# Syllabus for Master of Science (Data Science) Academic Year  (2022)

 1 Semester - 2022 - 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 Discipline Specific Elective 2 2 50 MDS161B INTRODUCTION TO COMPUTERS AND PROGRAMMING Discipline Specific Elective 2 2 50 MDS161C LINUX ADMINISTRATION Discipline Specific Elective 2 2 50 MDS171 DATA BASE TECHNOLOGIES Core Courses 6 5 150 MDS171Y DATA BASE TECHNOLOGIES Discipline Specific Elective 6 5 150 MDS172 INFERENTIAL STATISTICS Core Courses 6 5 150 MDS172Y INFERENTIAL STATISTICS Discipline Specific Elective 6 6 150 MDS173 PROGRAMMING FOR DATA SCIENCE IN PYTHON Core Courses 6 4 100 MDS173Y PROGRAMMING FOR DATA SCIENCE IN PYTHON Discipline Specific Elective 6 6 100 2 Semester - 2022 - Batch Course Code Course Type Hours Per Week Credits Marks MDS231 MATHEMATICAL FOUNDATION FOR DATA SCIENCE - II - 4 4 100 MDS232 REGRESSION ANALYSIS - 4 4 100 MDS241A MULTIVARIATE ANALYSIS - 4 4 100 MDS241B STOCHASTIC PROCESS - 4 4 100 MDS241C CATEGORICAL DATA ANALYSIS - 4 4 100 MDS271 MACHINE LEARNING - 6 5 150 MDS272A WEB ANALYTICS - 6 5 150 MDS272B IMAGE AND VIDEO ANALYTICS - 6 5 150 MDS272C INTERNET OF THINGS - 6 5 150 MDS273 JAVA PROGRAMMING - 5 4 100 MDS281 SEMINAR - 2 1 50 3 Semester - 2021 - 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 BIO-STATISTICS Discipline Specific Elective 4 4 100 MDS371 CLOUD ANALYTICS Core Courses 6 5 150 MDS372 JAVA PROGRAMMING Core Courses 5 4 100 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 - 2021 - Batch Course Code Course Type Hours Per Week Credits Marks MDS481 INDUSTRY PROJECT - 2 12 300

 MDS131 - MATHEMATICAL FOUNDATION FOR DATA SCIENCE - I (2022 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 spacesCO2: Use the properties of Linear Maps in solving problems on Linear AlgebraCO3: Demonstrate proficiency on the topics Eigenvalues, Eigenvectors and Inner Product SpacesC04: Apply mathematics for some applications in Data Science
 Unit-1 Teaching Hours:12 INTRODUCTION TO VECTOR SPACES Vector Spaces: Rn and Cn, lists, Fn and digression on Fields, Definition of Vector spaces, Subspaces, sums of Subspaces, Direct Sums, Span and Linear Independence, bases, dimension. Unit-2 Teaching Hours:12 LINEAR MAPS DefinitionofLinearMaps-AlgebraicOperationson L(V,W) - Null spaces and Injectivity-RangeandSurjectivity-FundamentalTheoremsofLinearMaps-Representing aLinearMapbyaMatrix-InvertibleLinearMaps-IsomorphicVectorspaces-LinearMap as Matrix Multiplication - Operators - Products of Vector Spaces - Product of Direct Sum - Quotients of Vector spaces. Unit-3 Teaching Hours:12 EIGENVALUES, EIGENVECTORS, AND INNER PRODUCT SPACES Eigenvalues and Eigenvectors - Eigenvectors and Upper Triangular matrices - Eigenspaces and Diagonal Matrices - Inner Products and Norms - Linear functionals on Inner Product spaces. Unit-4 Teaching Hours:12 BASIC MATRIX METHODS FOR APPLICATIONS Matrix Norms – Least square problem - Singular value decomposition- Householder Transformation and QR decomposition- Non Negative Matrix Factorization – bidiagonalization. Unit-5 Teaching Hours:12 MATHEMATICS APPLIED TO DATA SCIENCE Handwritten digits recognition using simple algorithm - Classification of handwritten digits using SVD bases and Tangent distance - Text Mining using Latent semantic index, Clustering, Non-negative Matrix Factorization and LGK bidiagonalization. Text Books And Reference Books:1. S. Axler, Linear algebra done right, Springer, 2017. 2. Eldén Lars, Matrix methods in data mining and pattern recognition, Society for Industrial and Applied Mathematics, 2007. Essential Reading / Recommended Reading1. 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 PatternCIA - 50% ESE - 50% MDS131L - MATHEMATICAL FOUNDATION FOR DATA SCIENCE I (2022 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 spacesCO2: Use the properties of Linear Maps in solving problems in Linear AlgebraCO3: Demonstrate proficiency in the topics Eigenvalues, Eigenvectors, and Inner Product SpacesCO4: Apply mathematics for some applications in Data Science
 Unit-1 Teaching Hours:12 INTRODUCTION TO VECTOR SPACES Vector Spaces: Rn and Cn, lists, Fn and digression on Fields, Definition of Vector spaces, Subspaces, sums of Subspaces, Direct Sums, Span and Linear Independence, bases, dimension. Unit-2 Teaching Hours:12 LINEAR MAPS DefinitionofLinearMaps-AlgebraicOperationson L(V,W) - Null spaces and InjectivityRangeandSurjectivity-FundamentalTheoremsofLinearMaps-Representing a Linear Mapbya Matrix-InvertibleL inearMaps-IsomorphicVectorspaces-LinearMap as Matrix Multiplication - Operators - Products of Vector Spaces - Product of Direct Sum - Quotients of Vector spaces. Unit-3 Teaching Hours:12 EIGENVALUES, EIGENVECTORS, AND INNER PRODUCT SPACES Eigenvalues and Eigenvectors - Eigenvectors and Upper Triangular matrices - Eigenspaces and Diagonal Matrices - Inner Products and Norms - Linear functionals on Inner Product spaces. Unit-4 Teaching Hours:12 BASIC MATRIX METHODS FOR APPLICATIONS Matrix Norms – Least square problem - Singular value decompositionHouseholder Transformation and QR decomposition- Non Negative Matrix Factorization – bidiagonalization. Unit-5 Teaching Hours:12 MATHEMATICS APPLIED TO DATA SCIENCE Handwritten digits recognition using simple algorithm - Classification of handwritten digits using SVD bases and Tangent distance - Text Mining using Latent semantic index, Clustering, Non-negative Matrix Factorization and LGK bidiagonalization. Text Books And Reference Books:1. S. Axler, Linear algebra done right, Springer, 2017. 2. Eldén Lars, Matrix methods in data mining and pattern recognition, Society for Industrial and Applied Mathematics, 2007. Essential Reading / Recommended Reading1. 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 PatternCIA - 50% ESE - 50% MDS132 - PROBABILITY AND DISTRIBUTION THEORY (2022 Batch) Total Teaching Hours for Semester:60 No of Lecture Hours/Week:4 Max Marks:100 Credits:4 Course Objectives/Course Description Probability and probability distributions play an essential role in modeling data from the real-world phenomenon. This course will equip students with thorough knowledge in probability and various probability distributions and model real-life data sets with an appropriate probability distribution Course Outcome CO1: Describe random event and probability of eventsCO2: Identify various discrete and continuous distributions and their usage.CO3: Evaluate condition probabilities and conditional expectationsC04: Apply Chebychev?s inequality to verify the convergence of sequence in probability
 Unit-1 Teaching Hours:12 DESCRIPTIVE STATISTICS AND PROBABILITY Data – types of variables: numeric vs categorical - measures of central tendency – measures of dispersion - random experiment - sample space and random events – probability - probability axioms - finite sample space with equally likely outcomes - conditional probability - independent events - Baye’s theorem Unit-2 Teaching Hours:12 PROBABILITY DISTRIBUTIONS FOR DISCRETE DATA Random variable – data as observed values of a random variable - expectation – moments & moment generating function - mean and variance in terms of moments - discrete sample space and discrete random variable – Bernoulli experiment and Binary variable: Bernoulli and binomial distributions – Count data: Poisson distribution – overdispersion in count data: negative binomial distribution – dependent Bernoulli  trails: hypergeometric distribution. Unit-3 Teaching Hours:12 PROBABILITY DISTRIBUTIONS FOR CONTINUOUS DATA Continuous sample space - Interval data - continuous random variable – uniform distribution - normal distribution (Gaussian distribution) – modeling lifetime data: exponential distribution, gamma distribution, Weibull distribution. Unit-4 Teaching Hours:12 JOINTLY DISTRIBUTED RANDOM VARIABLES Joint distribution of vector random variables – joint moments – covariance – correlation - the correlation - independent random variables - conditional distribution – conditional expectation - sampling distributions: chi-square, t, F (central). Unit-5 Teaching Hours:12 LIMIT THEOREMS Chebychev’s inequality - weak law of large n u mbers (iid): examples - strong law of large numbers (statement only) - central limit theorems (iid case): examples. Text Books And Reference Books:1. Ross, Sheldon. A first course in probability. 10th Edition. Pearson, 2019. 2. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3rd Edition, 2015 Essential Reading / Recommended Reading1. Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGraw-Hill, 3rd Edition (Reprint), 2017. 2. Ross, Sheldon M. Introduction to probability models. 12th Edition, Academic Press, 2019. Evaluation PatternCIA: 50% ESE: 50% MDS132L - PROBABILITY AND DISTRIBUTION THEORY (2022 Batch) Total Teaching Hours for Semester:60 No of Lecture Hours/Week:4 Max Marks:100 Credits:4 Course Objectives/Course Description Probability and probability distributions play an essential role in modeling data from the real-world phenomenon. This course will equip students with thorough knowledge in probability and various probability distributions and model real-life data sets with an appropriate probability distribution. Course Outcome CO1: Describe random event and probability of eventsCO2: Identify various discrete and continuous distributions and their usage.CO3: Evaluate condition probabilities and conditional expectationsCO4: Apply Chebychev?s inequality to verify the convergence of sequence in probability
 Unit-1 Teaching Hours:12 DESCRIPTIVE STATISTICS AND PROBABILITY Data – types of variables: numeric vs categorical - measures of central tendency –measures of dispersion - random experiment - sample space and random events –probability - probability axioms - finite sample space with equally likely outcomes -conditional probability - independent events - Baye’s theorem Unit-2 Teaching Hours:12 PROBABILITY DISTRIBUTIONS FOR DISCRETE DATA Random variable – data as observed values of a random variable - expectation – moments & moment generating function - mean and variance in terms of moments – discrete sample space and discrete random variable – Bernoulli experiment and Binary variable:Bernoulli and binomial distributions – Count data: Poisson distribution – overdispersion in count data: negative binomial distribution – dependent Bernoulli trails: hypergeometric distribution. Unit-3 Teaching Hours:12 PROBABILITY DISTRIBUTIONS FOR CONTINUOUS DATA Continuous sample space - Interval data - continuous random variable – uniform distribution - normal distribution (Gaussian distribution) – modeling lifetime data: exponential distribution, gamma distribution, Weibull distribution. Unit-4 Teaching Hours:12 JOINTLY DISTRIBUTED RANDOM VARIABLES Joint distribution of vector random variables – joint moments – covariance – correlation -the correlation - independent random variables - conditional distribution –conditional expectation - sampling distributions: chi-square, t, F (central). Unit-5 Teaching Hours:12 LIMIT THEOREMS Chebychev’s inequality - weak law of large numbers (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 Reading1. Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGraw-Hill, 3rd Edition (Reprint), 2017. 2. Ross, Sheldon M. Introduction to probability models. 12th Edition, Academic Press, 2019. Evaluation PatternCIA: 50% ESE: 50% MDS133 - PRINCIPLES OF DATA SCIENCE (2022 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 scienceCO2: Understand data analysis techniques for applications handling large dataCO3: Understand various machine learning algorithms used in data science processC04: Visualize and present the inference using various toolsCO5: Learn to think through the ethics surrounding privacy, data sharing and algorithmic decision-making
Unit-1
Teaching Hours:10
INTRODUCTION TO DATA SCIENCE

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

Unit-2
Teaching Hours:12
BIG DATA

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

Unit-3
Teaching Hours:12
MACHINE LEARNING

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

Unit-4
Teaching Hours:12
DEEP LEARNING

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

Unit-5
Teaching Hours:14
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.

