CHRIST (Deemed to University), BangaloreDEPARTMENT OF COMPUTER SCIENCESchool of Sciences 

Syllabus for

1 Semester  2021  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MDS131  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  I  Core Courses  4  4  100 
MDS132  PROBABILITY AND DISTRIBUTION THEORY  Core Courses  4  4  100 
MDS133  PRINCIPLES OF DATA SCIENCE  Core Courses  4  4  100 
MDS134  RESEARCH METHODOLOGY  Core Courses  2  2  50 
MDS161A  INTRODUCTION TO STATISTICS  Generic Elective  2  2  50 
MDS161B  INTRODUCTION TO COMPUTERS AND PROGRAMMING  Generic Elective  2  2  50 
MDS161C  LINUX ADMINISTRATION  Generic Elective  2  2  50 
MDS171  DATA BASE TECHNOLOGIES  Core Courses  6  5  150 
MDS172  INFERENTIAL STATISTICS  Core Courses  6  5  150 
MDS173  PROGRAMMING FOR DATA SCIENCE IN PYTHON  Core Courses  6  4  100 
2 Semester  2021  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MDS231  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  II  Core Courses  4  4  100 
MDS232  REGRESSION ANALYSIS  Core Courses  4  4  100 
MDS241A  MULTIVARIATE ANALYSIS  Discipline Specific Elective  4  4  100 
MDS241B  STOCHASTIC PROCESS  Discipline Specific Elective  4  4  100 
MDS241C  CATEGORICAL DATA ANALYSIS  Discipline Specific Elective  4  4  100 
MDS271  MACHINE LEARNING  Core Courses  6  5  150 
MDS272A  HADOOP  Discipline Specific Elective  6  5  150 
MDS272B  IMAGE AND VIDEO ANALYTICS  Discipline Specific Elective  6  5  150 
MDS272C  INTERNET OF THINGS  Discipline Specific Elective  6  5  150 
MDS273  PROGRAMMING FOR DATA SCIENCE IN R  Core Courses  6  4  100 
3 Semester  2020  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MDS331  NEURAL NETWORKS AND DEEP LEARNING  Core Courses  4  4  100 
MDS341A  TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES  Discipline Specific Elective  4  4  100 
MDS341B  BAYESIAN INFERENCE  Discipline Specific Elective  4  4  100 
MDS341C  ECONOMETRICS  Discipline Specific Elective  4  4  100 
MDS341D  BIOSTATISTICS  Discipline Specific Elective  4  4  100 
MDS371  CLOUD ANALYTICS  Core Courses  6  5  150 
MDS372A  NATURAL LANGUAGE PROCESSING  Discipline Specific Elective  6  5  150 
MDS372B  WEB ANALYTICS  Discipline Specific Elective  6  5  150 
MDS372C  BIO INFORMATICS  Discipline Specific Elective  6  5  150 
MDS372D  EVOLUTIONARY ALGORITHMS  Discipline Specific Elective  6  5  150 
MDS372E  OPTIMIZATION TECHNIQUE  Discipline Specific Elective  6  5  150 
MDS381  SPECIALIZATION PROJECT  Core Courses  4  2  100 
MDS382  SEMINAR  Skill Enhancement Course  2  1  50 
4 Semester  2020  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MDS481  INDUSTRY PROJECT  Core Courses  2  12  300 
 
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.  
Assesment Pattern  
CIA  50% ESE  50%  
Examination And Assesments  
CIA  50% ESE  50%  
 
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.  
Programme Outcome/Programme Learning Goals/Programme Learning Outcome: PO1: Understand the abstract concepts that lead to various data science theories in Mathematics, Statistics and Computer science.PO2: Identify analyze and design solutions for data science problems using fundamental principles of mathematics, Statistics, computing sciences, and relevant domain disciplines. PO3: Demonstrate the skills in handling data science programming tools towards problem solving and solution analysis for domain specific problems. PO4: Produce innovative IT solutions and services based on global needs and trends. PO5: Utilize the data science theories for societal and environmental concerns. PO6: Understand and commit to professional ethics and cyber regulations, responsibilities, and norms of professional computing practices. PO7: Use researchbased knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the researchbased knowledge and research methods including design of information to provide valid conclusions. PO8: Function effectively as an individual and as a member or leader in diverse teams and in multidisciplinary environments. PO9: Understand the role of statistical approaches and apply the same to solve the real life problems in the fields of data science. PO10: Apply the researchbased knowledge to analyse and solve advanced problems in data science  
Assesment Pattern  
5050  
Examination And Assesments  
CIA & ESE 
MDS131  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  I (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Understand the properties of Vector spaces CO2: Use the properties of Linear Maps in solving problems on Linear Algebra CO3: Demonstrate proficiency on the topics Eigenvalues, Eigenvectors and Inner Product Spaces. C04: Apply mathematics for some applications in Data Science 
Unit1 
Teaching Hours:12 
INTRODUCTION TO VECTOR SPACES


Vector Spaces: R^{n} and C^{n}, lists, F^{n} and digression on Fields, Definition of Vector spaces, Subspaces, sums of Subspaces, Direct Sums, Span and Linear Independence, bases, dimension.  
Unit2 
Teaching Hours:12 
LINEAR MAPS


DefinitionofLinearMapsAlgebraicOperationson L(V,W)  Null spaces and InjectivityRangeandSurjectivityFundamentalTheoremsofLinearMapsRepresenting aLinearMapbyaMatrixInvertibleLinearMapsIsomorphicVectorspacesLinearMap as Matrix Multiplication  Operators  Products of Vector Spaces  Product of Direct Sum  Quotients of Vector spaces.  
Unit3 
Teaching Hours:12 
EIGENVALUES, EIGENVECTORS, AND INNER PRODUCT SPACES


Eigenvalues and Eigenvectors  Eigenvectors and Upper Triangular matrices  Eigenspaces and Diagonal Matrices  Inner Products and Norms  Linear functionals on Inner Product spaces.  
Unit4 
Teaching Hours:12 
BASIC MATRIX METHODS FOR APPLICATIONS


Matrix Norms – Least square problem  Singular value decomposition Householder Transformation and QR decomposition Non Negative Matrix Factorization – bidiagonalization.
 
Unit5 
Teaching Hours:12 
MATHEMATICS APPLIED TO DATA SCIENCE


Handwritten digits recognition using simple algorithm  Classification of handwritten digits using SVD bases and Tangent distance  Text Mining using Latent semantic index, Clustering, Nonnegative Matrix Factorization and LGK bidiagonalization.  
Text Books And Reference Books: 1. S. Axler, Linear algebra done right, Springer, 2017. 2. Eldén Lars, Matrix methods in data mining and pattern recognition, Society for Industrial and Applied Mathematics, 2007.  
Essential Reading / Recommended Reading 1. E. Davis, Linear algebra and probability for computer science applications, CRC Press, 2012. 2. J. V. Kepner and J. R. Gilbert, Graph algorithms in the language of linear algebra, Society for Industrial and Applied Mathematics, 2011. 3. D. A. Simovici, Linear algebra tools for data mining, World Scientific Publishing, 2012. 4. P. N. Klein, Coding the matrix: linear algebra through applications to computer science, Newtonian Press, 2015.  
Evaluation Pattern CIA  50% ESE  50%  
MDS131L  MATHEMATICAL FOUNDATION FOR DATA SCIENCE I (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 


Unit1 
Teaching Hours:12 
INTRODUCTION TO VECTOR SPACES


Vector Spaces: R^{n} and C^{n}, lists, F^{n} and digression on Fields, Definition of Vector spaces, Subspaces, sums of Subspaces, Direct Sums, Span and Linear Independence, bases, dimension  
Unit2 
Teaching Hours:12 
LINEAR MAPS


Definition of LinearMapsAlgebraicOperationson L(V,W)  Null spaces and InjectivityRangeandSurjectivityFundamentalTheoremsofLinearMapsRepresenting aLinearMapbyaMatrixInvertibleLinearMapsIsomorphicVectorspacesLinearMap as Matrix Multiplication  Operators  Products of Vector Spaces  Product of Direct Sum  Quotients of Vector spaces  
Unit3 
Teaching Hours:12 
EIGENVALUES, EIGENVECTORS, AND INNER PRODUCT SPACES


Eigenvalues and Eigenvectors  Eigenvectors and Upper Triangular matrices  Eigenspaces and Diagonal Matrices  Inner Products and Norms  Linear functionals on Inner Product spaces.  
Unit4 
Teaching Hours:12 
BASIC MATRIX METHODS FOR APPLICATIONS


Matrix Norms – Least square problem  Singular value decomposition Householder Transformation and QR decomposition Non Negative Matrix Factorization – bidiagonalization.  
Unit5 
Teaching Hours:12 
MATHEMATICS APPLIED TO DATA SCIENCE


