Department of COMPUTER SCIENCE

Syllabus for
Master of Science (Data Science)
Academic Year  (2021)

 
1 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MDS131 MATHEMATICAL FOUNDATION FOR DATA SCIENCE - I - 4 4 100
MDS132 PROBABILITY AND DISTRIBUTION THEORY - 4 4 100
MDS133 PRINCIPLES OF DATA SCIENCE - 4 4 100
MDS134 RESEARCH METHODOLOGY - 2 2 50
MDS161A INTRODUCTION TO STATISTICS - 2 2 50
MDS161B INTRODUCTION TO COMPUTERS AND PROGRAMMING - 2 2 50
MDS161C LINUX ADMINISTRATION - 2 2 50
MDS171 DATA BASE TECHNOLOGIES - 6 5 150
MDS172 INFERENTIAL STATISTICS - 6 5 150
MDS173 PROGRAMMING FOR DATA SCIENCE IN PYTHON - 6 4 100
2 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MDS231 MATHEMATICAL FOUNDATION FOR DATA SCIENCE - II - 4 4 100
MDS232 REGRESSION ANALYSIS - 4 4 100
MDS241A MULTIVARIATE ANALYSIS - 4 4 100
MDS241B STOCHASTIC PROCESS - 4 4 100
MDS241C CATEGORICAL DATA ANALYSIS - 4 4 100
MDS271 MACHINE LEARNING - 6 5 150
MDS272A HADOOP - 6 5 150
MDS272B IMAGE AND VIDEO ANALYTICS - 6 5 150
MDS272C INTERNET OF THINGS - 6 5 150
MDS273 PROGRAMMING FOR DATA SCIENCE IN R - 6 4 100
3 Semester - 2020 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MDS331 NEURAL NETWORKS AND DEEP LEARNING - 4 4 100
MDS341A TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES - 4 4 100
MDS341B BAYESIAN INFERENCE - 4 4 100
MDS341C ECONOMETRICS - 4 4 100
MDS341D BIO-STATISTICS - 4 4 100
MDS371 CLOUD ANALYTICS - 6 5 150
MDS372A NATURAL LANGUAGE PROCESSING - 6 5 150
MDS372B WEB ANALYTICS - 6 5 150
MDS372C BIO INFORMATICS - 6 5 150
MDS372D EVOLUTIONARY ALGORITHMS - 6 5 150
MDS372E OPTIMIZATION TECHNIQUE - 6 5 150
MDS381 SPECIALIZATION PROJECT - 4 2 100
MDS382 SEMINAR - 2 1 50
4 Semester - 2020 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MDS481 INDUSTRY PROJECT - 2 12 300
      

        

    

Department Overview:

Department of Computer Science of CHRIST (Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation?s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field.

Mission Statement:

Vision The Department of Computer Science endeavours to imbibe the vision of the University "Excellence and Service". The department is committed to this philosophy which pervades every aspect and functioning of the department.

Mission: To develop IT professionals with ethical and human values. To accomplish our mission, the department encourages students to apply their acquired knowledge and skills towards professional achievements in their career. The department also moulds the

Introduction to Program:

Data Science is popular in all academia, business sectors, and research and development to make effective decision in day to day activities. MSc in Data Science is a two year programme with four semesters. This programme aims to provide opportunity to all candidates to master the skill sets specific to data science with research bent. The curriculum supports the students to obtain adequate knowledge in theory of data science with hands on experience in relevant domains and tools. Candidate gains exposure to research models and industry standard applications in data science through guest lectures, seminars, projects, internships, etc.

Program Objective:

Programme Objective

To acquire in-depth understanding of the theoretical concepts in statistics, data analysis, data mining, machine learning and other advanced data science techniques.

To gain practical experience in programming tools for data sciences, database systems, machine learning and big data tools.

To strengthen the analytical and problem solving skill through developing real time applications.

To empower students with tools and techniques for handling, managing, analyzing and interpreting data.

To imbibe quality research and develop solutions to the social issues.

Programme Outcome

PO1 Engage in continuous reflective learning in the context of technology and scientific advancement.

PO2 Identify the need and scope of the Interdisciplinary research.

PO3 Enhance research culture and uphold the scientific integrity and objectivity 

PO4 Understand the professional, ethical and social responsibilities

PO5 Understand the importance and the judicious use of technology for the sustainability of the environment

PO6 Enhance disciplinary competency, employability and leadership skills

 

Programme Specific Outcomes

PSO1: Abstract thinking: Ability to understand the abstract concepts that lead to various data science theories in Mathematics, Statistics and Computer science.

PSO2: Problem Analysis and Design Ability to identify ana

Assesment Pattern

CIA - 50%

ESE - 50%

Examination And Assesments

CIA - 50%

ESE - 50%

Department Overview:

The Department of Data Science at CHRIST (Deemed to be University), Pune Lavasa Campus was established to shape students into outstanding Data Scientists and Analytics  professionals with ethical and human values. The department offers various  undergraduate and postgraduate programmes viz., Bachelor of Science in Data Science,  Master of Science in Data Science, Bachelor of Science in Economics & Analytics, and  Doctor of Philosophy in the area of Computer Science and Engineering. The department  has rich expertise in faculty resources who are trained in various fields like Data Science,  Data Security, Data Analytics, Artificial Intelligence, Machine Learning, Networking, Data  Mining, Big Data, Text Mining, Knowledge Representation, Soft Computing, and Cloud  Computing. The department has a wide variety of labs set up, namely the Machine  Learning Lab, Data Analytics Lab, Open Source Lab, etc., which are exclusively for  students' hands-on training for their lab-oriented courses and research. 

 

Mission Statement:

VISION 

  

Introduction to Program:

 

Data Science is popular in all academia, business sectors, and research and development to make effective decision in day to day activities. MSc in Data Science is a two year programme with four semesters. This programme aims to provide opportunity to all candidates to master the skill sets specific to data science with research bent. The curriculum supports the students to obtain adequate knowledge in theory of data science with hands on experience in relevant domains and tools. Candidate gains exposure to research models and industry standard applications in data science through guest lectures, seminars, projects, internships, etc. 

Program Objective:

 

Programme Objective ? To acquire in-depth understanding of the theoretical concepts in statistics, data analysis, data mining, machine learning and other advanced data science techniques. ? To gain practical experience in programming tools for data sciences, database systems, machine learning and big data tools. ? To strengthen the analytical and problem solving skill through developing real time applications. ? To empower students with tools and techniques for handling, managing, analyzing and interpreting data. ? To imbibe quality research and develop solutions to the social issues. Programme Specific Outcomes PSO1: Abstract thinking: Ability to understand the abstract concepts that lead to various data science theories in Mathematics, Statistics and Computer science. PSO2: Problem Analysis and Design Ability to identify analyze and design solutions for data science problems using fundamental principles of mathematics, Statistics, computing sciences, and relevant domain disciplines. PSO3: Modern software tool usage: Acquire the skills in handling data science programming tools towards problem solving and solution analysis for domain specific pr

Assesment Pattern

50-50

Examination And Assesments

CIA & ESE

MDS131 - MATHEMATICAL FOUNDATION FOR DATA SCIENCE - I (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Linear Algebra plays a fundamental role in the theory of Data Science. This course aims at introducing the basic notions of vector spaces, Linear Algebra and the use of Linear Algebra in applications to Data Science.

Learning Outcome

CO1: Understand the properties of Vector spaces

CO2: Use the properties of Linear Maps in solving problems on Linear Algebra

CO3: Demonstrate proficiency on the topics Eigenvalues, Eigenvectors and Inner Product Spaces

CO4: Apply mathematics for some applications in Data Science

Unit-1
Teaching Hours:12
INTRODUCTION TO VECTOR SPACES
 

Vector Spaces: Rn and Cn, lists, Fn and digression on Fields, Definition of Vector spaces, Subspaces, sums of Subspaces, Direct Sums, Span and Linear Independence, bases, dimension.

Unit-2
Teaching Hours:12
LINEAR MAPS
 

DefinitionofLinearMaps-AlgebraicOperationson L(V,W) - Null spaces and Injectivity-RangeandSurjectivity-FundamentalTheoremsofLinearMaps-Representing aLinearMapbyaMatrix-InvertibleLinearMaps-IsomorphicVectorspaces-LinearMap as Matrix Multiplication - Operators - Products of Vector Spaces - Product of Direct Sum - Quotients of Vector spaces.

Unit-3
Teaching Hours:12
EIGENVALUES, EIGENVECTORS, AND INNER PRODUCT SPACES
 

Eigenvalues and Eigenvectors - Eigenvectors and Upper Triangular matrices - Eigenspaces and Diagonal Matrices - Inner Products and Norms - Linear functionals on Inner Product spaces.

Unit-4
Teaching Hours:12
BASIC MATRIX METHODS FOR APPLICATIONS
 

 Matrix Norms – Least square problem - Singular value decomposition- Householder Transformation and QR decomposition- Non Negative Matrix Factorization – bidiagonalization.

 

Unit-5
Teaching Hours:12
MATHEMATICS APPLIED TO DATA SCIENCE
 

Handwritten digits recognition using simple algorithm - Classification of handwritten digits using SVD bases and Tangent distance - Text Mining using Latent semantic index, Clustering, Non-negative Matrix Factorization and LGK bidiagonalization.

Text Books And Reference Books:

1. S. Axler, Linear algebra done right, Springer, 2017.

2. Eldén Lars, Matrix methods in data mining and pattern recognition, Society for Industrial and Applied Mathematics, 2007.

Essential Reading / Recommended Reading

1. E. Davis, Linear algebra and probability for computer science applications, CRC Press, 2012.

2. J. V. Kepner and J. R. Gilbert, Graph algorithms in the language of linear algebra, Society for Industrial and Applied Mathematics, 2011.

3. D. A. Simovici, Linear algebra tools for data mining, World Scientific Publishing, 2012.

4. P. N. Klein, Coding the matrix: linear algebra through applications to computer science, Newtonian Press, 2015.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS131L - MATHEMATICAL FOUNDATION FOR DATA SCIENCE I (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Linear Algebra plays a fundamental role in the theory of Data Science. This course aims at introducing the basic notions of vector spaces, Linear Algebra and the use of Linear Algebra in applications to Data Science

Learning Outcome

  •  Understand the properties of Vector spaces
  •  Use the properties of Linear Maps in solving problems on Linear Algebra
  •  Demonstrate proficiency on the topics Eigenvalues, Eigenvectors and Inner Product Spaces
  •  Apply mathematics for some applications in Data Science

Unit-1
Teaching Hours:12
INTRODUCTION TO VECTOR SPACES
 

Vector Spaces: Rn and Cn, lists, Fn and digression on Fields, Definition of Vector spaces, Subspaces, sums of Subspaces, Direct Sums, Span and Linear Independence, bases, dimension

Unit-2
Teaching Hours:12
LINEAR MAPS
 

Definition of LinearMaps-AlgebraicOperationson L(V,W) - Null spaces and Injectivity-RangeandSurjectivity-FundamentalTheoremsofLinearMaps-Representing aLinearMapbyaMatrix-InvertibleLinearMaps-IsomorphicVectorspaces-LinearMap as Matrix Multiplication - Operators - Products of Vector Spaces - Product of Direct Sum - Quotients of Vector spaces

Unit-3
Teaching Hours:12
EIGENVALUES, EIGENVECTORS, AND INNER PRODUCT SPACES
 

Eigenvalues and Eigenvectors - Eigenvectors and Upper Triangular matrices - Eigenspaces and Diagonal Matrices - Inner Products and Norms - Linear functionals on Inner Product spaces.

Unit-4
Teaching Hours:12
BASIC MATRIX METHODS FOR APPLICATIONS
 

Matrix Norms – Least square problem - Singular value decomposition- Householder Transformation and QR decomposition- Non Negative Matrix Factorization – bidiagonalization.

Unit-5
Teaching Hours:12
MATHEMATICS APPLIED TO DATA SCIENCE
 

Handwritten digits recognition using simple algorithm - Classification of handwritten digits using SVD bases and Tangent distance - Text Mining using Latent semantic index, Clustering, Non-negative Matrix Factorization and LGK bidiagonalization

Text Books And Reference Books:

1. S. Axler, Linear algebra done right, Springer, 2017.

2. Eldén Lars, Matrix methods in data mining and pattern recognition, Society for Industrial and Applied Mathematics, 2007.

Essential Reading / Recommended Reading

1. E. Davis, Linear algebra and probability for computer science applications, CRC Press, 2012.

2. J. V. Kepner and J. R. Gilbert, Graph algorithms in the language of linear algebra, Society for Industrial and Applied Mathematics, 2011.

3. D. A. Simovici, Linear algebra tools for data mining, World Scientific Publishing, 2012.

4. P. N. Klein, Coding the matrix: linear algebra through applications to computer science, Newtonian Press,2015

Evaluation Pattern

CIA I : 10%

CIA II : 25%

CIA III : 10%

ATTENDANCE : 5%

ESE : 50%

MDS132 - PROBABILITY AND DISTRIBUTION THEORY (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Probability and probability distributions play an essential role in modeling data from the real-world phenomenon. This course will equip students with thorough knowledge in probability and various probability distributions and model real-life data sets with an appropriate probability distribution

Learning Outcome

CO1: Describe random event and probability of events

CO2: Identify various discrete and continuous distributions and their usage.

CO3: Evaluate condition probabilities and conditional expectations

CO4: Apply Chebychev’s inequality to verify the convergence of sequence in probability

Unit-1
Teaching Hours:12
DESCRIPTIVE STATISTICS AND PROBABILITY
 

Data – types of variables: numeric vs categorical - measures of central tendency – measures of dispersion - random experiment - sample space and random events – probability - probability axioms - finite sample space with equally likely outcomes - conditional probability - independent events - Baye’s theorem

Unit-2
Teaching Hours:12
PROBABILITY DISTRIBUTIONS FOR DISCRETE DATA
 

Random variable – data as observed values of a random variable - expectation – moments & moment generating function - mean and variance in terms of moments - discrete sample space and discrete random variable – Bernoulli experiment and Binary variable: Bernoulli and binomial distributions – Count data: Poisson distribution – overdispersion in count data: negative binomial distribution – dependent Bernoulli  trails: hypergeometric distribution.

Unit-3
Teaching Hours:12
PROBABILITY DISTRIBUTIONS FOR CONTINUOUS DATA
 

Continuous sample space - Interval data - continuous random variable – uniform distribution - normal distribution (Gaussian distribution) – modeling lifetime data: exponential distribution, gamma distribution, Weibull distribution.

Unit-4
Teaching Hours:12
JOINTLY DISTRIBUTED RANDOM VARIABLES
 

Joint distribution of vector random variables – joint moments – covariance – correlation - the correlation - independent random variables - conditional distribution – conditional expectation - sampling distributions: chi-square, t, F (central).

Unit-5
Teaching Hours:12
LIMIT THEOREMS
 

Chebychev’s inequality - weak law of large n u mbers (iid): examples - strong law of large numbers (statement only) - central limit theorems (iid case): examples.

Text Books And Reference Books:

1. Ross, Sheldon. A first course in probability. 10th Edition. Pearson, 2019.

2. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3rd Edition, 2015

Essential Reading / Recommended Reading

1. Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGraw-Hill, 3rd Edition (Reprint), 2017.

2. Ross, Sheldon M. Introduction to probability models. 12th Edition, Academic Press, 2019. 

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS132L - PROBABILITY AND DISTRIBUTION THEORY (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Course Objectives

 To enable the students to understand the properties and applications of various probability functions.

Learning Outcome

CO1: Demonstrate the random variables and its functions

CO2: Infer the expectations for random variable functions and generating functions.

CO3: Demonstrate various discrete and continuous distributions and their usage

Unit-1
Teaching Hours:12
ALGEBRA OF PROBABILITY
 

Algebra of sets - fields and sigma - fields, Inverse function -Measurable function – Probability measure on a sigma field – simple properties - Probability space - Random variables and Random vectors – Induced Probability space – Distribution functions –Decomposition of distribution functions.

Unit-2
Teaching Hours:12
EXPECTATION AND MOMENTS OF RANDOM VARIABLES
 

Definitions and simple properties - Moment inequalities – Holder, Jenson Inequalities – Characteristic function – definition and properties – Inversion formula. Convergence of a sequence of random variables - convergence in distribution - convergence in probability almost sure convergence and convergence in quadratic mean - Weak and Complete convergence of distribution functions – Helly - Bray theorem.

Unit-3
Teaching Hours:12
LAW OF LARGE NUMBERS
 

Khintchin's weak law of large numbers, Kolmogorov strong law of large numbers (statement only) – Central Limit Theorem – Lindeberg – Levy theorem, Linderberg – Feller theorem (statement only), Liapounov theorem – Relation between Liapounov and Linderberg –Feller forms – Radon Nikodym theorem and derivative (without proof) – Conditional expectation – definition and simple properties.

Unit-4
Teaching Hours:12
DISTRIBUTION THEORY
 

Distribution of functions of random variables – Laplace, Cauchy, Inverse Gaussian, Lognormal, Logarithmic series and Power series distributions - Multinomial distribution - Bivariate Binomial – Bivariate Poisson – Bivariate Normal - Bivariate Exponential of Marshall and Olkin - Compound, truncated and mixture of distributions, Concept of convolution - Multivariate normal distribution (Definition and Concept only) - Sampling distributions: Non-central chi-square, t and F distributions and their properties.

Unit-5
Teaching Hours:12
ORDER STATISTICS
 

Order statistics, their distributions and properties - Joint and marginal distributions of order statistics - Distribution of range and mid range -Extreme values and their asymptotic distributions (concepts only) - Empirical distribution function and its properties – Kolmogorov - Smirnov distributions – Life time distributions -Exponential and Weibull distributions - Mills ratio – Distributions classified by hazard rate.

Text Books And Reference Books:

1. B.R Bhat, Modern Probability Theory,  New Age International, 4th Edition, 2014.

2. V.K Rohatgi and Saleh, An Introduction to Probability and Statistics, 3rd Edition, 2015.

Essential Reading / Recommended Reading

1. A.M Mood, F.A Graybill and D.C Boes, Introduction to the theory of statistics, Tata McGraw-Hill, 3rd Edition (Reprint), 2017.

2. H.A David and H.N Nagaraja, Order Statistics, John Wiley & Sons, 3rd Edition, 2003.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS133 - PRINCIPLES OF DATA SCIENCE (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To provide strong foundation for data science and application area related to information technology and understand the underlying core concepts and emerging technologies in data science

Learning Outcome

 CO1:Explore the fundamental concepts of data science

CO2:Understand data analysis techniques for applications handling large data

CO3:Understand various machine learning algorithms used in data science process

CO4:Visualize and present the inference using various tools

CO5:Learn to think through the ethics surrounding privacy, data sharing and algorithmic decision-making

 

Unit-1
Teaching Hours:10
INTRODUCTION TO DATA SCIENCE
 

Definition – Big Data and Data Science Hype – Why data science – Getting Past the Hype – The Current Landscape – Who is Data Scientist? - Data Science Process Overview – Defining goals – Retrieving data – Data preparation – Data exploration – Data modeling – Presentation.