Unit-5
Teaching Hours:14
DATA VISUALIZATION

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

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

[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 (2022 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 a strong foundation for Data Science and related areas of application. The course includes the fundamentals of data science, different techniques for handling 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 with the data is also a part of the course.

Course Objectives:

 To provide a 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

CO4: Visualize and present the inference using various tools

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

Unit-1
Teaching Hours:10
INTRODUCTION TO DATA SCIENCE

 Definition – Big Data and Data Science Hype – Why data science – Getting Past the Hype – The Current Landscape – Who is Data Scientist? - Data Science Process Overview – Defining goals – Retrieving data – Data preparation – Data exploration – Data modeling – Presentation.
Unit-2
Teaching Hours:12
BIG DATA

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

Unit-3
Teaching Hours:12
MACHINE LEARNING

 Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms.
Unit-4
Teaching Hours:12
DEEP LEARNING

 Introduction – Deep Feedforward Networks – Regularization – Optimization of Deep Learning – Convolutional Networks – Recurrent and Recursive Nets – Applications of Deep Learning.
Unit-5
Teaching Hours:14
DATA VISUALIZATION

 Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools – Creating an interactive dashboard with dc.js-summary. ETHICS AND RECENT TRENDS Data Science Ethics – Doing good data science – Owners of the data - Valuing different aspects of privacy - Getting informed consent - The Five Cs – Diversity – Inclusion – Future Trends.
Text Books And Reference Books:

[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

[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 I CIA  II CIA III Attendance ESE 10% 25% 10% 5% 50%

MDS134 - RESEARCH METHODOLOGY (2022 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.

 Unit-1 Teaching Hours:8 RESEARCH METHODOLOGY Defining research problem:Selecting the problem, Necessity of defining the problem ,Techniques involved in defining a problem- Ethics in Research. Unit-2 Teaching Hours:8 RESEARCH DESIGN Principles of experimental design,Working with Literature: Importance, finding literature, Using your resources, Managing the literature, Keep track of references,Using the literature, Literature review,On-line Searching: Database ,SCIFinder, Scopus, Science Direct ,Searching research articles , Citation Index ,Impact Factor ,H-index. Unit-3 Teaching Hours:7 RESEARCH DATA Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation. Unit-4 Teaching Hours:7 REPORT WRITING Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, Text, Tables, Figures, Equations, Citations, Referencing, and Templates (IEEE style), Paper writing for international journals, Writing scientific report. Text Books And Reference Books:[1] C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014. [2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005. Essential Reading / Recommended Reading[1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4thed. SAGE Publications, 2014. [2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010. Evaluation PatternCIA - 50% ESE - 50% MDS134L - RESEARCH METHODOLOGY (2022 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.
 Unit-1 Teaching Hours:8 RESEARCH METHODOLOGY Defining research problem:Selecting the problem, Necessity of defining the problem ,Techniques involved in defining a problem- Ethics in Research Unit-2 Teaching Hours:8 RESEARCH DESIGN Principles of experimental design,Working with Literature: Importance, finding literature, Using your resources, Managing the literature, Keep track of references,Using the literature, Literature review,On-line Searching: Database ,SCIFinder, Scopus, Science Direct ,Searching research articles , Citation Index ,Impact Factor ,H-index Unit-3 Teaching Hours:7 RESEARCH DATA Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation Unit-4 Teaching Hours:7 REPORT WRITING Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, Text, Tables, Figures, Equations, Citations, Referencing, and Templates (IEEE style), Paper writing for international journals, Writing scientific report. Text Books And Reference Books:1. C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014. 2. Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005 Essential Reading / Recommended Reading1.  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 PatternCIA - 50% ESE - 50% MDS161A - INTRODUCTION TO STATISTICS (2022 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.
 Unit-1 Teaching Hours:8 ORGANIZATION AND PRESENTATION OF DATA Origin and development of Statistics, Scope, limitation and misuse of statistics. Types of data: primary, secondary, quantitative and qualitative data. Types of Measurements: nominal, ordinal, discrete and continuous data. Presentation of data by tables: construction of frequency distributions for discrete and continuous data, graphical representation of a frequency distribution by histogram and frequency polygon, cumulative frequency distributions Unit-2 Teaching Hours:8 DESCRIPTIVE STATISTICS Measures of location or central tendency: Arthimetic mean, Median, Mode, Geometric mean, Harmonic mean. Partition values: Quartiles, Deciles and percentiles. Measures of dispersion: Mean deviation, Quartile deviation, Standard deviation, Coefficient of variation. Moments: measures of skewness, Kurtosis. Unit-3 Teaching Hours:7 CORRELATION AND REGRESSION Correlation: Scatter plot, Karl Pearson coefficient of correlation, Spearman's rank correlation coefficient, multiple and partial correlations (for 3 variates only). Regression: Concept of errors, Principles of Least Square, Simple linear regression and its properties. Unit-4 Teaching Hours:7 BASICS OF PROBABILITY Random experiment, sample point and sample space, event, algebra of events. Definition of Probability: classical, empirical and axiomatic approaches to probability, properties of probability. Theorems on probability, conditional probability and independent events, Laws of total probability, Baye’s theorem and its applications Text Books And Reference Books:[1]. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, John Wiley & Sons Inc., New Jersey, 2015. [2]. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11th edition, Sultan Chand & Sons, New Delhi, 2014. Essential Reading / Recommended Reading[1]. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015. [2]. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017. [3]. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013. [4]. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi, 2008. Evaluation PatternCIA - 50% ESE - 50% MDS161AL - INTRODUCTION TO STATISTICS (2022 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 3: CO1: Demonstrate the history of statistics and present the data in various forms. CO2: Infer the concept of correlation and regression for relating two or more related variables. CO3: Demonstrate the probabilities for various events.
 Unit-1 Teaching Hours:8 Organization and Presentation of data Origin and development of Statistics, Scope, limitation and misuse of statistics. Types of data: primary, secondary, quantitative and qualitative data. Types of Measurements: nominal, ordinal, discrete and continuous data, cumulative frequency distributions Unit-2 Teaching Hours:8 Descriptive Statistics Measures of location or central tendency: Arithmetic 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. Unit-3 Teaching Hours:8 Correlation and Regression Correlation: Scatter plot, Karl Pearson coefficient of correlation, Spearman's rank correlation coefficient.Simple linear regression and its properties. Unit-4 Teaching Hours:8 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. 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 (2022 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.CO2: Apply different programming structures with suitable logic for computational problems.
 Unit-1 Teaching Hours:10 COMPUTERS AND DIGITAL BASICS Number Representation – Decimal, Binary, Octal, Hexadecimal and BCD numbers – Binary Arithmetic – Binary addition – Unsigned and Signed numbers – one’s and two’s complements of Binary numbers – Arithmetic operations with signed numbers - Number system conversions – Boolean Algebra – Logic gates – Design of Circuits – K - Map Unit-2 Teaching Hours:5 GENERAL PROBLEM SOLVING CONCEPT Types of Problems – Problem solving with Computers – Difficulties with problem solving – problem solving concepts for the Computer – Constants and Variables – Rules for Naming and using variables – Data types – numeric data – character data – logical data – rules for data types – examples of data types – storing the data in computer - Functions – Operators – Expressions and Equations Unit-3 Teaching Hours:5 PLANNING FOR SOLUTION Communicating with computer – organizing the solution – Analyzing the problem – developing the interactivity chart – developing the IPO chart – Writing the algorithms – drawing the flow charts – pseudocode – internal and external documentation – testing the solution – coding the solution – software development life cycle. Unit-4 Teaching Hours:10 PROBLEM SOLVING Introduction to programming structure – pointers for structuring a solution – modules and their functions – cohesion and coupling – problem solving with logic structure. Problem solving with decisions – the decision logic structure – straight through logic – positive logic – negative logic – logic conversion – decision tables – case logic structure -  examples. Text Books And Reference Books:[1] Thomas L.Floyd and R.P.Jain,“Digital Fundamentals”,8th Edition, Pearson Education,2007. [2] Peter Norton “Introduction to Computers”,6th Edition, Tata Mc Graw Hill, New Delhi,2006. [3] Maureen Sprankle and Jim Hubbard, Problem-solving and programming concepts, PHI, 9th Edition, 2012 Essential Reading / Recommended Reading [1]. E Balagurusamy, Fundamentals of Computers, TMH, 2011 Evaluation PatternCIA: 50% ESE: 50% MDS161BL - INTRODUCTION TO COMPUTERS AND PROGRAMMING (2022 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.CO2: Apply different programming structure with suitable logic for computational problems.
 Unit-1 Teaching Hours:10 COMPUTERS AND DIGITAL BASICS Number Representation – Decimal, Binary, Octal, Hexadecimal and BCD numbers – Binary Arithmetic – Binary addition – Unsigned and Signed numbers – one’s and two’s complements of Binary numbers – Arithmetic operations with signed numbers - Number system conversions – Boolean Algebra – Logic gates – Design of Circuits – K - Map Unit-2 Teaching Hours:5 GENERAL PROBLEM SOLVING CONCEPT Types of Problems – Problem solving with Computers – Difficulties with problem solving – problem solving concepts for the Computer – Constants and Variables – Rules for Naming and using variables – Data types – numeric data – character data – logical data – rules for data types – examples of data types – storing the data in computer - Functions – Operators – Expressions and Equations Unit-3 Teaching Hours:5 PLANNING FOR SOLUTION Communicating with computer – organizing the solution – Analyzing the problem – developing the interactivity chart – developing the IPO chart – Writing the algorithms – drawing the flow charts – pseudocode – internal and external documentation – testing the solution – coding the solution – software development life cycle. Unit-4 Teaching Hours:10 PROBLEM SOLVING Introduction to programming structure – pointers for structuring a solution – modules and their functions – cohesion and coupling – problem solving with logic structure. Problem solving with decisions – the decision logic structure – straight through logic – positive logic – negative logic – logic conversion – decision tables – case logic structure - examples. Text Books And Reference Books:[1]Thomas L.Floyd and R.P.Jain,“Digital Fundamentals”,8th Edition, Pearson Education,2007. [2]Peter Norton “Introduction to Computers”,6th Edition, Tata Mc Graw Hill, New Delhi,2006. [3]Maureen Sprankle and Jim Hubbard, Problem solving and programming concepts, PHI, 9th Edition, 2012 Essential Reading / Recommended Reading[1]. EBalagurusamy,FundamentalsofComputers, TMH,2011 Evaluation PatternCIA:50%   ESE:50% MDS161C - LINUX ADMINISTRATION (2022 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: Demostrate the systematic approach for configure the Liux environmentCO2: Manage the Linux environment to work with open source data science tools
 Unit-1 Teaching Hours:10 Module-1 RHEL7.5,breaking root password, Understand and use essential tools for handling files, directories, command-line environments, and documentation - Configure local storage using partitions and logical volumes Unit-2 Teaching Hours:10 Module-2 Swapping, Extend LVM Partitions,LVM Snapshot - Manage users and groups, including use of a centralized directory for authentication Unit-3 Teaching Hours:10 Module-3 Kernel updations,yum and nmcli configuration, Scheduling jobs,at,crontab - Configure firewall settings using firewall config, firewall-cmd, or iptables , Configure key-based authentication for SSH ,Set enforcing and permissive modes for SELinux , List and identify SELinux file and process context ,Restore default file contexts Text Books And Reference Books:1.    https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/7/ 2.    https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/7/ Essential Reading / Recommended Reading- Evaluation PatternCIA:50% ESE:50% MDS171 - DATA BASE TECHNOLOGIES (2022 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 queriesCO2: Understanding the process of OLAP system constructionCO3: Develop applications using Relational and NoSQL databases.
 Unit-1 Teaching Hours:18 INTRODUCTION Concept & Overview of DBMS, Data Models, Database Languages, Database Administrator, Database Users, Three Schema architecture of DBMS. Basic concepts, Design Issues, Mapping Constraints, Keys, Entity-Relationship Diagram, Weak Entity Sets, Extended E-R features  Lab Exercises 1. Data Definition, 2. Table Creation 3. Constraints Unit-2 Teaching Hours:18 RELATIONAL MODEL AND DATABASE DESIGN SQL and Integrity Constraints, Concept of DDL, DML, DCL. Basic Structure, Set operations, Aggregate Functions, Null Values, Domain Constraints, Referential Integrity Constraints, assertions, views, Nested Subqueries, Functional Dependency, Different anomalies in designing a Database, Normalization: using functional dependencies, Boyce-Codd Normal Form, 4NF  Lab Exercises 1. Insert, Select, Update & Delete Commands 2. Nested Queries & Join Queries 3. Views Unit-3 Teaching Hours:18 DATA WAREHOUSE: THE BUILDING BLOCKS Defining Features, Data Warehouses and Data Marts, Architectural Types, Overview of the Components, Metadata in the Data warehouse, Data Design and Data Preparation: Principles of Dimensional Modeling, Dimensional Modeling Advanced Topics From Requirements To Data Design, The Star Schema, Star Schema Keys, Advantages of the Star Schema, Star Schema: Examples, Dimensional Modeling: Advanced Topics, Updates to the Dimension Tables, Miscellaneous Dimensions, The Snowflake Schema, Aggregate Fact Tables, Families Oo Stars  Lab Exercises: 1. Importing source data structures 2. Design Target Data Structures 3. Create target multidimensional cube Unit-4 Teaching Hours:18 DATA INTEGRATION and DATA FLOW (ETL) Requirements, ETL Data Structures, Extracting, Cleaning and Conforming, Delivering Dimension Tables, Delivering Fact Tables, Real-Time ETL Systems  Lab Exercises: 1. Perform the ETL process and transform into data map 2. Create the cube and process it 3. Generating Reports 4. Creating the Pivot table and pivot chart using some existing data Unit-5 Teaching Hours:18 NOSQL Databases Introduction to NOSQL Systems, The CAP Theorem, Document-Based NOSQL Systems and MongoDB, NOSQL Key-Value Stores, Column-Based or Wide Column NOSQL Systems, Graph databases, Multimedia databases.  Lab Exercises: 1. MongoDB Exercise - 1 2. MongoDB Exercise - 2 Text Books And Reference Books:[1]. Henry F. Korth and Silberschatz Abraham, “Database System Concepts”, Mc.Graw Hill. [2]. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education, 2007. [3]. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd John Wiley & Sons, Inc. New York, USA, 2002 Essential Reading / Recommended Reading[1] LiorRokach and OdedMaimon, Data Mining and Knowledge Discovery Handbook, Springer, 2nd edition, 2010. Evaluation PatternCIA: 50% ESE: 50% MDS171L - DATABASE TECHNOLOGIES (2022 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 queriesCO2: Understanding the process of OLAP system constructionCO3: Develop applications using Relational and NoSQL databases
Unit-1
Teaching Hours:18
INTRODUCTION