Handwritten digits recognition using simple algorithm  Classification of handwritten digits using SVD bases and Tangent distance  Text Mining using Latent semantic index, Clustering, Nonnegative Matrix Factorization and LGK bidiagonalization  
Text Books And Reference Books: 1. S. Axler, Linear algebra done right, Springer, 2017. 2. Eldén Lars, Matrix methods in data mining and pattern recognition, Society for Industrial and Applied Mathematics, 2007.  
Essential Reading / Recommended Reading 1. E. Davis, Linear algebra and probability for computer science applications, CRC Press, 2012. 2. J. V. Kepner and J. R. Gilbert, Graph algorithms in the language of linear algebra, Society for Industrial and Applied Mathematics, 2011. 3. D. A. Simovici, Linear algebra tools for data mining, World Scientific Publishing, 2012. 4. P. N. Klein, Coding the matrix: linear algebra through applications to computer science, Newtonian Press,2015  
Evaluation Pattern CIA I : 10% CIA II : 25% CIA III : 10% ATTENDANCE : 5% ESE : 50%  
MDS132  PROBABILITY AND DISTRIBUTION THEORY (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Describe random event and probability of events CO2: Identify various discrete and continuous distributions and their usage. CO3: Evaluate condition probabilities and conditional expectations C04: Apply Chebychev?s inequality to verify the convergence of sequence in probability 
Unit1 
Teaching Hours:12 
DESCRIPTIVE STATISTICS AND PROBABILITY


Data – types of variables: numeric vs categorical  measures of central tendency – measures of dispersion  random experiment  sample space and random events – probability  probability axioms  finite sample space with equally likely outcomes  conditional probability  independent events  Baye’s theorem  
Unit2 
Teaching Hours:12 
PROBABILITY DISTRIBUTIONS FOR DISCRETE DATA


Random variable – data as observed values of a random variable  expectation – moments & moment generating function  mean and variance in terms of moments  discrete sample space and discrete random variable – Bernoulli experiment and Binary variable: Bernoulli and binomial distributions – Count data: Poisson distribution – overdispersion in count data: negative binomial distribution – dependent Bernoulli trails: hypergeometric distribution.  
Unit3 
Teaching Hours:12 
PROBABILITY DISTRIBUTIONS FOR CONTINUOUS DATA


Continuous sample space  Interval data  continuous random variable – uniform distribution  normal distribution (Gaussian distribution) – modeling lifetime data: exponential distribution, gamma distribution, Weibull distribution.  
Unit4 
Teaching Hours:12 
JOINTLY DISTRIBUTED RANDOM VARIABLES


Joint distribution of vector random variables – joint moments – covariance – correlation  the correlation  independent random variables  conditional distribution – conditional expectation  sampling distributions: chisquare, t, F (central).  
Unit5 
Teaching Hours:12 
LIMIT THEOREMS


Chebychev’s inequality  weak law of large n u mbers (iid): examples  strong law of large numbers (statement only)  central limit theorems (iid case): examples.  
Text Books And Reference Books: 1. Ross, Sheldon. A first course in probability. 10th Edition. Pearson, 2019. 2. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3rd Edition, 2015  
Essential Reading / Recommended Reading 1. Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGrawHill, 3rd Edition (Reprint), 2017. 2. Ross, Sheldon M. Introduction to probability models. 12th Edition, Academic Press, 2019.  
Evaluation Pattern CIA: 50% ESE: 50%  
MDS132L  PROBABILITY AND DISTRIBUTION THEORY (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course 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 
Unit1 
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.  
Unit2 
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.  
Unit3 
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.  
Unit4 
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: Noncentral chisquare, t and F distributions and their properties.  
Unit5 
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, 4^{th} Edition, 2014. 2. V.K Rohatgi and Saleh, An Introduction to Probability and Statistics, 3^{rd} Edition, 2015.  
Essential Reading / Recommended Reading 1. A.M Mood, F.A Graybill and D.C Boes, Introduction to the theory of statistics, Tata McGrawHill, 3^{rd} Edition (Reprint), 2017. 2. H.A David and H.N Nagaraja, Order Statistics, John Wiley & Sons, 3^{rd} Edition, 2003.  
Evaluation Pattern CIA  50% ESE  50%  
MDS133  PRINCIPLES OF DATA SCIENCE (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course 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 C04: Visualize and present the inference using various tools CO5: Learn to think through the ethics surrounding privacy, data sharing and algorithmic decisionmaking 
Unit1 
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.  
Unit2 
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.  
Unit3 
Teaching Hours:12 

MACHINE LEARNING


Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms.  
Unit4 
Teaching Hours:12 

DEEP LEARNING


Introduction – Deep Feedforward Networks – Regularization – Optimization of Deep Learning – Convolutional Networks – Recurrent and Recursive Nets – Applications of Deep Learning.  
Unit5 
Teaching Hours:14 

DATA VISUALIZATION


Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools – Creating an interactive dashboard with dc.jssummary.  
Unit5 
Teaching Hours:14 

ETHICS AND RECENT TRENDS


Data Science Ethics – Doing good data science – Owners of the data  Valuing different aspects of privacy  Getting informed consent  The Five Cs – Diversity – Inclusion – Future Trends.  
Text Books And Reference Books: [1]. Introducing Data Science, Davy Cielen, Arno D. B. Meysman, Mohamed Ali, Manning Publications Co., 1st edition, 2016 [2]. An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Springer, 1st edition, 2013 [3]. Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 1st edition, 2016 [4]. Ethics and Data Science, D J Patil, Hilary Mason, Mike Loukides, O’ Reilly, 1st edition, 2018  
Essential Reading / Recommended Reading [1]. Data Science from Scratch: First Principles with Python, Joel Grus, O’Reilly, 1st edition, 2015 [2]. Doing Data Science, Straight Talk from the Frontline, Cathy O'Neil, Rachel Schutt, O’Reilly, 1st edition, 2013 [3]. Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Cambridge University Press, 2nd edition, 2014  
Evaluation Pattern CIA : 50 % ESE : 50 %  
MDS133L  PRINCIPLES OF DATA SCIENCE (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

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


Course 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 decisionmaking 
Unit1 
Teaching Hours:10 

INTRODUCTION TO DATA SCIENCE


 
Unit2 
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.  
Unit3 
Teaching Hours:12 

MACHINE LEARNING


 
Unit4 
Teaching Hours:12 

DEEP LEARNING


 
Unit5 
Teaching Hours:14 

DATA VISUALIZATION


 
Text Books And Reference Books: T1. Introducing Data Science, Davy Cielen, Amo D.B. Meysman, Mohammed Ali, Manning Publications Co., 1^{st} Edition, 2016 T2. An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten, Trevor Hastic, Robert Tibshirani, Springer, 1^{st} edition, 2013 T3. Deep learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 1^{st} Edition, 2016 T4. Ethics and Data Science, D J Patil, Hilary mason, Mike Loukides, O’ Reilly, 1^{st} Edition, 2018  
Essential Reading / Recommended Reading R1. Data Science from Scratch: First Principles with Python, Joel Grus, O’Reilly, 1^{st} Edition, 2015
R2.Doing Data Science, Straight talk from the Frontline, Cathy O’Neil, Rachel Schutt, O’ Reilly, 1^{st} Edition, 2013 R3. Mining of Massive Datasets, Jure Leskovee, Anand Rajaraman, Jeffrey David Ullman, Cambridge University Press, 2^{nd} edition, 2014  
Evaluation Pattern
 
MDS134  RESEARCH METHODOLOGY (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 

This course is intended to assist students in planning and carrying out research work.The students are exposed to the basic principles, procedures and techniques of implementing a research project. To introduce the research concept and the various research methodologies is the main objective. It focuses on finding out the research gap from the literature and encourages lateral, strategic and creative thinking. This course also introduces computer technology and basic statistics required for research and reporting the research outcomes scientifically emphasizing on research ethics.


Course Outcome 

CO1: Understand the essence 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. 
Unit1 
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.  
Unit2 
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,Online Searching: Database ,SCIFinder, Scopus, Science Direct ,Searching research articles , Citation Index ,Impact Factor ,Hindex.  
Unit3 
Teaching Hours:7 
RESEARCH DATA


Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation.  
Unit4 
Teaching Hours:7 
REPORT WRITING


Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, Text, Tables, Figures, Equations, Citations, Referencing, and Templates (IEEE style), Paper writing for international journals, Writing scientific report.  
Text Books And Reference Books: [1] C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014. [2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005.  
Essential Reading / Recommended Reading [1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4thed. SAGE Publications, 2014. [2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010.  
Evaluation Pattern CIA  50% ESE  50%  
MDS134L  RESEARCH METHODOLOGY (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

This course is intended to assist students in planning and carrying out research work.The students are exposed to the basic principles, procedures and techniques of implementing a research project. To introduce the research concept and the various research methodologies is the main objective. It focuses on finding out the research gap from the literature and encourages lateral, strategic and creative thinking. This course also introduces computer technology and basic statistics required for research and reporting the research outcomes scientifically emphasizing on research ethics. 