Unit-2
Teaching Hours:12
BIG DATA
 

Problems when handling large data – General techniques for handling large data – Case study – Steps in big data – Distributing data storage and processing with Frameworks – Case study.

Unit-3
Teaching Hours:12
MACHINE LEARNING
 

Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms.

Unit-4
Teaching Hours:12
DEEP LEARNING
 

Introduction – Deep Feedforward Networks – Regularization – Optimization of Deep Learning – Convolutional Networks – Recurrent and Recursive Nets – Applications of Deep Learning.

Unit-5
Teaching Hours:14
DATA VISUALIZATION
 

Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools – Creating an interactive dashboard with dc.js-summary.

Unit-5
Teaching Hours:14
ETHICS AND RECENT TRENDS
 

Data Science Ethics – Doing good data science – Owners of the data - Valuing different aspects of privacy - Getting informed consent - The Five Cs – Diversity – Inclusion – Future Trends.

Text Books And Reference Books:

[1]. Introducing Data Science, Davy Cielen, Arno D. B. Meysman, Mohamed Ali, Manning Publications Co., 1st edition, 2016

[2]. An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Springer, 1st edition, 2013

[3]. Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 1st edition, 2016

[4]. Ethics and Data Science, D J Patil, Hilary Mason, Mike Loukides, O’ Reilly, 1st edition, 2018

Essential Reading / Recommended Reading

[1]. Data Science from Scratch: First Principles with Python, Joel Grus, O’Reilly, 1st edition, 2015

[2]. Doing Data Science, Straight Talk from the Frontline, Cathy O'Neil, Rachel Schutt, O’Reilly, 1st edition, 2013

[3]. Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Cambridge University Press, 2nd edition, 2014

Evaluation Pattern

CIA : 50 %

ESE : 50 %

MDS133L - PRINCIPLES OF DATA SCIENCE (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Course Description:

To provide strong foundation for Data Science and related areas of application. The course includes with the fundamentals of data science, different techniques for handing big data and machine learning algorithms for supervised and unsupervised learning. The importance of handling data in an ethical manner and the ethical practices to be adopted while dealing the data is also  a part of the course.

Course Objectives:

 

To provide strong foundation for data science and application area related to information technology and understand the underlying core concepts and emerging technologies in data science

 

Learning Outcome

 

 CO1:Explore the fundamental concepts of data science

CO2:Understand data analysis techniques for applications handling large data

CO3:Understand various machine learning algorithms used in data science process

CO4:Visualize and present the inference using various tools

CO5:Learn to think through the ethics surrounding privacy, data sharing and algorithmic decision-making

 

Unit-1
Teaching Hours:10
INTRODUCTION TO DATA SCIENCE
 

Definition – Big Data and Data Science Hype – Why data science – Getting Past the Hype – The Current Landscape – Who is Data Scientist? - Data Science Process Overview – Defining goals – Retrieving data – Data preparation – Data exploration – Data modeling – Presentation.

Unit-2
Teaching Hours:12
BIG DATA
 

Problems when handling large data – General techniques for handling large data – Case study – Steps in big data – Distributing data storage and processing with Frameworks – Case study.

Unit-3
Teaching Hours:12
MACHINE LEARNING
 

Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms.

Unit-4
Teaching Hours:12
DEEP LEARNING
 

Introduction – Deep Feedforward Networks – Regularization – Optimization of Deep Learning – Convolutional Networks – Recurrent and Recursive Nets – Applications of Deep Learning.

Unit-5
Teaching Hours:14
DATA VISUALIZATION
 

Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools – Creating an interactive dashboard with dc.js-summary.

ETHICS AND RECENT TRENDS

Data Science Ethics – Doing good data science – Owners of the data - Valuing different aspects of privacy - Getting informed consent - The Five Cs – Diversity – Inclusion – Future Trends.

Text Books And Reference Books:

T1. Introducing Data Science, Davy Cielen, Amo D.B. Meysman, Mohammed Ali,  Manning Publications Co., 1st              Edition, 2016

T2. An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten, Trevor Hastic, Robert Tibshirani, Springer, 1st edition, 2013

T3. Deep learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 1st   Edition, 2016

T4. Ethics and Data Science, D J Patil, Hilary mason, Mike Loukides, O’ Reilly, 1st Edition, 2018

Essential Reading / Recommended Reading

R1. Data Science from Scratch: First Principles with Python, Joel Grus, O’Reilly, 1st Edition, 2015

 

R2.Doing Data Science, Straight talk from the Frontline, Cathy O’Neil, Rachel Schutt, O’ Reilly, 1st Edition, 2013

R3. Mining of Massive Datasets, Jure Leskovee, Anand Rajaraman, Jeffrey David Ullman, Cambridge University Press, 2nd edition, 2014

Evaluation Pattern

 

CIA I

CIA  II

CIA III

Attendance

ESE

10%

25%

10%

5%

50%

MDS134 - RESEARCH METHODOLOGY (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course is intended to assist students in planning and carrying out research work.The students are exposed to the basic principles, procedures and techniques of implementing a research project.

To introduce the research concept and the various research methodologies is the main objective. It focuses on finding out the research gap from the literature and encourages lateral, strategic and creative thinking. This course also introduces computer technology and basic statistics required for research and reporting the research outcomes scientifically emphasizing on research ethics.

 

Learning Outcome

CO1: Understand the essense of research and the necessity of defining a research problem.

CO2: Apply research methods and methodology including research design,data collection, data analysis, and interpretation.

CO3: Create scientific reports according to specified standards.

 

Unit-1
Teaching Hours:8
RESEARCH METHODOLOGY
 

Defining research problem:Selecting the problem, Necessity of defining the problem ,Techniques involved in defining a problem- Ethics in Research.

Unit-2
Teaching Hours:8
RESEARCH DESIGN
 

Principles of experimental design,Working with Literature: Importance, finding literature, Using your resources, Managing the literature, Keep track of references,Using the literature, Literature review,On-line Searching: Database ,SCIFinder, Scopus, Science Direct ,Searching research articles , Citation Index ,Impact Factor ,H-index.

Unit-3
Teaching Hours:7
RESEARCH DATA
 

Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation. 

Unit-4
Teaching Hours:7
REPORT WRITING
 

Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, Text, Tables, Figures, Equations, Citations, Referencing, and Templates (IEEE style), Paper writing for international journals, Writing scientific report. 

Text Books And Reference Books:

[1] C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014.

[2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005. 

Essential Reading / Recommended Reading

[1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4thed. SAGE Publications, 2014.

[2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010. 

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS134L - RESEARCH METHODOLOGY (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course is intended to assist students in planning and carrying out research work.The students are exposed to the basic principles, procedures and techniques of implementing a research project. 

To introduce the research concept and the various research methodologies is the main objective. It focuses on finding out the research gap from the literature and encourages lateral, strategic and creative thinking. This course also introduces computer technology and basic statistics required for research and reporting the research outcomes scientifically emphasizing on research ethics.

Learning Outcome

CO1: Understand the essense of research and the necessity of defining a research problem.

CO2: Apply research methods and methodology including research design,data collection, data analysis, and interpretation.

CO3: Create scientific reports according to specified standards.

Unit-1
Teaching Hours:8
RESEARCH METHODOLOGY
 

Defining research problem:Selecting the problem, Necessity of defining the problem ,Techniques involved in defining a problem- Ethics in Research.

Unit-2
Teaching Hours:8
RESEARCH DESIGN
 

Principles of experimental design,Working with Literature: Importance, finding literature, Using your resources, Managing the literature, Keep track of references,Using the literature, Literature review,On-line Searching: Database ,SCIFinder, Scopus, Science Direct, Searching research articles , Citation Index ,Impact Factor ,H-index.

Unit-3
Teaching Hours:7
RESEARCH DATA
 

Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation.

Unit-4
Teaching Hours:7
REPORT WRITING
 

Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, Text, Tables, Figures, Equations, Citations, Referencing, and Templates (IEEE style), Paper writing for international journals, Writing scientific report.

Text Books And Reference Books:

[1] C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014.

[2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005.

Essential Reading / Recommended Reading

[1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4thed. SAGE Publications, 2014.

[2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010.

Evaluation Pattern

CIA- 50%

ESE- 50%

MDS161A - INTRODUCTION TO STATISTICS (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

To enable the students to understand the fundamentals of statistics to apply descriptive measures and probability for data analysis.

Learning Outcome

CO1: Demonstrate the history of statistics and present the data in various forms.

CO2: Infer the concept of correlation and regression for relating two or more related variables.

CO3: Demonstrate the probabilities for various events.

Unit-1
Teaching Hours:8
ORGANIZATION AND PRESENTATION OF DATA
 

Origin and development of Statistics, Scope, limitation and misuse of statistics. Types of data: primary, secondary, quantitative and qualitative data. Types of Measurements: nominal, ordinal, discrete and continuous data. Presentation of data by tables: construction of frequency distributions for discrete and continuous data, graphical representation of a frequency distribution by histogram and frequency polygon, cumulative frequency distributions

Unit-2
Teaching Hours:8
DESCRIPTIVE STATISTICS
 

Measures of location or central tendency: Arthimetic mean, Median, Mode, Geometric mean, Harmonic mean. Partition values: Quartiles, Deciles and percentiles. Measures of dispersion: Mean deviation, Quartile deviation, Standard deviation, Coefficient of variation. Moments: measures of skewness, Kurtosis.

Unit-3
Teaching Hours:7
CORRELATION AND REGRESSION
 

Correlation: Scatter plot, Karl Pearson coefficient of correlation, Spearman's rank correlation coefficient, multiple and partial correlations (for 3 variates only). Regression: Concept of errors, Principles of Least Square, Simple linear regression and its properties.

Unit-4
Teaching Hours:7
BASICS OF PROBABILITY
 

Random experiment, sample point and sample space, event, algebra of events. Definition of Probability: classical, empirical and axiomatic approaches to probability, properties of probability. Theorems on probability, conditional probability and independent events, Laws of total probability, Baye’s theorem and its applications

Text Books And Reference Books:

[1]. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, John Wiley & Sons Inc., New Jersey, 2015.

[2]. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11th edition, Sultan Chand & Sons, New Delhi, 2014.

Essential Reading / Recommended Reading

[1]. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015.

[2]. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017.

[3]. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013.

[4]. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi, 2008.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS161B - INTRODUCTION TO COMPUTERS AND PROGRAMMING (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

To enable the students to understand the fundamental concepts of problem solving and programming structures.

Learning Outcome

CO1: Demonstrate the systematic approach for problem-solving using computers.

CO2: Apply different programming structures with suitable logic for computational problems.

Unit-1
Teaching Hours:10
COMPUTERS AND DIGITAL BASICS
 

Number Representation – Decimal, Binary, Octal, Hexadecimal and BCD numbers – Binary Arithmetic – Binary addition – Unsigned and Signed numbers – one’s and two’s complements of Binary numbers – Arithmetic operations with signed numbers - Number system conversions – Boolean Algebra – Logic gates – Design of Circuits – K - Map

Unit-2
Teaching Hours:5
GENERAL PROBLEM SOLVING CONCEPT
 

Types of Problems – Problem solving with Computers – Difficulties with problem solving – problem solving concepts for the Computer – Constants and Variables – Rules for Naming and using variables – Data types – numeric data – character data – logical data – rules for data types – examples of data types – storing the data in computer - Functions – Operators – Expressions and Equations

Unit-3
Teaching Hours:5
PLANNING FOR SOLUTION
 

Communicating with computer – organizing the solution – Analyzing the problem – developing the interactivity chart – developing the IPO chart – Writing the algorithms – drawing the flow charts – pseudocode – internal and external documentation – testing the solution – coding the solution – software development life cycle.

Unit-4
Teaching Hours:10
PROBLEM SOLVING
 

Introduction to programming structure – pointers for structuring a solution – modules and their functions – cohesion and coupling – problem solving with logic structure. Problem solving with decisions – the decision logic structure – straight through logic – positive logic – negative logic – logic conversion – decision tables – case logic structure -  examples.

Text Books And Reference Books:

[1] Thomas L.Floyd and R.P.Jain,“Digital Fundamentals”,8th Edition, Pearson Education,2007.

[2] Peter Norton “Introduction to Computers”,6th Edition, Tata Mc Graw Hill, New Delhi,2006.

[3] Maureen Sprankle and Jim Hubbard, Problem-solving and programming concepts, PHI, 9th Edition, 2012

Essential Reading / Recommended Reading

[1]. E Balagurusamy, Fundamentals of Computers, TMH, 2011

 

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS161BL - INTRODUCTION TO COMPUTERS AND PROGRAMMING (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

To enable the students to understand the fundamental concepts of problem solving and programming structures.

 

Learning Outcome

CO1: Demonstrate the systematic approach for problem solving using computers. EM

CO2: Apply different programming structure with suitable logic for computational problems. EM+S

Unit-1
Teaching Hours:10
COMPUTERS AND DIGITAL BASICS
 

Number Representation – Decimal, Binary, Octal, Hexadecimal and BCD numbers – Binary Arithmetic – Binary addition – Unsigned and Signed numbers – one’s and two’s complements of Binary numbers – Arithmetic operations with signed numbers - Number system conversions – Boolean Algebra – Logic gates – Design of Circuits – K - Map

Unit-2
Teaching Hours:5
GENERAL PROBLEM SOLVING CONCEPT
 

Types of Problems – Problem solving with Computers – Difficulties with problem solving – problem solving concepts for the Computer – Constants and Variables – Rules for Naming and using variables – Data types – numeric data – character data – logical data – rules for data types – examples of data types – storing the data in computer - Functions – Operators – Expressions and Equations

Unit-3
Teaching Hours:5
PLANNING FOR SOLUTION
 

Communicating with computer – organizing the solution – Analyzing the problem – developing the interactivity chart – developing the IPO chart – Writing the algorithms – drawing the flow charts – pseudocode – internal and external documentation – testing the solution – coding the solution – software development life cycle.

Unit-4
Teaching Hours:10
PROBLEM SOLVING
 

Introduction to programming structure – pointers for structuring a solution – modules and their functions – cohesion and coupling – problem solving with logic structure. Problem

solving with decisions – the decision logic structure – straight through logic – positive logic – negative logic – logic conversion – decision tables – case logic structure - examples.

Text Books And Reference Books:

[1]Thomas L.Floyd and R.P.Jain,“Digital Fundamentals”,8th Edition, Pearson Education,2007.

[2]Peter Norton “Introduction to Computers”,6th Edition, Tata Mc Graw Hill, New Delhi,2006.

[3]Maureen Sprankle and Jim Hubbard, Problem solving and programming concepts, PHI, 9th Edition, 2012

 

Essential Reading / Recommended Reading

[1]. EBalagurusamy,FundamentalsofComputers, TMH,2011

Evaluation Pattern

CIA:50%

 

ESE:50%

MDS161C - LINUX ADMINISTRATION (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

To Enable the students to excel in the Linux Platform

Learning Outcome

CO1: Demostrate the systematic approach for configure the Liux environment

CO2: Manage the Linux environment to work with open source data science tools

Unit-1
Teaching Hours:10
Module-1
 

RHEL7.5,breaking root password, Understand and use essential tools for handling files, directories, command-line environments, and documentation - Configure local storage using partitions and logical volumes

Unit-2
Teaching Hours:10
Module-2
 

Swapping, Extend LVM Partitions,LVM Snapshot - Manage users and groups, including use of a centralized directory for authentication

Unit-3
Teaching Hours:10
Module-3
 

Kernel updations,yum and nmcli configuration, Scheduling jobs,at,crontab - Configure firewall settings using firewall config, firewall-cmd, or iptables , Configure key-based authentication for SSH ,Set enforcing and permissive modes for SELinux , List and identify SELinux file and process context ,Restore default file contexts

Text Books And Reference Books:

1.    https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/7/

2.    https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/7/

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA:50%

ESE:50%

MDS161LA - INTRODUCTION TO STATISTICS (2021 Batch)

Total Teaching Hours for Semester:1
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

 

To enable the students to understand the fundamentals of statistics to apply descriptive measures and probability for data analysis.

Learning Outcome

 

CO1: Demonstrate the history of statistics and present the data in various forms.

CO2: Infer the concept of correlation and regression for relating two or more related variables.

CO3: Demonstrate the probabilities for various events.

Unit-1
Teaching Hours:8
ORGANIZATION AND PRESENTATION OF DATA
 

Origin and development of Statistics, Scope, limitation and misuse of statistics. Types of data: primary, secondary, quantitative and qualitative data. Types of Measurements: nominal, ordinal, discrete and continuous data. Presentation of data by tables: construction of frequency distributions for discrete and continuous data, graphical representation of a frequency distribution by histogram and frequency polygon, cumulative frequency distributions

Unit-2
Teaching Hours:8
DESCRIPTIVE STATISTICS
 

Measures of location or central tendency: Arthimetic mean, Median, Mode, Geometric mean, Harmonic mean. Partition values: Quartiles, Deciles and percentiles. Measures of dispersion: Mean deviation, Quartile deviation, Standard deviation, Coefficient of variation. Moments: measures of skewness, Kurtosis

Unit-3
Teaching Hours:7
CORRELATION AND REGRESSION
 

Correlation: Scatter plot, Karl Pearson coefficient of correlation, Spearman's rank correlation coefficient, multiple and partial correlations (for 3 variates only). Regression: Concept of errors, Principles of Least Square, Simple linear regression and its properties

Unit-4
Teaching Hours:7
BASICS OF PROBABILITY
 

Random experiment, sample point and sample space, event, algebra of events. Definition of Probability: classical, empirical and axiomatic approaches to probability, properties of probability. Theorems on probability, conditional probability and independent events, Laws of total probability, Baye’s theorem and its applications

Text Books And Reference Books:

[1]. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, John Wiley & Sons Inc., New Jersey, 2015.

[2]. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11th edition, Sultan Chand & Sons, New Delhi, 2014.

Essential Reading / Recommended Reading

[1]. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015.

[2]. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017.

[3]. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013.

[4]. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi, 2008.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS171 - DATA BASE TECHNOLOGIES (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

The main objective of this course is to fundamental knowledge and practical experience with, database concepts. It includes the concepts and terminologies which facilitate the construction of relational databases, writing effective queries comprehend data warehouse and NoSQL databases and its types

Learning Outcome

CO1: Demonstrate various databases and Compose effective queries

CO2: Understanding the process of OLAP system construction

CO3: Develop applications using Relational and NoSQL databases.

