Department of COMPUTER SCIENCE

Syllabus for
Master of Science (Data Science)
Academic Year  (2020)

 
1 Semester - 2020 - Batch
Course Code
Course
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 - 2020 - Batch
Course Code
Course
Hours Per
Week
Credits
Marks
MDS231 MATHEMATICAL FOUNDATION FOR DATA SCIENCE - II 4 04 100
MDS232 REGRESSION ANALYSIS 4 4 100
MDS241A MULTIVARIATE ANALYSIS 4 4 100
MDS241B STOCHASTIC PROCESS 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 - 2019 - Batch
Course Code
Course
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
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
MDS381 SPECIALIZATION PROJECT 4 2 100
MDS382 SEMINAR 2 1 50
MDS383 RESEARCH MODELLING AND IMPLEMENTATION 4 2 50
4 Semester - 2019 - Batch
Course Code
Course
Hours Per
Week
Credits
Marks
MDS481 INDUSTRY PROJECT 2 10 300
MDS482 RESEARCH PUBLICATION 4 2 100
        

          

  

Assesment Pattern

CIA - 50%

ESE - 50%

Examination And Assesments

CIA - 50%

ESE - 50%

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 st
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 problems. PSO4: Innovation And Entrepreneurship: Produce innovative IT solutions and services based on global needs and trends. PSO5: Societal And Environmental Concern: Utilize the data science theories for societal and environmental concerns. PSO6: Professional Ethics: Understand and commit to professional ethics and

Assesment Pattern

CIA-50%

ESE-50%

Examination And Assesments

CIA + ESE

Department Overview:
Department of Data Science of Christ (Deemed to be University), Lavasa is established to shape students into outstanding Data Scientist and Analytics professionals with ethical and human values. The department offers various under graduation and post-graduation 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 areas of Computer Science and Engineering. The department has rich expertise in terms of faculty resources who are well 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 Machine learning lab, Data Analytics Lab, Open Source lab, etc. exclusively for the hands-on training of students for their lab-oriented courses and research. The department intermittently organizes hands-on workshops on recent technologies like Machine learning, Cloud Computing, Hadoop, etc. for the students to keep them industry-ready. The department equips students with a holistic education to be better citizens.
Mission Statement:
*Vision ?Enrich Ethical Scientific Excellence? *Mission ?To develop Data Science professionals with ethical and social values.? ? Divulge state-of-art knowledge in the area of Data Science and Analytics.? ?Encourages research and innovation.? ?Accustoms the students with current industry practices, teamwork, and entrepreneurship.?
Introduction to Program:
Data Science is prevalent in all academia, business sectors, and research and development to make effective decisions in day to day activities. MSc in Data Science is a two-year programme with four semesters. This programme aims to provide opportunities 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 a 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 an 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 analytical and problem-solving skill through developing real-time applications. ? To empower students with tools and techniques for handling, managing, analyzing, and interpreting data. Ethics and Human Values 1. Only proprietary or open-source software would be used for academic teaching and learning purposes. 2. Copying of programs from the internet, friends, or other sources is strictly discouraged since it impairs the development of programming skills. 3. Unique Practical (Domain-based) exercises to ensure that the students don?t involve in code plagiarism. 4. Projects undertaken by students during the course are done in teams to improve collaborative work and synergy between team members. 5. Projects involve modularization, which initiates students to take individual responsibility for common goals. 6. Passion for excellence is promoted among the students, be it in software development or project documentation. 7. Giving due credit to sources during the seminar and research assignment is promoted among the students 8. The course and its design enforce the practice of proper referencing techniques to improve the sense of integri

MDS131 - MATHEMATICAL FOUNDATION FOR DATA SCIENCE - I (2020 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

On successful completion of this course, a student will be able to: 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:15
Indroduction to Vector Spacees
 

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:20
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 Vectorspaces.

Unit-3
Teaching Hours:10
Eigenvalues, Eigenvctors and inner product Spacees
 

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:15
Mathematics Applied to Data Scincee
 

Singular value decomposition - Handwritten digits and simple algorithm - Classification of handwritten digits using SVD bases - Tangent distance - Text Mining.