Course 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. 
Unit1 
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.  
Unit2 
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,Online Searching: Database ,SCIFinder, Scopus, Science Direct, Searching research articles , Citation Index ,Impact Factor ,Hindex.  
Unit3 
Teaching Hours:7 
RESEARCH DATA


Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation.  
Unit4 
Teaching Hours:7 
REPORT WRITING


Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, Text, Tables, Figures, Equations, Citations, Referencing, and Templates (IEEE style), Paper writing for international journals, Writing scientific report.  
Text Books And Reference Books: [1] C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014. [2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005.  
Essential Reading / Recommended Reading [1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4thed. SAGE Publications, 2014. [2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010.  
Evaluation Pattern CIA 50% ESE 50%  
MDS161A  INTRODUCTION TO STATISTICS (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

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

Course 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. 
Unit1 
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  
Unit2 
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.  
Unit3 
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.  
Unit4 
Teaching Hours:7 
BASICS OF PROBABILITY


Random experiment, sample point and sample space, event, algebra of events. Definition of Probability: classical, empirical and axiomatic approaches to probability, properties of probability. Theorems on probability, conditional probability and independent events, Laws of total probability, Baye’s theorem and its applications  
Text Books And Reference Books: [1]. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, John Wiley & Sons Inc., New Jersey, 2015. [2]. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11th edition, Sultan Chand & Sons, New Delhi, 2014.  
Essential Reading / Recommended Reading [1]. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015. [2]. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017. [3]. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013. [4]. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi, 2008.  
Evaluation Pattern CIA  50% ESE  50%  
MDS161B  INTRODUCTION TO COMPUTERS AND PROGRAMMING (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Demonstrate the systematic approach for problemsolving using computers. CO2: Apply different programming structures with suitable logic for computational problems. 
Unit1 
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  
Unit2 
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  
Unit3 
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.  
Unit4 
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, Problemsolving and programming concepts, PHI, 9th Edition, 2012  
Essential Reading / Recommended Reading [1]. E Balagurusamy, Fundamentals of Computers, TMH, 2011
 
Evaluation Pattern CIA: 50% ESE: 50%  
MDS161BL  INTRODUCTION TO COMPUTERS AND PROGRAMMING (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

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


Course Outcome 

CO1: Demonstrate the systematic approach for problem solving using computers. EM CO2: Apply different programming structure with suitable logic for computational problems. EM+S 
Unit1 
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  
Unit2 
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  
Unit3 
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.  
Unit4 
Teaching Hours:10 
PROBLEM SOLVING


Introduction to programming structure – pointers for structuring a solution – modules and their functions – cohesion and coupling – problem solving with logic structure. Problem solving with decisions – the decision logic structure – straight through logic – positive logic – negative logic – logic conversion – decision tables – case logic structure  examples.  
Text Books And Reference Books: [1]Thomas L.Floyd and R.P.Jain,“Digital Fundamentals”,8th Edition, Pearson Education,2007. [2]Peter Norton “Introduction to Computers”,6th Edition, Tata Mc Graw Hill, New Delhi,2006. [3]Maureen Sprankle and Jim Hubbard, Problem solving and programming concepts, PHI, 9th Edition, 2012
 
Essential Reading / Recommended Reading [1]. EBalagurusamy,FundamentalsofComputers, TMH,2011  
Evaluation Pattern CIA:50%
ESE:50%  
MDS161C  LINUX ADMINISTRATION (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

To Enable the students to excel in the Linux Platform 

Course Outcome 

CO1: Demonstrate the systematic approach for configure the Linux environment CO2: Demonstrate the systematic approach for configure the Liux environment 
Unit1 
Teaching Hours:10 

Module1


RHEL7.5,breaking root password, Understand and use essential tools for handling files, directories, commandline environments, and documentation  Configure local storage using partitions and logical volumes  
Unit2 
Teaching Hours:10 

Module2


Swapping, Extend LVM Partitions,LVM Snapshot  Manage users and groups, including use of a centralized directory for authentication  
Unit3 
Teaching Hours:10 

Module3


Kernel updations,yum and nmcli configuration, Scheduling jobs,at,crontab  Configure firewall settings using firewall config, firewallcmd, or iptables , Configure keybased 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/enUS/Red_Hat_Enterprise_Linux/7/ 2. https://access.redhat.com/documentation/enUS/Red_Hat_Enterprise_Linux/7/  
Essential Reading / Recommended Reading   
Evaluation Pattern CIA:50% ESE:50%  
MDS161LA  INTRODUCTION TO STATISTICS (2021 Batch)  
Total Teaching Hours for Semester:1 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 



Course 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. 
Unit1 
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  
Unit2 
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  
Unit3 
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  
Unit4 
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:
 
Essential Reading / Recommended Reading [1]. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015. [2]. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017. [3]. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013. [4]. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi, 2008.  
Evaluation Pattern CIA  50% ESE  50%  
MDS171  DATA BASE TECHNOLOGIES (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 

Max Marks:150 
Credits:5 

Course Objectives/Course Description 

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

Course Outcome 

CO1: Demonstrate various databases and Compose effective queries CO2: Understanding the process of OLAP system construction CO3: Understanding the process of OLAP system construction 
Unit1 
Teaching Hours:18 

INTRODUCTION


Concept & Overview of DBMS, Data Models, Database Languages, Database Administrator, Database Users, Three Schema architecture of DBMS. Basic concepts, Design Issues, Mapping Constraints, Keys, EntityRelationship Diagram, Weak Entity Sets, Extended ER features Lab Exercises 1. Data Definition, 2. Table Creation 3. Constraints  
Unit2 
Teaching Hours:18 

RELATIONAL MODEL AND DATABASE DESIGN


SQL and Integrity Constraints, Concept of DDL, DML, DCL. Basic Structure, Set operations, Aggregate Functions, Null Values, Domain Constraints, Referential Integrity Constraints, assertions, views, Nested Subqueries, Functional Dependency, Different anomalies in designing a Database, Normalization: using functional dependencies, BoyceCodd Normal Form, 4NF Lab Exercises 1. Insert, Select, Update & Delete Commands 2. Nested Queries & Join Queries 3. Views  
Unit3 
Teaching Hours:18 

DATA WAREHOUSE: THE BUILDING BLOCKS


Defining Features, Data Warehouses and Data Marts, Architectural Types, Overview of the Components, Metadata in the Data warehouse, Data Design and Data Preparation: Principles of Dimensional Modeling, Dimensional Modeling Advanced Topics From Requirements To Data Design, The Star Schema, Star Schema Keys, Advantages of the Star Schema, Star Schema: Examples, Dimensional Modeling: Advanced Topics, Updates to the Dimension Tables, Miscellaneous Dimensions, The Snowflake Schema, Aggregate Fact Tables, Families Oo Stars Lab Exercises: 1. Importing source data structures 2. Design Target Data Structures 3. Create target multidimensional cube  
Unit4 
Teaching Hours:18 

DATA INTEGRATION and DATA FLOW (ETL)


Requirements, ETL Data Structures, Extracting, Cleaning and Conforming, Delivering Dimension Tables, Delivering Fact Tables, RealTime ETL Systems Lab Exercises: 1. Perform the ETL process and transform into data map 2. Create the cube and process it 3. Generating Reports 4. Creating the Pivot table and pivot chart using some existing data  
Unit5 
Teaching Hours:18 

NOSQL Databases


Introduction to NOSQL Systems, The CAP Theorem, DocumentBased NOSQL Systems and MongoDB, NOSQL KeyValue Stores, ColumnBased or Wide Column NOSQL Systems, Graph databases, Multimedia databases. Lab Exercises: 1. MongoDB Exercise  1 2. MongoDB Exercise  2  
Text Books And Reference Books: [1]. Henry F. Korth and Silberschatz Abraham, “Database System Concepts”, Mc.Graw Hill. [2]. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education, 2007. [3]. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd John Wiley & Sons, Inc. New York, USA, 2002  
Essential Reading / Recommended Reading [1] LiorRokach and OdedMaimon, Data Mining and Knowledge Discovery Handbook, Springer, 2nd edition, 2010.  
Evaluation Pattern CIA: 50% ESE: 50%  
MDS171L  DATABASE TECHNOLOGIES (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 

Max Marks:150 
Credits:5 

Course Objectives/Course Description 



Course Outcome 

CO1: Demonstrate various databases and Compose effective queries CO2: Understanding the process of OLAP system construction CO3: Develop applications using Relational and NoSQL databases. 
Unit1 
Teaching Hours:18 

INTRODUCTION


Concept & Overview of DBMS, Data Models, Database Languages, Database Administrator, Database Users, Three Schema architecture of DBMS. Basic concepts, Design Issues, Mapping Constraints, Keys, EntityRelationship Diagram, Weak Entity Sets, Extended ER features Lab Exercises 1. Data Definition, 2. Table Creation 3. Constraints  
Unit2 
Teaching Hours:18 

RELATIONAL MODEL AND DATABASE DESIGN


SQL and Integrity Constraints, Concept of DDL, DML, DCL. Basic Structure, Set operations, Aggregate Functions, Null Values, Domain Constraints, Referential Integrity Constraints, assertions, views, Nested Subqueries, Functional Dependency, Different anomalies in designing a Database, Normalization: using functional dependencies, BoyceCodd Normal Form, 4NF Lab Exercises 1. Insert, Select, Update & Delete Commands 2. Nested Queries & Join Queries 3. Views  
Unit3 
Teaching Hours:18 

DATA WAREHOUSE: THE BUILDING BLOCKS


Defining Features, Data Warehouses and Data Marts, Architectural Types, Overview of the Components, Metadata in the Data warehouse, Data Design and Data Preparation: Principles of Dimensional Modeling, Dimensional Modeling Advanced Topics From Requirements To Data Design, The Star Schema, Star Schema Keys, Advantages of the Star Schema, Star Schema: Examples, Dimensional Modeling: Advanced Topics, Updates to the Dimension Tables, Miscellaneous Dimensions, The Snowflake Schema, Aggregate Fact Tables, Families Oo Stars Lab Exercises: 1. Importing source data structures 2. Design Target Data Structures 3. Create target multidimensional cube  
Unit4 
Teaching Hours:18 

DATA INTEGRATION and DATA FLOW (ETL)


 
Unit5 
Teaching Hours:18 

NOSQL DATABASES


Introduction to NOSQL Systems, The CAP Theorem, DocumentBased NOSQL Systems and MongoDB, NOSQL KeyValue Stores, ColumnBased or Wide Column NOSQL Systems, Graph databases, Multimedia databases. Lab Exercises: 1. MongoDB Exercise  1 2. MongoDB Exercise  2  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading [1] LiorRokach and OdedMaimon, Data Mining and Knowledge Discovery Handbook, Springer, 2nd edition, 2010.  
Evaluation Pattern
 
MDS172  INFERENTIAL STATISTICS (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 

Max Marks:150 
Credits:5 

Course Objectives/Course Description 

Statistical inference plays an important role in modeling data and decisionmaking from the realworld phenomenon. This course is designed to impart the knowledge of testing of hypothesis and estimation of parameters for reallife data sets. 