Unit-1
Teaching Hours:18
INTRODUCTION
 

 Concept & Overview of DBMS, Data Models, Database Languages, Database Administrator, Database Users, Three Schema architecture of DBMS. Basic concepts, Design Issues, Mapping Constraints, Keys, Entity-Relationship Diagram, Weak Entity Sets, Extended E-R features

 Lab Exercises

1. Data Definition,

2. Table Creation

3. Constraints

Unit-2
Teaching Hours:18
RELATIONAL MODEL AND DATABASE DESIGN
 

SQL and Integrity Constraints, Concept of DDL, DML, DCL. Basic Structure, Set operations, Aggregate Functions, Null Values, Domain Constraints, Referential Integrity Constraints, assertions, views, Nested Subqueries, Functional Dependency, Different anomalies in designing a Database, Normalization: using functional dependencies, Boyce-Codd Normal Form, 4NF

 Lab Exercises

1. Insert, Select, Update & Delete Commands

2. Nested Queries & Join Queries

3. Views

Unit-3
Teaching Hours:18
DATA WAREHOUSE: THE BUILDING BLOCKS
 

Defining Features, Data Warehouses and Data Marts, Architectural Types, Overview of the Components, Metadata in the Data warehouse, Data Design and Data Preparation: Principles of Dimensional Modeling, Dimensional Modeling Advanced Topics From Requirements To Data Design, The Star Schema, Star Schema Keys, Advantages of the Star Schema, Star Schema: Examples, Dimensional Modeling: Advanced Topics, Updates to the Dimension Tables, Miscellaneous Dimensions, The Snowflake Schema, Aggregate Fact Tables, Families Oo Stars

 Lab Exercises:

1. Importing source data structures

2. Design Target Data Structures

3. Create target multidimensional cube

Unit-4
Teaching Hours:18
DATA INTEGRATION and DATA FLOW (ETL)
 

Requirements, ETL Data Structures, Extracting, Cleaning and Conforming, Delivering Dimension Tables, Delivering Fact Tables, Real-Time ETL Systems

 Lab Exercises:

1. Perform the ETL process and transform into data map

2. Create the cube and process it

3. Generating Reports

4. Creating the Pivot table and pivot chart using some existing data

Unit-5
Teaching Hours:18
NOSQL Databases
 

Introduction to NOSQL Systems, The CAP Theorem, Document-Based NOSQL Systems and MongoDB, NOSQL Key-Value Stores, Column-Based or Wide Column NOSQL Systems, Graph databases, Multimedia databases.

 Lab Exercises:

1. MongoDB Exercise - 1

2. MongoDB Exercise - 2

Text Books And Reference Books:

[1]. Henry F. Korth and Silberschatz Abraham, “Database System Concepts”, Mc.Graw Hill.

[2]. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education, 2007.

[3]. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd John Wiley & Sons, Inc. New York, USA, 2002

Essential Reading / Recommended Reading

[1] LiorRokach and OdedMaimon, Data Mining and Knowledge Discovery Handbook, Springer, 2nd edition, 2010.

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS171L - DATABASE TECHNOLOGIES (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

Course Description and Course Objectives

 

The main objective of this course is to fundamental knowledge and practical experience with, database concepts. It includes the concepts and terminologies which facilitate the construction of relational databases, writing effective queries comprehend data warehouse and NoSQL databases and its types

Learning Outcome

CO1: Demonstrate various databases and Compose effective queries

CO2: Understanding the process of OLAP system construction

CO3: Develop applications using Relational and NoSQL databases.

Unit-1
Teaching Hours:18
INTRODUCTION
 

Concept & Overview of DBMS, Data Models, Database Languages, Database Administrator, Database Users, Three Schema architecture of DBMS. Basic concepts, Design Issues, Mapping Constraints, Keys, Entity-Relationship Diagram, Weak Entity Sets, Extended E-R features

 Lab Exercises

1. Data Definition,

2. Table Creation

3. Constraints

Unit-2
Teaching Hours:18
RELATIONAL MODEL AND DATABASE DESIGN
 

SQL and Integrity Constraints, Concept of DDL, DML, DCL. Basic Structure, Set operations, Aggregate Functions, Null Values, Domain Constraints, Referential Integrity Constraints, assertions, views, Nested Subqueries, Functional Dependency, Different anomalies in designing a Database, Normalization: using functional dependencies, Boyce-Codd Normal Form, 4NF

 Lab Exercises

1. Insert, Select, Update & Delete Commands

2. Nested Queries & Join Queries

3. Views

Unit-3
Teaching Hours:18
DATA WAREHOUSE: THE BUILDING BLOCKS
 

Defining Features, Data Warehouses and Data Marts, Architectural Types, Overview of the Components, Metadata in the Data warehouse, Data Design and Data Preparation: Principles of Dimensional Modeling, Dimensional Modeling Advanced Topics From Requirements To Data Design, The Star Schema, Star Schema Keys, Advantages of the Star Schema, Star Schema: Examples, Dimensional Modeling: Advanced Topics, Updates to the Dimension Tables, Miscellaneous Dimensions, The Snowflake Schema, Aggregate Fact Tables, Families Oo Stars

Lab Exercises:

1. Importing source data structures

2. Design Target Data Structures

3. Create target multidimensional cube

Unit-4
Teaching Hours:18
DATA INTEGRATION and DATA FLOW (ETL)
 
 

Requirements, ETL Data Structures, Extracting, Cleaning and Conforming, Delivering Dimension Tables, Delivering Fact Tables, Real-Time ETL Systems

 Lab Exercises:

1. Perform the ETL process and transform into data map

2. Create the cube and process it

3. Generating Reports

4. Creating the Pivot table and pivot chart using some existing data

Unit-5
Teaching Hours:18
NOSQL DATABASES
 

Introduction to NOSQL Systems, The CAP Theorem, Document-Based NOSQL Systems and MongoDB, NOSQL Key-Value Stores, Column-Based or Wide Column NOSQL Systems, Graph databases, Multimedia databases.

 Lab Exercises:

1. MongoDB Exercise - 1

2. MongoDB Exercise - 2

Text Books And Reference Books:
 

[1]. Henry F. Korth and Silberschatz Abraham, “Database System Concepts”, Mc.Graw Hill.

[2]. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education, 2007.

[3]. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd John Wiley & Sons, Inc. New York, USA, 2002

Essential Reading / Recommended Reading

[1] LiorRokach and OdedMaimon, Data Mining and Knowledge Discovery Handbook, Springer, 2nd edition, 2010.

Evaluation Pattern
 

CIA: 50%

ESE: 50%

MDS172 - INFERENTIAL STATISTICS (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

Statistical inference plays an important role in modeling data and decision-making from the real-world phenomenon. This course is designed to impart the knowledge of testing of hypothesis and estimation of parameters for real-life data sets.

Learning Outcome

CO1: Demonstrate the concepts of population and samples.

CO2: Apply the idea of sampling distribution of different statistics in testing of hypothesis

CO3: Test the hypothesis using nonparametric tests for real world problems.

CO4: Estimate the unknown population parameters using the concepts of point and interval estimations.

Unit-1
Teaching Hours:18
INTRODUCTION
 

Population and Statistics – Finite and Infinite population – Parameter and Statistics – Types of sampling - Sampling Distribution – Sampling Error - Standard Error – Test of significance –concept of hypothesis – types of hypothesis – Errors in hypothesis-testing – Critical region – level of significance - Power of the test – p-value.

Lab Exercise:

1. Calculation of sampling error and standard error

2. Calculation of probability of critical region using standard distributions

3. Calculation of power of the test using standard distributions.

Unit-2
Teaching Hours:18
TESTING OF HYPOTHESIS I
 

Concept of large and small samples – Tests concerning a single population mean for known σ – equality of two means for known σ – Test for Single variance - Test for equality of two variance for normal population – Tests for single proportion – Tests of equality of two proportions for the normal population.

 

Lab Exercise:

4. Test of the single sample mean for known σ.

5. Test of equality of two means when known σ

6. Tests of single variance and equality of variance for large samples

7. Tests for single proportion and equality of two proportion for large samples.

Unit-3
Teaching Hours:18
TESTING OF HYPOTHESIS II
 

Students t-distribution and its properties (without proofs) – Single sample mean test – Independent sample mean test – Paired sample mean test – Tests of proportion (based on t distribution) – F distribution and its properties (without proofs) – Tests of equality of two variances using F-test – Chi-square distribution and its properties (without proofs) – chisquare test for independence of attributes – Chi-square test for goodness of fit.

 

Lab Exercise:

8. Single sample mean test

9. Independent and Paired sample mean test

10. Tests of proportion of one and two samples based on t-distribution

11. Test of equality of two variances

12. Chi-square test for independence of attributes and goodness of fit.

Unit-4
Teaching Hours:18
ANALYSIS OF VARIANCE
 

Meaning and assumptions - Fixed, random and mixed effect models - Analysis of variance of one-way and two-way classified data with and without interaction effects – Multiple comparison tests: Tukey’s method - critical difference.

 

Lab Exercise:

13. Construction of one-way ANOVA

14. Construction of two-way ANOVA with interaction

15. Construction of two-way ANOVA without interaction

16. Multiple comparision test using Tukey’s method and critical difference methods

Unit-5
Teaching Hours:18
NONPARAMETRIC TESTS
 

Concept of Nonparametric tests - Run test for randomness - Sign test and Wilcoxon Signed Rank Test for one and paired samples - Run test - Median test and Mann-Whitney-Wilcoxon tests for two samples.

 

Lab Exercise:

17. Test of one sample using Run and sign tests

18. Test of paried sample using Wilcoxon signed rank test

19. Test of two samples using Run test and Median test

20. Test for two samples using Mann-Whitney-Wilcoxon tests

Text Books And Reference Books:

1. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 12th edition, Sultan Chand & Sons, New Delhi, 2020.

2. Brian Caffo, Statistical Inference for Data Science, Learnpub, 2016. 

Essential Reading / Recommended Reading

1. Walpole R.E, Myers R.H and Myers S.L, Probability and Statistics for Engineers and Scientists, 9th edition, Pearson, New Delhi, 2017.

2. John V, Using R for Introductory Statistics, 2nd edition, CRC Press, Boca Raton, 2014.

3. Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd, New Delhi, 2012.

4. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, JohnWiley & Sons Inc, New Jersey, 2015.

Evaluation Pattern

CIA: 50%

ESE:50%

MDS172L - INFERENTIAL STATISTICS (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

This course is designed to introduce the concepts of theory of estimation and testing of hypothesis. This paper also deals with the concept of parametric tests for large and small samples. It also provides knowledge about non-parametric tests and its applications

Learning Outcome

CO1: Demonstrate the concepts of point and interval estimation of unknown parameters and their significance using large and small samples.

CO2: Apply the idea of sampling distributions of different statistics in testing of hypotheses.

CO3: Infer the concept of nonparametric tests for single sample and two samples.

Unit-1
Teaching Hours:15
SUFFICIENT STATISTICS
 

Neyman - Fisher Factorisation theorem - the existence and construction of minimal sufficient statistics - Minimal sufficient statistics and exponential family - sufficiency and completeness - sufficiency and invariance.

Lab Excercise 

1. Drawing random samples using random number tables.

2. Point estimation of parameters and obtaining estimates of standard errors.

 

Unit-2
Teaching Hours:15
UNBIASED ESTIMATION
 

Minimum variance unbiased estimation - locally minimum variance unbiased estimators - Rao Blackwell – theorem – Completeness: Lehmann Scheffe theorems - Necessary and sufficient condition for unbiased estimators - Cramer- Rao lower bound - Bhattacharya system of lower bounds in the 1-parameter regular case - Chapman -Robbins inequality

Lab Excercise 

1. Comparison of estimators by plotting mean square error.

2. Computing maximum likelihood estimates -1

3. Computing maximum likelihood estimates - 2

4. Computing moment estimates

Unit-3
Teaching Hours:15
MAXIMUM LIKELIHOOD ESTIMATION
 

Computational routines - strong consistency of maximum likelihood estimators - Asymptotic Efficiency of maximum likelihood estimators - Best Asymptotically Normal estimators - Method of moments - Bayes’ and minimax estimation: The structure of Bayes’ rules - Bayes’ estimators for quadratic and convex loss functions - minimax estimation - interval estimation.

Lab Exercise: 

1. Constructing confidence intervals based on large samples.

2. Constructing confidence intervals based on small samples.

3. Generating random samples from discrete distributions.

4. Generating random samples from continuous distributions.

Unit-4
Teaching Hours:15
HYPOTHESIS TESTING
 

Uniformly most powerful tests - the Neyman-Pearson fundamental Lemma - Distributions with monotone likelihood ratio - Problems - Generalization of the fundamental lemma, two sided hypotheses - testing the mean and variance of a normal distribution.

Lab Excercise :

1. Evaluation of probabilities of Type-I and Type-II errors and powers of tests.

2. MP test for parameters of binomial and Poisson distributions.

3. MP test for the mean of a normal distribution and power curve.

4. Tests for mean, equality of means when variance is (i) known, (ii) unknown under normality

(small and large samples)

Unit-5
Teaching Hours:15
MEAN TESTS
 

Unbiased ness for hypotheses testing - similarity and completeness - UMP unbiased tests for multi-parameter exponential families - comparing two Poisson or Binomial populations - testing the parameters of a normal distribution (unbiased tests) - comparing the mean and variance of two normal distributions - Symmetry and invariance - maximal invariance - most powerful invariant tests.

Lab Excercise:

1. Tests for single proportion and equality of two proportions.

2. Tests for variance and equality of two variances under normality

3. Tests for correlation and regression coefficients.

Unit-6
Teaching Hours:15
SEQUENCTIAL TESTS
 

SPRT procedures - likelihood ratio tests - locally most powerful tests - the concept of confidence sets - non parametric tests.

Lab Exercise :

1. Tests for the independence of attributes, analysis of categorical data and tests for the goodness of fit.(For uniform, binomial and Poisson distributions)

2. Nonparametric tests.

3. SPRT for binomial proportion and mean of a normal distribution.

Text Books And Reference Books:

[1]. Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd, New Delhi, 2012.

[2]. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3rd Edition, 2015.

Essential Reading / Recommended Reading

[1]. Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGraw-Hill, 3rd Edition (Reprint), 2017.

[2]. Linear Statistical Inference and its Applications, Rao C.R, Willy Publications, 2nd Edition, 2001.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS173 - PROGRAMMING FOR DATA SCIENCE IN PYTHON (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The objective of this course is to provide comprehensive knowledge of python programming paradigms required for Data Science.

Learning Outcome

CO1: Demonstrate the use of built-in objects of Python

CO2:Demonstrate     significant     experience     with      python     program     development environment

CO3:Implement   numerical   programming,   data   handling   and   visualization   through NumPy, Pandas and MatplotLibmodules.

Unit-1
Teaching Hours:17
INTRODUCTION TO PYTHON
 

Structure of Python Program-Underlying mechanism of Module Execution-Branching and Looping-Problem Solving Using Branches and Loops-Functions - Lists and Mutability- Problem Solving Using Lists and Functions

 

Lab Exercises

1.      Demonstrate usage of branching and loopingstatements

2.      Demonstrate Recursivefunctions

3.      DemonstrateLists

Unit-2
Teaching Hours:17
SEQUENCE DATATYPES AND OBJECT-ORIENTED PROGRAMMING
 

 

Sequences, Mapping and Sets- Dictionaries- -Classes: Classes and Instances-Inheritance- Exceptional Handling-Introduction to Regular Expressions using “re” module.

Lab Exercises

1.      Demonstrate Tuples andSets

2.      DemonstrateDictionaries

3.      Demonstrate inheritance and exceptionalhandling

4.      Demonstrate use of“re”

Unit-3
Teaching Hours:13
USING NUMPY
 

 

Basics of NumPy-Computation on NumPy-Aggregations-Computation on Arrays- Comparisons, Masks and Boolean Arrays-Fancy Indexing-Sorting Arrays-Structured Data: NumPy’s Structured Array.

Lab Exercises

1.      DemonstrateAggregation

2.      Demonstrate Indexing andSorting

Unit-4
Teaching Hours:13
DATA MANIPULATION WITH PANDAS -I
 

 

Introduction to Pandas Objects-Data indexing and Selection-Operating on Data in Pandas- Handling Missing Data-Hierarchical Indexing - Combining Data Sets

Lab Exercises

1.      Demonstrate handling of missingdata

2.      Demonstrate hierarchicalindexing

Unit-5
Teaching Hours:17
DATA MANIPULATION WITH PANDAS -II
 

 

Aggregation and Grouping-Pivot Tables-Vectorized String Operations -Working with Time Series-High Performance Pandas- and query()

Lab Exercises

1.      Demonstrate usage of Pivottable

2.      Demonstrate use of andquery()

Unit-6
Teaching Hours:13
VISUALIZATION AND MATPLOTLIB
 

 

Basic functions of matplotlib-Simple Line Plot, Scatter Plot-Density and Contour Plots- Histograms, Binnings and Density-Customizing Plot Legends, Colour Bars-Three- Dimensional Plotting in Matplotlib.

Lab Exercises

1.      DemonstrateScatterPlot

2.      Demonstrate3Dplotting

Text Books And Reference Books:

[1]. Jake VanderPlas ,Python Data Science Handbook - Essential Tools for Working with Data, O’Reily Media,Inc, 2016

[2].   Zhang.Y   ,An   Introduction   to    Python   and   Computer   Programming,   Springer Publications,2016

Essential Reading / Recommended Reading

[1].JoelGrus,DataSciencefromScratchFirstPrincipleswithPython,O’ReillyMedia,2016

[2]. T.R.Padmanabhan, Programming with Python,SpringerPublications,2016

Evaluation Pattern
CIA:  50%
ESE: 50%

 

MDS173L - PROGRAMMING OF DATA SCIENCE IN PYTHON (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course aims at laying down the foundational concepts of python programming. Starting with the fundamental programming using python, it escalates to the advanced programming concepts required for Data Science. It enables the students to organize, process and visualize data using the packages available in Python.

The objective of this course is to provide knowledge of python programming paradigms required for Data Science.

Learning Outcome

CO1: Understand and demonstrate the usage of built-in objects in Python

CO2:Analyze the significance of python program development environment and apply it to solve real world applications

CO3: Implement numerical programming, data handling and visualization through NumPy, Pandas and MatplotLib modules.