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 (2020 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

1. Understand the properties of Vector spaces

 

2. Use the properties of Linear Maps in solving problems on Linear Algebra

 

3. Demonstrate proficiency on the topics Eigenvalues, Eigenvectors and Inner Product Spaces

 

4. Apply mathematics for some applications in Data Science

Unit-1
Teaching Hours:15
INTRODUCTION TO VECTOR SPACES
 

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

Unit-2
Teaching Hours:20
LINEAR MAPS
 

Definition of Linear Maps - Algebraic Operations on L(V,W) - Null spaces and Injectivity - Range and Surjectivity - Fundamental Theorems of Linear Maps - Representing a Linear Map by a Matrix - Invertible Linear Maps - Isomorphic Vector spaces - Linear Map as Matrix Multiplication - Operators - Products of Vector Spaces - Product of Direct Sum - Quotients of Vector spaces

Unit-3
Teaching Hours:10
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:15
MATHEMATICS APPLIED TO DATA SCIENCE
 

Singular value decomposition - Handwritten digits and simple algorithm - Classification of handwritten digits using SVD bases - Tangent distance - Text Mining

Text Books And Reference Books:

1. S. Axler, Linear algebra done right, 2nd ed., 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.Friedberg, Stephen H., Arnold J., & L.Spence , Linear algebra,4th ed., Pearson, 2014

 

3. Hoffman, Kenneth, & Kunze, Ray Alden , Linear Algebra,2nd ed., Pearson, 2015

 

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

 

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

 

6. 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 (2020 Batch)

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

Course Objectives/Course Description

 

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

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

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS132L - PROBABILITY AND DISTRBUTION THEORY (2020 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 (2020 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 - PRINCIPLE OF DATA SCIENCE (2020 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 enable students to understand the underlying core concepts and emerging technologies in Data Science.

 

Learning Outcome

CO1: Explore the fundamental concepts of Data Science

CO2:  Understand the data analysis techniques for applications handling large data

CO3: Understand and apply the 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 and follow ethical practices while dealing with data.

Unit-1
Teaching Hours:10
INTRODUCTION TO DATA SCIENCE
 

Big Data and Data Science Hype – Why data science – Getting Past the Hype – The current Landscape – Data Science Process Overview

Unit-2
Teaching Hours:12
BIG DATA
 

Problems when handling large data -General techniques for handling large dataCase study

Unit-3
Teaching Hours:12
MACHINE LEARNING
 

Machine learning –Modelling Process -Training model – Validating model

Supervised learning algorithms

Unsupervised learning algorithms

Unit-4
Teaching Hours:12
DEEP LEARNING
 

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
 

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  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 (2020 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 (2020 Batch)

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

Course Objectives/Course Description

 

The research methodology module is intended to assist students in planning and carrying out research projects.

The students are exposed to the principles, procedures and techniques of implementing a research project.

The course starts with an introduction to research and carries through the various methodologies involved.

It continues with finding out the literature using computer technology, basic statistics required for research and ends with linear regression.

Learning Outcome

CO1: Define research and describe the research process and research methods

CO2: Understand and apply basic research methods including research design, data analysis, and interpretation

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 etc.

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-1 

Evaluated out of = 20

Marks Converted to = 10

CIA-2 

Evaluated out of = 50

Marks Converted to = 25

CIA-3 

Evaluated out of = 20

Marks Converted to = 10

 

Total CIA marks after conversion = 45

Attendance Marks = 5

  Final Marks = 50

 

 

 

MDS161A - INTRODUCTION TO STATISTICS (2020 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 (2020 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 structure 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 (2020 Batch)

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

Course Objectives/Course Description

 

To provide foundation for the fundamental concepts of problem solving and programming. The course includes the fundamentals of programming, different types of problem-solving concepts and programming structures to build logic for suitable computational problems. 

Learning Outcome

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

 

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

Unit-1
Teaching Hours:10
COMPUTER AND DIGITAL BASICS
 

Number Representations - Hexa, octal, binary, decimal - 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 Circuits - K-Map

Unit-2
Teaching Hours:5
GENERAL PROBLEM-SOLVING CONCEPTS
 

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 - 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.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 I

CIA  II

CIA III

Attendance

End Sem

10%

25%

10%

5%

50%

 

MDS161C - LINUX ADMINISTRATION (2020 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%

MDS171 - DATA BASE TECHNOLOGIES (2020 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 database tables and write effective queries. Also, to Comprehend Data warehouse and its functions.

Learning Outcome

CO1: Design conceptual models of a database using ER modeling

CO2: Create and populate a RDBMS for a real life application, with constraints and keys, using SQL

CO3: Retrieve any type of information from a data base by formulating complex queries in SQL

CO4: Demonstrate various databases

 CO5: Distinguish database from data warehouse and examine ETL process

Unit-1
Teaching Hours:16
INTRODUCTION
 

Concept and 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.      Specification of Constraints

Unit-2
Teaching Hours:16
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, 5NF

Lab Exercises

1.   Insert, Select, Update & Delete Commands

2.   Nested Queries & Join Queries

3.  Views

Unit-3
Teaching Hours:10
INTELLIGENT DATABASES
 

Active databases, Deductive Databases, Knowledge bases, Multimedia Databases, Multidimensional Data Structures, Image Databases, Text/Document Databases, Video Databases, Audio Databases, Multimedia Database Design.