Course Outcome 

CO1: Demonstrate the concepts of population and samples. CO2: Apply the idea of sampling distribution of different statistics in testing of hypothesis CO3: Test the hypothesis using nonparametric tests for real world problems. C04: Estimate the unknown population parameters using the concepts of point and interval estimations. 
Unit1 
Teaching Hours:18 
INTRODUCTION


Population and Statistics – Finite and Infinite population – Parameter and Statistics – Types of sampling  Sampling Distribution – Sampling Error  Standard Error – Test of significance –concept of hypothesis – types of hypothesis – Errors in hypothesistesting – Critical region – level of significance  Power of the test – pvalue. Lab Exercise: 1. Calculation of sampling error and standard error 2. Calculation of probability of critical region using standard distributions 3. Calculation of power of the test using standard distributions.  
Unit2 
Teaching Hours:18 
TESTING OF HYPOTHESIS I


Concept of large and small samples – Tests concerning a single population mean for known σ – equality of two means for known σ – Test for Single variance  Test for equality of two variance for normal population – Tests for single proportion – Tests of equality of two proportions for the normal population.
Lab Exercise: 4. Test of the single sample mean for known σ. 5. Test of equality of two means when known σ 6. Tests of single variance and equality of variance for large samples 7. Tests for single proportion and equality of two proportion for large samples.  
Unit3 
Teaching Hours:18 
TESTING OF HYPOTHESIS II


Students tdistribution and its properties (without proofs) – Single sample mean test – Independent sample mean test – Paired sample mean test – Tests of proportion (based on t distribution) – F distribution and its properties (without proofs) – Tests of equality of two variances using Ftest – Chisquare distribution and its properties (without proofs) – chisquare test for independence of attributes – Chisquare test for goodness of fit.
Lab Exercise: 8. Single sample mean test 9. Independent and Paired sample mean test 10. Tests of proportion of one and two samples based on tdistribution 11. Test of equality of two variances 12. Chisquare test for independence of attributes and goodness of fit.  
Unit4 
Teaching Hours:18 
ANALYSIS OF VARIANCE


Meaning and assumptions  Fixed, random and mixed effect models  Analysis of variance of oneway and twoway classified data with and without interaction effects – Multiple comparison tests: Tukey’s method  critical difference.
Lab Exercise: 13. Construction of oneway ANOVA 14. Construction of twoway ANOVA with interaction 15. Construction of twoway ANOVA without interaction 16. Multiple comparision test using Tukey’s method and critical difference methods  
Unit5 
Teaching Hours:18 
NONPARAMETRIC TESTS


Concept of Nonparametric tests  Run test for randomness  Sign test and Wilcoxon Signed Rank Test for one and paired samples  Run test  Median test and MannWhitneyWilcoxon tests for two samples.
Lab Exercise: 17. Test of one sample using Run and sign tests 18. Test of paried sample using Wilcoxon signed rank test 19. Test of two samples using Run test and Median test 20. Test for two samples using MannWhitneyWilcoxon tests  
Text Books And Reference Books: 1. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 12th edition, Sultan Chand & Sons, New Delhi, 2020. 2. Brian Caffo, Statistical Inference for Data Science, Learnpub, 2016.  
Essential Reading / Recommended Reading 1. Walpole R.E, Myers R.H and Myers S.L, Probability and Statistics for Engineers and Scientists, 9th edition, Pearson, New Delhi, 2017. 2. John V, Using R for Introductory Statistics, 2nd edition, CRC Press, Boca Raton, 2014. 3. Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd, New Delhi, 2012. 4. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, JohnWiley & Sons Inc, New Jersey, 2015.  
Evaluation Pattern CIA: 50% ESE:50%  
MDS172L  INFERENTIAL STATISTICS (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Course 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. 
Unit1 
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.
 
Unit2 
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 1parameter 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  
Unit3 
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.  
Unit4 
Teaching Hours:15 
HYPOTHESIS TESTING


Uniformly most powerful tests  the NeymanPearson 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 TypeI and TypeII 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)  
Unit5 
Teaching Hours:15 
MEAN TESTS


Unbiased ness for hypotheses testing  similarity and completeness  UMP unbiased tests for multiparameter 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.  
Unit6 
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 McGrawHill, 3rd Edition (Reprint), 2017. [2]. Linear Statistical Inference and its Applications, Rao C.R, Willy Publications, 2nd Edition, 2001.  
Evaluation Pattern CIA  50% ESE  50%  
MDS173  PROGRAMMING FOR DATA SCIENCE IN PYTHON (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Demonstrate the use of builtin objects of Python CO2: Demonstrate significant experience with python program development environment CO3: Implement numerical programming, data handling and visualization through NumPy, Pandas and MatplotLib modules. 
Unit1 
Teaching Hours:17 
INTRODUCTION TO PYTHON


Structure of Python ProgramUnderlying mechanism of Module ExecutionBranching and LoopingProblem Solving Using Branches and LoopsFunctions  Lists and Mutability Problem Solving Using Lists and Functions
Lab Exercises1. Demonstrate usage of branching and loopingstatements 2. Demonstrate Recursivefunctions 3. DemonstrateLists  
Unit2 
Teaching Hours:17 
SEQUENCE DATATYPES AND OBJECTORIENTED PROGRAMMING


Sequences, Mapping and Sets Dictionaries Classes: Classes and InstancesInheritance Exceptional HandlingIntroduction to Regular Expressions using “re” module. Lab Exercises1. Demonstrate Tuples andSets 2. DemonstrateDictionaries 3. Demonstrate inheritance and exceptionalhandling 4. Demonstrate use of“re”  
Unit3 
Teaching Hours:13 
USING NUMPY


Basics of NumPyComputation on NumPyAggregationsComputation on Arrays Comparisons, Masks and Boolean ArraysFancy IndexingSorting ArraysStructured Data: NumPy’s Structured Array. Lab Exercises1. DemonstrateAggregation 2. Demonstrate Indexing andSorting  
Unit4 
Teaching Hours:13 
DATA MANIPULATION WITH PANDAS I


Introduction to Pandas ObjectsData indexing and SelectionOperating on Data in Pandas Handling Missing DataHierarchical Indexing  Combining Data Sets Lab Exercises1. Demonstrate handling of missingdata 2. Demonstrate hierarchicalindexing  
Unit5 
Teaching Hours:17 
DATA MANIPULATION WITH PANDAS II


Aggregation and GroupingPivot TablesVectorized String Operations Working with Time SeriesHigh Performance Pandas and query() Lab Exercises1. Demonstrate usage of Pivottable 2. Demonstrate use of andquery()  
Unit6 
Teaching Hours:13 
VISUALIZATION AND MATPLOTLIB


Basic functions of matplotlibSimple Line Plot, Scatter PlotDensity and Contour Plots Histograms, Binnings and DensityCustomizing Plot Legends, Colour BarsThree Dimensional Plotting in Matplotlib. Lab Exercises1. DemonstrateScatterPlot 2. Demonstrate3Dplotting  
Text Books And Reference Books: [1]. Jake VanderPlas ,Python Data Science Handbook  Essential Tools for Working with Data, O’Reily Media,Inc, 2016 [2]. Zhang.Y ,An Introduction to Python and Computer Programming, Springer Publications,2016  
Essential Reading / Recommended Reading [1].JoelGrus,DataSciencefromScratchFirstPrincipleswithPython,O’ReillyMedia,2016 [2]. T.R.Padmanabhan, Programming with Python,SpringerPublications,2016  
Evaluation Pattern CIA: 50%ESE: 50%
 
MDS173L  PROGRAMMING OF DATA SCIENCE IN PYTHON (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Understand and demonstrate the usage of builtin 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. 
Unit1 
Teaching Hours:17 

INTRODUCTION TO PYTHON


Structure of Python ProgramUnderlying mechanism of Module ExecutionBranching and LoopingProblem Solving Using Branches and LoopsFunctions  Lists and Mutability Problem Solving Using Lists and Functions  
Unit2 
Teaching Hours:17 

SEQUENCE DATATYPES AND OBJECTORIENTED PROGRAMMING


Sequences, Mapping and Sets Dictionaries Classes: Classes and InstancesInheritance Exceptional HandlingIntroduction to Regular Expressions using “re” module.  
Unit3 
Teaching Hours:13 

USING NUMPY


Basics of NumPyComputation on NumPyAggregationsComputation on Arrays Comparisons, Masks and Boolean ArraysFancy IndexingSorting ArraysStructured Data: NumPy’s Structured Array.  
Unit4 
Teaching Hours:13 

DATA MANIPULATION WITH PANDAS I


Introduction to Pandas ObjectsData indexing and SelectionOperating on Data in Pandas Handling Missing DataHierarchical Indexing  Combining Data Sets  
Unit5 
Teaching Hours:17 

DATA MANIPULATION WITH PANDAS II


Aggregation and GroupingPivot TablesVectorized String Operations Working with Time SeriesHigh Performance Pandas and query()  
Unit6 
Teaching Hours:13 

VISUALIZATION AND MATPLOTLIB


Basic functions of matplotlibSimple Line Plot, Scatter PlotDensity and Contour Plots Histograms, Binnings and DensityCustomizing Plot Legends, Colour BarsThree 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
 
Evaluation Pattern
 
MDS231  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  II (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

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

Course 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 C04: Know the about the basic terminologies and properties in Graph Theory 
Unit1 
Teaching Hours:14 
Calculus of Several Variables


Functions of Several Variables: Functions of two, three variables  Limits and continuity in HIgher Dimensions: Limits for functions of two variables, Functions of more than two variables  Partial Derivatives: partial derivative of functions of two variables, partial derivatives of functions of more than two variables, partial derivatives and continuity, second order partial derivatives  The Chain Rule: chain rule on functions of two, three variables, chain rule on functions defined on surfaces  Directional Derivative and Gradient vectors: Directional derivatives in a plane, Interpretation of directional derivative, calculation and gradients, Gradients and tangents to level curves.  
Unit2 
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 GramSchmidt orthogonalization  
Unit3 
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 halfspaces  Euclidean balls and ellipsoids Norm balls and Norm cones  polyhedra  simplexes, Convex hull description of polyhedra  The positive semidefinitecone.
 
Unit4 
Teaching Hours:12 
Graph Theory  Basics


Graph Classes: Definition of a Graph and Graph terminology, isomorphism of graphs, Completegraphs, bipartite graphs, complete bipartite graphsVertex degree: adjacency and incidence, regular graphs  subgraphs, spanning subgraphs, induced subgraphs, removing or adding edges of a graph, removing vertices from graphs  Graph Operations: Graph Union, intersection, complement, self complement, Paths and Cycles, Connected graphs, Eulerian and HamiltonianGraphs.
 
Unit5 
Teaching Hours:12 
Graph Theory  More concepts


Matrix Representation of Graphs, Adjacency matrices, Incidence Matrices, Trees and its properties, Bridges (cutedges), spanning trees, weighted Graphs, minimal spanning tree problems, Shortest path problems, cut vertices, cuts, vertex and edge connectivity, Graph Algorithms  Applications of Graph Theory
 
Text Books And Reference Books: 1. M. D. Weir, J. Hass, and G. B. Thomas, Thomas' calculus. Pearson, 2016. (Unit 1) 2. G Strang, Linear Algebra and its Applications, 4th ed., Cengage, 2006. (Unit 2) 3. S. P. Boyd and L.Vandenberghe, Convex optimization.Cambridge Univ. Pr., 2011.(Unit 3) 4. J Clark, D A Holton, A first look at Graph Theory, Allied Publishers India, 1995. (Unit 4)  
Essential Reading / Recommended Reading 1.J. Patterson and A. Gibson, Deep learning: a practitioner's approach. O'Reilly Media, 2017. 2.S. Sra, S. Nowozin, and S. J. Wright, Optimization for machine learning. MIT Press, 2012. 3.D. Jungnickel, Graphs, networks and algorithms. Springer, 2014. 4.D Samovici, Mathematical Analysis for Machine Learning and Data Mining, World Scientific Publishing Co. Pte. Ltd, 2018 5.P. N. Klein, Coding the matrix: linear algebra through applications to computer science. Newtonian Press, 2015. 6.K H Rosen, Discrete Mathematics and its applications, 7th ed., McGraw Hill, 2016  
Evaluation Pattern CIA:50% ESE :50%  
MDS231L  MATHEMATICAL FOUNDATION FOR DATA SCIENCE II (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 


Unit1 
Teaching Hours:14 
Calculus of Several Variables


Functions of Several Variables: Functions of two, three variables  Limits and continuity in HIgher Dimensions: Limits for functions of two variables, Functions of more than two variables  Partial Derivatives: partial derivative of functions of two variables, partial derivatives of functions of more than two variables, partial derivatives and continuity, second order partial derivatives  The Chain Rule: chain rule on functions of two, three variables, chain rule on functions defined on surfaces  Directional Derivative and Gradient vectors: Directional derivatives in a plane, Interpretation of directional derivative, calculation and gradients, Gradients and tangents to level curves.  
Unit2 
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 GramSchmidt orthogonalization  
Unit3 
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 halfspaces  Euclidean balls and ellipsoids Norm balls and Norm cones  polyhedra  simplexes, Convex hull description of polyhedra  The positive semidefinitecone.  
Unit4 
Teaching Hours:12 
Graph Theory  Basics


Graph Classes: Definition of a Graph and Graph terminology, isomorphism of graphs, Complete graphs, bipartite graphs, complete bipartite graphs Vertex degree: adjacency and incidence, regular graphs  subgraphs, spanning subgraphs, induced subgraphs, removing or adding edges of a graph, removing vertices from graphs  Graph Operations: Graph Union, intersection, complement, self complement, Paths and Cycles, Connected graphs, Euclerian and Hamiltonian graphs.  
Unit5 
Teaching Hours:12 
Graph Theory  More concepts


Matrix Representation of Graphs, Adjacency matrices, Incidence Matrices, Trees and its properties, Bridges (cutedges), spanning trees, weighted Graphs, minimal spanning tree problems, Shortest path problems, cut vertices, cuts, vertex and edge connectivity, Graph Algorithms  Applications of Graph Theory  
Text Books And Reference Books: 1. M. D. Weir, J. Hass, and G. B. Thomas, Thomas' calculus. Pearson, 2016. 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 (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Demonstrate deeper understanding of the linear regression model. CO2: Evaluate Rsquare 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 
Unit1 
Teaching Hours:13 
SIMPLE LINEAR REGRESSION


Introduction to regression analysis: Modelling a response, overview and applications of regression analysis, major steps in regression analysis. Simple linear regression (Two variables): assumptions, estimation and properties of regression coefficients, significance and confidence intervals of regression coefficients, measuring the quality of the fit.  
Unit2 
Teaching Hours:13 
MULTIPLE LINEAR REGRESSION


Multiple linear regression model: assumptions, ordinary least square estimation of regression coefficients, interpretation and properties of regression coefficient, significance and confidence intervals of regression coefficients.  
Unit3 
Teaching Hours:12 
CRITERIA FOR MODEL SELECTION


Mean Square error criteria, R2 and criteria for model selection; Need of the transformation of variables; BoxCox transformation; Forward, Backward and Stepwise procedures.  
Unit4 
Teaching Hours:12 
RESIDUAL ANALYSIS


Residual analysis, Departures from underlying assumptions, Effect of outliers, Collinearity, Nonconstant variance and serial correlation, Departures from normality, Diagnostics and remedies.  
Unit5 
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, 4^{th} 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 (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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


Course Outcome 

CO1: Demonstrate deeper understanding of the linear regression model. CO2: Evaluate Rsquare 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 
Unit1 
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.  
Unit2 
Teaching Hours:15 
MULTIPLE LINEAR REGRESSION


Unit3 
Teaching Hours:10 
CRITERIA FOR MODEL SELECTION


Mean Square error criteria, R2 and criteria for model selection; Need of the transformation of variables; BoxCox transformation; Forward, Backward and Stepwise procedures.  
Unit4 
Teaching Hours:10 
RESIDUAL ANALYSIS


Residual analysis, Departures from underlying assumptions, Effect of outliers, Collinearity, Nonconstant variance and serial correlation, Departures from normality, Diagnostics and remedies.  
Unit5 
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:
a. Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Cengage learning. b. Gujarati, D. N., Porter, D. C., & Gunasekar, S. (2012). Basic econometrics. Tata McGrawHill Education. c. Studenmund, A. H. (2014). Using econometrics, a practical guide. Pearson  
Essential Reading / Recommended Reading
1. Iain Pardoe, Applied Regression Modelling, John Wiley and Sons, Inc, 2012. 2. P. McCullagh, J.A. Nelder, Generalized Linear Models, Chapman & Hall, 1989. 3. D.C Montgomery, E.A Peck and G.G Vining, Introduction to Linear Regression Analysis, John Wiley and Sons,Inc.NY, 2003. 4. S. Chatterjee and AHadi, Regression Analysis by Example, 4th Ed., John Wiley and Sons, Inc, 2006 5. Seber, A.F. and Lee, A.J. (2003) Linear Regression Analysis, John Wiley, Relevant sections from chapters 3, 4, 5, 6, 7, 9, 10.
 
Evaluation Pattern CIA I: 10% CIA II: 25% CIA III: 10% Attendance: 5% ESE: 50%  
MDS241A  MULTIVARIATE ANALYSIS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Understand multivariate data structure, multinomial and multivariate normal distribution CO2: Apply Multivariate analysis of variance (MANOVA) of one and twoway classified data. 
Unit1 
Teaching Hours:12 
INTRODUCTION


Basic concepts on multivariate variable. Multivariate normal distribution, Marginal and conditional distribution, Concept of random vector: Its expectation and VarianceCovariance matrix. Marginal and joint distributions. Conditional distributions and Independence of random vectors. Multinomial distribution. Sample mean vector and its distribution.  
Unit2 
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.  
Unit3 
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.  
Unit4 
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  
Unit5 
Teaching Hours:12 
PRINCIPAL COMPONENT and FACTOR ANALYSIS


Principal components, sample principal components asymptotic properties. Canonical variables and canonical correlations: definition, estimation, computations. Test for significance of canonical correlations. Factor analysis: Orthogonal factor model, factor loadings, estimation of factor loadings, factor scores. Applications  
Text Books And Reference Books: [1]. Anderson, T.W. 2009. An Introduction to Multivariate Statistical Analysis, 3rd Edition, John Wiley. [2]. Everitt B, Hothorn T, 2011. An Introduction to Applied Multivariate Analysis with R, Springer. [3]. Barry J. Babin, Hair, Rolph E Anderson, and William C. Blac, 2013, Multivariate Data Analysis, Pearson New International Edition,  
Essential Reading / Recommended Reading [1] Giri, N.C. 1977. Multivariate Statistical Inference. Academic Press. [2] Chatfield, C. and Collins, A.J. 1982. Introduction to Multivariate analysis. Prentice Hall [3] Srivastava, M.S. and Khatri, C.G. 1979. An Introduction to Multivariate Statistics. North Holland  
Evaluation Pattern CIA  50% ESE  50%  
MDS241B  STOCHASTIC PROCESS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course 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. 
Unit1 
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.  
Unit2 
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.  
Unit3 
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.  
Unit4 
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.  
Unit5 
Teaching Hours:12 
STATIONARY PROCESS


Stationary Processes: Discrete Parameter Stochastic Process – Application to Time Series. Autocovariance and Autocorrelation functions and their properties. Moving Average, Autoregressive, Autoregressive Moving Average, Autoregressive Integrated Moving Average Processes. Basic ideas of residual analysis, diagnostic checking, forecasting.  
Text Books And Reference Books: [1]. Stochastic Processes, R.G Gallager, Cambridge University Press, 2013. [2]. Stochastic Processes, S.M Ross, Wiley India Pvt. Ltd, 2008.  
Essential Reading / Recommended Reading [1]. Stochastic Processes from Applications to Theory, P.D Moral and S. Penev, CRC Press, 2016 [2]. Introduction to Probability and Stochastic Processes with Applications, B..C. Liliana, A Viswanathan, S. Dharmaraja, Wiley Pvt. Ltd, 2012.  
Evaluation Pattern CIA  50% ESE  50%  
MDS241C  CATEGORICAL DATA ANALYSIS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Describe the categorical response. CO2: Identify tests for contingency tables. CO3: Apply regression models for categorical response variable CO4: Analyse contingency tables using loglinear models. 
Unit1 
Teaching Hours:12 
INTRODUCTION


Categorical response data  Probability distributions for categorical data  Statistical inference for discrete data  
Unit2 
Teaching Hours:12 
CONTINGENCY TABLES


Probability structure for contingency tables  Comparing proportions with 2x2 tables  The odds ratio  Tests for independence  Exact inference  Extension to threeway and larger tables  
Unit3 
Teaching Hours:12 
GENERALIZED LINEAR MODELS


Components of a generalized linear model  GLM for binary and count data  Statistical inference and model checking  Fitting GLMs  
Unit4 
Teaching Hours:12 
LOGISTIC REGRESSION


Interpreting the logistic regression model  Inference for logistic regression  Logistic regression with categorical predictors  Multiple logistic regression  Summarising effects  Building and applying logistic regression models  Multicategory logit models  
Unit5 
Teaching Hours:12 
LOGLINEAR MODELS FOR CONTINGENCY TABLES


Loglinear models for twoway and threeway tables  Inference for Loglinear models  the loglinearlogistic connection  Independence graphs and collapsibility  Models for matched pairs: Comparing dependent proportions, Logistic regression for matched pairs  Comparing margins of square contingency tables  symmetry issues  
Text Books And Reference Books: 1. Agresti, A. (2012). Categorical Data Analysis, 3rd Edition. New York: Wiley  
Essential Reading / Recommended Reading 1. Le, C.T. (2009). Applied Categorical Data Analysis and Translational Research, 2nd Ed., John Wiley and Sons. 2. Agresti, A. (2010). Analysis of ordinal categorical. John Wiley & Sons. 3. Stokes, M. E., Davis, C. S., & Koch, G. G. (2012). Categorical data analysis using SAS. SAS Institute. 4. Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons. 5. Bilder, C. R., & Loughin, T. M. (2014). Analysis of categorical data with R. Chapman and Hall/CRC.  
Evaluation Pattern CIA:50% ESE:50%  
MDS241LA  MULTIVARIATE ANALYSIS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Course Description and Course Objectives 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. 

Course Outcome 

Course Outcomes CO1: Understand multivariate data structure, multinomial and multivariate normal distribution CO2: Apply Multivariate analysis of variance (MANOVA) of one and twoway classified data. 
Unit1 
Teaching Hours:12 

INTRODUCTION


Basic concepts on multivariate variable. Multivariate normal distribution, Marginal and conditional distribution, Concept of random vector: Its expectation and VarianceCovariance matrix. Marginal and joint distributions. Conditional distributions and Independence of random vectors. Multinomial distribution. Sample mean vector and its distribution.  
Unit2 
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.  
Unit3 
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.  
Unit4 
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  
Unit5 
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%
 
MDS241LB  STOCHASTIC PROCESS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

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

Course 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. 
Unit1 
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.  
Unit2 
Teaching Hours:12 

POISSON PROCESS


Classification of States, Recurrent and Transient States  Transient Markov Chain, Random Walk , and Gambler's Ruin Problem. ContinuousTime Markov Process: Poisson Processes, Birth and Death Processes, Kolmogorov’s Differential Equations, Applications.  
Unit3 
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.  
Unit4 
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.  
Unit5 
Teaching Hours:12 

STATIONARY PROCESS


Stationary Processes: Discrete Parameter Stochastic Process – Application to Time Series. Autocovariance and Autocorrelation 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
 
Evaluation Pattern
 
MDS271  MACHINE LEARNING (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 

Max Marks:150 
Credits:5 

Course Objectives/Course Description 

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

Course Outcome 

CO1: Understand the basic principles of machine learning techniques. CO2: Understand how machine learning problems are formulated and solved CO3: Apply machine learning algorithms to solve real world problems. 
Unit1 
Teaching Hours:18 
INRTODUCTION


MachineLearningExamplesofMachineApplicationsLearningAssociationsClassification RegressionUnsupervisedLearningReinforcement Learning.Supervised Learning: Learning class from examples Probably Approach Correct(PAC) LearningNoiseLearning Multiple classes. RegressionModel Selection and Generalization. IntroductiontoParametricmethodsMaximumLikelihood Estimation:Bernoulli Density Multinomial DensityGaussian Density, Nonparametric Density Estimation: Histogram EstimatorKernel EstimatorKNearest NeighbourEstimator. Lab Exercise: 1. Data Exploration using parametric methods 2. Data Exploration using nonparametric methods 3. Regression analysis  
Unit2 
Teaching Hours:18 
DIMENSIONALITY REDUCTION


Dimensionality Reduction: Introduction Subset SelectionPrincipal Component Analysis, Feature EmbeddingFactor AnalysisSingular Value DecompositionMultidimensional ScalingLinear Discriminant Analysis Bayesian Decision Theory. Lab Exercise: 1. Data reduction using Principal ComponentAnalysis 2. Data reduction using multidimensional scaling  
Unit3 
Teaching Hours:18 
SUPERVISED LEARNING  I


Linear Discrimination: Introduction Generalizing the Linear ModelGeometry of the Linear Discriminant Pairwise SeparationGradient DescentLogistic Discrimination. Kernel Machines: Introduction optical separating hyperplane vSVM, kernel tricks vertical kernel vertical kernel defining kernel multiclass kernel machines oneclass kernel machines. Lab Exercise 1. Lineardiscrimination 2. Logisticdiscrimination 3. Classification using kernel machines  
Unit4 
Teaching Hours:18 
SUPERVISED LEARNING  II


Multilayer Perceptron:Introduction, training a perceptron learning Boolean functions multilayer perceptron backpropogation algorithm training procedures. Combining Multiple Learners RationaleGenerating diverse learners Model combination schemes voting, Bagging Boosting fine tuning an Ensemble. Lab Exercise 1. Classification using MLP 2. Ensemble Learning
 
Unit5 
Teaching Hours:18 
UNSUPERVISED LEARNING


Clustering IntroductionMixture Densities, KMeans Clustering ExpectationMaximization algorithm Mixtures of Latent Varaible ModelsSupervised Learning after ClusteringSpectral Clustering Hierachial ClusteringClustering Choosing the number of Clusters. Lab Exercise 1. K means clustering 2. Hierarchical clustering  
Text Books And Reference Books: [1]. E. Alpaydin, Introduction to Machine Learning, 3rd Edition, MIT Press, 2014.  
Essential Reading / Recommended Reading 1. C.M.Bishop,PatternRecognitionandMachineLearning,Springer,2016. 2. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer, 2nd Edition,2009 3. K.P.Murphy,MachineLearning:AProbabilisticPerspective,MITPress,2012.  
Evaluation Pattern CIA: 50% ESE: 50%  
MDS271L  MACHINE LEARNING (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

The objectives 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. 

Course Outcome 

This course enables students to 

Unit1 
Teaching Hours:18 
INTRODUCTION


Machine Learning  Examples of Machine Applications  Learning Associations  Classification  Regression Unsupervised Learning  Reinforcement Learning Supervised Learning: Learning class from examples  Probably Approach Correct (PAC) Learning  Noise  Learning Multiple classes. RegressionModel Selection and Generalization. Introduction to Parametric methods  Maximum Likelihood Estimation: Bernoulli Density  Multinomial DensityGaussian Density, Nonparametric Density Estimation: Histogram EstimatorKernel EstimatorKNearest Neighbour Estimator.  
Unit1 
Teaching Hours:18 
Lab Exercises


 
Unit2 
Teaching Hours:18 
DIMENSIONALITY REDUCTION


Dimensionality Reduction: Introduction Subset Selection  Principal Component Analysis, Feature EmbeddingFactor AnalysisSingular Value DecompositionMultidimensional Scaling  Linear Discriminant Analysis  Bayesian Decision Theory.  
Unit2 
Teaching Hours:18 
Lab Exercise


 
Unit3 
Teaching Hours:18 
KERNEL METHODS


Introduction  optical separating hyperplane vSVM, kernel tricks  vertical kernel  vertical kernel  defining kernel  multiclass kernel machines  oneclass kernel machines.  
Unit3 
Teaching Hours:18 
SUPERVISED LEARNING


Linear Discrimination: Introduction  Generalizing the Linear ModelGeometry of the Linear Discriminant  Pairwise Separation  Gradient Descent  Logistic Discrimination  
Unit3 
Teaching Hours:18 
Lab Exercises


 
Unit4 
Teaching Hours:18 
MULTILAYER PERCEPTRON


Introduction, training a perceptron  learning Boolean functions  multilayer perceptron  backpropogation algorithm  training procedures  
Unit4 
Teaching Hours:18 
Lab Exercise


 
Unit4 
Teaching Hours:18 
COMBINING MULTIPLE LEARNERS


Rationale  Generating diverse learners  Model combination schemes  voting, Bagging Boosting  fine tuning an Ensemble.  
Unit5 
Teaching Hours:18 
UNSUPERVISED LEARNING


Clustering  Introduction  Mixture Densities, KMeans Clustering  ExpectationMaximization algorithm  Mixtures of Latent Varaible Models  Supervised Learning after Clustering  Spectral Clustering  Hierachial Clustering  Clustering  Choosing the number of Clusters  
Unit5 
Teaching Hours:18 
Lab Exercises


 
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern CIA: 50%, ESE: 50%  
MDS272A  HADOOP (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

The subject is intended to give the knowledge of Big Data evolving in every realtime 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. 

Course 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. 
Unit1 
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, MatrixVector Multiplication by Map Reduce. Apache Hadoop– Moving Data in and out of Hadoop – Understanding inputs and outputs ofMapReduce  Data Serialization, Problems with traditional largescale systemsRequirements for a new approachHadoop – ScalingDistributed FrameworkHadoop v/s RDBMSBrief history of Hadoop.
Lab Exercise
1. Installing and Configuring Hadoop  
Unit2 
Teaching Hours:15 
CONFIGURATIONS OF HADOOP


Hadoop Processes (NN, SNN, JT, DN, TT)Temporary directory – UICommon errors when running Hadoop cluster, solutions. Setting up Hadoop on a local Ubuntu host: Prerequisites, downloading Hadoop, setting up SSH, configuring the pseudodistributed 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, Hadoopspecific 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.  
Unit3 
Teaching Hours:15 
ADVANCED MAPREDUCE TECHNIQUES


Simple, advanced, and inbetween Joins, Graph algorithms, using languageindependent 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 Kmeans Clustering using MapReduce. 3. Generation of Frequent Itemset using MapReduce.  
Unit4 
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 Plugins 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 mapreduce, 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.  
Unit5 
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.  
Unit6 
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, HighPerformance BigData Analytics: Computing Systems and Approaches, Springer, 2015. [2] Jonathan R. Owens, Jon Lentz and Brian Femiano, Hadoop RealWorld Solutions Cookbook, Packt Publishing, 2013. [3] Tom White, HADOOP: The definitive Guide, O Reilly, 2012.  
Evaluation Pattern CIA  50% ESE  50%  
MDS272B  IMAGE AND VIDEO ANALYTICS (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Understand the fundamental principles of image and video analysis CO2: Apply the image and video analysis approaches to solve real world problems 
Unit1 
Teaching Hours:18 
INTRODUCTION TO DIGITAL IMAGE AND VIDEO PROCESSING


Digital image representation, Sampling and Quantization, Types of Images, Basic Relations between Pixels  Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations, Introduction to Digital Video, Sampled Video, Video Transmission.GrayLevel Processing: Image Histogram, Linear and Nonlinear point operations on Images, Arithmetic Operations between Images, Geometric Image Operations, Image Thresholding, Region labeling, Binary Image Morphology.Lab Programs:1. Program to perform Resize, Rotation of binary, Grayscale and color images using various methods.2. Program to implement contrast stretching.  
Unit2 
Teaching Hours:18 
IMAGE AND VIDEO ENHANCEMENT AND RESTORATION


Spatial domainLinear and Nonlinear Filtering, Introduction to Fourier Transform and the frequency Domain– Filtering in Frequency domain, Homomorphic Filtering, Brief introduction towards Wavelets, Wavelet based image denoising, A model of The Image Degradation / Restoration, Noise Models and basic methods for image restoration. Blotch detection and Removal.Lab Programs:3. Program to implement various image enhancement techniques using Builtin and user defined functions.4. Program to implement Nonlinear Spatial Filtering using Builtin and userdefined functions.  
Unit3 
Teaching Hours:18 
IMAGE AND VIDEO ANALYSIS


Image Compression: Huffman Coding, Run length Coding, LZW Coding, Basics of Wavelets based image compression.Video Compression: Basic Concepts and Techniques of Video compression, MPEG1 and MPEG2 Video Standards.Lab Programs:5. Program to implement homomorphic Filtering6. Extraction of frames from videos and analyzing frames  
Unit4 
Teaching Hours:18 
FEATURE DETECTION AND DESCRIPTION


Introduction to feature detectors, descriptors, matching and tracking, Basic edge detectors – canny, sobel, prewitt etc., Image Segmentation  Region Based Segmentation – Region Growing and Region Splitting and Merging, Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method.Lab Programs:7. Implement multiresolution image decomposition and reconstruction using wavelet.8. Implement image compression using wavelets.  
Unit5 
Teaching Hours:18 
OBJECT DETECTION AND RECOGNITION


Descriptors: Boundary descriptors  Fourier descriptors  Regional descriptors  Topological descriptors  moment invariantsObject detection and recognition in image and video: Minimum distance classifier, KNN classifier and Bayes, Applications in image and video analysis, object tracking in videos.Lab Programs:9. Extracting feature descriptors from the image dataset.10. Implement image classification using extracted relevant features.  
Text Books And Reference Books: [1] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 4th Edition, Pearson Education, 2018. [2] Alan Bovik, Handbook of Image and Video Processing, Second Edition, Academic Press, 2005.
 
Essential Reading / Recommended Reading [1] Anil K Jain, Fundamentals of Digital Image Processing, PHI, 2011. [2] RichardSzeliski,ComputerVision–AlgorithmsandApplications,Springer,2011. [3] Oge Marques, Practical Image and Video Processing Using MatLab, Wiley, 2011. [4] John W. Woods, Multidimensional Signal, Image, Video Processing and Coding, Academic Press, 2006.  
Evaluation Pattern CIA: 50% ESE: 50%  
MDS272C  INTERNET OF THINGS (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Understand the concepts of IoT and IoT enabling technologies CO2: Gain knowledge on IoT programming and able to develop IoT applications CO3: Identify different issues in wireless ad hoc and sensor networks CO4: Develop an understanding of sensor network architectures from a design and performance perspective CO5: Understand the layered approach in sensor networks and WSN protocols 
Unit1 
Teaching Hours:18 
Lab Exercises


1. 1. Introduction to ICs and Sensors. A basic program can be shown which makes use of logic gates IC s for understanding the basics of sensor nodes. Different sensors which find application in IoT projects can be shown,their working explained. 2. 2.Introduction to Arduino/Raspberry Pi. Sample sketches or code can be selected from theArduinosoftwareandexecuted,making use of different sensors.  
Unit1 
Teaching Hours:18 
Introduction to IOT


Introduction to IoT  Definition and Characteristics, Physical Design Things Protocols, Logical Design Functional Blocks, Communication Models Communication APIs Introductiontomeasurethephysicalquantities,IoTEnablingTechnologiesWirelessSensor Networks, Cloud Computing Big Data Analytics, Communication Protocols Embedded System IoT Levels and DeploymentTemplates.  
Unit2 
Teaching Hours:18 
IOT Programming


Introduction to Smart Systems using IoT  IoT Design Methodology IoT Boards (Raspberry Pi,Arduino)andIDECaseStudy:WeatherMonitoringLogicalDesignusingPython, Data types & Data Structures Control Flow, Functions Modules Packages, File Handling  Date/Time Operations, Classes Python Packages of Interest forIoT.  
Unit2 
Teaching Hours:18 
Lab Exercises


3. Use of sensors to detect the temperature/humidity in a room and having appropriate actions performed such as changing the LED color and turning the speaker on as an alarm and using serial monitor to see these values. 4. A basic parking system making use of multiple IR sensors, Ultrasonic Sensors, LED bulbs, Speakers etc, to identify if a slot is empty or full and using the LED and speakers to alert the user about the availability.  
Unit3 
Teaching Hours:18 
IOT Applications


Home Automation – Smart Cities Environment, Energy Retail, Logistics Agriculture, Industry Health and Lifestyle IoT and M2M.  
Unit3 
Teaching Hours:18 
Lab Exercises


5. An Agricultural System (Greenhouse System) that makes use of sensors like humidity, temperature etc, to identify the current situation of the agricultural area and taking necessary measures such as activating the water spraying motor, the alarm system (to indicate if there is excess heat) etc. 6. Create a basic sound system by making use of knobs, speakers, LED bulbs etc., to mimic the sound produced by a race car, ambulance, siren etc. 7. A basic obstacle avoiding robot by making use of Ultrasonic sensors, dc motors, and the chassis kit for robotic car.  
Unit4 
Teaching Hours:18 
Network of wireless sensor nodes


SensingandSensorsWirelessSensorNetworks,ChallengesandConstraintsApplications: Structural Health Monitoring, Traffic Control, Health Care  Node Architecture  Operating system.  
Unit4 
Teaching Hours:18 
Lab Exercise


8. Making use of GSM for communication in the obstacle avoiding robot. Using sensors such as flame sensors, PIR human motion sensor, IR sensor, LED bulbs etc for better inputs regarding the environment. 9. A garbage level indicator which makes use of IR proximity sensors, WiFi modules etc to detect the rising amount of garbage and sending data to a server and channelling that data to the owner of the module. Can be introduced as the application IoT. If needed, IoT introduction can be done much earlier and the sharing of data can be shown, for better functionality of later projects. 10. Elderly care: We want to monitor very senior citizens whether they had a sudden fall. If a very senior citizen falls suddenly while walking, due to stroke or slippery ground etc, a notification should be sent out so that he/she can get immediate medical attention. shown, for better functionality of later projects.  
Unit5 
Teaching Hours:18 
MAC, Routing and Transport Protocols in WSN


Introduction – Fundamentals of MAC Protocols – MAC protocols for WSN – Sensor MAC CaseStudy–RoutingChallengesandDesignIssues–RoutingStrategies–TransportControl Protocols–TransportProtocolDesignIssues–PerformanceofTransportProtocols  
Unit5 
Teaching Hours:18 
Lab Exercise


11. Smart street lights: The street lights should increase or decrease their intensity based on the actual requirements of the amount of light needed at that time of the day. This will save a lot of energy for the municipal corporation. 12. Implement 3bit Binary Counter using 3 LED Module. a. Glow RED if the Binary bit is '0'. Glow GREEN if the binary bit is '1' i. For example: ii. 000 = 0 (all LED should be RED) iii. 001 = 1 (Two LEDs Should be RED , and one LED should be GREEN) iv. If Button is pressed in between, Reset the counter and Restart from 0. Theft prevention system for night: When the room is dark and Board is moved or tilted (say around 90 degree), it should alarm.  
Text Books And Reference Books: [1] Arshdeep Bahgaand, Vijay Madisetti, Internet of Things: Handson Approach, Hyderabad University Press, 2015. [2] Kazem Sohraby, Daniel Minoli and TaiebZnati, Wireless Sensor Networks: Technology. Protocols and Application, Wiley Publications, 2010. [3] Waltenegus Dargie and Christian Poellabauer, Fundamentals of Wireless Sensor Networks: Theory and Practice, A John Wiley and Sons Ltd., 2010.  
Essential Reading / Recommended Reading [1] Edgar Callaway, Wireless Sensor Networks: Architecture and Protocols, Auerbach Publications, 2003. [2] Michael Miller, The Internet of Things, Pearson Education, 2015. [3] Holger Karl and Andreas Willig, Protocols and Architectures for Wireless Sensor Networks, John Wiley & Sons Inc., 2005. [4] Erdal Çayırcı and Chunming Rong, SecurityinWirelessAdHocandSensorNetworks,John Wiley and Sons, 2009. [5] Carlos De MoraisCordeiro and Dharma Prakash Agrawal, Ad Hoc and Sensor Networks: Theory and Applications, World Scientific Publishing, 2011. [6] Adrian Perrig and J.D.Tygar, Secure Broadcast Communication: In Wired and Wireless Networks, Springer, 2006.  
Evaluation Pattern CIA  50% ESE  50%  
MDS272LA  HADOOP (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

The subject is intended to give the knowledge of Big Data evolving in every realtime 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. 

Course 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. 
Unit1 
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, MatrixVector Multiplication by Map Reduce. Apache Hadoop– Moving Data in and out of Hadoop – Understanding inputs and outputs ofMapReduce  Data Serialization, Problems with traditional largescale systemsRequirements for a new approachHadoop – ScalingDistributed FrameworkHadoop v/s RDBMSBrief history of Hadoop. Lab Exercise 1. Installing and Configuring Hadoop  
Unit2 
Teaching Hours:15 
CONFIGURATIONS OF HADOOP


Hadoop Processes (NN, SNN, JT, DN, TT)Temporary directory – UICommon errors when running Hadoop cluster, solutions. Setting up Hadoop on a local Ubuntu host: Prerequisites, downloading Hadoop, setting up SSH, configuring the pseudodistributed 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, Hadoopspecific data types, Input/output. Developing MapReduce Programs: Using languages other than Java with Hadoop, Analysing a large dataset. Lab Exercise 1. Word count application in Hadoop. 2. Sorting the data using MapReduce. 3. Finding max and min value in Hadoop  
Unit3 
Teaching Hours:15 
ADVANCED MAPREDUCE TECHNIQUES


Simple, advanced, and inbetween Joins, Graph algorithms, using languageindependent 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 Kmeans Clustering using MapReduce. 3. Generation of Frequent Itemset using MapReduce.  
Unit4 
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 Plugins for Jobs. Lab Exercise: 1. Count the number of missing and invalid values through joining two large given datasets. 2. Using hadoop’s mapreduce, Evaluating Number of Products Sold in Each Country in the online shopping portal. Dataset is given. 3. Analyze the sentiment for product reviews, this work proposes a MapReduce technique provided by Apache Hadoop.  
Unit5 
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.  
Unit6 
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. 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, HighPerformance BigData Analytics: Computing Systems and Approaches, Springer, 2015. [2] Jonathan R. Owens, Jon Lentz and Brian Femiano, Hadoop RealWorld Solutions Cookbook, Packt Publishing, 2013. [3] Tom White, HADOOP: The definitive Guide, O Reilly, 2012.  
Evaluation Pattern CIA50% ESE50%  
MDS272LC  INTERNET OF THINGS (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Understand the concepts of IoT and IoT enabling technologies
CO2: Gain knowledge on IoT programming and able to develop IoT applications
CO3: Identify different issues in wireless ad hoc and sensor networks
CO4: Develop an understanding of sensor network architectures from a design and performance perspective CO5: Understand the layered approach in sensor networks and WSN protocols 
Unit1 
Teaching Hours:18 
Introduction to IOT


Introduction to IoT  Definition and Characteristics, Physical Design Things Protocols, Logical Design Functional Blocks, Communication Models Communication APIs Introductiontomeasurethephysicalquantities,IoTEnablingTechnologiesWirelessSensor Networks, Cloud Computing Big Data Analytics, Communication Protocols Embedded System IoT Levels and DeploymentTemplates.  
Unit2 
Teaching Hours:18 
IOT Programming


Introduction to Smart Systems using IoT  IoT Design Methodology IoT Boards (Raspberry Pi,Arduino)andIDECaseStudy:WeatherMonitoringLogicalDesignusingPython, Data types & Data Structures Control Flow, Functions Modules Packages, File Handling  Date/Time Operations, Classes Python Packages of Interest for IoT.
 
Unit3 
Teaching Hours:18 
IOT Applications


Home Automation – Smart Cities Environment, Energy Retail, Logistics Agriculture, Industry Health and Lifestyle IoT and M2M.  
Unit4 
Teaching Hours:18 
Network of wireless sensor nodes


SensingandSensorsWirelessSensorNetworks,ChallengesandConstraintsApplications: Structural Health Monitoring, Traffic Control, Health Care  Node Architecture  Operating system 