Unit-1
Teaching Hours:17
INTRODUCTION TO PYTHON
 

Structure of Python Program-Underlying mechanism of Module Execution-Branching and Looping-Problem Solving Using Branches and Loops-Functions - Lists and Mutability- Problem Solving Using Lists and Functions

Unit-2
Teaching Hours:17
SEQUENCE DATATYPES AND OBJECT-ORIENTED PROGRAMMING
 

Sequences, Mapping and Sets- Dictionaries- -Classes: Classes and Instances-Inheritance- Exceptional Handling-Introduction to Regular Expressions using “re” module.

Unit-3
Teaching Hours:13
USING NUMPY
 

Basics of NumPy-Computation on NumPy-Aggregations-Computation on Arrays- Comparisons, Masks and Boolean Arrays-Fancy Indexing-Sorting Arrays-Structured Data: NumPy’s Structured Array.

Unit-4
Teaching Hours:13
DATA MANIPULATION WITH PANDAS -I
 

Introduction to Pandas Objects-Data indexing and Selection-Operating on Data in Pandas- Handling Missing Data-Hierarchical Indexing - Combining Data Sets

Unit-5
Teaching Hours:17
DATA MANIPULATION WITH PANDAS -II
 

Aggregation and Grouping-Pivot Tables-Vectorized String Operations -Working with Time Series-High Performance Pandas- and query()

Unit-6
Teaching Hours:13
VISUALIZATION AND MATPLOTLIB
 

Basic functions of matplotlib-Simple Line Plot, Scatter Plot-Density and Contour Plots- Histograms, Binnings and Density-Customizing Plot Legends, Colour Bars-Three- Dimensional Plotting in Matplotlib

Text Books And Reference Books:

1. Jake VanderPlas ,Python Data Science Handbook - Essential Tools for Working with   Data, O’Reily Media,Inc, 2016

2. Zhang.Y ,An Introduction to Python and Computer Programming, Springer Publications,2016

Essential Reading / Recommended Reading

  1.   Joel Grus ,Data Science from Scratch First Principles with Python, O’Reilly Media,2016.
  2.   T.R.Padmanabhan, Programming with Python,Springer Publications,2016
  3. "CS41 - The Python Programming Language", Stanfordpython.com, 2019. [Online]. Available: https://stanfordpython.com/#overview. [Accessed: 20- Jun- 2019].
  4.  "Python for Data Science", Cognitive Class, 2019. [Online]. Available: https://cognitiveclass.ai/courses/python-for-data-science/. [Accessed: 20- Jun- 2019].

 

Evaluation Pattern

 

CIA I

CIA  II

CIA III

Attendance

ESE

10%

25%

10%

5%

50%

 

MDS231 - MATHEMATICAL FOUNDATION FOR DATA SCIENCE - II (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course aims at introducing data science related essential mathematics concepts such as fundamentals of topics on Calculus of several variables, Orthogonality, Convex optimization and Graph Theory.

Learning Outcome

CO1: Demonstrate the properties of multivariate calculus

CO2: Use the idea of orthogonality and projections effectively

CO3: Have a clear understanding of Convex Optimization

CO4: Know the about the basic terminologies and properties in Graph Theory

Unit-1
Teaching Hours:14
Calculus of Several Variables
 

Functions of Several Variables: Functions of two, three variables - Limits and continuity in HIgher Dimensions: Limits for functions of two variables, Functions of more than two variables - Partial Derivatives: partial derivative of functions of two variables, partial derivatives of functions of more than two variables, partial derivatives and continuity, second order partial derivatives - The Chain Rule: chain rule on functions of two, three variables, chain rule on functions defined on surfaces - Directional Derivative and Gradient vectors: Directional derivatives in a plane, Interpretation of directional derivative, calculation and gradients, Gradients and tangents to level curves.

Unit-2
Teaching Hours:10
Orthogonality
 

Perpendicular vectors and Orthogonality - Inner Products and Projections onto lines - Projections of Rank one - Projections and Least Squares Approximations - Projection Matrices - Orthogonal Bases, Orthogonal Matrices and Gram-Schmidt orthogonalization

Unit-3
Teaching Hours:12
Introduction to Convex Optimization
 

Affine and Convex Sets: Lines and Line segments, affine sets, affine dimension andrelative interior, convexsets, cones - Hyperplanes and half-spaces - Euclidean balls and ellipsoids- Norm balls and Norm cones - polyhedra - simplexes, Convex hull description of polyhedra - The positive semidefinitecone.

 

Unit-4
Teaching Hours:12
Graph Theory - Basics
 

Graph Classes: Definition of a Graph and Graph terminology, isomorphism of graphs, Completegraphs, bipartite graphs, complete bipartite graphs-Vertex degree: adjacency and incidence, regular graphs - subgraphs, spanning subgraphs, induced subgraphs, removing or adding edges of a graph, removing vertices from graphs - Graph Operations: Graph Union, intersection, complement, self complement, Paths and Cycles, Connected graphs, Eulerian and HamiltonianGraphs.

 

Unit-5
Teaching Hours:12
Graph Theory - More concepts
 

Matrix Representation of Graphs, Adjacency matrices, Incidence Matrices, Trees and its properties, Bridges (cut-edges), spanning trees, weighted Graphs, minimal spanning tree problems, Shortest path problems, cut vertices, cuts, vertex and edge connectivity,  Graph Algorithms - Applications of Graph Theory

 

Text Books And Reference Books:

1.     M. D. Weir, J. Hass, and G. B. Thomas, Thomas' calculus. Pearson, 2016. (Unit 1)

2.     G Strang, Linear Algebra and its Applications, 4th ed., Cengage, 2006. (Unit 2)

3.     S. P. Boyd and L.Vandenberghe, Convex optimization.Cambridge Univ. Pr., 2011.(Unit 3)

4.     J Clark, D A Holton, A first look at Graph Theory, Allied Publishers India, 1995. (Unit 4)

Essential Reading / Recommended Reading

1.J. Patterson and A. Gibson, Deep learning: a practitioner's approach. O'Reilly Media, 2017.

2.S. Sra, S. Nowozin, and S. J. Wright, Optimization for machine learning. MIT Press, 2012.

3.D. Jungnickel, Graphs, networks and algorithms. Springer, 2014.

4.D Samovici, Mathematical Analysis for Machine Learning and Data Mining, World Scientific Publishing Co. Pte. Ltd, 2018

5.P. N. Klein, Coding the matrix: linear algebra through applications to computer science. Newtonian Press, 2015.

6.K H Rosen, Discrete Mathematics and its applications, 7th ed., McGraw Hill, 2016

Evaluation Pattern

CIA:50%

ESE :50%

MDS232 - REGRESSION ANALYSIS (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course aims to provide the grounding knowledge about the regression model building of simple and multiple regression.

Learning Outcome

CO1: Demonstrate deeper understanding of the linear regression model.

CO2: Evaluate R-square criteria for model selection

CO3: Understand the forward, backward and stepwise methods for selecting the variables

CO4: Understand the importance of multicollinearity in regression modelling

CO5: Ability touse and understand generalizations of the linear model to binary and count data

Unit-1
Teaching Hours:13
SIMPLE LINEAR REGRESSION
 

Introduction to regression analysis: Modelling a response, overview and applications of regression analysis, major steps in regression analysis. Simple linear regression (Two variables): assumptions, estimation and properties of regression coefficients, significance and confidence intervals of regression coefficients, measuring the quality of the fit.

Unit-2
Teaching Hours:13
MULTIPLE LINEAR REGRESSION
 

Multiple linear regression model: assumptions, ordinary least square estimation of regression coefficients, interpretation and properties of regression coefficient, significance and confidence intervals of regression coefficients.

Unit-3
Teaching Hours:12
CRITERIA FOR MODEL SELECTION
 

Mean Square error criteria, R2 and  criteria for model selection; Need of the transformation of variables; Box-Cox transformation; Forward, Backward and Stepwise procedures.

Unit-4
Teaching Hours:12
RESIDUAL ANALYSIS
 

Residual analysis, Departures from underlying assumptions, Effect of outliers, Collinearity, Non-constant variance and serial correlation, Departures from normality, Diagnostics and remedies.

Unit-5
Teaching Hours:10
NON LINEAR REGRESSION
 

Introduction to nonlinear regression, Least squares in the nonlinear case and estimation of parameters, Models for binary response variables, estimation and diagnosis methods for logistic and Poisson regressions. Prediction and residual analysis.

Text Books And Reference Books:

[1].D.C Montgomery, E.A Peck and G.G Vining, Introduction to Linear Regression Analysis, John Wiley and Sons,Inc.NY, 2003.

[2]. S. Chatterjee and AHadi, Regression Analysis by Example, 4th Ed., John Wiley and Sons, Inc, 2006

[3].Seber, A.F. and Lee, A.J. (2003) Linear Regression Analysis, John Wiley, Relevant sections from chapters 3, 4, 5, 6, 7, 9, 10.

Essential Reading / Recommended Reading

[1]. Iain Pardoe, Applied Regression Modeling, John Wiley and Sons, Inc, 2012.

[2]. P. McCullagh, J.A. Nelder, Generalized Linear Models, Chapman & Hall, 1989.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS241A - MULTIVARIATE ANALYSIS (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

 This course lays the foundation of Multivariate data analysis. The exposure provided to multivariate data structure, multinomial and multivariate normal distribution, estimation and testing of parameters, various data reduction methods would help the students in having a better understanding of research data, its presentation and analysis.

Learning Outcome

CO1: Understand multivariate data structure, multinomial and multivariate normal distribution

CO2: Apply Multivariate analysis of variance (MANOVA) of one and two-way classified data.

Unit-1
Teaching Hours:12
INTRODUCTION
 

Basic concepts on multivariate variable. Multivariate normal distribution, Marginal and conditional distribution, Concept of random vector: Its expectation and Variance-Covariance matrix. Marginal and joint distributions. Conditional distributions and Independence of random vectors. Multinomial distribution. Sample mean vector and its distribution.

Unit-2
Teaching Hours:12
DISTRIBUTION
 

Sample mean vector and its distribution. Likelihood ratio tests: Tests of hypotheses about the mean vectors and covariance matrices for multivariate normal populations. Independence of sub vectors and sphericity test.

Unit-3
Teaching Hours:12
MULTIVARIATE ANALYSIS
 

Multivariate analysis of variance (MANOVA) of one and two- way classified data. Multivariate analysis of covariance.  Wishart distribution, Hotelling’s T2 and Mahalanobis’ D2 statistics, Null distribution of Hotelling’s T2. Rao’s U statistics and its distribution.

Unit-4
Teaching Hours:12
CLASSIFICATION AND DISCRIMINANT PROCEDURES
 

Bayes, minimax, and Fisher’s criteria for discrimination between two multivariate normal populations. Sample discriminant function. Tests associated with discriminant functions. Probabilities of misclassification and their estimation. Discrimination for several multivariate normal populations

Unit-5
Teaching Hours:12
PRINCIPAL COMPONENT and FACTOR ANALYSIS
 

Principal components, sample principal components asymptotic properties. Canonical variables and canonical correlations: definition, estimation, computations. Test for significance of canonical correlations.

Factor analysis: Orthogonal factor model, factor loadings, estimation of factor loadings, factor scores.  Applications

Text Books And Reference Books:

[1]. Anderson, T.W. 2009. An Introduction to Multivariate Statistical Analysis, 3rd Edition, John Wiley.

[2]. Everitt B, Hothorn T, 2011. An Introduction to Applied Multivariate Analysis with R, Springer.

[3]. Barry J. Babin, Hair, Rolph E Anderson, and William C. Blac, 2013,  Multivariate Data Analysis, Pearson New International Edition, 

Essential Reading / Recommended Reading

[1] Giri, N.C. 1977. Multivariate Statistical Inference. Academic Press.

[2] Chatfield, C. and Collins, A.J. 1982. Introduction to Multivariate analysis. Prentice Hall

[3] Srivastava, M.S. and Khatri, C.G. 1979. An Introduction to Multivariate Statistics. North Holland

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS241B - STOCHASTIC PROCESS (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course is designed to introduce the concepts of theory of estimation and testing of hypothesis. This paper also deals with the concept of parametric tests for large and small samples. It also provides knowledge about non-parametric tests and its applications.

Learning Outcome

CO1: Demonstrate the concepts of point and interval estimation of unknown parameters and their significance using large and small samples.

CO2: Apply the idea of sampling distributions of difference statistics in testing of hypotheses.

CO3: Infer the concept of nonparametric tests for single sample and two samples.

Unit-1
Teaching Hours:12
INTRODUCTION TO STOCHASTIC PROCESSES
 

Classification of Stochastic Processes, Markov Processes – Markov Chain - Countable State Markov Chain. Transition Probabilities, Transition Probability Matrix. Chapman - Kolmogorov's Equations, Calculation of n - step Transition Probability and its limit.

Unit-2
Teaching Hours:12
POISSON PROCESS
 

Classification of States, Recurrent and Transient States - Transient Markov Chain, Random Walk and Gambler's Ruin Problem. Continuous Time Markov Process:, Poisson Processes, Birth and Death Processes, Kolmogorov’s Differential Equations, Applications.

Unit-3
Teaching Hours:12
BRANCHING PROCESS
 

Branching Processes – Galton – Watson Branching Process - Properties of Generating Functions – Extinction Probabilities – Distribution of Total Number of Progeny. Concept of Weiner Process.

Unit-4
Teaching Hours:12
RENEWAL PROCESS
 

Renewal Processes – Renewal Process in Discrete and Continuous Time – Renewal Interval – Renewal Function and Renewal Density – Renewal Equation – Renewal theorems: Elementary Renewal Theorem. Probability Generating Function of Renewal Processes.

Unit-5
Teaching Hours:12
STATIONARY PROCESS
 

Stationary Processes: Discrete Parameter Stochastic Process – Application to Time Series. Auto-covariance and Auto-correlation functions and their properties. Moving Average, Autoregressive, Autoregressive Moving Average, Autoregressive Integrated Moving Average Processes. Basic ideas of residual analysis, diagnostic checking, forecasting.

Text Books And Reference Books:

[1]. Stochastic Processes, R.G Gallager, Cambridge University Press, 2013.

[2]. Stochastic Processes, S.M Ross, Wiley India Pvt. Ltd, 2008.

Essential Reading / Recommended Reading

[1]. Stochastic Processes from Applications to Theory, P.D Moral and S. Penev, CRC Press, 2016

[2]. Introduction to Probability and Stochastic Processes with Applications, B..C. Liliana, A Viswanathan, S. Dharmaraja, Wiley Pvt. Ltd, 2012.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS241C - CATEGORICAL DATA ANALYSIS (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Categorical data analysis deals with the study of information captured through expressions or verbal forms. This course equips the students with the theory and methods to analyse and categorical responses.

Learning Outcome

CO1: Describe the categorical response.

CO2: Identify tests for contingency tables.

CO3: Apply regression models for categorical response variables.

CO4: Analyse contingency tables using log-linear models.

Unit-1
Teaching Hours:12
INTRODUCTION
 

Categorical response data - Probability distributions for categorical data - Statistical inference for discrete data

Unit-2
Teaching Hours:12
CONTINGENCY TABLES
 

Probability structure for contingency tables - Comparing proportions with 2x2 tables - The odds ratio - Tests for independence - Exact inference - Extension to three-way and larger tables

Unit-3
Teaching Hours:12
GENERALIZED LINEAR MODELS
 

Components of a generalized linear model - GLM for binary and count data - Statistical inference and model checking - Fitting GLMs

Unit-4
Teaching Hours:12
LOGISTIC REGRESSION
 

Interpreting the logistic regression model - Inference for logistic regression - Logistic regression with categorical predictors - Multiple logistic regression - Summarising effects - Building and applying logistic regression models - Multicategory logit models

Unit-5
Teaching Hours:12
LOGLINEAR MODELS FOR CONTINGENCY TABLES
 

Loglinear models for two-way and three-way tables - Inference for Loglinear models - the log-linear-logistic connection - Independence graphs and collapsibility - Models for matched pairs: Comparing dependent proportions, Logistic regression for matched pairs - Comparing margins of square contingency tables - symmetry issues

Text Books And Reference Books:

1. Agresti, A. (2012). Categorical Data Analysis, 3rd Edition. New York: Wiley

Essential Reading / Recommended Reading

 1. Le, C.T. (2009). Applied Categorical Data Analysis and Translational Research, 2nd Ed., John Wiley and Sons.

 2. Agresti, A. (2010). Analysis of ordinal categorical. John Wiley & Sons.

 3. Stokes, M. E., Davis, C. S., & Koch, G. G. (2012). Categorical data analysis using SAS. SAS Institute.

 4. Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons.

 5. Bilder, C. R., & Loughin, T. M. (2014). Analysis of categorical data with R. Chapman and Hall/CRC.

Evaluation Pattern

CIA:50%

ESE:50%

MDS271 - MACHINE LEARNING (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

Theobjectiveofthiscourseistoprovideintroductiontotheprinciplesanddesignofmachine learning algorithms. The course is aimed at providing foundations for conceptual aspects of machine learning algorithms along with their applications to solve real world problems.

Learning Outcome

CO1: Understand the basic principles of machine learning techniques.

CO2:Understandhowmachinelearningproblemsareformulatedandsolved.

CO3:Applymachinelearningalgorithmstosolverealworldproblems.

Unit-1
Teaching Hours:18
INRTODUCTION
 

MachineLearning-ExamplesofMachineApplications-LearningAssociations-Classification- Regression-UnsupervisedLearning-Reinforcement Learning.Supervised Learning: Learning class from examples- Probably Approach Correct(PAC) Learning-Noise-Learning Multiple classes. Regression-Model Selection and Generalization.

IntroductiontoParametricmethods-MaximumLikelihood Estimation:Bernoulli Density- Multinomial Density-Gaussian Density, Nonparametric Density Estimation: Histogram Estimator-Kernel Estimator-K-Nearest NeighbourEstimator.

 Lab Exercise:

1.      Data Exploration using parametric methods

2.      Data Exploration using non-parametric methods

3.      Regression analysis

Unit-2
Teaching Hours:18
DIMENSIONALITY REDUCTION
 

Dimensionality Reduction: Introduction- Subset Selection-Principal Component Analysis, Feature Embedding-Factor Analysis-Singular Value Decomposition-Multidimensional Scaling-Linear Discriminant Analysis- Bayesian Decision Theory.

Lab Exercise:

1.      Data reduction using Principal ComponentAnalysis

2.      Data reduction using multi-dimensional scaling

Unit-3
Teaching Hours:18
SUPERVISED LEARNING - I
 

Linear Discrimination: Introduction- Generalizing the Linear Model-Geometry of the Linear Discriminant- Pairwise Separation-Gradient Descent-Logistic Discrimination. 

Kernel Machines: Introduction- optical separating hyperplane- v-SVM, kernel tricks- vertical kernel- vertical kernel- defining kernel- multiclass kernel machines- one-class kernel machines.

Lab Exercise

1.   Lineardiscrimination

2.    Logisticdiscrimination

3.   Classification using kernel machines 

Unit-4
Teaching Hours:18
SUPERVISED LEARNING - II
 

Multilayer Perceptron:

Introduction, training a perceptron- learning Boolean functions- multilayer perceptron- backpropogation algorithm- training procedures.

Combining Multiple Learners

Rationale-Generating diverse learners- Model combination schemes- voting, Bagging- Boosting- fine tuning an Ensemble.

Lab Exercise

1.  Classification using MLP

2.  Ensemble Learning

 

Unit-5
Teaching Hours:18
UNSUPERVISED LEARNING
 

Clustering

Introduction-Mixture Densities, K-Means Clustering- Expectation-Maximization algorithm- Mixtures of Latent Varaible Models-Supervised Learning after Clustering-Spectral Clustering- Hierachial Clustering-Clustering- Choosing the number of Clusters.

Lab Exercise

1.  K means clustering

2.  Hierarchical clustering

Text Books And Reference Books:

[1]. E. Alpaydin, Introduction to Machine Learning, 3rd Edition, MIT Press, 2014.

Essential Reading / Recommended Reading

1.  C.M.Bishop,PatternRecognitionandMachineLearning,Springer,2016. 

2.   T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer, 2nd Edition,2009

3.  K.P.Murphy,MachineLearning:AProbabilisticPerspective,MITPress,2012. 

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS272A - HADOOP (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

The subject is intended to give the knowledge of Big Data evolving in every real-time applications and how they are manipulated using the emerging technologies. This course breaks down the walls of complexity in processing Big Data by providing a practical approach to developing Java applications on top of the Hadoop platform. It describes the Hadoop architecture and how to work with the Hadoop Distributed File System (HDFS) and HBase in Ubuntu platform.

Learning Outcome

CO1: Understand the Big Data concepts in real time scenario

CO2: Understand the big data systems and identify the main sources of Big Data in the real world.

CO3: Demonstrate an ability to use Hadoop framework for processing Big Data for Analytics. 

CO4: Evaluate the Map reduce approach for different domain problems. 

 

Unit-1
Teaching Hours:15
INTRODUCTION
 

Distributed file system – Big Data and its importance, Four Vs, Drivers for Big data, Big data analytics, Big data applications, Algorithms using map reduce, Matrix-Vector Multiplication by Map Reduce.

Apache Hadoop– Moving Data in and out of Hadoop – Understanding inputs and outputs ofMapReduce - Data Serialization, Problems with traditional large-scale systems-Requirements for a new approach-Hadoop – Scaling-Distributed Framework-Hadoop v/s RDBMS-Brief history of Hadoop.

 

Lab Exercise

 

1. Installing and Configuring Hadoop

Unit-2
Teaching Hours:15
CONFIGURATIONS OF HADOOP
 

 

Hadoop Processes (NN, SNN, JT, DN, TT)-Temporary directory – UI-Common errors when running Hadoop cluster, solutions.

Setting up Hadoop on a local Ubuntu host: Prerequisites, downloading Hadoop, setting up SSH, configuring the pseudo-distributed mode, HDFS directory, NameNode, Examples of MapReduce, Using Elastic MapReduce, Comparison of local versus EMR Hadoop.

Understanding MapReduce:Key/value pairs,TheHadoop Java API for MapReduce, Writing MapReduce programs, Hadoop-specific data types, Input/output.

Developing MapReduce Programs: Using languages other than Java with Hadoop, Analysing a large dataset.

Lab Exercise

1.      1. Word count application in Hadoop.

2.      2. Sorting the data using MapReduce.

3.      3. Finding max and min value in Hadoop.

Unit-3
Teaching Hours:15
ADVANCED MAPREDUCE TECHNIQUES
 

Simple, advanced, and in-between Joins, Graph algorithms, using language-independent data structures.

Hadoop configuration properties - Setting up a cluster, Cluster access control, managing the NameNode, Managing HDFS, MapReduce management, Scaling.

Lab Exercise: 

1. Implementation of decision tree algorithms using MapReduce.

 2. Implementation of K-means Clustering using MapReduce.

3. Generation of  Frequent Itemset using MapReduce. 

Unit-4
Teaching Hours:15
HADOOP STREAMING
 

Hadoop Streaming  -   Streaming  Command  Options - Specifying  a  Java  Class  as  the  Mapper/Reducer - Packaging Files With Job Submissions - Specifying Other Plug-ins for Jobs.

Lab Exercise: 

1.      1. Count the number of missing and invalid values through joining two large given datasets.

2.      2. Using hadoop’s map-reduce, Evaluating Number of Products Sold in Each Country in the online shopping portal. Dataset is given.

3.      3. Analyze the sentiment for product reviews, this work proposes a MapReduce technique provided by Apache Hadoop.

Unit-5
Teaching Hours:15
HIVE & PIG
 

Architecture, Installation, Configuration, Hive vs RDBMS, Tables, DDL & DML, Partitioning & Bucketing, Hive Web Interface, Pig, Use case of Pig, Pig Components, Data Model, Pig Latin.

Lab Exercise

1. Trend Analysis based on Access Pattern over Web Logs using Hadoop.

2. Service Rating Prediction by Exploring Social Mobile Users Geographical Locations.

Unit-6
Teaching Hours:15
Hbase
 

RDBMS VsNoSQL, HBasics, Installation, Building an online query application – Schema design, Loading Data, Online Queries, Successful service.

Hands On: Single Node Hadoop Cluster Set up in any cloud service provider- How to create instance.How to connect that Instance Using putty.InstallingHadoop framework on this instance. Run sample programs which come with Hadoop framework.

Lab Exercise:

1.      1. Big Data Analytics Framework Based Simulated Performance and Operational Efficiencies Through Billons of Patient Records in Hospital System.

Text Books And Reference Books:

[1] Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, Professional Hadoop Solutions, Wiley, 2015.

[2] Tom White, Hadoop: The Definitive Guide, O’Reilly Media Inc., 2015.

[3] Garry Turkington, Hadoop Beginner's Guide, Packt Publishing, 2013.

Essential Reading / Recommended Reading

[1] Pethuru Raj, Anupama Raman, DhivyaNagaraj and Siddhartha Duggirala, High-Performance Big-Data Analytics: Computing Systems and Approaches, Springer, 2015.

[2] Jonathan R. Owens, Jon Lentz and Brian Femiano, Hadoop Real-World Solutions Cookbook, Packt Publishing, 2013.

[3] Tom White, HADOOP: The definitive Guide, O Reilly, 2012.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS272B - IMAGE AND VIDEO ANALYTICS (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

This course will provide a basic foundation towards digital image processing and video analysis. This course will also provide brief introduction about various Object Detection, Recognition, Segmentation and Compression methods which will help the students to demonstrate real-time image and video analytics applications.

Learning Outcome

CO1: Understand the fundamental principles of image and video analysis

CO2: Apply the image and video analysis approaches to solve real world problems

Unit-1
Teaching Hours:18
INTRODUCTION TO DIGITAL IMAGE AND VIDEO PROCESSING
 

Digital image representation, Sampling and Quantization, Types of Images, Basic Relations between Pixels - Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations, Introduction to Digital Video, Sampled Video, Video Transmission.

Gray-Level Processing: Image Histogram, Linear and Non-linear point operations on Images, Arithmetic Operations between Images, Geometric Image Operations, Image Thresholding, Region labeling, Binary Image Morphology.

Lab Programs:

1.  Program to perform Resize, Rotation of binary, Gray-scale and color images using various methods.

2. Program to implement contrast stretching.

 

Unit-2
Teaching Hours:18
IMAGE AND VIDEO ENHANCEMENT AND RESTORATION
 

Spatial domain-Linear and Non-linear Filtering, Introduction to Fourier Transform and the frequency Domain– Filtering in Frequency domain, Homomorphic Filtering, Brief introduction towards Wavelets, Wavelet based image denoising, A model of The Image Degradation / Restoration, Noise Models and basic methods for image restoration. Blotch detection and Removal.

Lab Programs:

3.  Program to implement various image enhancement techniques using Built-in and user defined functions.

4. Program to implement Non-linear Spatial Filtering using Built-in and userdefined functions.

Unit-3
Teaching Hours:18
IMAGE AND VIDEO ANALYSIS
 

Image Compression: Huffman Coding, Run length Coding, LZW Coding, Basics of Wavelets based image compression.

Video Compression: Basic Concepts and Techniques of Video compression, MPEG-1 and MPEG-2 Video Standards.

Lab Programs: 

5.     Program to implement homomorphic Filtering

6.     Extraction of frames from videos and analyzing frames

Unit-4
Teaching Hours:18
FEATURE DETECTION AND DESCRIPTION
 

Introduction to feature detectors, descriptors, matching and tracking, Basic edge detectors – canny, sobel, prewitt etc., Image Segmentation - Region Based Segmentation – Region Growing and Region Splitting and Merging, Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method.

Lab Programs:

7.     Implement multi-resolution image decomposition and reconstruction using wavelet. 

8.     Implement image compression using wavelets.

Unit-5
Teaching Hours:18
OBJECT DETECTION AND RECOGNITION
 

Descriptors: Boundary descriptors - Fourier descriptors - Regional descriptors - Topological descriptors - moment invariants

Object detection and recognition in image and video: Minimum distance classifier, K-NN classifier and Bayes, Applications in image and video analysis, object tracking in videos.

 Lab Programs:

9.   Extracting feature descriptors from the image dataset.

10. Implement image classification using extracted relevant features.

Text Books And Reference Books:

[1] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 4th Edition, Pearson Education, 2018.

 [2] Alan Bovik, Handbook of Image and Video Processing, Second Edition, Academic Press, 2005.

 

Essential Reading / Recommended Reading

[1] Anil K Jain, Fundamentals of Digital Image Processing, PHI, 2011.

[2] RichardSzeliski,ComputerVision–AlgorithmsandApplications,Springer,2011.

[3] Oge Marques, Practical Image and Video Processing Using MatLab, Wiley, 2011.

[4] John W. Woods, Multidimensional Signal, Image, Video Processing and Coding, Academic Press, 2006.

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS272C - INTERNET OF THINGS (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

The explosive growth of the “Internet of Things” is changing our world and the rapid growth of IoT components is allowing people to innovate new designs and products at home. Wireless Sensor Networks form the basis of the Internet of Things. To latch on to the applications in the field of IoT of the recent times, this course provides a deeper understanding of the underlying concepts of IoT and Wireless Sensor Networks.

Learning Outcome

CO1: Understand the concepts of IoT and IoT enabling technologies

CO2: Gain knowledge on IoT programming and able to develop IoT applications

CO3: Identify different issues in wireless ad hoc and sensor networks

CO4: Develop an understanding of sensor network architectures from a design and performance perspective

CO5: Understand the layered approach in sensor networks and WSN protocols

Unit-1
Teaching Hours:18
Lab Exercises
 

1.   1. Introduction to ICs and Sensors. A basic program can be shown which makes use of logic gates IC s for understanding the basics of sensor nodes. Different sensors which find application in IoT projects can be shown,their working explained.

2.    2.Introduction to Arduino/Raspberry Pi. Sample sketches or code can be selected from theArduinosoftwareandexecuted,making use of different sensors.

Unit-1
Teaching Hours:18
Introduction to IOT
 

Introduction to IoT - Definition and Characteristics, Physical Design Things- Protocols, Logical Design- Functional Blocks, Communication Models- Communication APIs- Introductiontomeasurethephysicalquantities,IoTEnablingTechnologies-WirelessSensor Networks, Cloud Computing Big Data Analytics, Communication Protocols- Embedded System- IoT Levels and DeploymentTemplates.

Unit-2
Teaching Hours:18
IOT Programming
 

Introduction to Smart Systems using IoT - IoT Design Methodology- IoT Boards (Raspberry Pi,Arduino)andIDE-CaseStudy:WeatherMonitoring-LogicalDesignusingPython, Data types & Data Structures- Control Flow, Functions- Modules- Packages, File Handling - Date/Time Operations, Classes- Python Packages of Interest forIoT.

Unit-2
Teaching Hours:18
Lab Exercises
 

3. Use of sensors to detect the temperature/humidity in a room and having appropriate actions performed such as changing the LED color and turning the speaker on as an alarm and using serial monitor to see these values.

4. A basic parking system making use of multiple IR sensors, Ultrasonic Sensors, LED bulbs, Speakers etc, to identify if a slot is empty or full and using the LED and speakers to alert the user about the availability.

Unit-3
Teaching Hours:18
IOT Applications
 

Home Automation – Smart Cities- Environment, Energy- Retail, Logistics- Agriculture, Industry- Health and Lifestyle- IoT and M2M.

Unit-3
Teaching Hours:18
Lab Exercises
 

5. An Agricultural System (Greenhouse System) that makes use of sensors like humidity, temperature etc, to identify the current situation of the agricultural area and taking necessary measures such as activating the water spraying motor, the alarm system (to indicate if there is excess heat) etc.

6. Create a basic sound system by making use of knobs, speakers, LED bulbs etc., to mimic the sound produced by a race car, ambulance, siren etc.

7. A basic obstacle avoiding robot by making use of Ultrasonic sensors, dc motors, and the chassis kit for robotic car.

Unit-4
Teaching Hours:18
Network of wireless sensor nodes
 

SensingandSensors-WirelessSensorNetworks,ChallengesandConstraints-Applications: Structural Health Monitoring, Traffic Control, Health Care - Node Architecture - Operating system.

Unit-4
Teaching Hours:18
Lab Exercise
 

8. Making use of GSM for communication in the obstacle avoiding robot. Using sensors such as flame sensors, PIR human motion sensor, IR sensor, LED bulbs etc for better inputs regarding the environment.

9. A garbage level indicator which makes use of IR proximity sensors, WiFi modules etc to detect the rising amount of garbage and sending data to a server and channelling that data to the owner of the module. Can be introduced as the application IoT. If needed, IoT introduction can be done much earlier and the sharing of data can be shown, for better functionality of later projects.

10. Elderly care: We want to monitor very senior citizens whether they had a sudden fall. If a very senior citizen falls suddenly while walking, due to stroke or slippery ground etc, a notification should be sent out so that he/she can get immediate medical attention. shown, for better functionality of later projects. 

Unit-5
Teaching Hours:18
MAC, Routing and Transport Protocols in WSN
 

Introduction – Fundamentals of MAC Protocols – MAC protocols for WSN – Sensor MAC CaseStudy–RoutingChallengesandDesignIssues–RoutingStrategies–TransportControl Protocols–TransportProtocolDesignIssues–PerformanceofTransportProtocols

Unit-5
Teaching Hours:18
Lab Exercise
 

11. Smart street lights: The street lights should increase or decrease their intensity based on the actual requirements of the amount of light needed at that time of the day. This will save a lot of energy for the municipal corporation.

12. Implement 3-bit Binary Counter using 3 LED Module. 

a. Glow RED if the Binary bit is '0'. Glow GREEN if the binary bit is '1'

i. For example:

ii. 000 = 0 (all LED should be RED)

iii. 001 = 1 (Two LEDs Should be RED , and one LED should be GREEN)

iv. If Button is pressed in between, Reset the counter and Re-start from 0.

Theft prevention system for night: When the room is dark and Board is moved or tilted (say around 90 degree), it should alarm.

Text Books And Reference Books:

[1]   Arshdeep Bahgaand, Vijay Madisetti, Internet of Things: Hands-on Approach, Hyderabad University Press, 2015.

[2]   Kazem Sohraby, Daniel Minoli and TaiebZnati, Wireless Sensor Networks: Technology. Protocols and Application, Wiley Publications, 2010.

[3]    Waltenegus Dargie and Christian Poellabauer, Fundamentals of Wireless Sensor Networks: Theory and Practice, A John Wiley and Sons Ltd., 2010.

Essential Reading / Recommended Reading

[1]    Edgar Callaway, Wireless Sensor Networks: Architecture and Protocols, Auerbach Publications, 2003.

[2]   Michael Miller, The Internet of Things, Pearson Education, 2015.

[3]    Holger Karl and Andreas Willig, Protocols and Architectures for Wireless Sensor Networks, John Wiley & Sons Inc., 2005.

[4]   Erdal Çayırcı and Chunming Rong, SecurityinWirelessAdHocandSensorNetworks,John Wiley and Sons, 2009.

[5]   Carlos De MoraisCordeiro and Dharma Prakash Agrawal, Ad Hoc and Sensor Networks: Theory and Applications, World Scientific Publishing, 2011.


[6]   Adrian Perrig and J.D.Tygar, Secure Broadcast Communication: In Wired and Wireless Networks, Springer, 2006.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS273 - PROGRAMMING FOR DATA SCIENCE IN R (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This lab is designed to introduce implementation of practical machine learning algorithms using R programming language. The lab will extensively use datasets from real life situations.

Learning Outcome

CO1: Demonstrate to use R in any OS (Windows / Mac / Linux).

CO2: Analyse the use of basic functions of R Package.

CO3: Demonstrate exploratory data analysis (EDA) for a given data set.

CO4: Create and edit visualizations with R

CO5: Implement and assess relevance and effectiveness of machine learning algorithms for a given dataset.

Unit-1
Teaching Hours:18
R INSTALLTION, SETUP AND LINEAR REGRESSION
 

Download and install R – R IDE environments – Why R – Getting started with R – Vectors and Data Frames – Loading Data Frames – Data analysis with summary statistics and scatter plots – Summary tables -  Working with Script Files

 Linear Regression – Introduction – Regression model for one variable regression – Selecting best model – Error measures SSE, SST, RMSE, R2 – Interpreting R– Multiple linear regression – Lasso and ridge regression – Correlation – Recitation – A minimum of  3 data sets for practice

Unit-2
Teaching Hours:18
LOGISTIC REGRESSION
 

Logistic Regression – The Logit – Confusion matrix – sensitivity, specificity – ROC curve – Threshold selection with ROC curve – Making predictions – Area under the ROC curve (AUC)  - Recitation – A minimum of 3 data sets for practice

Unit-3
Teaching Hours:18
DECISION TREES
 

Approaches to missing data – Data imputation – Multiple imputation – Classification and Regression Tress (CART) – CART with Cross Validation – Predictions from CART – ROC curve for CART – Random Forests – Building many trees – Parameter selection – K-fold Cross Validation – Recitation – A minimum of 3 data sets for practice

Unit-4
Teaching Hours:18
TEXT ANALYTICS AND NLP
 

Using text as data – Text analytics – Natural language processing – Bag of words – Stemming – word clouds – Recitation – min 3 data sets for practice – Time series analysis – Clustering – k-mean clustering – Random forest with clustering – Understanding cluster patterns – Impact of clustering – Heatmaps – Recitation – min 3 data sets for practice

Unit-5
Teaching Hours:18
ENSEMBLE MODELLING
 

Support Vector Machines – Gradient Boosting – Naive Bayes - Bayesian GLM – GLMNET - Ensemble modeling – Experimenting with all of the above approaches (Units 1-5) with and without data imputation and assessing predictive accuracy – Recitation – min 3 data sets for practice PROJECT – A concluding project work carried out individually for a common data set

Text Books And Reference Books:

[1]. Statistics : An Introduction Using R, Michael J. Crawley, WILEY, Second Edition, 2015.