 

Unit-4
Teaching Hours:16
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.

Unit-5
Teaching Hours:16
REQUIREMENTS, REALITIES, ARCHITECTURE AND DATA FLOW
 

Requirements, ETL Data Structures, Extracting, Cleaning and Conforming, Delivering Dimension Tables, Delivering Fact Tables (CH:1,2,3,4,5,6)

Lab Exercises:

1.      Importing source data structures

2.      Design Target Data Structures

3.      Create target structure

4.      Design and build the ETL mapping

Unit-6
Teaching Hours:16
IMPLEMENTATION, OPERATIONS AND ETL SYSTEMS
 

Development, Operations, Metadata, Real-Time ETL Systems. (CH:7,8,9,11)

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

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 TECHNOLOGY (2020 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 database tables and write effective queries. Also, to Comprehend Data warehouse and its functions.

Learning Outcome

CO1: Design conceptual models of a database using ER modelling

CO2: Create and populate a RDBMS for a real-life application, with constraint and keys, using SQL

CO3: Retrieve any type of information from a database by formulating complex queries in SQL

CO 4: Demonstrate various databases

CO 5: Distinguish database from data warehouse and examine ETL process

Unit-1
Teaching Hours:16
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

Unit-1
Teaching Hours:16
Lab Exercises
 

1. Data Definition

2. Table Creation

3. Specialization of Constraints

Unit-2
Teaching Hours:16
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, 5NF

Unit-2
Teaching Hours:16
Lab Exercises
 

1. Insert, Select, Update & Delete Commands

2. Nested Queries & Join Queries

3. Views

Unit-3
Teaching Hours:10
INTELLIGENT DATABASES
 

Active databases, Deductive Databases, Knowledge bases, Multimedia Databases, Multidimensional Data Structures, Image Databases, Text/Document Databases, Video Databases, Audio Databases, Multimedia Database Design.

Unit-4
Teaching Hours:16
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

Unit-5
Teaching Hours:16
REQUIREMENTS, REALITIES, ARCHITECTURE AND DATA FLOW
 

Requirements, ETL Data Structures, Extracting, Cleaning and Conforming, Delivering Dimension Tables, Delivering Fact Tables (CH:1,2,3,4,5,6)

 

Lab Exercises:

1. Importing source data structures

2. Design Target Data Structures

3. Create a target structure

4. Design and build the ETL mapping

Unit-6
Teaching Hours:16
IMPLEMENTATION, OPERATIONS AND ETL SYSTEMS:
 

Development, Operations, Metadata, Real-Time ETL Systems. (CH:7,8,9,11) 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

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 (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 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: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 Exercise

  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 Exercise

  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 Exercise

  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
 

Unbiasedness 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 Exercise

  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
SEQUENTIAL 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%

MDS172L - INFERENTIAL STATISTICAL LABORATORY (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 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 (2020 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
ESE 50%

CIA 50%

MDS173L - PROGRAMMING OF DATA SCIENCE IN PYTHON (2020 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 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 (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

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:18
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:20
Basic Graph Theory
 

Graph Classes: Definition of a Graph and Graph terminology, isomorphism of graphs, Completegraphs,bipartitegraphs,completebipartitegraphs-Vertexdegree:adjacencyand 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, 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, Eulerian and HamiltonianGraphs.

 

 

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%

MDS231L - MATHEMATICAL FOUNDATION FOR DATA SCIENCE - II (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 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

  • Demonstrate the properties of multivariate calculus
  • Use the idea of orthogonality and projections effectively
  • Have a clear understanding of Convex Optimization
  • Know the about the basic terminologies and properties in Graph Theory

 

Unit-1
Teaching Hours:18
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:20
Basic Graph Theory
 

Graph Classes: Definition of a Graph and Graph terminology, isomorphism of graphs, Completegraphs,bipartitegraphs,completebipartitegraphs-Vertexdegree:adjacencyand 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, 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, Eulerian and HamiltonianGraphs

Text Books And Reference Books:

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

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

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

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

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 I : 10%

CIA II : 25%

CIA III : 10%

ATTENDANCE : 5%

ESE : 50%

 

MDS232 - REGRESSION ANALYSIS (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 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:15
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:15
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:10
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:10
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%

MDS232L - REGRESSION ANALYSIS (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 aims to provide the grounding knowledge about the regression model building of simple and multiple regression.