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

Lab Exercises

1. Data Definition,

2. Table Creation

3. Constraints

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

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

Lab Exercises

1. Insert, Select, Update & Delete Commands

2. Nested Queries & Join Queries

3. Views

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

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

Lab Exercises:

1. Importing source data structures

2. Design Target Data Structures

3. Create target multidimensional cube

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

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

Lab Exercises:

1. Perform the ETL process and transform into data map

2. Create the cube and process it

3. Generating Reports

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

Unit-5
Teaching Hours:18
NoSQL DATABASES

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

Lab Exercises:

1. MongoDB Exercise - 1

2. MongoDB Exercise - 2

Text Books And Reference Books:

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

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

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

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

50% CIA, 50% ESE

MDS171Y - DATA BASE TECHNOLOGIES (2022 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

Unit-1
Teaching Hours:18
INTRODUCTION

INTRODUCTION

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

Lab Exercises

1. Data Definition,
2. Table Creation
3. Constraints
Unit-2
Teaching Hours:18
RELATIONAL MODEL AND DATABASE DESIGN

RELATIONAL MODEL AND DATABASE DESIGN

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

Lab Exercises

1. Insert, Select, Update & Delete Commands

2. Nested Queries & Join Queries

3. Views

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

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

Lab Exercises:

1. Importing source data structures

2. Design Target Data Structures

3. Create target multidimensional cube

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

DATA INTEGRATION and DATA FLOW (ETL)

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

Lab Exercises:

1. Perform the ETL process and transform into data map

2. Create the cube and process it

3. Generating Reports

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

Unit-5
Teaching Hours:18
NOSQL Databases

NOSQL Databases

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

Lab Exercises:

1. MongoDB Exercise - 1

2. MongoDB Exercise - 2

Text Books And Reference Books:
 [1]. Henry F. Korth and Silberschatz Abraham, “Database System Concepts”, Mc.Graw Hill. [2]. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education, 2007. [3]. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd John Wiley & Sons, Inc. New York, USA, 2002
 [1] LiorRokach and OdedMaimon, Data Mining and Knowledge Discovery Handbook, Springer, 2nd edition, 2010.
Evaluation Pattern

CIA: 50%

ESE: 50%

MDS172 - INFERENTIAL STATISTICS (2022 Batch)

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

Course Objectives/Course Description

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

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.

 Unit-1 Teaching Hours:18 INTRODUCTION Population and Statistics – Finite and Infinite population – Parameter and Statistics – Types of sampling - Sampling Distribution – Sampling Error - Standard Error – Test of significance –concept of hypothesis – types of hypothesis – Errors in hypothesis-testing – Critical region – level of significance - Power of the test – p-value. Lab Exercise: 1. Calculation of sampling error and standard error 2. Calculation of probability of critical region using standard distributions 3. Calculation of power of the test using standard distributions. Unit-2 Teaching Hours:18 TESTING OF HYPOTHESIS I Concept of large and small samples – Tests concerning a single population mean for known σ – equality of two means for known σ – Test for Single variance - Test for equality of two variance for normal population – Tests for single proportion – Tests of equality of two proportions for the normal population.   Lab Exercise: 4. Test of the single sample mean for known σ. 5. Test of equality of two means when known σ 6. Tests of single variance and equality of variance for large samples 7. Tests for single proportion and equality of two proportion for large samples. Unit-3 Teaching Hours:18 TESTING OF HYPOTHESIS II Students t-distribution and its properties (without proofs) – Single sample mean test – Independent sample mean test – Paired sample mean test – Tests of proportion (based on t distribution) – F distribution and its properties (without proofs) – Tests of equality of two variances using F-test – Chi-square distribution and its properties (without proofs) – chisquare test for independence of attributes – Chi-square test for goodness of fit.   Lab Exercise: 8. Single sample mean test 9. Independent and Paired sample mean test 10. Tests of proportion of one and two samples based on t-distribution 11. Test of equality of two variances 12. Chi-square test for independence of attributes and goodness of fit. Unit-4 Teaching Hours:18 ANALYSIS OF VARIANCE Meaning and assumptions - Fixed, random and mixed effect models - Analysis of variance of one-way and two-way classified data with and without interaction effects – Multiple comparison tests: Tukey’s method - critical difference.   Lab Exercise: 13. Construction of one-way ANOVA 14. Construction of two-way ANOVA with interaction 15. Construction of two-way ANOVA without interaction 16. Multiple comparision test using Tukey’s method and critical difference methods Unit-5 Teaching Hours:18 NONPARAMETRIC TESTS Concept of Nonparametric tests - Run test for randomness - Sign test and Wilcoxon Signed Rank Test for one and paired samples - Run test - Median test and Mann-Whitney-Wilcoxon tests for two samples.   Lab Exercise: 17. Test of one sample using Run and sign tests 18. Test of paried sample using Wilcoxon signed rank test 19. Test of two samples using Run test and Median test 20. Test for two samples using Mann-Whitney-Wilcoxon tests Text Books And Reference Books:1. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 12th edition, Sultan Chand & Sons, New Delhi, 2020. 2. Brian Caffo, Statistical Inference for Data Science, Learnpub, 2016. Essential Reading / Recommended Reading1. 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 PatternCIA: 50% ESE:50% MDS172L - INFERENTIAL STATISTICS (2022 Batch) Total Teaching Hours for Semester:90 No of Lecture Hours/Week:6 Max Marks:150 Credits:5 Course Objectives/Course Description This course is designed to introduce the concepts of theory of estimation and testing of hypothesis. This paper also deals with the concept of parametric tests for large and small samples. It also provides knowledge about non-parametric tests and its applications 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. CO4: Estimate the unknown population parameters using the concepts of point and interval estimations.
 Unit-1 Teaching Hours:17 INTRODUCTION Population and Statistics – Finite and Infinite population – Parameter and Statistics – Types of sampling - Sampling Distribution – Sampling Error - Standard Error – Test of significance –concept of hypothesis – types of hypothesis – Errors in hypothesis-testing – Critical region – level of significance - Power of the test – p-value.   Lab Excercise  1. Calculation of standard error and plotting sampling distribution using histogram Unit-2 Teaching Hours:19 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: 1. Test of the single sample mean for known σ. 2. Test of equality of two means when known σ 3. Tests of single variance and equality of variance for large samples Unit-3 Teaching Hours:18 TESTING OF HYPOTHESIS II Students t-distribution and its properties (without proofs) – Single sample mean test – Independent sample mean test – Paired sample mean test – Tests of proportion (based on t distribution) – F distribution and its properties (without proofs) – Tests of equality of two variances using F-test – Chi-square distribution and its properties (without proofs) –chisquare test for independence of attributes – Chi-square test for goodness of fit.   Lab Exercise: 1. Single sample mean test 2. Test of equality of two variances 3.Tests of proportion of one and two samples based on t-distribution 4. Test of equality of two variances   5. Chi-square test for independence of attributes Unit-4 Teaching Hours:18 ANALYSIS OF VARIANCE Meaning and assumptions - Fixed, random and mixed effect models - Analysis of variance of one-way and two-way classified data with and without interaction effects – Multiple comparison tests: Tukey’s method - critical difference. Lab Exercise: 1. Construction of one-way ANOVA 2. Construction of two-way ANOVA with interaction 3. Construction of two-way ANOVA without interaction Unit-5 Teaching Hours:18 NONPARAMETRIC TESTS Concept of Nonparametric tests - Run test for randomness - Sign test and Wilcoxon Signed Rank Test for one and paired samples - Run test - Median test and Mann Whitney-Wilcoxon tests for two samples. Lab Exercise: 1. Test of one sample using Run and sign tests 2. Test of paried sample using Wilcoxon signed rank test Text Books And Reference Books:[1]. Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd, New Delhi, 2012. [2]. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3rd Edition, 2015. Essential Reading / Recommended Reading[1]. Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGraw-Hill, 3rd Edition (Reprint), 2017. [2]. Linear Statistical Inference and its Applications, Rao C.R, Willy Publications, 2nd Edition, 2001. Evaluation PatternCIA - 50% ESE - 50% MDS172Y - INFERENTIAL STATISTICS (2022 Batch) Total Teaching Hours for Semester:90 No of Lecture Hours/Week:6 Max Marks:150 Credits:6 Course Objectives/Course Description Statistical inference plays an important role in modeling data and decision-making from the real-world phenomenon. This course is designed to impart the knowledge of testing of hypothesis and estimation of parameters for real-life data sets. Course Outcome C1: Demonstrate the concepts of population and samples.C2: Apply the idea of sampling distribution of different statistics in testing of hypothesisC3: Test the hypothesis using nonparametric tests for real world problems.C4: Estimate the unknown population parameters using the concepts of point and interval estimations.
 Unit-1 Teaching Hours:18 INTRODUCTION Population and Statistics – Finite and Infinite population – Parameter and Statistics – Types of sampling - Sampling Distribution – Sampling Error - Standard Error – Test of significance –concept of hypothesis – types of hypothesis – Errors in hypothesis-testing – Critical region – level of significance - Power of the test – p-value. Lab Exercise: 1. Calculation of sampling error and standard error 2. Calculation of probability of critical region using standard distributions 3. Calculation of power of the test using standard distributions. Unit-2 Teaching Hours:18 TESTING OF HYPOTHESIS I Concept of large and small samples – Tests concerning a single population mean for known σ – equality of two means for known σ – Test for Single variance - Test for equality of two variance for normal population – Tests for single proportion – Tests of equality of two proportions for the normal population. Lab Exercise: 4. Test of the single sample mean for known σ. 5. Test of equality of two means when known σ 6. Tests of single variance and equality of variance for large samples 7. Tests for single proportion and equality of two proportions for large samples. Unit-3 Teaching Hours:18 TESTING OF HYPOTHESIS II Students t-distribution and its properties (without proofs) – Single sample mean test – Independent sample mean test – Paired sample mean test – Tests of proportion (based on t distribution) – F distribution and its properties (without proofs) – Tests of equality of two variances using F-test – Chi-square distribution and its properties (without proofs) – chisquare test for independence of attributes – Chi-square test for goodness of fit. Lab Exercise: 8. Single sample mean test 9. Independent and Paired sample mean test 10. Tests of proportion of one and two samples based on t-distribution 11. Test of equality of two variances 12. Chi-square test for independence of attributes and goodness of fit. Unit-4 Teaching Hours:18 ANALYSIS OF VARIANCE Meaning and assumptions - Fixed, random and mixed effect models - Analysis of variance of one-way and two-way classified data with and without interaction effects – Multiple comparison tests: Tukey’s method - critical difference. Lab Exercise: 13. Construction of one-way ANOVA 14. Construction of two-way ANOVA with interaction 15. Construction of two-way ANOVA without interaction 16. Multiple comparision test using Tukey’s method and critical difference methods Unit-5 Teaching Hours:18 NONPARAMETRIC TESTS Concept of Nonparametric tests - Run test for randomness - Sign test and Wilcoxon Signed Rank Test for one and paired samples - Run test - Median test and Mann-Whitney-Wilcoxon tests for two samples. Lab Exercise: 17. Test of one sample using Run and sign tests 18. Test of paried sample using Wilcoxon signed rank test 19. Test of two samples using Run test and Median test 20. Test for two samples using Mann-Whitney-Wilcoxon tests Text Books And Reference Books:[1]Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 12th edition, Sultan Chand & Sons, New Delhi, 2020. [2] Brian Caffo, Statistical Inference for Data Science, Learnpub, 2016. Essential Reading / Recommended Reading[1]  Walpole R.E, Myers R.H and Myers S.L, Probability and Statistics for Engineers        and Scientists, 9th edition, Pearson, New Delhi, 2017. [2]  John V, Using R for Introductory Statistics, 2nd edition, CRC Press, Boca Raton, 2014. [3]  Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd,            New Delhi, 2012. [4]  Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd       edition, JohnWiley & Sons Inc, New Jersey, 2015. Evaluation PatternCIA: 50% ESE:50% MDS173 - PROGRAMMING FOR DATA SCIENCE IN PYTHON (2022 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 built-in objects of PythonCO2: Demonstrate significant experience with python program development environmentCO3: Implement numerical programming, data handling and visualization through NumPy, Pandas and MatplotLibmodules
Unit-1
Teaching Hours:17
INTRODUCTION TO PYTHON

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

## Lab Exercises

1.      Demonstrate usage of branching and loopingstatements

2.      Demonstrate Recursivefunctions

3.      DemonstrateLists

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

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

## Lab Exercises

1.      Demonstrate Tuples andSets

2.      DemonstrateDictionaries

3.      Demonstrate inheritance and exceptionalhandling

4.      Demonstrate use of“re”

Unit-3
Teaching Hours:13
USING NUMPY

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

## Lab Exercises

1.      DemonstrateAggregation

2.      Demonstrate Indexing andSorting

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

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

## Lab Exercises

1.      Demonstrate handling of missingdata

2.      Demonstrate hierarchicalindexing

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

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

## Lab Exercises

1.      Demonstrate usage of Pivottable

2.      Demonstrate use of andquery()

Unit-6
Teaching Hours:13
VISUALIZATION AND MATPLOTLIB

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

## Lab Exercises

1.      DemonstrateScatterPlot

2.      Demonstrate3Dplotting

Text Books And Reference Books:

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

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

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

Evaluation Pattern
##### ESE: 50%

MDS173L - PROGRAMMING FOR DATA SCIENCE IN PYTHON (2022 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 built-in objects in Python

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

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

Unit-1
Teaching Hours:17
INTRODUCTION TO PYTHON

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

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

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

Unit-3
Teaching Hours:13
USING NUMPY

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

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

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

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

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

Unit-6
Teaching Hours:13
VISUALIZATION AND MATPLOTLIB

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

Text Books And Reference Books:

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

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

1. R1.  Joel Grus, Data Science from Scratch First Principles with Python, O’ReillyMedia,2016

2. R2.  T. R. Padmanabhan, Programming with Python, Springer Publications, 2016

Evaluation Pattern

 CIA I CIA  II CIA III Attendance ESE 10% 25% 10% 5% 50%

MDS173Y - PROGRAMMING FOR DATA SCIENCE IN PYTHON (2022 Batch)

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

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 built-in objects of Python

CO2: Demonstrate significant experience with python program development environment

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

Unit-1
Teaching Hours:17
INTRODUCTION TO PYTHON

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

Unit-1
Teaching Hours:17
Lab Exercises

1. Demonstrate usage of branching and looping statements
2. Demonstrate Recursive functions
3. Demonstrate Lists
Unit-2
Teaching Hours:17
SEQUENCE DATATYPES AND OBJECT-ORIENTED PROGRAMMING

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

Unit-2
Teaching Hours:17
Lab Exercises

1. Demonstrate Tuples and Sets
2. Demonstrate Dictionaries
3. Demonstrate inheritance and exception handling
4. Demonstrate use of “re”
Unit-3
Teaching Hours:13
USING NUMPY

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

Unit-3
Teaching Hours:13
Lab Exercises

1. Demonstrate Aggregation
2. Demonstrate Indexing and Sorting
Unit-4
Teaching Hours:13
DATA MANIPULATION WITH PANDAS -I

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

Unit-4
Teaching Hours:13
Lab Exercises

1. Demonstrate handling of missing data
2. Demonstrate hierarchical indexing
Unit-5
Teaching Hours:17
DATA MANIPULATION WITH PANDAS -II

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

Unit-5
Teaching Hours:17
Lab Exercises

1. Demonstrate usage of Pivot table
2. Demonstrate use of and query()
Unit-6
Teaching Hours:13
Lab Exercises

1. Demonstrate Scatter Plot
2. Demonstrate 3D plotting
Unit-6
Teaching Hours:13
VISUALIZATION AND MATPLOTLIB

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

Text Books And Reference Books:

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

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

[1]. JoelGrus, Data Science from Scratch First Principles with Python, O’Reilly, Media,2016

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

Evaluation Pattern
##### CIA:  50%

ESE: 50%

MDS231 - MATHEMATICAL FOUNDATION FOR DATA SCIENCE - II (2022 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

 Unit-1 Teaching Hours:14 Calculus of Several Variables Functions of Several Variables: Functions of two, three variables - Limits and continuity in HIgher Dimensions: Limits for functions of two variables, Functions of more than two variables - Partial Derivatives: partial derivative of functions of two variables, partial derivatives of functions of more than two variables, partial derivatives and continuity, second order partial derivatives - The Chain Rule: chain rule on functions of two, three variables, chain rule on functions defined on surfaces - Directional Derivative and Gradient vectors: Directional derivatives in a plane, Interpretation of directional derivative, calculation and gradients, Gradients and tangents to level curves. Unit-2 Teaching Hours:10 Orthogonality Perpendicular vectors and Orthogonality - Inner Products and Projections onto lines - Projections of Rank one - Projections and Least Squares Approximations - Projection Matrices - Orthogonal Bases, Orthogonal Matrices and Gram-Schmidt orthogonalization Unit-3 Teaching Hours:12 Introduction to Convex Optimization Affine and Convex Sets: Lines and Line segments, affine sets, affine dimension andrelative interior, convexsets, cones - Hyperplanes and half-spaces - Euclidean balls and ellipsoids- Norm balls and Norm cones - polyhedra - simplexes, Convex hull description of polyhedra - The positive semidefinitecone. Unit-4 Teaching Hours:12 Graph Theory - Basics Graph Classes: Definition of a Graph and Graph terminology, isomorphism of graphs, Completegraphs, bipartite graphs, complete bipartite graphs-Vertex degree: adjacency and incidence, regular graphs - subgraphs, spanning subgraphs, induced subgraphs, removing or adding edges of a graph, removing vertices from graphs - Graph Operations: Graph Union, intersection, complement, self complement, Paths and Cycles, Connected graphs, Eulerian and HamiltonianGraphs. Unit-5 Teaching Hours:12 Graph Theory - More concepts Matrix Representation of Graphs, Adjacency matrices, Incidence Matrices, Trees and its properties, Bridges (cut-edges), spanning trees, weighted Graphs, minimal spanning tree problems, Shortest path problems, cut vertices, cuts, vertex and edge connectivity,  Graph Algorithms - Applications of Graph Theory Text Books And Reference Books:1.     M. D. Weir, J. Hass, and G. B. Thomas, Thomas' calculus. Pearson, 2016. (Unit 1) 2.     G Strang, Linear Algebra and its Applications, 4th ed., Cengage, 2006. (Unit 2) 3.     S. P. Boyd and L.Vandenberghe, Convex optimization.Cambridge Univ. Pr., 2011.(Unit 3) 4.     J Clark, D A Holton, A first look at Graph Theory, Allied Publishers India, 1995. (Unit 4) Essential Reading / Recommended Reading1.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 PatternCIA:50% ESE :50% MDS231L - MATHEMATICAL FOUNDATION FOR DATA SCIENCE-II (2022 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 calculusCO2: Understand the idea of orthogonality and projections effectivelyCO3: Identify the use of Convex OptimizationCO4: Analyse the properties in Graph Theory and its applications
 Unit-1 Teaching Hours:14 Calculus of Several Variables Functions of Several Variables: Functions of two, three variables - Limits and continuity in HIgher Dimensions: Limits for functions of two variables, Functions of more than two variables - Partial Derivatives: partial derivative of functions of two variables, partial derivatives of functions of more than two variables, partial derivatives and continuity, second order partial derivatives - The Chain Rule: chain rule on functions of two, three variables, chain rule on functions defined on surfaces - Directional Derivative and Gradient vectors: Directional derivatives in a plane, Interpretation of directional derivative, calculation and gradients, Gradients and tangents to level curves. Unit-2 Teaching Hours:10 Orthogonality Perpendicular vectors and Orthogonality - Inner Products and Projections onto lines - Projections of Rank one - Projections and Least Squares Approximations - Projection Matrices - Orthogonal Bases, Orthogonal Matrices and Gram-Schmidt orthogonalization Unit-3 Teaching Hours:12 Introduction to Convex Optimization Affine and Convex Sets: Lines and Line segments, affine sets, affine dimensi andrelative interior, convexsets, cones - Hyperplanes and half-spaces - Euclidean ba and ellipsoids- Norm balls and Norm cones - polyhedra - simplexes, Convex h description of polyhedra - The positive semidefinite cone Unit-4 Teaching Hours:12 Graph Theory - Basics Graph Classes: Definition of a Graph and Graph terminology, isomorphism of graphs, Completegraphs, bipartite graphs, complete bipartite graphs-Vertex degree: adjacency and incidence, regular graphs - subgraphs, spanning subgraphs, induced subgraphs, removing or adding edges of a graph, removing vertices from graphs - Graph Operations: Graph Union, intersection, complement, self complement, Paths and Cycles, Connected graphs, Eulerian and HamiltonianGraphs. Unit-5 Teaching Hours:12 Graph Theory - More concepts Matrix Representation of Graphs, Adjacency matrices, Incidence Matrices, Trees and it properties, Bridges (cut-edges), spanning trees, weighted Graphs, minimal spanning tree problems, Shortest path problems, cut vertices, cuts, vertex and edge connectivity, Graph Algorithms - Applications of Graph Theory Text Books And Reference Books:  1. M. D. Weir, J. Hass, and G. B. Thomas, Thomas' calculus. Pearson, 2016. 2. S. P. Boyd and L. Vandenberghe, Convex optimization. Cambridge Univ. Pr., 2011. 3. D. Jungnickel, Graphs, networks and algorithms. Springer, 2014. Essential Reading / Recommended Reading  J. Patterson and A. Gibson, Deep learning: a practitioner's approach. O'Reilly Media, 2017. S. Sra, S. Nowozin, and S. J. Wright, Optimization for machine learning. MIT Press, 2012. .D. Jungnickel, Graphs, networks and algorithms. Springer, 2014. D Samovici, Mathematical Analysis for Machine Learning and Data Mining, World Scientific Publishing Co. Pte. Ltd, 2018. P. N. Klein, Coding the matrix: linear algebra through applications to computer science. Newtonian Press, 2015. K H Rosen, Discrete Mathematics and its applications, 7th ed., McGraw Hill, 2016. Evaluation PatternCIA : 50 % ESE : 50 % MDS232 - REGRESSION ANALYSIS (2022 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 R-square criteria for model selectionCO3: Understand the forward, backward and stepwise methods for selecting the variablesCO4: Understand the importance of multicollinearity in regression modellingCO5: Ability touse and understand generalizations of the linear model to binary and count data
 Unit-1 Teaching Hours:13 SIMPLE LINEAR REGRESSION Introduction to regression analysis: Modelling a response, overview and applications of regression analysis, major steps in regression analysis. Simple linear regression (Two variables): assumptions, estimation and properties of regression coefficients, significance and confidence intervals of regression coefficients, measuring the quality of the fit. Unit-2 Teaching Hours:13 MULTIPLE LINEAR REGRESSION Multiple linear regression model: assumptions, ordinary least square estimation of regression coefficients, interpretation and properties of regression coefficient, significance and confidence intervals of regression coefficients. Unit-3 Teaching Hours:12 CRITERIA FOR MODEL SELECTION Mean Square error criteria, R2 and  criteria for model selection; Need of the transformation of variables; Box-Cox transformation; Forward, Backward and Stepwise procedures. Unit-4 Teaching Hours:12 RESIDUAL ANALYSIS Residual analysis, Departures from underlying assumptions, Effect of outliers, Collinearity, Non-constant variance and serial correlation, Departures from normality, Diagnostics and remedies. Unit-5 Teaching Hours:10 NON LINEAR REGRESSION Introduction to nonlinear regression, Least squares in the nonlinear case and estimation of parameters, Models for binary response variables, estimation and diagnosis methods for logistic and Poisson regressions. Prediction and residual analysis. Text Books And Reference Books:[1].D.C Montgomery, E.A Peck and G.G Vining, Introduction to Linear Regression Analysis, John Wiley and Sons,Inc.NY, 2003. [2]. S. Chatterjee and AHadi, Regression Analysis by Example, 4th Ed., John Wiley and Sons, Inc, 2006 [3].Seber, A.F. and Lee, A.J. (2003) Linear Regression Analysis, John Wiley, Relevant sections from chapters 3, 4, 5, 6, 7, 9, 10. Essential Reading / Recommended Reading[1]. Iain Pardoe, Applied Regression Modeling, John Wiley and Sons, Inc, 2012. [2]. P. McCullagh, J.A. Nelder, Generalized Linear Models, Chapman & Hall, 1989. Evaluation PatternCIA - 50% ESE - 50% MDS232L - REGRESSION ANALYSIS (2022 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 : To build a foundation on the basic tools of regression analysis.  To apply econometric modelling on different types of data To learn how to identify the goodness of fit of some basic econometric models To diagnose common problems in linear regression modelling Course Outcome CO1: Demonstrate deeper understanding of the linear regression model.CO2: Evaluate R-square criteria for model selection.CO3: Understand the forward, backward and stepwise methods for selecting the variables.CO4: Understand the importance of multicollinearity in regression modelling.CO5: Ability to use and understand generalizations of the linear model to binary and count data.
 Unit-1 Teaching Hours:13 SIMPLE LINEAR REGRESSION Introduction to regression analysis: Modelling a response, overview and applications of regression analysis, major steps in regression analysis. Simple linear regression (Two variables): assumptions, estimation and properties of regression coefficients, significance and confidence intervals of regression coefficients, measuring the quality of the fit. Unit-2 Teaching Hours:13 MULTIPLE LINEAR REGRESSION Multiple linear regression model: assumptions, ordinary least square estimation of regression coefficients, interpretation and properties of regression coefficient, significance and confidence intervals of regression coefficients. Unit-3 Teaching Hours:12 CRITERIA FOR MODEL SELECTION Mean Square error criteria, R2 and  criteria for model selection; Need of the transformation of variables; Box-Cox transformation; Forward, Backward and Stepwise procedures. Unit-4 Teaching Hours:10 RESIDUAL ANALYSIS Residual analysis, Departures from underlying assumptions, Effect of outliers, Collinearity, Non-constant variance and serial correlation, Departures from normality, Diagnostics and remedies. Unit-5 Teaching Hours:10 NON LINEAR REGRESSION Introduction to nonlinear regression, Least squares in the nonlinear case and estimation of parameters, Models for binary response variables, estimation and diagnosis methods for logistic and Poisson regressions. Prediction and residual analysis. Text Books And Reference Books: D.C Montgomery, E.A Peck and G.G Vining, Introduction to Linear Regression Analysis, John Wiley and Sons,Inc.NY, 2003. S. Chatterjee and AHadi, Regression Analysis by Example, 4th Ed., John Wiley and Sons, Inc, 2006 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 Iain Pardoe, Applied Regression Modeling, John Wiley and Sons, Inc, 2012. P. McCullagh, J.A. Nelder, Generalized Linear Models, Chapman & Hall, 1989. Evaluation PatternCIA I: 10% CIA II: 25% CIA III: 10% Attendance: 5% ESE: 50% MDS241A - MULTIVARIATE ANALYSIS (2022 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 distributionCO2: Apply Multivariate analysis of variance (MANOVA) of one and two-way classified data.
 Unit-1 Teaching Hours:12 INTRODUCTION Basic concepts on multivariate variable. Multivariate normal distribution, Marginal and conditional distribution, Concept of random vector: Its expectation and Variance-Covariance matrix. Marginal and joint distributions. Conditional distributions and Independence of random vectors. Multinomial distribution. Sample mean vector and its distribution. Unit-2 Teaching Hours:12 DISTRIBUTION Sample mean vector and its distribution. Likelihood ratio tests: Tests of hypotheses about the mean vectors and covariance matrices for multivariate normal populations. Independence of sub vectors and sphericity test. Unit-3 Teaching Hours:12 MULTIVARIATE ANALYSIS Multivariate analysis of variance (MANOVA) of one and two- way classified data. Multivariate analysis of covariance.  Wishart distribution, Hotelling’s T2 and Mahalanobis’ D2 statistics, Null distribution of Hotelling’s T2. Rao’s U statistics and its distribution. Unit-4 Teaching Hours:12 CLASSIFICATION AND DISCRIMINANT PROCEDURES Bayes, minimax, and Fisher’s criteria for discrimination between two multivariate normal populations. Sample discriminant function. Tests associated with discriminant functions. Probabilities of misclassification and their estimation. Discrimination for several multivariate normal populations Unit-5 Teaching Hours:12 PRINCIPAL COMPONENT and FACTOR ANALYSIS Principal components, sample principal components asymptotic properties. Canonical variables and canonical correlations: definition, estimation, computations. Test for significance of canonical correlations. Factor analysis: Orthogonal factor model, factor loadings, estimation of factor loadings, factor scores.  Applications Text Books And Reference Books:[1]. Anderson, T.W. 2009. An Introduction to Multivariate Statistical Analysis, 3rd Edition, John Wiley. [2]. Everitt B, Hothorn T, 2011. An Introduction to Applied Multivariate Analysis with R, Springer. [3]. Barry J. Babin, Hair, Rolph E Anderson, and William C. Blac, 2013,  Multivariate Data Analysis, Pearson New International Edition, Essential Reading / Recommended Reading[1] Giri, N.C. 1977. Multivariate Statistical Inference. Academic Press. [2] Chatfield, C. and Collins, A.J. 1982. Introduction to Multivariate analysis. Prentice Hall [3] Srivastava, M.S. and Khatri, C.G. 1979. An Introduction to Multivariate Statistics. North Holland Evaluation PatternCIA - 50% ESE - 50% MDS241AL - MULTIVARIATE ANALYSIS (2022 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 CO1: Understand multivariate data structure, multinomial and multivariate normal distributionCO2: Apply Multivariate analysis of variance (MANOVA) of one and two-way classified data.
Unit-1
Teaching Hours:12
INTRODUCTION

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

Unit-2
Teaching Hours:12
DISTRIBUTION

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

Unit-3
Teaching Hours:12
MULTIVARIATE ANALYSIS

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

Unit-4
Teaching Hours:12
CLASSIFICATION AND DISCRIMINANT PROCEDURES

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

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

Principal components, sample principal components asymptotic properties. Canonical variables and canonical correlations: definition, estimation, computations. Test for significance of canonical correlations. Factor analysis: Orthogonal factor model, factor loadings, estimation of factor loadings, factor scores. Applications

Text Books And Reference Books:

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

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

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

[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%

 CIA I - 1 CIA II CIA III Attendance ESE 10% 25% 10% 5% 50%

MDS241B - STOCHASTIC PROCESS (2022 Batch)

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

Course Objectives/Course Description

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

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.

 Unit-1 Teaching Hours:12 INTRODUCTION TO STOCHASTIC PROCESSES Classification of Stochastic Processes, Markov Processes – Markov Chain - Countable State Markov Chain. Transition Probabilities, Transition Probability Matrix. Chapman - Kolmogorov's Equations, Calculation of n - step Transition Probability and its limit. Unit-2 Teaching Hours:12 POISSON PROCESS Classification of States, Recurrent and Transient States - Transient Markov Chain, Random Walk and Gambler's Ruin Problem. Continuous Time Markov Process:, Poisson Processes, Birth and Death Processes, Kolmogorov’s Differential Equations, Applications. Unit-3 Teaching Hours:12 BRANCHING PROCESS Branching Processes – Galton – Watson Branching Process - Properties of Generating Functions – Extinction Probabilities – Distribution of Total Number of Progeny. Concept of Weiner Process. Unit-4 Teaching Hours:12 RENEWAL PROCESS Renewal Processes – Renewal Process in Discrete and Continuous Time – Renewal Interval – Renewal Function and Renewal Density – Renewal Equation – Renewal theorems: Elementary Renewal Theorem. Probability Generating Function of Renewal Processes. Unit-5 Teaching Hours:12 STATIONARY PROCESS Stationary Processes: Discrete Parameter Stochastic Process – Application to Time Series. Auto-covariance and Auto-correlation functions and their properties. Moving Average, Autoregressive, Autoregressive Moving Average, Autoregressive Integrated Moving Average Processes. Basic ideas of residual analysis, diagnostic checking, forecasting. Text Books And Reference Books:[1]. Stochastic Processes, R.G Gallager, Cambridge University Press, 2013. [2]. Stochastic Processes, S.M Ross, Wiley India Pvt. Ltd, 2008. Essential Reading / Recommended Reading[1]. Stochastic Processes from Applications to Theory, P.D Moral and S. Penev, CRC Press, 2016 [2]. Introduction to Probability and Stochastic Processes with Applications, B..C. Liliana, A Viswanathan, S. Dharmaraja, Wiley Pvt. Ltd, 2012. Evaluation PatternCIA - 50% ESE - 50% MDS241C - CATEGORICAL DATA ANALYSIS (2022 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 variables.CO4: Analyse contingency tables using log-linear models.
 Unit-1 Teaching Hours:12 INTRODUCTION Categorical response data - Probability distributions for categorical data - Statistical inference for discrete data Unit-2 Teaching Hours:12 CONTINGENCY TABLES Probability structure for contingency tables - Comparing proportions with 2x2 tables - The odds ratio - Tests for independence - Exact inference - Extension to three-way and larger tables Unit-3 Teaching Hours:12 GENERALIZED LINEAR MODELS Components of a generalized linear model - GLM for binary and count data - Statistical inference and model checking - Fitting GLMs Unit-4 Teaching Hours:12 LOGISTIC REGRESSION Interpreting the logistic regression model - Inference for logistic regression - Logistic regression with categorical predictors - Multiple logistic regression - Summarising effects - Building and applying logistic regression models - Multicategory logit models Unit-5 Teaching Hours:12 LOGLINEAR MODELS FOR CONTINGENCY TABLES Loglinear models for two-way and three-way tables - Inference for Loglinear models - the log-linear-logistic connection - Independence graphs and collapsibility - Models for matched pairs: Comparing dependent proportions, Logistic regression for matched pairs - Comparing margins of square contingency tables - symmetry issues Text Books And Reference Books:1. Agresti, A. (2012). Categorical Data Analysis, 3rd Edition. New York: Wiley Essential Reading / Recommended Reading 1. Le, C.T. (2009). Applied Categorical Data Analysis and Translational Research, 2nd Ed., John Wiley and Sons.  2. Agresti, A. (2010). Analysis of ordinal categorical. John Wiley & Sons.  3. Stokes, M. E., Davis, C. S., & Koch, G. G. (2012). Categorical data analysis using SAS. SAS Institute.  4. Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons.  5. Bilder, C. R., & Loughin, T. M. (2014). Analysis of categorical data with R. Chapman and Hall/CRC. Evaluation PatternCIA:50% ESE:50% MDS271 - MACHINE LEARNING (2022 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.
Unit-1
Teaching Hours:18
INRTODUCTION

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

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

Lab Exercise:

1.      Data Exploration using parametric methods

2.      Data Exploration using non-parametric methods

3.      Regression analysis

Unit-2
Teaching Hours:18
DIMENSIONALITY REDUCTION

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

Lab Exercise:

1.      Data reduction using Principal ComponentAnalysis

2.      Data reduction using multi-dimensional scaling

Unit-3
Teaching Hours:18
SUPERVISED LEARNING - I

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

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

Lab Exercise

1.   Lineardiscrimination

2.    Logisticdiscrimination

3.   Classification using kernel machines

Unit-4
Teaching Hours:18
SUPERVISED LEARNING - II

## Multilayer Perceptron:

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

Combining Multiple Learners

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

Lab Exercise

1.  Classification using MLP

2.  Ensemble Learning

Unit-5
Teaching Hours:18
UNSUPERVISED LEARNING

Clustering

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

Lab Exercise

1.  K means clustering

2.  Hierarchical clustering

Text Books And Reference Books:

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

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 (2022 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

1: Understand the basic principles of machine learning techniques.

2: Understand how machine learning problems are formulated and solved.

3: Apply machine learning algorithms to solve real world problems.

 Unit-1 Teaching Hours:18 Lab Exercises Data Exploration using Parametric Methods Data Exploration using Non-Parametric Methods Regression Analysis Unit-1 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. Regression-Model Selection and Generalization. Introduction to Parametric methods - Maximum Likelihood Estimation: Bernoulli Density - Multinomial Density-Gaussian Density, Nonparametric Density Estimation: Histogram Estimator-Kernel Estimator-K-Nearest Neighbour Estimator. Unit-2 Teaching Hours:18 Lab Exercise Data reduction using Principal Component Analysis Data reduction using Multi-Dimensional Scaling Unit-2 Teaching Hours:18 DIMENSIONALITY REDUCTION Dimensionality Reduction: Introduction- Subset Selection - Principal Component Analysis, Feature Embedding-Factor Analysis-Singular Value Decomposition-Multidimensional Scaling - Linear Discriminant Analysis - Bayesian Decision Theory. Unit-3 Teaching Hours:18 SUPERVISED LEARNING Linear Discrimination: Introduction - Generalizing the Linear Model-Geometry of the Linear Discriminant - Pairwise Separation - Gradient Descent - Logistic Discrimination Unit-3 Teaching Hours:18 Lab Exercises Linear Discrimination Logistic Discrimination Classification using Kernel Machines Unit-3 Teaching Hours:18 KERNEL METHODS Introduction - optical separating hyperplane- v-SVM, kernel tricks - vertical kernel - vertical kernel - defining kernel - multiclass kernel machines - one-class kernel machines. Unit-4 Teaching Hours:18 Lab Exercise Classification using MLP Enesemble Learning Unit-4 Teaching Hours:18 COMBINING MULTIPLE LEARNERS Rationale - Generating diverse learners - Model combination schemes - voting, Bagging- Boosting - fine tuning an Ensemble. Unit-4 Teaching Hours:18 MULTILAYER PERCEPTRON Introduction, training a perceptron - learning Boolean functions - multilayer perceptron - backpropogation algorithm - training procedures Unit-5 Teaching Hours:18 Lab Exercises K Means Clustering Hierarchical Clustering Unit-5 Teaching Hours:18 UNSUPERVISED LEARNING Clustering - Introduction - Mixture Densities, K-Means Clustering - Expectation-Maximization algorithm - Mixtures of Latent Varaible Models - Supervised Learning after Clustering - Spectral Clustering - Hierachial Clustering - Clustering - Choosing the number of Clusters Text Books And Reference Books: E. Alpaydin, Introduction to Machine Learning, 3rd Edition, MIT Press, 2014. Essential Reading / Recommended Reading C.M.Bishop,PatternRecognitionandMachineLearning,Springer,2016. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer, 2nd Edition,2009 K.P.Murphy,MachineLearning:AProbabilisticPerspective,MITPress,2012. Evaluation PatternCIA: 50%, ESE: 50% MDS272A - WEB ANALYTICS (2022 Batch) Total Teaching Hours for Semester:90 No of Lecture Hours/Week:6 Max Marks:150 Credits:5 Course Objectives/Course Description The objective of this course is to provide an overview and the importance of Web analytics and helps to understand role of Web analytic. This course also explores the effective of Web analytic strategies and implementation. Course Outcome CO1 : Understand the concept and importance of Web analytics in an organization and the role of Web analytic in collecting, analyzing and reporting website traffic.CO2: Identify key tools and diagnostics associated with Web analytics.CO3: Explore effective Web analytics strategies and implementation and Understand the importance of web analytic as a tool for e-Commerce, business research, and market research.
Unit-1
Teaching Hours:18
INTRODUCTION TO WEB ANALYTICS

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

Lab Exercise

1. Working concept of web analytics

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

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

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

Lab Exercise

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

2. Delivering reports based on collected data

3. Implement the concept of web analytics ecosystem

Unit-3
Teaching Hours:18

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

Lab Exercise

1. Creation of segmentation in web analytics

2. Visualization, acquisition and conversions of web analytics data

Unit-4
Teaching Hours:18
OVERVIEW OF QUALITATIVE ANALYSIS

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

Lab Exercise

1. Performing site search analytics

2. Analyse the web analytic reports and visualizations

3. Performing visual web analytics

Unit-5
Teaching Hours:18
VISUAL ANALYTICS

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

Lab Exercise

1. Assignments and final discussions

2. Web Analytics case studies

Text Books And Reference Books:

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

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

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

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

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

3. Burby J, Atchison S, (2007), Actionable web analytics: using data to make smart

business decisions, John Wiley & Sons.

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

Evaluation Pattern
 CIA-50% Marks ESE-50% Marks CAT1 CAC1 Regular Lab Attendance CAT2 CAC2 CAT3 25% 20% 45% 10% 30% 30% 40%

MDS272B - IMAGE AND VIDEO ANALYTICS (2022 Batch)

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

Course Objectives/Course Description

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

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

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

#### 2. Program to implement contrast stretching.

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

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

Unit-3
Teaching Hours:18
IMAGE AND VIDEO ANALYSIS

#### 6.     Extraction of frames from videos and analyzing frames

Unit-4
Teaching Hours:18
FEATURE DETECTION AND DESCRIPTION

#### 8.     Implement image compression using wavelets.

Unit-5
Teaching Hours:18
OBJECT DETECTION AND RECOGNITION

#### 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.

[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 (2022 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

 Unit-1 Teaching Hours:18 Introduction to IOT Introduction to IoT - Definition and Characteristics, Physical Design Things- Protocols,  Logical Design- Functional Blocks, Communication Models- Communication APIs Introductiontomeasurethephysicalquantities,IoTEnablingTechnologies-WirelessSensor  Networks, Cloud Computing Big Data Analytics, Communication Protocols- Embedded  System- IoT Levels and DeploymentTemplates. Unit-2 Teaching Hours:18 IOT Programming Introduction to Smart Systems using IoT - IoT Design Methodology- IoT Boards  (Raspberry Pi,Arduino)andIDE-CaseStudy:Weather Monitoring- Logical Designusing Python, Data types & Data Structures- Control Flow, Functions- Modules- Packages, File  Handling - Date/Time Operations, Classes- Python Packages of Interest for IoT. Unit-3 Teaching Hours:18 IOT Applications Home Automation – Smart Cities- Environment, Energy- Retail, Logistics- Agriculture,  Industry- Health and Lifestyle- IoT and M2M. Unit-4 Teaching Hours:18 Network of wireless sensor nodes SensingandSensors-WirelessSensorNetworks,ChallengesandConstraints-Applications:  Structural Health Monitoring, Traffic Control, Health Care - Node Architecture - Operating system. Unit-5 Teaching Hours:18 MAC, Routing and Transport Protocols in WSN Introduction – Fundamentals of MAC Protocols – MAC protocols for WSN – Sensor MAC CaseStudy–RoutingChallengesandDesignIssues–RoutingStrategies– TransportControl Protocols–TransportProtocolDesignIssues– PerformanceofTransportProtocols Text Books And Reference Books:[1] Arshdeep Bahgaand, Vijay Madisetti, Internet of Things: Hands-on Approach,  Hyderabad University Press, 2015.  [2] Kazem Sohraby, Daniel Minoli and TaiebZnati, Wireless Sensor Networks:  Technology. Protocols and Application, Wiley Publications, 2010.    [3] Waltenegus Dargie and Christian Poellabauer, Fundamentals of Wireless Sensor  Networks: Theory and Practice, A John Wiley and Sons Ltd., 2010. Essential Reading / Recommended Reading[1] Edgar Callaway, Wireless Sensor Networks: Architecture and Protocols,  Auerbach Publications, 2003.  [2] Michael Miller, The Internet of Things, Pearson Education, 2015. [3] Holger Karl and Andreas Willig, Protocols and Architectures for Wireless Sensor  Networks, John Wiley & Sons Inc., 2005.  [4] Erdal Çayırcı and Chunming   Rong, SecurityinWirelessAdHocandSensorNetworks,John Wiley and Sons, 2009. [5] Carlos De MoraisCordeiro and Dharma Prakash Agrawal, Ad Hoc and Sensor  Networks: Theory and Applications, World Scientific Publishing, 2011.    [6] Adrian Perrig and J.D.Tygar, Secure Broadcast Communication: In Wired and  Wireless Networks, Springer, 2006. Evaluation PatternCIA - 50%    ESE - 50% MDS273 - JAVA PROGRAMMING (2022 Batch) Total Teaching Hours for Semester:75 No of Lecture Hours/Week:5 Max Marks:100 Credits:4 Course Objectives/Course Description This course of study builds on the skills gained by students in Java Fundamentals to help them to apply Java programming skills in Data science applications. Students will design object-oriented applications with Java and will create Java programs using hands-on, engaging activities.This course will help the learner to gain a sound knowledge in object-oriented principles, GUI application design with data base and Servlets. Course Outcome CO1 : Understanding and applying the principles and practice of object-oriented programming in the construction of robust maintainable programs.CO2: Competence in the use of Java Programming Language in the development of small to medium sized applications that demonstrate professionally acceptable coding and performance standards.CO3: prepare the students to address the challenging requirements coming from the enterprise applications.
Unit-1
Teaching Hours:9
INTRODUCTION-Overview of JVM & JAVA Basics

Overview of JVM

Introduction to JVM-JVM Architecture-JDK&JRE-Class Loader-Overview of Bootstrap, Extension and Application Class Loader

Java Basics

Class and Object Concept-Method Overloading and Overriding-Constructor-this and static keyword-finalize () method in java

Unit-2
Teaching Hours:9
INHERITANCE, INTERFACES & PACKAGES AND EXCEPTION HANDLING IN JAVA

Inheritance in Java

Inheritance Basics - Multilevel Hierarchy- Using super - Dynamic Method Dispatch- Abstract keyword- Using final with inheritance – Aggregation and Composition in Java

Interfaces and Packages

Defining Interfaces - Implementing Interfaces - Extending Interfaces- Creating Packages - Importing Packages - Interfaces in a Package.

Exception Handling in Java

try-catch-finally mechanism - throw statement - throws statement - Built-in-Exceptions – Custom Exceptions.

Unit-3
Teaching Hours:9
MULTITHREADING, GENERICS AND THE COLLECTIONS FRAMEWORK

Generics

Generics Concept - General Form of a Generic Class – Bounded Types – Generic Class Hierarchy - Generic Interfaces – Restrictions in Generics

The Collections Framework

The Collections Overview – Collection Interface – List Interface – Set Interface – SortedSet Interface – Queue Interface - ArrayList Class – LinkedList Class – HashSet Class – Using an Iterator – The For Each Statement

Unit-4
Teaching Hours:9
INTRODUCING GUI PROGRAMING WITH SWING, EVENT HANDLING

Introducing GUI Programing with Swing

Swing Basics – Components and Containers – JLabel and ImageIcons- JTextField – Swing Buttons – JTabbedPane – JScrollPane – JList – JComboBox – JTable – Swing Menus

Event Handling

Delegation Event Model - Event Classes – Key Event Class – Event Listener Interface - Adapter Classes

Unit-5
Teaching Hours:9
DATABASE PROGRAMMING AND DATA SCIENCE WITH JAVA

Database Programming

Connecting to and querying a database –Connecting to the database - Creating a Statement for executing query - Executing a query - Processing a Query’s ResultSet – PreparedStatements.

Data Science with Java

Importance of JAVA in Data Science-Creating Simple Plots-Plotting Mixed Chart Types-Saving a Plot to a File

Unit-5
Teaching Hours:9
LAB EXERCISES(30 hrs)

 1.         Implement the concept of class, data members, member functions and access specifiers. 2.         Implement the concept of function overloading & Constructor overloading 3.         Implement the static keyword – static variable, static block, static function and static class 4.         Implement String and String Buffer classes. 5.         Implement this keyword and command line arguments. 6.         Implement the concept of inheritance, super, abstract and final keywords 7.         Implement package and interface 8.         Implement Exception Handing in java 9.          Implement multithreading – Thread class, Runnable interface, thread synchronization and thread communication. 10.  Implement collection Interfaces and classes 11.  Implement basic CRUD operations in JDBC with SWING 12.  Visualizing Data with Plots 13. Implement Java Servlets
Text Books And Reference Books:

1. Schildt Herbert, Java: The Complete Reference, Tata McGraw-Hill, 12th Edition, 2021.

2. Michael R. Brzustowicz, Data Science with Java: Practical Methods for Scientists and Engineers, Shroff/O'Reilly; 1st edition,2017

1. Paul Deitel, Java How to Program, Pearson Education Asia, 11th Edition, 2017

2. Cay S Horstmann, Core Java Volume 1 Fundamentals, Prentice Hall, 11th Edition, 2018.

Online Resources:

3.      http://stackoverflow.com/

Evaluation Pattern

CIA-50%

ESE-50%

MDS273L - JAVA PROGRAMMING (2022 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

This course of study builds on the skills gained by students in Java Fundamentals to help them to apply Java programming skills in Data science applications. Students will design object-oriented applications with Java and will create Java programs using hands-on, engaging activities. This course will help the learner to gain sound knowledge in object-oriented principles, GUI application design with databases and Servlets.

Course Outcome

CO1: Understanding and applying the principles and practice of object-oriented programming in the construction of robust, maintainable programs.

CO2: Competence in the use of Java Programming Language in the development of small to medium-sized applications that demonstrate professionally acceptable coding and performance standards.

CO3: To prepare the students to address the challenging requirements coming from the enterprise applications.

Unit-1
Teaching Hours:30
Seminar

Students will be giving presentations on any advanced concepts and technologies in data science and submit the report

Text Books And Reference Books:
 Research Articles / Books / Web resources related to data science domain

Recommended References

Evaluation Pattern

CIA-100%

MDS281L - SEMINAR (2022 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description

The course is designed to provide to enhance the soft skills and technical undetstanding of the students.

Course Outcome

1: CO1: Understand new and latest trends in data science

2: CO2: Demonstrate the professional presentation abilities

3: CO3: Apply the acquired knowledge in their Research

 Unit-1 Teaching Hours:6 Unit-1 Identification of  any advanced concepts and technologies in data science Unit-2 Teaching Hours:6 Unit 2 Presentation of Topics Unit-3 Teaching Hours:6 Unit 3 Submission of Report Unit-4 Teaching Hours:6 Unit 4 Soft Skills Unit-5 Teaching Hours:6 Unit 5 Interview Skills Text Books And Reference Books:Research Articles / Books / Web resources related to data science domain Essential Reading / Recommended Reading  Research Articles / Books / Web resources related to data science domain Evaluation Pattern100% MDS331 - NEURAL NETWORKS AND DEEP LEARNING (2021 Batch) Total Teaching Hours for Semester:60 No of Lecture Hours/Week:4 Max Marks:100 Credits:4 Course Objectives/Course Description The main aim of this course is to provide fundamental knowledge of neural networks and deep learning. On successful completion of the course, students will acquire fundamental knowledge of neural networks and deep learning, such as Basics of neural networks, shallow neural networks, deep neural networks, forward & backward propagation process and build various research projects Course Outcome CO1: Understand the major technology trends in neural networks and deep learningCO2: Build, train and apply neural networks and fully connected deep neural networksCO3: Implement efficient (vectorized) neural networks for real time application
 Unit-1 Teaching Hours:12 INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS Neural Networks-Application Scope of Neural Networks- Fundamental Concept of ANN: The Artificial Neural Network-Biological Neural Network-Comparison between Biological Neuron and Artificial Neuron-Evolution of Neural Network. Basic models of ANN-Learning Methods-Activation Functions-Importance Terminologies of ANN. Unit-2 Teaching Hours:12 SUPERVISED LEARNING NETWORK Shallow neural networks- Perceptron Networks-Theory-Perceptron Learning RuleArchitecture-Flowchart for training Process-Perceptron Training Algorithm for Single and Multiple Output Classes. Back Propagation Network- Theory-Architecture-Flowchart for training process-Training Algorithm-Learning Factors for Back-Propagation Network. Radial Basis Function Network RBFN: Theory, Architecture, Flowchart and Algorithm. Unit-3 Teaching Hours:12 CONVOLUTIONAL NEURAL NETWORK Introduction - Components of CNN Architecture - Rectified Linear Unit (ReLU) Layer - Exponential Linear Unit (ELU, or SELU) - Unique Properties of CNN -Architectures of CNN -Applications of CNN. Unit-4 Teaching Hours:12 RECURRENT NEURAL NETWORK Introduction- The Architecture of Recurrent Neural Network- The Challenges of Training Recurrent Networks- Echo-State Networks- Long Short-Term Memory (LSTM) - Applications of RNN. Unit-5 Teaching Hours:12 AUTO ENCODER AND RESTRICTED BOLTZMANN MACHINE Introduction - Features of Auto encoder Types of Autoencoder Restricted Boltzmann Machine- Boltzmann Machine - RBM Architecture -Example - Types of RBM. Text Books And Reference Books:1. S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition, 2018. 2. Dr. S Lovelyn Rose, Dr. L Ashok Kumar, Dr. D Karthika Renuka, Deep Learning Using Python, Wiley-India, 1st Edition, 2019. Essential Reading / Recommended Reading1. Charu C. Aggarwal, Neural Networks and Deep Learning, Springer, September 2018. 2. Francois Chollet, Deep Learning with Python, Manning Publications; 1st edition, 2017 3. John D. Kelleher, Deep Learning (MIT Press Essential Knowledge series), The MIT Press, 2019. Evaluation PatternCIA: 50%  ESE: 50% MDS331L - NEURAL NETWORKS AND DEEP LEARNING (2021 Batch) Total Teaching Hours for Semester:60 No of Lecture Hours/Week:4 Max Marks:100 Credits:4 Course Objectives/Course Description The main aim of this course is to provide fundamental knowledge of neural networks and deep learning. On successful completion of the course, students will acquire fundamental knowledge of neural networks and deep learning, such as Basics of neural networks, shallow neural networks, deep neural networks, forward & backward propagation process and build various research projects Course Outcome CO1: Understand the major technology trends in neural networks and deep learning CO2: Build, train and apply neural networks and fully connected deep neural networks CO3: Implement efficient (vectorized) neural networks for real time application
Unit-1
Teaching Hours:12
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS

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

Unit-2
Teaching Hours:12
SUPERVISED LEARNING NETWORK

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

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

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

Unit-3
Teaching Hours:12
CONVOLUTIONAL NEURAL NETWORK

 Introduction - Components of CNN Architecture - Rectified Linear Unit (ReLU) Layer - Exponential Linear Unit (ELU, or SELU) - Unique Properties of CNN -Architectures of CNN -Applications of CNN.
Unit-4
Teaching Hours:12
RECURRENT NEURAL NETWORK

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

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

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

Text Books And Reference Books:
 1. S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition, 2018. 2. Dr. S Lovelyn Rose, Dr. L Ashok Kumar, Dr. D Karthika Renuka, Deep Learning Using Python, Wiley-India, 1st Edition, 2019.

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

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

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

Evaluation Pattern

CIA- 50%

ESE-50%

MDS341A - TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES (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 covers applied statistical methods pertaining to time series and forecasting techniques. Moving average models like simple, weighted and exponential are dealt with. Stationary time series models and non-stationary time series models like AR, MA, ARMA and ARIMA are introduced to analyse time series data.

Course Outcome

CO1: Ability to approach and analyze univariate time series

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

CO3: Evaluate stationary and non-stationary time series models

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

 Unit-1 Teaching Hours:12 INTRODUCTION TO TIME SERIES AND STOCHASTIC PROCESS Introduction to time series and stochastic process, graphical representation, components and classical decomposition of time series data.Auto-covariance and auto-correlation functions, Exploratory time series analysis, Test for trend and seasonality, Smoothing techniques such as Exponential and moving average smoothing, Holt- Winter smoothing, Forecasting based on smoothing. Unit-2 Teaching Hours:12 STATIONARY TIME SERIES MODELS Wold representation of linear stationary processes, Study of linear time series models: Autoregressive, Moving Average and Autoregressive Moving average models and their statistical properties like ACF and PACF function. Unit-3 Teaching Hours:12 ESTIMATION OF ARMA MODELS Estimation of ARMA models: Yule- Walker estimation of AR Processes, Maximum likelihood and least squares estimation for ARMA Processes, Residual analysis and diagnostic checking. Unit-4 Teaching Hours:12 NON-STATIONARY TIME SERIES MODELS Concept of non-stationarity, general unit root tests for testing non-stationarity; basic formulation of the ARIMA Model and their statistical properties-ACF and PACF; forecasting using ARIMA models Unit-5 Teaching Hours:12 MULTIVARIATE TIME SERIES ANALYSIS Stationary Multivariate Time series, Cross covariance and cross-correlation matrices,covariance stationary process,Vector white noise process, Vector AR models, Vector MA models,Vector ARMA models- its stationarity properties,Non stationarity and Cointegration. Text Books And Reference Books: 1. George E. P. Box, G.M. Jenkins, G.C. Reinsel and G. M. Ljung, Time Series analysis Forecasting and Control, 5th Edition, John Wiley & Sons, Inc., New Jersey, 2016. 2. Montgomery D.C, Jennigs C. L and Kulachi M,Introduction to Time Series analysis  and Forecasting, 2nd Edition,John Wiley & Sons, Inc., New Jersey, 2016. Essential Reading / Recommended Reading1.      Anderson T.W,Statistical Analysis of Time Series, John Wiley& Sons, Inc., New Jersey, 1971.  2.      Shumway R.H and Stoffer D.S, Time Series Analysis and its Applications with R Examples, Springer, 2011.  3.      P. J. Brockwell and R. A. Davis, Times series: Theory and Methods, 2nd Edition, Springer-Verlag, 2009.  4.      S.C. Gupta and V.K. Kapoor, Fundamentals of Applied Statistics, 4th Edition, Sultan Chand and Sons, 2008. Evaluation PatternCIA: 50% ESE: 50% MDS341AL - TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES (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 covers applied statistical methods pertaining to time series and forecasting techniques. Moving average models like simple, weighted and exponential are dealt with. Stationary time series models and non-stationary time series models like AR, MA, ARMA and ARIMA are introduced to analyse time series data. Course Outcome 4: CO1: Ability to approach and analyze univariate time series CO2: Able to differentiate between various time series models like AR, MA, ARMA and ARIMA models CO3: Evaluate stationary and non-stationary time series models CO4: Able to forecast future observations of the time series.
 Unit-1 Teaching Hours:12 Introduction To Time Series And Stochastic Process Introduction to time series and stochastic process, graphical representation, components and classical decomposition of time series data.Auto-covariance and auto-correlation functions, Exploratory time series analysis, Test for trend and seasonality, Smoothing techniques such as Exponential and moving average smoothing, Holt- Winter smoothing, Forecasting based on smoothing Unit-2 Teaching Hours:12 Stationary time series models Wold representation of linear stationary processes, Study of linear time series models: Autoregressive, Moving Average and Autoregressive Moving average models and their statistical properties like ACF and PACF function. Unit-3 Teaching Hours:12 Estimation of ARMA models Estimation of ARMA models: Yule- Walker estimation of AR Processes, Maximum likelihood and least squares estimation for ARMA Processes, Residual analysis and diagnostic checking. Unit-4 Teaching Hours:12 Non-Stationary Time Series Models Concept of non-stationarity, general unit root tests for testing non-stationarity; basic formulation of the ARIMA Model and their statistical properties-ACF and PACF; forecasting using ARIMA models Unit-5 Teaching Hours:12 Multivariate Time Series Stationary Multivariate Time series, Cross covariance and cross-correlation matrices,covariance stationary process, Vector white noise process, Vector AR models, Vector MA models, Vector ARMA models- its stationarity properties,Non stationarity and Cointegration. Text Books And Reference Books: 1.      George E. P. Box, G.M. Jenkins, G.C. Reinsel and G. M. Ljung, Time Series analysis Forecasting and Control, 5th Edition, John Wiley & Sons, Inc., New Jersey,2016. 2.     Montgomery D.C, Jennigs C. L and Kulachi M, Introduction to Time Series analysis and Forecasting, 2nd Edition,John Wiley & Sons, Inc., New Jersey,2016. Essential Reading / Recommended Reading1.       Anderson T.W,Statistical Analysis of Time Series, John Wiley& Sons, Inc., New Jersey,1971. 2.       Shumway R.H and Stoffer D.S, Time Series Analysis and its Applications with R Examples, Springer,2011. 3.       P. J. Brockwell and R. A. Davis, Times series: Theory and Methods, 2nd Edition, Springer-Verlag,2009. 4.       S.C. Gupta and V.K. Kapoor, Fundamentals of Applied Statistics, 4th Edition, Sultan Chand and Sons,2008. Evaluation Pattern  CIA: 50% ESE: 50% MDS341B - BAYESIAN INFERENCE (2021 Batch) Total Teaching Hours for Semester:60 No of Lecture Hours/Week:4 Max Marks:100 Credits:4 Course Objectives/Course Description To equip the students with the knowledge of conceptual, computational, and practical methods of Bayesian data analysis. Course Outcome CO1: Understand Bayesian models and their specific model assumptions.CO2: Identify suitable informative and non-informative prior distributions to derive posterior distributionsCO3: Apply computer intensive methods like MCMC for approximating the posterior distribution.CO4: Analyse the results obtained by Bayesian methods.
 Unit-1 Teaching Hours:12 INTRODUCTION Basics on minimaxity: subjective and frequents probability, Bayesian inference, Bayesian estimation , prior distributions, posterior distribution, loss function, principle of minimum expected posterior loss, quadratic and other common loss functions, Advantages of being a Bayesian HPD confidence intervals, testing, credible intervals, prediction of a future observation. Unit-2 Teaching Hours:12 BAYESIAN ANALYSIS WITH PRIOR INFORMATION Robustness and sensitivity, classes of priors, conjugate class, neighbourhood class, density ratio class different methods of objective priors: Jeffrey’s prior, probability matching prior, conjugate priors and mixtures, posterior robustness: measures and techniques Unit-3 Teaching Hours:12 MULTIPARAMETER AND MULTIVARIABLE MODELS Basics of decision theory, multi-parameter models, Multivariate models, linear regression, asymptotic approximation to posterior distributions Unit-4 Teaching Hours:12 MODEL SELECTION AND HYPOTHESIS TESTING Selection criteria and testing of hypothesis based on objective probabilities and Bayes’ factors, large sample methods: limit of posterior distribution, consistency of posterior distribution, asymptotic normality of posterior distribution. Unit-5 Teaching Hours:12 BAYESIAN COMPUTATIONS Analytic approximation, E- M Algorithm, Monte Carlo sampling, Markov Chain Monte Carlo Methods, Metropolis – Hastings Algorithm, Gibbs sampling, examples, convergence issues Text Books And Reference Books:1. Albert Jim (2009) Bayesian Computation with R, second edition, Springer, New York 2. Bolstad W. M. and Curran, J.M. (2016) Introduction to Bayesian Statistics 3rd Ed. Wiley, New York 3. Christensen R. Johnson, W. Branscum A. and Hanson T.E. (2011) Bayesian Ideas and data analysis : A introduction for scientist and Statisticians, Chapman and Hall, London  4. A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin (2004). Bayesian Data Analysis, 2nd Ed. Chapman & Hall Essential Reading / Recommended Reading 1. Congdon P. (2006) Bayesian Statistical Modeling, Wiley, New York. 2. Ghosh, J.K. Delampady M. and T. Samantha (2006). An Introduction to Bayesian Analysis: Theory and Methods, Springer, New York. 3. Lee P.M. (2012) Bayesian Statistics: An Introduction-4th Ed. Hodder Arnold, New York. 4. Rao C.R. Day D. (2006) Bayesian Thinking, Modeling and Computation, Handbook of Statistics, Vol.25. Evaluation PatternCIA: 50% ESE: 50% MDS341C - ECONOMETRICS (2021 Batch) Total Teaching Hours for Semester:60 No of Lecture Hours/Week:4 Max Marks:100 Credits:4 Course Objectives/Course Description The course is designed to impart the learning of principles of econometric methods and tools. This is expected to improve student’s ability to understand of econometrics in the study of economics and finance. The learning objective of the course is to provide students to get the basic knowledge and skills of econometric analysis, so that they should be able to apply it to the investigation of economic relationships and processes, and also understand the econometric methods, approaches, ideas, results and conclusions met in the majority of economic books and articles. Introduce the students to the traditional econometric methods developed mostly for the work with cross-sections data. Course Outcome CO1: Demonstrate Simple and multiple Econometric modelsCO2: Interpret the models adequacy through various methodsCO3: Demonstrate simultaneous Linear Equations model.
 Unit-1 Teaching Hours:15 INTRODUCTION Introduction to Econometrics- Meaning and Scope – Methodology of Econometrics – Nature and Sources of Data for Econometric analysis – Types of Econometrics Unit-2 Teaching Hours:15 CORRELATION Aitken’s Generalised Least Squares(GLS) Estimator, Heteroscedasticity, Auto-correlation, Multicollinearity, Auto-Correlation, Test of Auto-correlation, Multicollinearity, Tools for Handling Multicollinearity Unit-3 Teaching Hours:15 REGRESSION Linear Regression with Stochastic Regressors, Errors in Variable Models and Instrumental Variable Estimation, Independent Stochastic linear Regression, Auto regression, Linear regression, Lag Models Unit-4 Teaching Hours:15 LINEAR EQUATIONS MODEL Simultaneous Linear Equations Model : Structure of Linear Equations Model, Identification Problem, Rank and Order Conditions, Single Equation and Simultaneous Equations, Methods of Estimation- Indirect Least squares, Least Variance Ratio and Two-Stage Least Square Text Books And Reference Books:1.  Johnston, J. (1997). Econometric Methods, Fourth Edition, McGraw Hill 2.  Gujarathi, D., and Porter, D. (2008). Basic Econometrics, Fifth Edition, McGraw-Hill Essential Reading / Recommended Reading1.   Intriligator, M. D. (1980). Econometric Models-Techniques and Applications, Prentice Hall. 2.  Theil, H. (1971). Principles of Econometrics, John Wiley. 3.  Walters, A. (1970). An Introduction to Econometrics, McMillan and Co. Evaluation PatternCIA : 50% ESE : 50% MDS341D - BIO-STATISTICS (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 provides an understanding of various statistical methods in describing and analyzing biological data. Students will be equipped with an idea about the applications of statistical hypothesis testing, related concepts and interpretation in biological data. Course Outcome CO1: Demonstrate the understanding of basic concepts of biostatistics and the process involved in the scientific method of research.CO2: Identify how the data can be appropriately organized and displayed.CO3: Interpret the measures of central tendency and measures of dispersion.CO4: Interpret the data based on the discrete and continuous probability distributions.CO5: Apply parametric and non-parametric methods of statistical data analysis.
 Unit-1 Teaching Hours:12 INTRODUCTION TO BIOSTATISTICS Presentation of data - graphical and numerical representations of data - Types of variables, measures of location - dispersion and correlation - inferential statistics - probability and distributions - Binomial, Poisson, Negative Binomial, Hyper geometric and normal distribution. Unit-2 Teaching Hours:12 PARAMETRIC AND NON - PARAMETRIC METHODS Parametric methods - one sample t-test - independent sample t-test - paired sample t-test - one-way analysis of variance - two-way analysis of variance - analysis of covariance - repeated measures of analysis of variance - Pearson correlation coefficient - Non-parametric methods: Chi-square test of independence and goodness of fit - Mann Whitney U test - Wilcoxon signed-rank test - Kruskal Wallis test - Friedman’s test - Spearman’s correlation test. Unit-3 Teaching Hours:12 GENERALIZED LINEAR MODELS Review of simple and multiple linear regression - introduction to generalized linear models - parameter estimation of generalized linear models - models with different link functions - binary (logistic) regression - estimation and model fitting - Poisson regression for count data - mixed effect models and hierarchical models with practical examples. Unit-4 Teaching Hours:12 EPIDEMIOLOGY Introduction to epidemiology, measures of epidemiology, observational study designs: case report, case series correlational studies, cross-sectional studies, retrospective and prospective studies, analytical epidemiological studies-case control study and cohort study, odds ratio, relative risk, the bias in epidemiological studies. Unit-5 Teaching Hours:12 DEMOGRAPHY Introduction to demography, mortality and life tables, infant mortality rate, standardized death rates, life tables, fertility, crude and specific rates, migration-definition and concepts population growth, measurement of population growth-arithmetic, geometric and exponential, population projection and estimation, different methods of population projection, logistic curve, urban population growth, components of urban population growth. Text Books And Reference Books:1. Marcello Pagano and Kimberlee Gauvreau (2018), Principles of Biostatistics, 2nd Edition, Chapman and Hall/CRC press 2. David Moore S. and George McCabe P., (2017) Introduction to practice of statistics, 9th Edition, W. H. Freeman. 3. Sundar Rao and Richard J., (2012) Introduction to Biostatistics and research methods, PHI Learning Private limited, New Delhi Essential Reading / Recommended Reading1. Abhaya Indrayan and Rajeev Kumar M., (2018) Medical Biostatistics, 4th Edition, Chapman and Hall/CRC Press. 2. Gordis Leon (2018), Epidemiology, 6th Edition, Elsevier, Philadelphia 3. Ram, F. and Pathak K. B., (2016): Techniques of Demographic Analysis, Himalaya Publishing house, Bombay. 4. Park K., (2019), Park's Text Book of Preventive and Social Medicine, Banarsidas Bhanot, Jabalpur. Evaluation PatternCIA:50% ESE:50% MDS371 - CLOUD ANALYTICS (2021 Batch) Total Teaching Hours for Semester:90 No of Lecture Hours/Week:6 Max Marks:150 Credits:5 Course Objectives/Course Description The objective of this course is to explore the basics of cloud analytics and the major cloud solutions. Students will learn how to analyze extremely large data sets, and to create visual representations of that data. Also aim to provide students with hands-on experience working with data at scale. Course Outcome CO1: Interpret the deployment and service models of cloud applications.CO2: Describe big data analytical concepts.CO3: Ingest, store, and secure data.CO4: Process and Visualize structured and unstructured data.