Essential Reading / Recommended Reading

[1].Hands-on programming with R, Garrett Grolemund, O’Reilley, 1st Edition, 2014

[2]. R for everyone, Jared Lander, Pearson, 1st Edition, 2014

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS331 - NEURAL NETWORKS AND DEEP LEARNING (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The main aim of this course is to provide fundamental knowledge of neural networks and deep learning. On successful completion of the course, students will acquire fundamental knowledge of neural networks and deep learning, such as Basics of neural networks, shallow neural networks, deep neural networks, forward & backward propagation process and build various research projects

Learning Outcome

CO1: Understand the major technology trends in neural networks and deep learning

CO2: Build, train and apply neural networks and fully connected deep neural networks

CO3: Implement efficient (vectorized) neural networks for real time application

Unit-1
Teaching Hours:12
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
 

Neural Networks-Application Scope of Neural Networks- Fundamental Concept of ANN: The Artificial Neural Network-Biological Neural Network-Comparison between Biological Neuron and Artificial Neuron-Evolution of Neural Network. Basic models of ANN-Learning Methods-Activation Functions-Importance Terminologies of ANN. 

Unit-2
Teaching Hours:12
SUPERVISED LEARNING NETWORK
 

Shallow neural networks- Perceptron Networks-Theory-Perceptron Learning RuleArchitecture-Flowchart for training Process-Perceptron Training Algorithm for Single and Multiple Output Classes.

Back Propagation Network- Theory-Architecture-Flowchart for training process-Training Algorithm-Learning Factors for Back-Propagation Network.

Radial Basis Function Network RBFN: Theory, Architecture, Flowchart and Algorithm.

Unit-3
Teaching Hours:12
CONVOLUTIONAL NEURAL NETWORK
 

Introduction - Components of CNN Architecture - Rectified Linear Unit (ReLU) Layer - Exponential Linear Unit (ELU, or SELU) - Unique Properties of CNN -Architectures of CNN -Applications of CNN. 

Unit-4
Teaching Hours:12
RECURRENT NEURAL NETWORK
 

Introduction- The Architecture of Recurrent Neural Network- The Challenges of Training Recurrent Networks- Echo-State Networks- Long Short-Term Memory (LSTM) - Applications of RNN.

Unit-5
Teaching Hours:12
AUTO ENCODER AND RESTRICTED BOLTZMANN MACHINE
 

Introduction - Features of Auto encoder Types of Autoencoder Restricted Boltzmann Machine- Boltzmann Machine - RBM Architecture -Example - Types of RBM.

Text Books And Reference Books:

1. S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition, 2018.

2. Dr. S Lovelyn Rose, Dr. L Ashok Kumar, Dr. D Karthika Renuka, Deep Learning Using Python, Wiley-India, 1st Edition, 2019. 

Essential Reading / Recommended Reading

1. Charu C. Aggarwal, Neural Networks and Deep Learning, Springer, September 2018.

2. Francois Chollet, Deep Learning with Python, Manning Publications; 1st edition, 2017

3. John D. Kelleher, Deep Learning (MIT Press Essential Knowledge series), The MIT Press, 2019. 

Evaluation Pattern

CIA: 50% 

ESE: 50%

MDS331L - NEURAL NETWORKS AND DEEP LEARNING (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The main aim of this course is to provide fundamental knowledge of neural networks and deep learning. On successful completion of the course, students will acquire fundamental knowledge of neural networks and deep learning, such as Basics of neural networks, shallow neural networks, deep neural networks, forward & backward propagation process and build various research projects

Learning Outcome

CO1: Understand the major technology trends in neural networks and deep learning

CO2: Build, train and apply neural networks and fully connected deep neural networks

CO3: Implement efficient (vectorized) neural networks for real time application

Unit-1
Teaching Hours:12
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
 

Neural Networks-Application Scope of Neural Networks- Fundamental Concept of ANN: The Artificial Neural Network-Biological Neural Network-Comparison between Biological Neuron and Artificial Neuron-Evolution of Neural Network. Basic models of ANN-Learning Methods-Activation Functions-Importance Terminologies of ANN. 

Unit-2
Teaching Hours:12
SUPERVISED LEARNING NETWORK
 

Shallow neural networks- Perceptron Networks-Theory-Perceptron Learning RuleArchitecture-Flowchart for training Process-Perceptron Training Algorithm for Single and Multiple Output Classes.

Back Propagation Network- Theory-Architecture-Flowchart for training process-Training Algorithm-Learning Factors for Back-Propagation Network.

Radial Basis Function Network RBFN: Theory, Architecture, Flowchart and Algorithm.

Unit-3
Teaching Hours:12
CONVOLUTIONAL NEURAL NETWORK
 

Introduction - Components of CNN Architecture - Rectified Linear Unit (ReLU) Layer - Exponential Linear Unit (ELU, or SELU) - Unique Properties of CNN -Architectures of CNN -Applications of CNN. 

Unit-4
Teaching Hours:12
RECURRENT NEURAL NETWORK
 

Introduction- The Architecture of Recurrent Neural Network- The Challenges of Training Recurrent Networks- Echo-State Networks- Long Short-Term Memory (LSTM) - Applications of RNN.

Unit-5
Teaching Hours:12
AUTO ENCODER AND RESTRICTED BOLTZMANN MACHINE
 

Introduction - Features of Auto encoder Types of Autoencoder Restricted Boltzmann Machine- Boltzmann Machine - RBM Architecture -Example - Types of RBM.

Text Books And Reference Books:

1. S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition, 2018.

2. Dr. S Lovelyn Rose, Dr. L Ashok Kumar, Dr. D Karthika Renuka, Deep Learning Using Python, Wiley-India, 1st Edition, 2019. 

Essential Reading / Recommended Reading

1. Charu C. Aggarwal, Neural Networks and Deep Learning, Springer, September 2018.

2. Francois Chollet, Deep Learning with Python, Manning Publications; 1st edition, 2017

3. John D. Kelleher, Deep Learning (MIT Press Essential Knowledge series), The MIT Press, 2019. 

Evaluation Pattern

CIA- 50%

ESE-50%

MDS341A - TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course covers applied statistical methods pertaining to time series and forecasting techniques. Moving average models like simple, weighted and exponential are dealt with. Stationary time series models and non-stationary time series models like AR, MA, ARMA and ARIMA are introduced to analyse time series data.

Learning Outcome

CO1: Ability to approach and analyze univariate time series

CO2: Able to differentiate between various time series models like AR, MA, ARMA and ARIMA models

CO3: Evaluate stationary and non-stationary time series models

CO4: Able to forecast future observations of the time series.

Unit-1
Teaching Hours:12
INTRODUCTION TO TIME SERIES AND STOCHASTIC PROCESS
 

Introduction to time series and stochastic process, graphical representation, components and classical decomposition of time series data.Auto-covariance and auto-correlation functions, Exploratory time series analysis, Test for trend and seasonality, Smoothing techniques such as Exponential and moving average smoothing, Holt- Winter smoothing, Forecasting based on smoothing.

Unit-2
Teaching Hours:12
STATIONARY TIME SERIES MODELS
 

Wold representation of linear stationary processes, Study of linear time series models: Autoregressive, Moving Average and Autoregressive Moving average models and their statistical properties like ACF and PACF function.

Unit-3
Teaching Hours:12
ESTIMATION OF ARMA MODELS
 

Estimation of ARMA models: Yule- Walker estimation of AR Processes, Maximum likelihood and least squares estimation for ARMA Processes, Residual analysis and diagnostic checking.

Unit-4
Teaching Hours:12
NON-STATIONARY TIME SERIES MODELS
 

Concept of non-stationarity, general unit root tests for testing non-stationarity; basic formulation of the ARIMA Model and their statistical properties-ACF and PACF; forecasting using ARIMA models

Unit-5
Teaching Hours:12
STATE SPACE MODELS
 

Filtering, smoothing and forecasting using state space models, Kalman smoother, Maximum likelihood estimation, Missing data modifications

Text Books And Reference Books:

 1. George E. P. Box, G.M. Jenkins, G.C. Reinsel and G. M. Ljung, Time Series analysis Forecasting and Control, 5th Edition, John Wiley & Sons, Inc., New Jersey, 2016.

2. Montgomery D.C, Jennigs C. L and Kulachi M,Introduction to Time Series analysis  and Forecasting, 2nd Edition,John Wiley & Sons, Inc., New Jersey, 2016.

Essential Reading / Recommended Reading

1.      Anderson T.W,Statistical Analysis of Time Series, John Wiley& Sons, Inc., New Jersey, 1971. 

2.      Shumway R.H and Stoffer D.S, Time Series Analysis and its Applications with R Examples, Springer, 2011. 

3.      P. J. Brockwell and R. A. Davis, Times series: Theory and Methods, 2nd Edition, Springer-Verlag, 2009. 

4.      S.C. Gupta and V.K. Kapoor, Fundamentals of Applied Statistics, 4th Edition, Sultan Chand and Sons, 2008.

Evaluation Pattern

CIA: 50%

ESE: 50%

 

MDS341AL - TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course covers applied statistical methods pertaining to time series and forecasting techniques. Moving average models like simple, weighted and exponential are dealt with. Stationary time series models and non-stationary time series models like AR, MA, ARMA and ARIMA are introduced to analyse time series data.

 

Learning Outcome

CO1: Ability to approach and analyze univariate time series

CO2: Able to differentiate between various time series models like AR, MA, ARMA and ARIMA models

CO3: Evaluate stationary and non-stationary time series models

         CO4: Able to forecast future observations of the time series.

Unit-1
Teaching Hours:15
Introduction To Time Series And Stochastic Process
 

Introduction to time series and stochastic process, graphical representation, components and classical decomposition of time series data.Auto-covariance and auto-correlation functions, Exploratory time series analysis, Test for trend and seasonality, Smoothing techniques such as Exponential and moving average smoothing, Holt- Winter smoothing, Forecasting based on smoothing

Unit-2
Teaching Hours:15
Stationary time series models
 

Wold representation of linear stationary processes, Study of linear time series models: Autoregressive, Moving Average and Autoregressive Moving average models and their statistical properties like ACF and PACF function.

 

Unit-3
Teaching Hours:15
Estimation of ARMA models
 

 

Estimation of ARMA models: Yule- Walker estimation of AR Processes, Maximum likelihood and least squares estimation for ARMA Processes, Residual analysis and diagnostic checking.

 

Unit-4
Teaching Hours:15
Non-Stationary Time Series Models
 

Concept of non-stationarity, general unit root tests for testing non-stationarity; basic formulation of the ARIMA Model and their statistical properties-ACF and PACF; forecasting using ARIMA models

 

Text Books And Reference Books:

 

 

T1 George E. P. Box, G.M. Jenkins, G.C. Reinsel and G. M. Ljung, Time Series analysis Forecasting and Control, 5th Edition, John Wiley & Sons, Inc., New Jersey,2016.

 

T2Montgomery D.C, Jennigs C. L and Kulachi M, Introduction to Time Series analysis and Forecasting, 2nd Edition,John Wiley & Sons, Inc., New Jersey,2016.

Essential Reading / Recommended Reading

1.       Anderson T.W,Statistical Analysis of Time Series, John Wiley& Sons, Inc., New Jersey,1971.

2.       Shumway R.H and Stoffer D.S, Time Series Analysis and its Applications with R Examples, Springer,2011.

3.       P. J. Brockwell and R. A. Davis, Times series: Theory and Methods, 2nd Edition, Springer-Verlag,2009.

4.       S.C. Gupta and V.K. Kapoor, Fundamentals of Applied Statistics, 4th Edition, Sultan Chand and Sons,2008.

Evaluation Pattern
CIA: 50%
ESE: 50%

MDS341B - BAYESIAN INFERENCE (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To equip the students with the knowledge of conceptual, computational, and practical methods of Bayesian data analysis.

Learning Outcome

CO1: Understand Bayesian models and their specific model assumptions.

CO2: Identify suitable informative and non-informative prior distributions to derive posterior distributions

CO3: Apply computer intensive methods like MCMC for approximating the posterior distribution.

CO4: Analyse the results obtained by Bayesian methods.

Unit-1
Teaching Hours:12
INTRODUCTION
 

Basics on minimaxity: subjective and frequents probability, Bayesian inference, Bayesian estimation , prior distributions, posterior distribution, loss function, principle of minimum expected posterior loss, quadratic and other common loss functions, Advantages of being a Bayesian HPD confidence intervals, testing, credible intervals, prediction of a future observation.

Unit-2
Teaching Hours:12
BAYESIAN ANALYSIS WITH PRIOR INFORMATION
 

Robustness and sensitivity, classes of priors, conjugate class, neighbourhood class, density ratio class different methods of objective priors: Jeffrey’s prior, probability matching prior, conjugate priors and mixtures, posterior robustness: measures and techniques

Unit-3
Teaching Hours:12
MULTIPARAMETER AND MULTIVARIABLE MODELS
 

Basics of decision theory, multi-parameter models, Multivariate models, linear regression, asymptotic approximation to posterior distributions

Unit-4
Teaching Hours:12
MODEL SELECTION AND HYPOTHESIS TESTING
 

Selection criteria and testing of hypothesis based on objective probabilities and Bayes’ factors, large sample methods: limit of posterior distribution, consistency of posterior distribution, asymptotic normality of posterior distribution.

Unit-5
Teaching Hours:12
BAYESIAN COMPUTATIONS
 

Analytic approximation, E- M Algorithm, Monte Carlo sampling, Markov Chain Monte Carlo Methods, Metropolis – Hastings Algorithm, Gibbs sampling, examples, convergence issues

Text Books And Reference Books:

1. Albert Jim (2009) Bayesian Computation with R, second edition, Springer, New York

2. Bolstad W. M. and Curran, J.M. (2016) Introduction to Bayesian Statistics 3rd Ed. Wiley, New York

3. Christensen R. Johnson, W. Branscum A. and Hanson T.E. (2011) Bayesian Ideas and data analysis : A introduction for scientist and Statisticians, Chapman and Hall, London 

4. A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin (2004). Bayesian Data Analysis, 2nd Ed. Chapman & Hall

Essential Reading / Recommended Reading

 1. Congdon P. (2006) Bayesian Statistical Modeling, Wiley, New York.

2. Ghosh, J.K. Delampady M. and T. Samantha (2006). An Introduction to Bayesian Analysis: Theory and Methods, Springer, New York.

3. Lee P.M. (2012) Bayesian Statistics: An Introduction-4th Ed. Hodder Arnold, New York.

4. Rao C.R. Day D. (2006) Bayesian Thinking, Modeling and Computation, Handbook of Statistics, Vol.25.

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS341C - ECONOMETRICS (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The course is designed to impart the learning of principles of econometric methods and tools. This is expected to improve student’s ability to understand of econometrics in the study of economics and finance. The learning objective of the course is to provide students to get the basic knowledge and skills of econometric analysis, so that they should be able to apply it to the investigation of economic relationships and processes, and also understand the econometric methods, approaches, ideas, results and conclusions met in the majority of economic books and articles. Introduce the students to the traditional econometric methods developed mostly for the work with cross-sections data.

Learning Outcome

CO1: Demonstrate Simple and multiple Econometric models

CO2: Interpret the models adequacy through various methods

CO3: Demonstrate simultaneous Linear Equations model.

Unit-1
Teaching Hours:15
INTRODUCTION
 

Introduction to Econometrics- Meaning and Scope – Methodology of Econometrics – Nature and Sources of Data for Econometric analysis – Types of Econometrics

Unit-2
Teaching Hours:15
CORRELATION
 

Aitken’s Generalised Least Squares(GLS) Estimator, Heteroscedasticity, Auto-correlation, Multicollinearity, Auto-Correlation, Test of Auto-correlation, Multicollinearity, Tools for Handling Multicollinearity

Unit-3
Teaching Hours:15
REGRESSION
 

Linear Regression with Stochastic Regressors, Errors in Variable Models and Instrumental Variable Estimation, Independent Stochastic linear Regression, Auto regression, Linear regression, Lag Models

Unit-4
Teaching Hours:15
LINEAR EQUATIONS MODEL
 

Simultaneous Linear Equations Model : Structure of Linear Equations Model, Identification Problem, Rank and Order Conditions, Single Equation and Simultaneous Equations, Methods of Estimation- Indirect Least squares, Least Variance Ratio and Two-Stage Least Square

Text Books And Reference Books:

1.  Johnston, J. (1997). Econometric Methods, Fourth Edition, McGraw Hill

2.  Gujarathi, D., and Porter, D. (2008). Basic Econometrics, Fifth Edition, McGraw-Hill

Essential Reading / Recommended Reading

1.   Intriligator, M. D. (1980). Econometric Models-Techniques and Applications, Prentice Hall.

2.  Theil, H. (1971). Principles of Econometrics, John Wiley.

3.  Walters, A. (1970). An Introduction to Econometrics, McMillan and Co.

Evaluation Pattern

CIA : 50%

ESE : 50%

MDS341D - BIO-STATISTICS (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course provides an understanding of various statistical methods in describing and analyzing biological data. Students will be equipped with an idea about the applications of statistical hypothesis testing, related concepts and interpretation in biological data.

Learning Outcome

CO1: Demonstrate the understanding of basic concepts of biostatistics and the process involved in the scientific method of research.

CO2: Identify how the data can be appropriately organized and displayed.

CO3: Interpret the measures of central tendency and measures of dispersion.

CO4: Interpret the data based on the discrete and continuous probability distributions.

CO5: Apply parametric and non-parametric methods of statistical data analysis. 

Unit-1
Teaching Hours:12
INTRODUCTION TO BIOSTATISTICS
 

Presentation of data - graphical and numerical representations of data - Types of variables, measures of location - dispersion and correlation - inferential statistics - probability and distributions - Binomial, Poisson, Negative Binomial, Hyper geometric and normal distribution.

Unit-2
Teaching Hours:12
PARAMETRIC AND NON - PARAMETRIC METHODS
 

Parametric methods - one sample t-test - independent sample t-test - paired sample t-test - one-way analysis of variance - two-way analysis of variance - analysis of covariance - repeated measures of analysis of variance - Pearson correlation coefficient - Non-parametric methods: Chi-square test of independence and goodness of fit - Mann Whitney U test - Wilcoxon signed-rank test - Kruskal Wallis test - Friedman’s test - Spearman’s correlation test.

Unit-3
Teaching Hours:12
GENERALIZED LINEAR MODELS
 

Review of simple and multiple linear regression - introduction to generalized linear models - parameter estimation of generalized linear models - models with different link functions - binary (logistic) regression - estimation and model fitting - Poisson regression for count data - mixed effect models and hierarchical models with practical examples.

Unit-4
Teaching Hours:12
EPIDEMIOLOGY
 

Introduction to epidemiology, measures of epidemiology, observational study designs: case report, case series correlational studies, cross-sectional studies, retrospective and prospective studies, analytical epidemiological studies-case control study and cohort study, odds ratio, relative risk, the bias in epidemiological studies.

Unit-5
Teaching Hours:12
DEMOGRAPHY
 

Introduction to demography, mortality and life tables, infant mortality rate, standardized death rates, life tables, fertility, crude and specific rates, migration-definition and concepts population growth, measurement of population growth-arithmetic, geometric and exponential, population projection and estimation, different methods of population projection, logistic curve, urban population growth, components of urban population growth.

Text Books And Reference Books:

1. Marcello Pagano and Kimberlee Gauvreau (2018), Principles of Biostatistics, 2nd Edition, Chapman and Hall/CRC press

2. David Moore S. and George McCabe P., (2017) Introduction to practice of statistics, 9th Edition, W. H. Freeman.

3. Sundar Rao and Richard J., (2012) Introduction to Biostatistics and research methods, PHI Learning Private limited, New Delhi

Essential Reading / Recommended Reading

1. Abhaya Indrayan and Rajeev Kumar M., (2018) Medical Biostatistics, 4th Edition, Chapman and Hall/CRC Press.

2. Gordis Leon (2018), Epidemiology, 6th Edition, Elsevier, Philadelphia

3. Ram, F. and Pathak K. B., (2016): Techniques of Demographic Analysis, Himalaya Publishing house, Bombay.

4. Park K., (2019), Park's Text Book of Preventive and Social Medicine, Banarsidas Bhanot, Jabalpur. 

Evaluation Pattern

CIA:50%

ESE:50%

MDS371 - CLOUD ANALYTICS (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

The objective of this course is to explore the basics of cloud analytics and the major cloud solutions. Students will learn how to analyze extremely large data sets, and to create visual representations of that data. Also aim to provide students with hands-on experience working with data at scale.

Learning Outcome

 CO1Interpret the deployment and service models of cloud applications.

 CO2Describe big data analytical concepts.

CO3Ingest, store, and secure data.

CO4Process and Visualize structured and unstructured data.

Unit-1
Teaching Hours:18
INTRODUCTION
 

Introduction to cloud computing - Major benefits of cloud computing - Cloud computing deployment models - Private cloud - Public cloud - Hybrid cloud - Types of cloud computing services -Infrastructure as a Service – PaaS – SaaS - Emerging cloud technologies and services - Different ways to secure the cloud - Risks and challenges with the cloud - What is cloud analytics? Parameters before adopting cloud strategy - Technologies utilized by cloud computing

1.Creating Virtual Machines using Hypervisors

2.IaaS: Compute service - Creating and running Virtual Machines

Unit-2
Teaching Hours:18
CLOUD ENABLING TECHNOLOGIES
 

Virtualization - Load Balancing - Scalability & Elasticity – Deployment –Replication – Monitoring - Software Defined Networking - Network Function Virtualization – MapReduce - Identity and Access Management - Service Level Agreements - Billing

1.      Storage as a Service: Ingesting & Querying data into cloud

2.      Database as a Service: Building DB Server

Unit-3
Teaching Hours:18
BASIC CLOUD SERVICES & PLATFORMS
 

Compute Services

Amazon Elastic Compute Cloud - Google Compute Engine -     Windows     Azure     Virtual Machines

Storage Services

Amazon Simple Storage Service - Google Cloud Storage - Windows Azure Storage

Database Services

Amazon Relational Data Store - Amazon DynamoDB - Google Cloud SQL - Google Cloud Datastore - Windows Azure SQL Database - Windows Azure Table Service

1.      PaaS: Working with GoogleAppEngine

Unit-4
Teaching Hours:18
DATA INGESTION AND STORING
 

Cloud Dataflow - The Dataflow programming model - Cloud Pub/Sub - Cloud storage - Cloud SQL - Cloud BigTable - Cloud Spanner - Cloud Datastore - Persistent disks 

1. Database as a Service: Building DB Server

2. Transforming data

Unit-4
Teaching Hours:18
PROCESSING AND VISUALIZING
 

 Google BigQuery - Cloud Dataproc - Google Cloud Datalab - Google Data Studio

 1.      Visualize structured data and unstructureddata

Unit-5
Teaching Hours:18
MACHINE LEARNING, DEEP LEARNING AND AI
 

Services on Artificial intelligence - Machine learning - Cloud Natural Language API – TensorFlow - Cloud Speech API - Cloud Translation API - Cloud Vision API - Cloud Video Intelligence – Dialogflow – AutoML

1. Load and query data in a data warehouse

2. Setting up and executing a data pipeline job to load data into cloud

Text Books And Reference Books:

1.  Sanket Thodge, Cloud Analytics with Google Cloud Platform, Packt Publishing, 2018.

2.  Arshdeep Bahga and Vijay Madisetti, Cloud computing - A Hands-On Approach, Create Space Independent Publishing Platform, 2014.

Essential Reading / Recommended Reading

1.     Deven Shah, Kailash Jayaswal, Donald J. Houde, Jagannath Kallakurchi, Cloud Computing - Black Book, Wiley, 2014.

2.      Thomas Erl, Ricardo Puttini, Zaigham Mahmood, Cloud Computing: Concepts, Technology & Architecture, Prentice Hall, 2014.

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS371L - CLOUD ANALYTICS (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

The objective of this course is to explore the basics of cloud analytics and the major cloud solutions. Students will learn how to analyze extremely large data sets, and to create visual representations of that data. Also aim to provide students with hands-on experience working with data at scale.

Learning Outcome

 CO1: Interpret the deployment and service models of cloud applications.

 CO2: Describe big data analytical concepts.

CO3: Ingest, store, and secure data.

CO4: Process and Visualize structured and unstructured data.

Unit-1
Teaching Hours:18
INTRODUCTION
 

Introduction to cloud computing - Major benefits of cloud computing - Cloud computing deployment models - Private cloud - Public cloud - Hybrid cloud - Types of cloud computing services -Infrastructure as a Service – PaaS – SaaS - Emerging cloud technologies and services - Different ways to secure the cloud - Risks and challenges with the cloud - What is cloud analytics? Parameters before adopting cloud strategy - Technologies utilized by cloud computing

1.Creating Virtual Machines using Hypervisors

2.IaaS: Compute service - Creating and running Virtual Machines

Unit-2
Teaching Hours:18
CLOUD ENABLING TECHNOLOGIES
 

Virtualization - Load Balancing - Scalability & Elasticity – Deployment –Replication – Monitoring - Software Defined Networking - Network Function Virtualization – MapReduce - Identity and Access Management - Service Level Agreements - Billing

1.      Storage as a Service: Ingesting & Querying data into cloud

2.      Database as a Service: Building DB Server

Unit-3
Teaching Hours:18
BASIC CLOUD SERVICES & PLATFORMS
 

Compute Services

Amazon Elastic Compute Cloud - Google Compute Engine -     Windows     Azure     Virtual Machines

Storage Services

Amazon Simple Storage Service - Google Cloud Storage - Windows Azure Storage

Database Services

Amazon Relational Data Store - Amazon DynamoDB - Google Cloud SQL - Google Cloud Datastore - Windows Azure SQL Database - Windows Azure Table Service

1.      PaaS: Working with GoogleAppEngine

Unit-4
Teaching Hours:18
DATA INGESTION AND STORING
 

Cloud Dataflow - The Dataflow programming model - Cloud Pub/Sub - Cloud storage - Cloud SQL - Cloud BigTable - Cloud Spanner - Cloud Datastore - Persistent disks 

1. Database as a Service: Building DB Server

2. Transforming data

PROCESSING AND VISUALIZING

 Google BigQuery - Cloud Dataproc - Google Cloud Datalab - Google Data Studio

 1.      Visualize structured data and unstructureddata

Unit-5
Teaching Hours:18
MACHINE LEARNING, DEEP LEARNING AND AI
 

Services on Artificial intelligence - Machine learning - Cloud Natural Language API – TensorFlow - Cloud Speech API - Cloud Translation API - Cloud Vision API - Cloud Video Intelligence – Dialogflow – AutoML

1. Load and query data in a data warehouse

2. Setting up and executing a data pipeline job to load data into cloud

Text Books And Reference Books:

1. Sanket Thodge, Cloud Analytics with Google Cloud Platform, Packt Publishing, 2018.

2.  Arshdeep Bahga and Vijay Madisetti, Cloud computing - A Hands-On Approach, Create Space

Essential Reading / Recommended Reading

1. Deven Shah, Kailash Jayaswal, Donald J. Houde, Jagannath Kallakurchi, Cloud Computing - Black Book, Wiley, 2014.

2. Thomas Erl, Ricardo Puttini, Zaigham Mahmood, Cloud Computing: Concepts, Technology & Architecture, Prentice Hall, 2014.

Evaluation Pattern

CIA-50%

ESE-50%

MDS372A - NATURAL LANGUAGE PROCESSING (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

 The goal is to make familiar with the concepts of the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning concepts.

 

 

Learning Outcome

 CO1: Understand various approaches on syntax and semantics in NLP

 CO2: Apply various methods to discourse, generation, dialogue, and summarization using NLP. 

 CO3: Analyze various methodologies used in machine translation, machine learning techniques used in NLP including unsupervised models and to  analyze real-time applications 

Unit-1
Teaching Hours:18
INTRODUCTION
 

Introduction to NLP- Background and overview- NLP Applications -NLP hard Ambiguity- Algorithms and models,Knowledge Bottlenecks in NLP-Introduction to NLTK,Case study.

 Lab Exercises:

1.     Write a program to tokenize text

2.     Write a program to count word frequency and toremove stopwords

Unit-2
Teaching Hours:18
PARSING AND SYNTAX
 

WordLevelAnalysis: RegularExpressions,Text Normalization, Edit Distance, Parsing and Syntax-Spelling, Error Detection and correction-Words and Word classes-Part-of speech Tagging, Naive Bayes and Sentiment Classification: Case study

 Lab Exercises:

 1.     Write a program to tokenize Non-English Languages

 2.     Write a program to get synonyms from WordNet

Unit-3
Teaching Hours:18
SMOOTHED ESTIMATION AND LANGUAGE MODELLING
 

N-gram Language Models:N-Grams,Evaluating Language Models-The language modelling problem

Unit-3
Teaching Hours:18
SEMANTIC ANALYSIS AND DISCOURSE PROCESSING
 

Semantic Analysis: Meaning Representation-Lexical Semantics- Ambiguity-Word Sense Disambiguation. Discourse Processing: cohesion-Reference Resolution- Discourse Coherence and Structure.

 Lab Exercises:

 1.     Write a program to get Antonyms from WordNet

 2.       Write a program for stemming Non-English words

 

Unit-4
Teaching Hours:18
NATURAL LANGUAGE GENERATION AND MACHINE TRANSLATION
 

 Natural Language Generation: Architecture of NLG Systems, Applications

 Machine Translation: Problems in Machine Translation- Machine Translation Approaches-

Evaluation of Machine Translation systems.Case study: Characteristics of Indian Languages

 LabExercises:  

1.  Write a program for lemmatizing words UsingWordNet

2.  Write a program to differentiate stemming and lemmatizing words

 

Unit-5
Teaching Hours:18
INFORMATION RETRIEVAL AND LEXICAL RESOURCES
 

Information Retrieval: Design features of Information Retrieval Systems-Classical, Non- classical, Alternative

Models of Information Retrieval – valuation Lexical Resources: Word Embeddings - Word2vec- Glove.

Unit-5
Teaching Hours:18
UNSUPERVISED METHODS IN NLP
 

Graphical Models for Sequence Labelling in NLP

 Lab Exercises

 1.     Write a program for POS Tagging or Word Embeddings.

 2.     Case study-based program (IBM) or Sentiment analysis.

Text Books And Reference Books:

1.   Speech and Language Processing, Daniel Jurafsky and James H., 2nd Edition, Martin PrenticeHall,2013.

2.   Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press, 1999.

 

Essential Reading / Recommended Reading

 1.  Foundations of Computational Linguistics: Human-computer Communication in Natural Language, Roland R. Hausser, Springer,2014.

2.  Steven Bird, Ewan Klein and Edward Loper Natural Language Processing with Python, O’Reilly Media; 1 edition,2009.

Web resources:

1.     https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf

2.     https://nptel.ac.in/courses/106101007/

3.     NLTK – Natural Language Tool Kit-http://www.nltk.org

Evaluation Pattern

CIA:50%

ESE:50%

MDS372AL - NATURAL LANGUAGE PROCESSING (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

The goal is to make familiar with the concepts of the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning concepts.

Learning Outcome

  CO1: Understand various approaches on syntax and semantics in NLP

  CO2: Apply various methods to discourse, generation, dialogue, and summarization using NLP. 

  CO3: Analyze various methodologies used in machine translation, machine learning techniques used in NLP including unsupervised models and   to  analyze realtime applications

Unit-1
Teaching Hours:18
INTRODUCTION
 

Introduction to NLP- Background and overview- NLP Applications -NLP hard Ambiguity- Algorithms and models, Knowledge Bottlenecks in NLP-Introduction to NLTK , Case study.

 

Lab Exercises:

           1.     Write a program to tokenize text

 2.     Write a program to count word frequency and to remove stop words

Unit-2
Teaching Hours:18
PARSING AND SYNTAX
 

WordLevelAnalysis: RegularExpressions,Text Normalization,Edit Distance, Parsing and Syntax-Spelling,Error Detection and correction-Words and Word classes-Part-ofSpeech Tagging, Naive Bayes and Sentiment Classification: Case study

 

Lab Exercises:

 

           1.     Write a program to tokenize Non-English Languages

 2.     Write a program to get synonyms from WordNet

Unit-3
Teaching Hours:18
SMOOTHED ESTIMATION AND LANGUAGE MODELLING
 

N-gram Language Models:N-Grams,Evaluating Language Models-The language modelling problem

Unit-3
Teaching Hours:18
SEMANTIC ANALYSIS AND DISCOURSE PROCESSING
 

Semantic Analysis: Meaning Representation-Lexical Semantics- Ambiguity-Word Sense Disambiguation. Discourse Processing: cohesion-Reference Resolution- Discourse Coherence and Structure.

 

Lab Exercises:

 

           1.     Write a program to get Antonyms from WordNet

 2.       Write a program for stemming Non-English words

Unit-4
Teaching Hours:18
NATURAL LANGUAGE GENERATION AND MACHINE TRANSLATION
 

Natural Language Generation: Architecture of NLG Systems, Applications.

          Machine Translation: Problems in Machine Translation- Machine Translation Approaches-

Evaluation of Machine Translation systems.Case study: Characteristics of Indian Languages

          Lab Exercises:  

          1.  Write a program for lemmatizing words UsingWordNet

 

          2.  Write a program to differentiate stemming and lemmatizing  words

Unit-5
Teaching Hours:18
INFORMATION RETRIEVAL AND LEXICAL RESOURCES
 

Information Retrieval: Design features of Information Retrieval Systems-Classical, Non- classical, Alternative

           Models of Information Retrieval – valuation Lexical Resources: Word Embeddings - Word2vec- Glove.

 

Unit-5
Teaching Hours:18
UNSUPERVISED METHODS IN NLP
 

Graphical Models for Sequence Labelling in NLP

 

Lab Exercises

 

          1.     Write a program for POS Tagging or Word Embeddings.

 2.     Case study-based program (IBM) or Sentiment analysis.

Text Books And Reference Books:

1.   Speech and Language Processing, Daniel Jurafsky and James H., 2nd Edition, Martin PrenticeHall,2013.

2.   Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press, 1999.

Essential Reading / Recommended Reading

1.  Foundations of Computational Linguistics: Human-computer Communication in Natural Language, Roland R. Hausser, Springer,2014.

2.  Steven Bird, Ewan Klein and Edward Loper Natural Language Processing with Python, O’Reilly Media; 1 edition,2009.

Evaluation Pattern

CIA-50% Marks

ESE-50% Marks

CAT1

CAC1

Regular Lab

Attendance

CAT2

CAC2

CAT3

25%

20%

45%

10%

30%

30%

40%

MDS372B - WEB ANALYTICS (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

The objective of this course is to provide an overview and the importance of Web analytics and helps to understand role of Web analytic. This course also explores the effective of Web analytic strategies and implementation

Learning Outcome

CO1: Understand the concept and importance of Web analytics in an organization and the role of Web analytic in collecting, analyzing and reporting website traffic.

CO2: Identify key tools and diagnostics associated with Web analytics.

CO3: Explore effective Web analytics strategies and implementation and Understand the importance of web analytic as a tool for e-Commerce, business research, and market research. 

 

Unit-1
Teaching Hours:18
INTRODUCTION TO WEB ANALYTICS
 

Introduction to Web Analytics: Web Analytics Approach – A Model of Analysis – Context matters – Data Contradiction – Working of Web Analytics: Log file analysis – Page tagging – Metrics and Dimensions – Interacting with data in Google Analytics

Lab Exercise

1. Working concept of web analytics

2. Evaluation with Intermediate metrics, custom metrics, calculated metrics.

 

Unit-2
Teaching Hours:18
LEARNING ABOUT USERS THROUGH WEB ANALYTICS
 

Goals: Introduction – Goals and Conversions – Conversion Rate – Goal reports in Google Analytics – Performance Indicators – Analyzing Web Users: Learning about users – Traffic Analysis – Analyzing user content – Click-Path analysis – Segmentation

Lab Exercise

1. Collection of web data and other internet data with the help of web analytics

2. Delivering reports based on collected data

3. Implement the concept of web analytics ecosystem

Unit-3
Teaching Hours:18
GOOGLE ANALYTICS
 

Different analytical tools - Key features and capabilities of Google analytics- How Google analytics works - Implementing Google analytics - Getting up and running with Google analytics -Navigating Google analytics – Using Google analytics reports -Google metrics - Using visitor data to drive website improvement- Focusing on key performance indicators- Integrating Google analytics with third-Party applications 

Lab Exercise

1. Creation of segmentation in web analytics

2. Visualization, acquisition and conversions of web analytics data

Unit-4
Teaching Hours:18
OVERVIEW OF QUALITATIVE ANALYSIS
 

Lab Usability Testing- Heuristic Evaluations- Site Visits- Surveys (Questionnaires) - Testing and Experimentation: A/B Testing and Multivariate Testing-Competitive Intelligence - Analysis Search Analytics: Performing Internal Site Search Analytics, Search Engine Optimization (SEO) and Pay per Click (PPC)-Website Optimization against KPIs- Content optimization- Funnel/Goal optimization - Text Analytics: Natural Language Processing (NLP)- Supervised Machine Learning (ML) Algorithms-API and Web data scarping using R and Python 

Lab Exercise

1. Performing site search analytics

2. Analyse the web analytic reports and visualizations

3. Performing visual web analytics

Unit-5
Teaching Hours:18
VISUAL ANALYTICS
 

VISUAL ANALYTICS:  Drill down and hierarchies-Sorting-Grouping- Additional Ways to Group- Creating Sets- Analysis with Cubes and MDX- Filtering for Top and Top N- Using the Filter Shelf- The Formatting Pane- Trend Lines- Forecasting- Formatting- Parameters -  SOCIAL NETWORK ANALYSIS:  Types of social network-Graph Visualization-Network Relationships-Network structures: equivalence-Network Evolution-Diffusion in networks- Descriptive Modeling-Predictive Modeling-Customer Profiling-Network targeting 

 Lab Exercise

1. Assignments and final discussions

2. Web Analytics case studies 

Text Books And Reference Books:

1. Beasley M, (2013), Practical web analytics for user experience: How analytics can help you understand your users. Newnes, 1st edition, Morgan Kaufmann.

2. Sponder M, (2013), Social media analytics: Effective tools for building, interpreting, and using metrics, 1st edition, McGraw Hill Professional.

3. Clifton B, (2012), Advanced Web Metrics with Google Analytics, 3rd edition, John Wiley & Sons.. 

 

Essential Reading / Recommended Reading

1. Peterson E. T, (2004), Web Analytics Demystified: AMarketer's Guide to Understanding How Your Web Site Affects Your Business. Ingram.

2. Sostre P, LeClaire J, (2007), Web Analytics for dummies, John Wiley & Sons.

3. Burby J, Atchison S, (2007), Actionable web analytics: using data to make smart business decisions, John Wiley & Sons.

4. Dykes B, (2011), Web analytics action hero: Using analysis to gain insight and optimize your business, Adobe Press.  

Evaluation Pattern

CIA 50%

ESE 50%

MDS372C - BIO INFORMATICS (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

To enable the students to learn the information search and retrieval, Genome analysis and Gene mapping, alignment of multiple sequences, and PERL for Bioinformatics. 

 

Learning Outcome

CO1: To understand the molecular Biology and Bioinformatics applications. 

CO2: Apply the modeling and simulation technologies in Biology and medicine.

CO3: Evaluate the algorithms to find the similarity between protein and DNA sequences. 

Unit-1
Teaching Hours:18
BIOINFORMATICS
 

Introduction, Historical Overview and Definition, Applications, Major databases in Bioinformatics, Data management and Analysis, Central Dogma of Molecular Biology.

INFORMATION SEARCH AND RETRIEVAL       

Introduction, Tools for web search, Data retrieval tools, Data mining of Biological databases. 

Lab Exercise

1. Test and verify the basic Linux commands and Filters.

2. Create the file(s) and verify the file handling commands.

Unit-2
Teaching Hours:18
GENOME ANALYSIS AND GENE MAPPING
 

GENOME ANALYSIS AND GENE MAPPING Introduction, Genome analysis, Genome mapping, Sequence assembly problem, Genetic mapping and linkage analysis, Physical maps, Cloning the entire Genome, Genome sequencing, Applications of Genetic maps, Identification of Genes in Contigs, Human Genome Project.  ALIGNMENT OF PAIRS OF SEQUENCES Introduction, Biological motivation of alignment, Methods of sequence alignments, Using score matrices, Measuring sequence detection

Lab Exercise

1. Create directories and verify the directory commands.

2. Perform basic mathematical operations using PERL.

3. Write a PERL script to demonstrate the Array operations and Regular expressions.

Unit-3
Teaching Hours:18
ALIGNMENT OF MULTIPLE SEQUENCES
 

ALIGNMENT OF MULTIPLE SEQUENCES Methods of multiple sequence alignment, Evaluating multiple alignments, Applications of multiple alignments, Phylogenetic analysis, Methods of phylogenetic analysis, Tree evaluation, Problems in Phylogenetic analysis. 

TOOLS FOR SIMILARITY SEARCH AND SEQUENCE ALIGNMENT Introduction, Working with FASTA, Working with BLAST, Filtering and Gapped BLAST, FASTA and BLAST algorithm comparison. 

 Lab Exercise

1. Write a PERL script to concatenate DNA sequences. 

2. Write a PERL script to transcribe DNA sequence into RNA sequence

3. Write a PERL script to calculate the reverse complement of a strand of DNA.

Unit-4
Teaching Hours:18
PERL FOR BIOINFORMATICS
 

Sequences and Strings: Representing sequence data, Program to store a DNA sequence, Concatenating DNA fragments, Transcription DNA to RNA, Proteins, Files and Arrays, Reading Proteins in Files, Arrays, Scalar and List Context. 

 

Motifs and Loops: Flow control, Code layout, Finding motifs, Counting Nucleotides, Exploding strings and arrays, Operating on strings. Subroutine and Bugs: Subroutines, Scoping and Subroutines, Command line arguments and Arrays, Passing data to Subroutines, Modules and Libraries of Subroutines. 

 Lab Exercise

1. Write a PERL script to read protein sequence data from a file.

2. Write a PERL script to search for a motif in a DNA sequence.

Unit-5
Teaching Hours:18
THE GENETIC CODE
 

Hashes, Data structure and algorithms for Biology, Translating DNA into Proteins, Reading DNA from the files in FASTA format, Reading Frames. GenBank: GenBank files, GenBank Libraries, Separating Sequence and Annotation, Parsing Annotations, Indexing GenBank with DBM. Protein Data Bank: Files and Folders, PDB Files, Parsing PDB Files. 

 1. Write a PERL script to append ACGT to DNA using a subroutine.

2 . Case Study: a. To retrieve the sequence of the Human keratin protein from UniProt database and to interpret the results. b. To retrieve the sequence of the Human keratin protein from GenBank database and to interpret the results. 

Text Books And Reference Books:

[1] Bioinformatics: Methods and Applications, S. C. Rastogi, Namita Mendirata and Parag Rastogi, 4th Edition, PHI Learning, 2013.   

[2] Beginning Perl for Bioinformatics, Tisdall James, 1st edition, Shroff Publishers (O’Reilly), 2009.    

Essential Reading / Recommended Reading

[1] Introduction to Bioinformatics, Arthur M Lesk, 2nd Edition, Oxford University Press,4th edition, 2014.

[2] Bioinformatics Technologies, Yi-Ping Phoebe Chen (Ed), 1st edition, Springer, 2005.

[3] Bioinformatics Computing, Bryan Bergeron, 2nd Edition, Prentice Hall, 1st edition, 2003.

Web resources:

[1] http://cac.annauniv.edu/PhpProject1/aidetails/afug_2013_fu/24.%20BIO%20MED.pdf

[2] https://www.amrita.edu/school/biotechnology/academics/pg/introduction-bioinformaticsbif410

[3] https://canvas.harvard.edu/courses/8084/assignments/syllabus

[4] https://www.coursera.org/specializations/bioinformatics

[5] http://www.dtc.ox.ac.uk/modules/introduction-bioinformatics-bioscientists.html 

Evaluation Pattern

CIA 50%

ESE 50%

MDS372D - EVOLUTIONARY ALGORITHMS (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

Able to understand the core concepts of evolutionary computing techniques and popular evolutionary algorithms that are used in solving optimization problems.Students will be able to implement custom solutions for real-time problems applicable with evolutionary computing.

Learning Outcome

CO1:Basic understanding of evolutionary computing concepts and techniques

CO2:Classifyrelevantreal-time problems for the applications of evolutionary algorithms

CO3:Design solutions using evolutionary algorithms

Unit-1
Teaching Hours:18
Lab Program
 

1.     Implementation of single and multi-objectivefunctions

2.     Implementation of binaryGA

Unit-1
Teaching Hours:18
INTRODUCTION TO EVOLUTIONARY COMPTUTING
 

Terminologies – Notations – Problems to be solved – Optimization – Modeling – Simulation

– Search problems – Optimization constraints

Unit-2
Teaching Hours:18
EVOLUTION STRATEGY
 

One plus one evolution strategy – The 1/5 Rule – (μ+1) evolution strategy – Self adaptive evolution strategy

Unit-2
Teaching Hours:18
Lab Program
 

1.     Implementation of continuousGA

2.     Implementation of evolutionaryprogramming

Unit-2
Teaching Hours:18
EVOLUTIONARY PROGRAMMING
 

Continuous evolutionary programming – Finite state machine optimization – Discrete evolutionary programming – The Prisoner’s dilemma

Unit-3
Teaching Hours:18
GENETIC PROGRAMMING
 

Fundamentals of genetic programming – Genetic programming for minimal time control

Unit-3
Teaching Hours:18
EVOLUTIONARY ALGORITHM VARIATION
 

Initialization – Convergence – Population diversity – Selection option – Recombination – Mutation

Unit-3
Teaching Hours:18
Lab Program
 

1.     Implementation of geneticprogramming

2.     Implementation of Ant ColonyOptimization

Unit-4
Teaching Hours:18
Lab Program
 

1.     Implementation of Particle SwarmOptimization

2.     Implementation of Multi-ObjectOptimization

Unit-4
Teaching Hours:18
ANT COLONY OPTIMIZATION
 

Pheromone models – Ant system – Continuous Optimization – Other Ant System

Unit-4
Teaching Hours:18
PARTICLE SWARM OPTIMIZATION
 

Velocity limiting – Inertia weighting – Global Velocity updates – Fully informed Particle Swarm

Unit-5
Teaching Hours:18
Lab Program
 

1.     Simulation of EA in Planning problems (routing, scheduling, packing) and Design problems (Circuit, structure,art)

2.     Simulation of EA in classification/predictionmodelling

Unit-5
Teaching Hours:18
MULT-OBJECTIVE OPTIMIATION
 

Pareto Optimality – Hyper volume – Relative coverage – Non-pareto based EAs – Pareto based EAs – Multi-objective Biogeography based optimization

Text Books And Reference Books:

[1] D. Simon, Evolutionary optimization algorithms: biologically inspired and population-based approaches to computer intelligence. New Jersey: John Wiley, 2013.

Essential Reading / Recommended Reading

1. Eiben and J. Smith, Introduction to evolutionary computing. 2nd ed.  Berlin:  Springer,  2015.

2.     D.Goldberg,Geneticalgorithmsinsearch,optimization,andmachinelearning.Boston: Addison-Wesley,2012.

3.     K. Deb, Multi-objective optimization using evolutionary algorithms. Chichester: John Wiley & Sons,2009.

4.     R. Poli, W. Langdon, N. McPhee and J. Koza, A field guide to genetic programming. [S.l.]: Lulu Press,2008.

5.     T.Bäck,Evolutionaryalgorithmsintheoryandpractice.NewYork:OxfordUniv.Press, 1996.

 

Web Resources:

 

1      E.A.EandS.J.E,"IntroductiontoEvolutionaryComputing|Theon-line accompaniment to the book Introduction toEvolutionary Computing",Evolutionarycomputation.org,2015.[Online].Available: http://www.evolutionarycomputation.org/.

2      F.Lobo,"EvolutionaryComputation2018/2019",Fernandolobo.info,2018.[Online]. Available:http://www.fernandolobo.info/ec1819.

3    "EClabTools",Cs.gmu.edu,2008.[Online].Available: https://cs.gmu.edu/~eclab/tools.html.

4    "Kanpur Genetic Algorithms Laboratory", Iitk.ac.in, 2008. [Online]. Available: https://www.iitk.ac.in/kangal/codes.shtml.

5    "Course webpage Evolutionary Algorithms", Liacs.leidenuniv.nl, 2017. [Online]. Available:http://liacs.leidenuniv.nl/~csnaco/EA/misc/ga_demo.htm.

Evaluation Pattern

CIA: 50%

ESE : 50%

MDS372E - OPTIMIZATION TECHNIQUE (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

This course will help the students to acquire and demonstrate the implementation of the necessary algorithms for solving advanced level Optimization techniques.

Learning Outcome

CO1: Apply the notions of linear programming in solving transportation problems

CO2: Understand the theory of games for solving simple games

CO3: Use linear programming in the formulation of the shortest route problem.

CO4: Apply algorithmic approach in solving various types of network problems

CO5: Create applications using dynamic programming.

Unit-1
Teaching Hours:18
INTRODUCTION
 

Operations Research Methods - Solving the OR model - Queuing and Simulation models – Art of modelling – phases of OR study.

Unit-1
Teaching Hours:18
MODELLING WITH LINEAR PROGRAMMING
 

Two variable LP model – Graphical LP solution – Applications. Simplex method and sensitivity analysis – Duality and post-optimal Analysis- Formulation of the dual problem.

Lab Exercise

 1.    Simplex Method 

 2.   Dual Simplex Method

Unit-2
Teaching Hours:18
TRANSPORTATION MODEL
 

Determination of the Starting Solution – Iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method – Simplex explanation of the Hungarian Method – The trans-shipment Model. 

Lab Exercise

1.   Balanced Transportation Problem

2.   Unbalanced Transportation Problem

3.   Assignment Problems

 

Unit-3
Teaching Hours:18
CPM and PERT
 

Network Representation – Critical Path Computations – Construction of the time Schedule – Linear Programming formulation of CPM – PERT networks. 

Lab Exercise:

1. Shortest path computations in a network

2.Maximum flow problem

 

Unit-3
Teaching Hours:18
NETWORK MODELS
 

Minimal Spanning tree Algorithm – Linear Programming formulation of the shortest-route problem. Maximal Flow Model: Enumeration of cuts – Maximal Flow Diagram – Linear Programming Formulation of Maximal Flow Model.

Unit-4
Teaching Hours:18
GOAL PROGRAMMING
 

Formulation – Tax Planning Problem – Goal Programming algorithms – Weights method – Preemptive method.

Lab Exercise:

1.  Critical path Computations

2.   Game Programming

Unit-4
Teaching Hours:18
GAME THEORY
 

Strategic Games and examples - Nash equilibrium and examples - Optimal Solution of two person zero sum games - Solution of Mixed strategy games - Mixed strategy Nash equilibrium - Dominated action with example.

Unit-5
Teaching Hours:18
DYNAMIC PROGRAMMING
 

Recursive nature of computation in Dynamic Programming – Forward and Backward Recursion – Knapsack / Fly Away / Cargo-Loading Model – Equipment Replacement Model.

Lab Exercise:

1. Goal Programming

2. Dynamic Programming

Unit-5
Teaching Hours:18
MARKOV CHAINS
 

Definition – Absolute and n-step Transition Probability – Classification of states.

Text Books And Reference Books:

1.      Hamdy A Taha, Operations Research, 9th Edition, Pearson Education, 2012.

2.      Garrido José M. Introduction to Computational Models with Python. CRC Press, 2016.

Essential Reading / Recommended Reading

1.      Rathindra P Sen, Operations Research – Algorithms and Applications, PHI Learning Pvt. Limited, 2011 

2.      R. Ravindran, D. T. Philips and J. J. Solberg, Operations Research: Principles and Practice, 2nd ed., John Wiley & Sons, 2007. 

3.      F. S. Hillier and G. J. Lieberman, Introduction to operations research, 8th ed., McGraw-Hill Higher Education, 2004. 

4.      K.C. Rao and S. L. Mishra, Operations research, Alpha Science International, 2005. 

5.      Hart, William E. Pyomo: Optimization Modeling in Python. Springer, 2012. 

6.      Martin J. Osborne, An introduction to Game theory, Oxford University Press, 2008

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS381 - SPECIALIZATION PROJECT (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

The course is designed to provide a real-world project development and deployment environment for the students. 

Learning Outcome

CO1: Identify the problem and relevant analytics for the selected domain.

CO2: Apply appropriate design/development strategy and tools. 

Unit-1
Teaching Hours:60
Specialization Project
 

Project will be based on the specialization domains which students are opted for during this semester.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS381L - SPECIALIZATION PROJECT (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

The course is designed to provide a real-world project development and deployment environment for the students. 

Learning Outcome

CO1: Identify the problem and relevant analytics for the selected domain.

CO2: Apply appropriate design/development strategy and tools. 

Unit-1
Teaching Hours:60
Specialization Project
 

Project will be based on the specialization domains which students are opted for during this semester.

Text Books And Reference Books:

NOT APPLICABLE

Essential Reading / Recommended Reading

NOT APPLICABLE

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS382 - SEMINAR (2020 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description

 

The course is designed to provide to enhance the soft skills and technical undetstanding of the students.  

 

Learning Outcome

CO1: Understand new and latest trends in data science

CO2: Demonstrate the professional presentation abilities

CO3: Apply the acquired knowledge in their Research

 

 

 

Unit-1
Teaching Hours:30
Students will be giving presentations on any advanced concepts and technologies in data science and submit the report
 

-

Text Books And Reference Books:

Research Articles / Books / Web resources related to data science domain

Essential Reading / Recommended Reading

Recommended References

Evaluation Pattern

CIA 100%

 

MDS382L - SEMINAR (2020 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description

 

The course is designed to provide to enhance the soft skills and technical understanding of the students.

Learning Outcome

CO1:Understand new and latest trends in data science

CO2:Demonstrate the professional presentation abilities

CO3:Apply the acquired knowledge in their Research

Unit-1
Teaching Hours:30
Seminar
 

Seminar

Text Books And Reference Books:

NA

Essential Reading / Recommended Reading

NA

Evaluation Pattern

 100% CIA

Two Minute Talks

(Audio,Video)

4 minutes talk

Talk on emerging trends (10 Minutes)

GD

Improv

Interview

10

5

5

10

10

10

MDS481 - INDUSTRY PROJECT (2020 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:300
Credits:12

Course Objectives/Course Description

 

 This course helps the student to develop students to become globally competent and to inculcate Entrepreneurial skills among students.

Learning Outcome

CO1: Develop Real time Projects.

CO2: Practices different data science principles and strategies in the project.

Unit-1
Teaching Hours:30
Project Work
 

It is a full time project to be taken up either in the industry or in an R&D organization

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA: 50%

ESE: 50%