 The Course enables Students to

 

  • To build a foundation on the basic tools of regression analysis. 

  • To apply econometric modelling on different types of data

  • To learn how to identify the goodness of fit of some basic econometric models

  • To diagnose common problems in linear regression modelling

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 to use and understand generalizations of the linear model to binary and count data

Unit-1
Teaching Hours:15
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:15
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:10
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:10
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. G. S. Madala, Introduction to  Econometrics, Wiley.

2. C. Brooks, Introductory Econometrics for Finance, 4th Ed., Cambridge University Press, 2019

3. G.G Vining, Introduction to Linear Regression Analysis, John Wiley and Sons,Inc.NY, 2003

 

Essential Reading / Recommended Reading

 

1. J M.  Wooldridge, Introductory Econometrics: A Modern Approach, 5th Ed., South-Western, Cengage Learning, 2013.

2. G.G Vining, Introduction to Linear Regression Analysis, John Wiley and Sons,Inc.NY, 2003

3. S. Chatterjee and A. Hadi, Regression Analysis by Example, 4th Ed., John Wiley and Sons, Inc, 2006

4.  Iain Pardoe, Applied Regression Modeling, John Wiley and Sons, Inc, 2012.

Evaluation Pattern

CIA I: 10%

CIA II: 25%

CIA III: 10%

Attendance: 5%

ESE: 50%

MDS241A - MULTIVARIATE ANALYSIS (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 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%

MDS241AL - MULTIVARIATE ANALYSIS (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 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 (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 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%

MDS241BL - STOCHASTIC PROCESS (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 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 the difference statistics in the 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 it's 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 I

CIA  II

CIA III

Attendance

ESE

10%

25%

10%

5%

50%

MDS271 - MACHINE LEARNING (2020 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 machinelearningalgorithmsalongwiththeirapplicationstosolverealworldproblems.

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-ReinforcementLearning.SupervisedLearning:Learning class from examples- Probably Approach Correct(PAC) Learning-Noise-Learning Multiple classes. Regression-Model Selection andGeneralization.

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

 

Lab Exercise:

1.      Data Exploration using parametricMethods

2.      Data Exploration using non-parametricMethods

3.      Regressionanalysis

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-dimensionalscaling

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 kernelmachines

 

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 usingMLP

2.  EnsembleLearning

 

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 meansclustering

 

2.  Hierarchicalclustering

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

ESE 50

CIA 50

MDS271L - MACHINE LEARNING (2020 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 objective of this course is to provide introduction to the principles and design of machine 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
Introduction
 

Machine Learning-Examples of Machine Applications-LearningAssociations-Classification- Regression-UnsupervisedLearning-Reinforcement Learning.SupervisedLearning:Learning class from examples- Probably Approach Correct(PAC) Learning-Noise-Learning Multiple classes. Regression-Model Selection and Generalization.

Introduction to Parametric methods-Maximum Likelihood Estimation:Bernoulli Density- Multinomial Density-Gaussian Density, Nonparametric Density Estimation: Histogram Estimator-Kernel Estimator-K-Nearest NeighbourEstimator.

Unit-1
Teaching Hours:18
Lab Exercises:
 
  1. Data Exploration using parametricMethods
  2. Data Exploration using non-parametricMethods
  3. Regressionanalysis
Unit-2
Teaching Hours:18
DIMENSIONALITY REDUCTION
 

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

Unit-2
Teaching Hours:18
Lab Exercise:
 
  1. Data reduction using Principal ComponentAnalysis
  2. Data reduction using multi-dimensionalscaling
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.

Unit-3
Teaching Hours:18
Lab Excercises
 
  1. Lineardiscrimination
  2. Logisticdiscrimination
  3. Classification using kernelmachines
Unit-3
Teaching Hours:18
Kernel Machines
 

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

Unit-4
Teaching Hours:18
Lab Exercises
 
  1. Classification using MLP
  2. Ensemble Learning
Unit-5
Teaching Hours:18
Lab exercises
 
  1. K meansclustering
  2. Hierarchicalclustering
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.

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 (2020 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%

MDS272AL - HADOOP (2020 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

 

  • Understand the Big Data concepts in real time scenario
  • Understand the big data systems and identify the main sources of Big Data in the real world.
  • Demonstrate an ability to use Hadoop framework for processing Big Data for Analytics. 
  • Evaluate the Map reduce approach for different domain problems. 

 

Unit-1
Teaching Hours:15
INTRODUCTION
 

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.  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: