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

Syllabus for

1 Semester  2021  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MDS131  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  I  Core Courses  4  4  100 
MDS132  PROBABILITY AND DISTRIBUTION THEORY  Core Courses  4  4  100 
MDS133  PRINCIPLES OF DATA SCIENCE  Core Courses  4  4  100 
MDS134  RESEARCH METHODOLOGY  Core Courses  2  2  50 
MDS161A  INTRODUCTION TO STATISTICS  Generic Elective Courses  2  2  50 
MDS161B  INTRODUCTION TO COMPUTERS AND PROGRAMMING  Generic Elective Courses  2  2  50 
MDS161C  LINUX ADMINISTRATION  Generic Elective Courses  2  2  50 
MDS171  DATA BASE TECHNOLOGIES  Core Courses  6  5  150 
MDS172  INFERENTIAL STATISTICS  Core Courses  6  5  150 
MDS173  PROGRAMMING FOR DATA SCIENCE IN PYTHON  Core Courses  6  4  100 
2 Semester  2021  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MDS231  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  II  Core Courses  4  4  100 
MDS232  REGRESSION ANALYSIS  Core Courses  4  4  100 
MDS241A  MULTIVARIATE ANALYSIS  Discipline Specific Elective Courses  4  4  100 
MDS241B  STOCHASTIC PROCESS  Discipline Specific Elective Courses  4  4  100 
MDS241C  CATEGORICAL DATA ANALYSIS  Discipline Specific Elective Courses  4  4  100 
MDS271  MACHINE LEARNING  Core Courses  6  5  150 
MDS272A  HADOOP  Discipline Specific Elective Courses  6  5  150 
MDS272B  IMAGE AND VIDEO ANALYTICS  Discipline Specific Elective Courses  6  5  150 
MDS272C  INTERNET OF THINGS  Discipline Specific Elective Courses  6  5  150 
MDS273  PROGRAMMING FOR DATA SCIENCE IN R  Core Courses  6  4  100 
3 Semester  2020  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MDS331  NEURAL NETWORKS AND DEEP LEARNING  Core Courses  4  4  100 
MDS341A  TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES  Discipline Specific Elective Courses  4  4  100 
MDS341B  BAYESIAN INFERENCE  Discipline Specific Elective Courses  4  4  100 
MDS341C  ECONOMETRICS  Discipline Specific Elective Courses  4  4  100 
MDS341D  BIOSTATISTICS  Discipline Specific Elective Courses  4  4  100 
MDS371  CLOUD ANALYTICS  Core Courses  6  5  150 
MDS372A  NATURAL LANGUAGE PROCESSING  Discipline Specific Elective Courses  6  5  150 
MDS372B  WEB ANALYTICS  Discipline Specific Elective Courses  6  5  150 
MDS372C  BIO INFORMATICS  Discipline Specific Elective Courses  6  5  150 
MDS372D  EVOLUTIONARY ALGORITHMS  Discipline Specific Elective Courses  6  5  150 
MDS372E  OPTIMIZATION TECHNIQUE  Discipline Specific Elective Courses  6  5  150 
MDS381  SPECIALIZATION PROJECT  Core Courses  4  2  100 
MDS382  SEMINAR  Skill Enhancement Courses  2  1  50 
4 Semester  2020  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MDS481  INDUSTRY PROJECT  Core Courses  2  12  300 
 
Introduction to Program:  
Data Science is popular in all academia, business sectors, and research and development to make effective decision in day to day activities. MSc in Data Science is a two year programme with four semesters. This programme aims to provide opportunity to all candidates to master the skill sets specific to data science with research bent. The curriculum supports the students to obtain adequate knowledge in theory of data science with hands on experience in relevant domains and tools. Candidate gains exposure to research models and industry standard applications in data science through guest lectures, seminars, projects, internships, etc.  
Assesment Pattern  
CIA  50% ESE  50%  
Examination And Assesments  
CIA  50% ESE  50% 
MDS131  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  I (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

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


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


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


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


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


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

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

Course Outcome 

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


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


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


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


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


Chebychev’s inequality  weak law of large n u mbers (iid): examples  strong law of large numbers (statement only)  central limit theorems (iid case): examples.  
Text Books And Reference Books: 1. Ross, Sheldon. A first course in probability. 10th Edition. Pearson, 2019. 2. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3rd Edition, 2015  
Essential Reading / Recommended Reading 1. Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGrawHill, 3rd Edition (Reprint), 2017. 2. Ross, Sheldon M. Introduction to probability models. 12th Edition, Academic Press, 2019.  
Evaluation Pattern CIA: 50% ESE: 50%  
MDS133  PRINCIPLES OF DATA SCIENCE (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Explore the fundamental concepts of data science CO2: Understand data analysis techniques for applications handling large data CO3: Understand various machine learning algorithms used in data science process C04: Visualize and present the inference using various tools CO5: Learn to think through the ethics surrounding privacy, data sharing and algorithmic decisionmaking 
Unit1 
Teaching Hours:10 
INTRODUCTION TO DATA SCIENCE


Definition – Big Data and Data Science Hype – Why data science – Getting Past the Hype – The Current Landscape – Who is Data Scientist?  Data Science Process Overview – Defining goals – Retrieving data – Data preparation – Data exploration – Data modeling – Presentation.  
Unit2 
Teaching Hours:12 
BIG DATA


Problems when handling large data – General techniques for handling large data – Case study – Steps in big data – Distributing data storage and processing with Frameworks – Case study.  
Unit3 
Teaching Hours:12 
MACHINE LEARNING


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


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


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


Data Science Ethics – Doing good data science – Owners of the data  Valuing different aspects of privacy  Getting informed consent  The Five Cs – Diversity – Inclusion – Future Trends.  
Text Books And Reference Books: [1]. Introducing Data Science, Davy Cielen, Arno D. B. Meysman, Mohamed Ali, Manning Publications Co., 1st edition, 2016 [2]. An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Springer, 1st edition, 2013 [3]. Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 1st edition, 2016 [4]. Ethics and Data Science, D J Patil, Hilary Mason, Mike Loukides, O’ Reilly, 1st edition, 2018  
Essential Reading / Recommended Reading [1]. Data Science from Scratch: First Principles with Python, Joel Grus, O’Reilly, 1st edition, 2015 [2]. Doing Data Science, Straight Talk from the Frontline, Cathy O'Neil, Rachel Schutt, O’Reilly, 1st edition, 2013 [3]. Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Cambridge University Press, 2nd edition, 2014  
Evaluation Pattern CIA : 50 % ESE : 50 %  
MDS134  RESEARCH METHODOLOGY (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

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


Course Outcome 

CO1: Understand the essence of research and the necessity of defining a research problem. CO2: Apply research methods and methodology including research design,data collection, data analysis, and interpretation. CO3: Create scientific reports according to specified standards. 
Unit1 
Teaching Hours:8 
RESEARCH METHODOLOGY


Defining research problem:Selecting the problem, Necessity of defining the problem ,Techniques involved in defining a problem Ethics in Research.  
Unit2 
Teaching Hours:8 
RESEARCH DESIGN


Principles of experimental design,Working with Literature: Importance, finding literature, Using your resources, Managing the literature, Keep track of references,Using the literature, Literature review,Online Searching: Database ,SCIFinder, Scopus, Science Direct ,Searching research articles , Citation Index ,Impact Factor ,Hindex.  
Unit3 
Teaching Hours:7 
RESEARCH DATA


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


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

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

Course Outcome 

CO1: Demonstrate the history of statistics and present the data in various forms. CO2: Infer the concept of correlation and regression for relating two or more related variables CO3: Demonstrate the probabilities for various events. 
Unit1 
Teaching Hours:8 
ORGANIZATION AND PRESENTATION OF DATA


Origin and development of Statistics, Scope, limitation and misuse of statistics. Types of data: primary, secondary, quantitative and qualitative data. Types of Measurements: nominal, ordinal, discrete and continuous data. Presentation of data by tables: construction of frequency distributions for discrete and continuous data, graphical representation of a frequency distribution by histogram and frequency polygon, cumulative frequency distributions  
Unit2 
Teaching Hours:8 
DESCRIPTIVE STATISTICS


Measures of location or central tendency: Arthimetic mean, Median, Mode, Geometric mean, Harmonic mean. Partition values: Quartiles, Deciles and percentiles. Measures of dispersion: Mean deviation, Quartile deviation, Standard deviation, Coefficient of variation. Moments: measures of skewness, Kurtosis.  
Unit3 
Teaching Hours:7 
CORRELATION AND REGRESSION


Correlation: Scatter plot, Karl Pearson coefficient of correlation, Spearman's rank correlation coefficient, multiple and partial correlations (for 3 variates only). Regression: Concept of errors, Principles of Least Square, Simple linear regression and its properties.  
Unit4 
Teaching Hours:7 
BASICS OF PROBABILITY


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

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

Course Outcome 

CO1: Demonstrate the systematic approach for problemsolving using computers. CO2: Apply different programming structures with suitable logic for computational problems. 
Unit1 
Teaching Hours:10 
COMPUTERS AND DIGITAL BASICS


Number Representation – Decimal, Binary, Octal, Hexadecimal and BCD numbers – Binary Arithmetic – Binary addition – Unsigned and Signed numbers – one’s and two’s complements of Binary numbers – Arithmetic operations with signed numbers  Number system conversions – Boolean Algebra – Logic gates – Design of Circuits – K  Map  
Unit2 
Teaching Hours:5 
GENERAL PROBLEM SOLVING CONCEPT


Types of Problems – Problem solving with Computers – Difficulties with problem solving – problem solving concepts for the Computer – Constants and Variables – Rules for Naming and using variables – Data types – numeric data – character data – logical data – rules for data types – examples of data types – storing the data in computer  Functions – Operators – Expressions and Equations  
Unit3 
Teaching Hours:5 
PLANNING FOR SOLUTION


Communicating with computer – organizing the solution – Analyzing the problem – developing the interactivity chart – developing the IPO chart – Writing the algorithms – drawing the flow charts – pseudocode – internal and external documentation – testing the solution – coding the solution – software development life cycle.  
Unit4 
Teaching Hours:10 
PROBLEM SOLVING


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

To Enable the students to excel in the Linux Platform 

Course Outcome 

CO1: Demonstrate the systematic approach for configure the Linux environment CO2: Demonstrate the systematic approach for configure the Liux environment 
Unit1 
Teaching Hours:10 
Module1


RHEL7.5,breaking root password, Understand and use essential tools for handling files, directories, commandline environments, and documentation  Configure local storage using partitions and logical volumes  
Unit2 
Teaching Hours:10 
Module2


Swapping, Extend LVM Partitions,LVM Snapshot  Manage users and groups, including use of a centralized directory for authentication  
Unit3 
Teaching Hours:10 
Module3


Kernel updations,yum and nmcli configuration, Scheduling jobs,at,crontab  Configure firewall settings using firewall config, firewallcmd, or iptables , Configure keybased authentication for SSH ,Set enforcing and permissive modes for SELinux , List and identify SELinux file and process context ,Restore default file contexts  
Text Books And Reference Books: 1. https://access.redhat.com/documentation/enUS/Red_Hat_Enterprise_Linux/7/ 2. https://access.redhat.com/documentation/enUS/Red_Hat_Enterprise_Linux/7/  
Essential Reading / Recommended Reading   
Evaluation Pattern CIA:50% ESE:50%  
MDS171  DATA BASE TECHNOLOGIES (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Course Outcome 

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


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


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


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


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


Introduction to NOSQL Systems, The CAP Theorem, DocumentBased NOSQL Systems and MongoDB, NOSQL KeyValue Stores, ColumnBased or Wide Column NOSQL Systems, Graph databases, Multimedia databases. Lab Exercises: 1. MongoDB Exercise  1 2. MongoDB Exercise  2  
Text Books And Reference Books: [1]. Henry F. Korth and Silberschatz Abraham, “Database System Concepts”, Mc.Graw Hill. [2]. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education, 2007. [3]. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd John Wiley & Sons, Inc. New York, USA, 2002  
Essential Reading / Recommended Reading [1] LiorRokach and OdedMaimon, Data Mining and Knowledge Discovery Handbook, Springer, 2nd edition, 2010.  
Evaluation Pattern CIA: 50% ESE: 50%  
MDS172  INFERENTIAL STATISTICS (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Course Outcome 

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


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


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


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


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


Concept of Nonparametric tests  Run test for randomness  Sign test and Wilcoxon Signed Rank Test for one and paired samples  Run test  Median test and MannWhitneyWilcoxon tests for two samples.
Lab Exercise: 17. Test of one sample using Run and sign tests 18. Test of paried sample using Wilcoxon signed rank test 19. Test of two samples using Run test and Median test 20. Test for two samples using MannWhitneyWilcoxon tests  
Text Books And Reference Books: 1. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 12th edition, Sultan Chand & Sons, New Delhi, 2020. 2. Brian Caffo, Statistical Inference for Data Science, Learnpub, 2016.  
Essential Reading / Recommended Reading 1. Walpole R.E, Myers R.H and Myers S.L, Probability and Statistics for Engineers and Scientists, 9th edition, Pearson, New Delhi, 2017. 2. John V, Using R for Introductory Statistics, 2nd edition, CRC Press, Boca Raton, 2014. 3. Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd, New Delhi, 2012. 4. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, JohnWiley & Sons Inc, New Jersey, 2015.  
Evaluation Pattern CIA: 50% ESE:50%  
MDS173  PROGRAMMING FOR DATA SCIENCE IN PYTHON (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

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


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


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


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


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


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


Basic functions of matplotlibSimple Line Plot, Scatter PlotDensity and Contour Plots Histograms, Binnings and DensityCustomizing Plot Legends, Colour BarsThree Dimensional Plotting in Matplotlib. Lab Exercises1. DemonstrateScatterPlot 2. Demonstrate3Dplotting  
Text Books And Reference Books: [1]. Jake VanderPlas ,Python Data Science Handbook  Essential Tools for Working with Data, O’Reily Media,Inc, 2016 [2]. Zhang.Y ,An Introduction to Python and Computer Programming, Springer Publications,2016  
Essential Reading / Recommended Reading [1].JoelGrus,DataSciencefromScratchFirstPrincipleswithPython,O’ReillyMedia,2016 [2]. T.R.Padmanabhan, Programming with Python,SpringerPublications,2016  
Evaluation Pattern CIA: 50%ESE: 50%
 
MDS231  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  II (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Demonstrate the properties of multivariate calculus CO2: Use the idea of orthogonality and projections effectively CO3: Have a clear understanding of Convex Optimization C04: Know the about the basic terminologies and properties in Graph Theory 
Unit1 
Teaching Hours:14 
Calculus of Several Variables


Functions of Several Variables: Functions of two, three variables  Limits and continuity in HIgher Dimensions: Limits for functions of two variables, Functions of more than two variables  Partial Derivatives: partial derivative of functions of two variables, partial derivatives of functions of more than two variables, partial derivatives and continuity, second order partial derivatives  The Chain Rule: chain rule on functions of two, three variables, chain rule on functions defined on surfaces  Directional Derivative and Gradient vectors: Directional derivatives in a plane, Interpretation of directional derivative, calculation and gradients, Gradients and tangents to level curves.  
Unit2 
Teaching Hours:10 
Orthogonality


Perpendicular vectors and Orthogonality  Inner Products and Projections onto lines  Projections of Rank one  Projections and Least Squares Approximations  Projection Matrices  Orthogonal Bases, Orthogonal Matrices and GramSchmidt orthogonalization  
Unit3 
Teaching Hours:12 
Introduction to Convex Optimization


Affine and Convex Sets: Lines and Line segments, affine sets, affine dimension andrelative interior, convexsets, cones  Hyperplanes and halfspaces  Euclidean balls and ellipsoids Norm balls and Norm cones  polyhedra  simplexes, Convex hull description of polyhedra  The positive semidefinitecone.
 
Unit4 
Teaching Hours:12 
Graph Theory  Basics


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


Matrix Representation of Graphs, Adjacency matrices, Incidence Matrices, Trees and its properties, Bridges (cutedges), spanning trees, weighted Graphs, minimal spanning tree problems, Shortest path problems, cut vertices, cuts, vertex and edge connectivity, Graph Algorithms  Applications of Graph Theory
 
Text Books And Reference Books: 1. M. D. Weir, J. Hass, and G. B. Thomas, Thomas' calculus. Pearson, 2016. (Unit 1) 2. G Strang, Linear Algebra and its Applications, 4th ed., Cengage, 2006. (Unit 2) 3. S. P. Boyd and L.Vandenberghe, Convex optimization.Cambridge Univ. Pr., 2011.(Unit 3) 4. J Clark, D A Holton, A first look at Graph Theory, Allied Publishers India, 1995. (Unit 4)  
Essential Reading / Recommended Reading 1.J. Patterson and A. Gibson, Deep learning: a practitioner's approach. O'Reilly Media, 2017. 2.S. Sra, S. Nowozin, and S. J. Wright, Optimization for machine learning. MIT Press, 2012. 3.D. Jungnickel, Graphs, networks and algorithms. Springer, 2014. 4.D Samovici, Mathematical Analysis for Machine Learning and Data Mining, World Scientific Publishing Co. Pte. Ltd, 2018 5.P. N. Klein, Coding the matrix: linear algebra through applications to computer science. Newtonian Press, 2015. 6.K H Rosen, Discrete Mathematics and its applications, 7th ed., McGraw Hill, 2016  
Evaluation Pattern CIA:50% ESE :50%  
MDS232  REGRESSION ANALYSIS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Demonstrate deeper understanding of the linear regression model. CO2: Evaluate Rsquare criteria for model selection CO3: Understand the forward, backward and stepwise methods for selecting the variables CO4: Understand the importance of multicollinearity in regression modelling CO5: Ability touse and understand generalizations of the linear model to binary and count data 
Unit1 
Teaching Hours:13 
SIMPLE LINEAR REGRESSION


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


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


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


Residual analysis, Departures from underlying assumptions, Effect of outliers, Collinearity, Nonconstant variance and serial correlation, Departures from normality, Diagnostics and remedies.  
Unit5 
Teaching Hours:10 
NON LINEAR REGRESSION


Introduction to nonlinear regression, Least squares in the nonlinear case and estimation of parameters, Models for binary response variables, estimation and diagnosis methods for logistic and Poisson regressions. Prediction and residual analysis.  
Text Books And Reference Books: [1].D.C Montgomery, E.A Peck and G.G Vining, Introduction to Linear Regression Analysis, John Wiley and Sons,Inc.NY, 2003. [2]. S. Chatterjee and AHadi, Regression Analysis by Example, 4^{th} Ed., John Wiley and Sons, Inc, 2006 [3].Seber, A.F. and Lee, A.J. (2003) Linear Regression Analysis, John Wiley, Relevant sections from chapters 3, 4, 5, 6, 7, 9, 10.  
Essential Reading / Recommended Reading [1]. Iain Pardoe, Applied Regression Modeling, John Wiley and Sons, Inc, 2012. [2]. P. McCullagh, J.A. Nelder, Generalized Linear Models, Chapman & Hall, 1989.  
Evaluation Pattern CIA  50% ESE  50%  
MDS241A  MULTIVARIATE ANALYSIS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

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


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


Sample mean vector and its distribution. Likelihood ratio tests: Tests of hypotheses about the mean vectors and covariance matrices for multivariate normal populations. Independence of sub vectors and sphericity test.  
Unit3 
Teaching Hours:12 
MULTIVARIATE ANALYSIS


Multivariate analysis of variance (MANOVA) of one and two way classified data. Multivariate analysis of covariance. Wishart distribution, Hotelling’s T2 and Mahalanobis’ D2 statistics, Null distribution of Hotelling’s T2. Rao’s U statistics and its distribution.  
Unit4 
Teaching Hours:12 
CLASSIFICATION AND DISCRIMINANT PROCEDURES


Bayes, minimax, and Fisher’s criteria for discrimination between two multivariate normal populations. Sample discriminant function. Tests associated with discriminant functions. Probabilities of misclassification and their estimation. Discrimination for several multivariate normal populations  
Unit5 
Teaching Hours:12 
PRINCIPAL COMPONENT and FACTOR ANALYSIS


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

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

Course Outcome 

CO1: Demonstrate the concepts of point and interval estimation of unknown parameters and their significance using large and small samples. CO2: Apply the idea of sampling distributions of difference statistics in testing of hypotheses CO3: Infer the concept of nonparametric tests for single sample and two samples. 
Unit1 
Teaching Hours:12 
INTRODUCTION TO STOCHASTIC PROCESSES


Classification of Stochastic Processes, Markov Processes – Markov Chain  Countable State Markov Chain. Transition Probabilities, Transition Probability Matrix. Chapman  Kolmogorov's Equations, Calculation of n  step Transition Probability and its limit.  
Unit2 
Teaching Hours:12 
POISSON PROCESS


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


Branching Processes – Galton – Watson Branching Process  Properties of Generating Functions – Extinction Probabilities – Distribution of Total Number of Progeny. Concept of Weiner Process.  
Unit4 
Teaching Hours:12 
RENEWAL PROCESS


Renewal Processes – Renewal Process in Discrete and Continuous Time – Renewal Interval – Renewal Function and Renewal Density – Renewal Equation – Renewal theorems: Elementary Renewal Theorem. Probability Generating Function of Renewal Processes.  
Unit5 
Teaching Hours:12 
STATIONARY PROCESS


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

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

Course Outcome 

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


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


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


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


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


Loglinear models for twoway and threeway tables  Inference for Loglinear models  the loglinearlogistic connection  Independence graphs and collapsibility  Models for matched pairs: Comparing dependent proportions, Logistic regression for matched pairs  Comparing margins of square contingency tables  symmetry issues  
Text Books And Reference Books: 1. Agresti, A. (2012). Categorical Data Analysis, 3rd Edition. New York: Wiley  
Essential Reading / Recommended Reading 1. Le, C.T. (2009). Applied Categorical Data Analysis and Translational Research, 2nd Ed., John Wiley and Sons. 2. Agresti, A. (2010). Analysis of ordinal categorical. John Wiley & Sons. 3. Stokes, M. E., Davis, C. S., & Koch, G. G. (2012). Categorical data analysis using SAS. SAS Institute. 4. Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons. 5. Bilder, C. R., & Loughin, T. M. (2014). Analysis of categorical data with R. Chapman and Hall/CRC.  
Evaluation Pattern CIA:50% ESE:50%  
MDS271  MACHINE LEARNING (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Course Outcome 

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


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


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


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


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


Clustering IntroductionMixture Densities, KMeans Clustering ExpectationMaximization algorithm Mixtures of Latent Varaible ModelsSupervised Learning after ClusteringSpectral Clustering Hierachial ClusteringClustering Choosing the number of Clusters. Lab Exercise 1. K means clustering 2. Hierarchical clustering  
Text Books And Reference Books: [1]. E. Alpaydin, Introduction to Machine Learning, 3rd Edition, MIT Press, 2014.  
Essential Reading / Recommended Reading 1. C.M.Bishop,PatternRecognitionandMachineLearning,Springer,2016. 2. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer, 2nd Edition,2009 3. K.P.Murphy,MachineLearning:AProbabilisticPerspective,MITPress,2012.  
Evaluation Pattern CIA: 50% ESE: 50%  
MDS272A  HADOOP (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Understand the Big Data concepts in real time scenario CO2: Understand the big data systems and identify the main sources of Big Data in the real world. CO3: Demonstrate an ability to use Hadoop framework for processing Big Data for Analytics. CO4: Evaluate the Map reduce approach for different domain problems. 
Unit1 
Teaching Hours:15 
INTRODUCTION


Distributed file system – Big Data and its importance, Four Vs, Drivers for Big data, Big data analytics, Big data applications, Algorithms using map reduce, MatrixVector Multiplication by Map Reduce. Apache Hadoop– Moving Data in and out of Hadoop – Understanding inputs and outputs ofMapReduce  Data Serialization, Problems with traditional largescale systemsRequirements for a new approachHadoop – ScalingDistributed FrameworkHadoop v/s RDBMSBrief history of Hadoop.
Lab Exercise
1. Installing and Configuring Hadoop  
Unit2 
Teaching Hours:15 
CONFIGURATIONS OF HADOOP


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


Simple, advanced, and inbetween Joins, Graph algorithms, using languageindependent data structures. Hadoop configuration properties  Setting up a cluster, Cluster access control, managing the NameNode, Managing HDFS, MapReduce management, Scaling. Lab Exercise: 1. Implementation of decision tree algorithms using MapReduce. 2. Implementation of Kmeans Clustering using MapReduce. 3. Generation of Frequent Itemset using MapReduce.  
Unit4 
Teaching Hours:15 
HADOOP STREAMING


Hadoop Streaming  Streaming Command Options  Specifying a Java Class as the Mapper/Reducer  Packaging Files With Job Submissions  Specifying Other Plugins for Jobs. Lab Exercise: 1. 1. Count the number of missing and invalid values through joining two large given datasets. 2. 2. Using hadoop’s mapreduce, Evaluating Number of Products Sold in Each Country in the online shopping portal. Dataset is given. 3. 3. Analyze the sentiment for product reviews, this work proposes a MapReduce technique provided by Apache Hadoop.  
Unit5 
Teaching Hours:15 
HIVE & PIG


Architecture, Installation, Configuration, Hive vs RDBMS, Tables, DDL & DML, Partitioning & Bucketing, Hive Web Interface, Pig, Use case of Pig, Pig Components, Data Model, Pig Latin. Lab Exercise 1. Trend Analysis based on Access Pattern over Web Logs using Hadoop. 2. Service Rating Prediction by Exploring Social Mobile Users Geographical Locations.  
Unit6 
Teaching Hours:15 
Hbase


RDBMS VsNoSQL, HBasics, Installation, Building an online query application – Schema design, Loading Data, Online Queries, Successful service. Hands On: Single Node Hadoop Cluster Set up in any cloud service provider How to create instance.How to connect that Instance Using putty.InstallingHadoop framework on this instance. Run sample programs which come with Hadoop framework. Lab Exercise: 1. 1. Big Data Analytics Framework Based Simulated Performance and Operational Efficiencies Through Billons of Patient Records in Hospital System.  
Text Books And Reference Books: [1] Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, Professional Hadoop Solutions, Wiley, 2015. [2] Tom White, Hadoop: The Definitive Guide, O’Reilly Media Inc., 2015. [3] Garry Turkington, Hadoop Beginner's Guide, Packt Publishing, 2013.  
Essential Reading / Recommended Reading [1] Pethuru Raj, Anupama Raman, DhivyaNagaraj and Siddhartha Duggirala, HighPerformance BigData Analytics: Computing Systems and Approaches, Springer, 2015. [2] Jonathan R. Owens, Jon Lentz and Brian Femiano, Hadoop RealWorld Solutions Cookbook, Packt Publishing, 2013. [3] Tom White, HADOOP: The definitive Guide, O Reilly, 2012.  
Evaluation Pattern CIA  50% ESE  50%  
MDS272B  IMAGE AND VIDEO ANALYTICS (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Course Outcome 

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


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


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


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


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


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

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

Course Outcome 

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


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


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


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


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


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


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


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


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


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


11. Smart street lights: The street lights should increase or decrease their intensity based on the actual requirements of the amount of light needed at that time of the day. This will save a lot of energy for the municipal corporation. 12. Implement 3bit Binary Counter using 3 LED Module. a. Glow RED if the Binary bit is '0'. Glow GREEN if the binary bit is '1' i. For example: ii. 000 = 0 (all LED should be RED) iii. 001 = 1 (Two LEDs Should be RED , and one LED should be GREEN) iv. If Button is pressed in between, Reset the counter and Restart from 0. Theft prevention system for night: When the room is dark and Board is moved or tilted (say around 90 degree), it should alarm.  
Text Books And Reference Books: [1] Arshdeep Bahgaand, Vijay Madisetti, Internet of Things: Handson Approach, Hyderabad University Press, 2015. [2] Kazem Sohraby, Daniel Minoli and TaiebZnati, Wireless Sensor Networks: Technology. Protocols and Application, Wiley Publications, 2010. [3] Waltenegus Dargie and Christian Poellabauer, Fundamentals of Wireless Sensor Networks: Theory and Practice, A John Wiley and Sons Ltd., 2010.  
Essential Reading / Recommended Reading [1] Edgar Callaway, Wireless Sensor Networks: Architecture and Protocols, Auerbach Publications, 2003. [2] Michael Miller, The Internet of Things, Pearson Education, 2015. [3] Holger Karl and Andreas Willig, Protocols and Architectures for Wireless Sensor Networks, John Wiley & Sons Inc., 2005. [4] Erdal Çayırcı and Chunming Rong, SecurityinWirelessAdHocandSensorNetworks,John Wiley and Sons, 2009. [5] Carlos De MoraisCordeiro and Dharma Prakash Agrawal, Ad Hoc and Sensor Networks: Theory and Applications, World Scientific Publishing, 2011. [6] Adrian Perrig and J.D.Tygar, Secure Broadcast Communication: In Wired and Wireless Networks, Springer, 2006.  
Evaluation Pattern CIA  50% ESE  50%  
MDS273  PROGRAMMING FOR DATA SCIENCE IN R (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Demonstrate to use R in any OS (Windows / Mac / Linux). CO2: Analyse the use of basic functions of R Package. CO3: Demonstrate exploratory data analysis (EDA) for a given data set. CO4: Create and edit visualizations with R CO5: Implement and assess relevance and effectiveness of machine learning algorithms for a given dataset. 
Unit1 
Teaching Hours:18 

R INSTALLTION, SETUP AND LINEAR REGRESSION


 
Unit2 
Teaching Hours:18 

LOGISTIC REGRESSION


 
Unit3 
Teaching Hours:18 

DECISION TREES


Approaches to missing data – Data imputation – Multiple imputation – Classification and Regression Tress (CART) – CART with Cross Validation – Predictions from CART – ROC curve for CART – Random Forests – Building many trees – Parameter selection – Kfold Cross Validation – Recitation – A minimum of 3 data sets for practice  
Unit4 
Teaching Hours:18 

TEXT ANALYTICS AND NLP


 
Unit5 
Teaching Hours:18 

ENSEMBLE MODELLING


Support Vector Machines – Gradient Boosting – Naive Bayes  Bayesian GLM – GLMNET  Ensemble modeling – Experimenting with all of the above approaches (Units 15) with and without data imputation and assessing predictive accuracy – Recitation – min 3 data sets for practice PROJECT – A concluding project work carried out individually for a common data set  
Text Books And Reference Books: [1]. Statistics : An Introduction Using R, Michael J. Crawley, WILEY, Second Edition, 2015.  
Essential Reading / Recommended Reading [1].Handson programming with R, Garrett Grolemund, O’Reilley, 1^{st} Edition, 2014 [2]. R for everyone, Jared Lander, Pearson, 1^{st} Edition, 2014  
Evaluation Pattern CIA  50% ESE  50%  
MDS331  NEURAL NETWORKS AND DEEP LEARNING (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

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

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 
Unit1 
Teaching Hours:12 
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS


Neural NetworksApplication Scope of Neural Networks Fundamental Concept of ANN: The Artificial Neural NetworkBiological Neural NetworkComparison between Biological Neuron and Artificial NeuronEvolution of Neural Network. Basic models of ANNLearning MethodsActivation FunctionsImportance Terminologies of ANN.  
Unit2 
Teaching Hours:12 
SUPERVISED LEARNING NETWORK


Shallow neural networks Perceptron NetworksTheoryPerceptron Learning RuleArchitectureFlowchart for training ProcessPerceptron Training Algorithm for Single and Multiple Output Classes. Back Propagation Network TheoryArchitectureFlowchart for training processTraining AlgorithmLearning Factors for BackPropagation Network. Radial Basis Function Network RBFN: Theory, Architecture, Flowchart and Algorithm.  
Unit3 
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.  
Unit4 
Teaching Hours:12 
RECURRENT NEURAL NETWORK


Introduction The Architecture of Recurrent Neural Network The Challenges of Training Recurrent Networks EchoState Networks Long ShortTerm Memory (LSTM)  Applications of RNN.  
Unit5 
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, WileyIndia, 3rd Edition, 2018. 2. Dr. S Lovelyn Rose, Dr. L Ashok Kumar, Dr. D Karthika Renuka, Deep Learning Using Python, WileyIndia, 1st Edition, 2019.  
Essential Reading / Recommended Reading 1. Charu C. Aggarwal, Neural Networks and Deep Learning, Springer, September 2018. 2. Francois Chollet, Deep Learning with Python, Manning Publications; 1st edition, 2017 3. John D. Kelleher, Deep Learning (MIT Press Essential Knowledge series), The MIT Press, 2019.  
Evaluation Pattern CIA: 50% ESE: 50%  
MDS341A  TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This course covers applied statistical methods pertaining to time series and forecasting techniques. Moving average models like simple, weighted and exponential are dealt with. Stationary time series models and nonstationary 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 nonstationary time series models CO4: Able to forecast future observations of the time series 
Unit1 
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.Autocovariance and autocorrelation 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.  
Unit2 
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.  
Unit3 
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.  
Unit4 
Teaching Hours:12 
NONSTATIONARY TIME SERIES MODELS


Concept of nonstationarity, general unit root tests for testing nonstationarity; basic formulation of the ARIMA Model and their statistical propertiesACF and PACF; forecasting using ARIMA models  
Unit5 
Teaching Hours:12 
STATE SPACE MODELS


Filtering, smoothing and forecasting using state space models, Kalman smoother, Maximum likelihood estimation, Missing data modifications  
Text Books And Reference Books: 1. George E. P. Box, G.M. Jenkins, G.C. Reinsel and G. M. Ljung, Time Series analysis Forecasting and Control, 5th Edition, John Wiley & Sons, Inc., New Jersey, 2016. 2. Montgomery D.C, Jennigs C. L and Kulachi M,Introduction to Time Series analysis and Forecasting, 2nd Edition,John Wiley & Sons, Inc., New Jersey, 2016.  
Essential Reading / Recommended Reading 1. Anderson T.W,Statistical Analysis of Time Series, John Wiley& Sons, Inc., New Jersey, 1971. 2. Shumway R.H and Stoffer D.S, Time Series Analysis and its Applications with R Examples, Springer, 2011. 3. P. J. Brockwell and R. A. Davis, Times series: Theory and Methods, 2nd Edition, SpringerVerlag, 2009. 4. S.C. Gupta and V.K. Kapoor, Fundamentals of Applied Statistics, 4th Edition, Sultan Chand and Sons, 2008.  
Evaluation Pattern CIA: 50% ESE: 50%
 
MDS341B  BAYESIAN INFERENCE (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Understand Bayesian models and their specific model assumptions CO2: Identify suitable informative and noninformative prior distributions to derive posterior distributions CO3: Apply computer intensive methods like MCMC for approximating the posterior distribution CO4: Analyse the results obtained by Bayesian methods 
Unit1 
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.  
Unit2 
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  
Unit3 
Teaching Hours:12 
MULTIPARAMETER AND MULTIVARIABLE MODELS


Basics of decision theory, multiparameter models, Multivariate models, linear regression, asymptotic approximation to posterior distributions  
Unit4 
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.  
Unit5 
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 Introduction4th Ed. Hodder Arnold, New York. 4. Rao C.R. Day D. (2006) Bayesian Thinking, Modeling and Computation, Handbook of Statistics, Vol.25.  
Evaluation Pattern CIA: 50% ESE: 50%  
MDS341C  ECONOMETRICS (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Demonstrate Simple and multiple Econometric models CO2: Interpret the models adequacy through various methods CO3: Demonstrate simultaneous Linear Equations model 
Unit1 
Teaching Hours:15 
INTRODUCTION


Introduction to Econometrics Meaning and Scope – Methodology of Econometrics – Nature and Sources of Data for Econometric analysis – Types of Econometrics  
Unit2 
Teaching Hours:15 
CORRELATION


Aitken’s Generalised Least Squares(GLS) Estimator, Heteroscedasticity, Autocorrelation, Multicollinearity, AutoCorrelation, Test of Autocorrelation, Multicollinearity, Tools for Handling Multicollinearity  
Unit3 
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  
Unit4 
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 TwoStage 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, McGrawHill  
Essential Reading / Recommended Reading 1. Intriligator, M. D. (1980). Econometric ModelsTechniques and Applications, Prentice Hall. 2. Theil, H. (1971). Principles of Econometrics, John Wiley. 3. Walters, A. (1970). An Introduction to Econometrics, McMillan and Co.  
Evaluation Pattern CIA : 50% ESE : 50%  
MDS341D  BIOSTATISTICS (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

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 nonparametric methods of statistical data analysis 
Unit1 
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.  
Unit2 
Teaching Hours:12 
PARAMETRIC AND NON  PARAMETRIC METHODS


Parametric methods  one sample ttest  independent sample ttest  paired sample ttest  oneway analysis of variance  twoway analysis of variance  analysis of covariance  repeated measures of analysis of variance  Pearson correlation coefficient  Nonparametric methods: Chisquare test of independence and goodness of fit  Mann Whitney U test  Wilcoxon signedrank test  Kruskal Wallis test  Friedman’s test  Spearman’s correlation test.  
Unit3 
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.  
Unit4 
Teaching Hours:12 
EPIDEMIOLOGY


Introduction to epidemiology, measures of epidemiology, observational study designs: case report, case series correlational studies, crosssectional studies, retrospective and prospective studies, analytical epidemiological studiescase control study and cohort study, odds ratio, relative risk, the bias in epidemiological studies.  
Unit5 
Teaching Hours:12 
DEMOGRAPHY


Introduction to demography, mortality and life tables, infant mortality rate, standardized death rates, life tables, fertility, crude and specific rates, migrationdefinition and concepts population growth, measurement of population growtharithmetic, geometric and exponential, population projection and estimation, different methods of population projection, logistic curve, urban population growth, components of urban population growth.  
Text Books And Reference Books: 1. Marcello Pagano and Kimberlee Gauvreau (2018), Principles of Biostatistics, 2nd Edition, Chapman and Hall/CRC press 2. David Moore S. and George McCabe P., (2017) Introduction to practice of statistics, 9th Edition, W. H. Freeman. 3. Sundar Rao and Richard J., (2012) Introduction to Biostatistics and research methods, PHI Learning Private limited, New Delhi  
Essential Reading / Recommended Reading 1. Abhaya Indrayan and Rajeev Kumar M., (2018) Medical Biostatistics, 4th Edition, Chapman and Hall/CRC Press. 2. Gordis Leon (2018), Epidemiology, 6th Edition, Elsevier, Philadelphia 3. Ram, F. and Pathak K. B., (2016): Techniques of Demographic Analysis, Himalaya Publishing house, Bombay. 4. Park K., (2019), Park's Text Book of Preventive and Social Medicine, Banarsidas Bhanot, Jabalpur.  
Evaluation Pattern CIA:50% ESE:50%  
MDS371  CLOUD ANALYTICS (2020 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

The objective of this course is to explore the basics of cloud analytics and the major cloud solutions. Students will learn how to analyze extremely large data sets, and to create visual representations of that data. Also aim to provide students with handson 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 
Unit1 
Teaching Hours:18 
INTRODUCTION


Introduction to cloud computing  Major benefits of cloud computing  Cloud computing deployment models  Private cloud  Public cloud  Hybrid cloud  Types of cloud computing services Infrastructure as a Service – PaaS – SaaS  Emerging cloud technologies and services  Different ways to secure the cloud  Risks and challenges with the cloud  What is cloud analytics? Parameters before adopting cloud strategy  Technologies utilized by cloud computing 1.Creating Virtual Machines using Hypervisors 2.IaaS: Compute service  Creating and running Virtual Machines  
Unit2 
Teaching Hours:18 
CLOUD ENABLING TECHNOLOGIES


Virtualization  Load Balancing  Scalability & Elasticity – Deployment –Replication – Monitoring  Software Defined Networking  Network Function Virtualization – MapReduce  Identity and Access Management  Service Level Agreements  Billing 1. Storage as a Service: Ingesting & Querying data into cloud 2. Database as a Service: Building DB Server  
Unit3 
Teaching Hours:18 
BASIC CLOUD SERVICES & PLATFORMS


Compute Services Amazon Elastic Compute Cloud  Google Compute Engine  Windows Azure Virtual Machines Storage Services Amazon Simple Storage Service  Google Cloud Storage  Windows Azure Storage Database Services Amazon Relational Data Store  Amazon DynamoDB  Google Cloud SQL  Google Cloud Datastore  Windows Azure SQL Database  Windows Azure Table Service 1. PaaS: Working with GoogleAppEngine  
Unit4 
Teaching Hours:18 
DATA INGESTION AND STORING


Cloud Dataflow  The Dataflow programming model  Cloud Pub/Sub  Cloud storage  Cloud SQL  Cloud BigTable  Cloud Spanner  Cloud Datastore  Persistent disks 1. Database as a Service: Building DB Server 2. Transforming data  
Unit4 
Teaching Hours:18 
PROCESSING AND VISUALIZING


Google BigQuery  Cloud Dataproc  Google Cloud Datalab  Google Data Studio 1. Visualize structured data and unstructureddata  
Unit5 
Teaching Hours:18 
MACHINE LEARNING, DEEP LEARNING AND AI


Services on Artificial intelligence  Machine learning  Cloud Natural Language API – TensorFlow  Cloud Speech API  Cloud Translation API  Cloud Vision API  Cloud Video Intelligence – Dialogflow – AutoML 1. Load and query data in a data warehouse 2. Setting up and executing a data pipeline job to load data into cloud  
Text Books And Reference Books: 1. Sanket Thodge, Cloud Analytics with Google Cloud Platform, Packt Publishing, 2018. 2. Arshdeep Bahga and Vijay Madisetti, Cloud computing  A HandsOn Approach, Create Space Independent Publishing Platform, 2014.  
Essential Reading / Recommended Reading 1. Deven Shah, Kailash Jayaswal, Donald J. Houde, Jagannath Kallakurchi, Cloud Computing  Black Book, Wiley, 2014. 2. Thomas Erl, Ricardo Puttini, Zaigham Mahmood, Cloud Computing: Concepts, Technology & Architecture, Prentice Hall, 2014.  
Evaluation Pattern CIA: 50% ESE: 50%  
MDS372A  NATURAL LANGUAGE PROCESSING (2020 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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


Course Outcome 

CO1: Understand various approaches on syntax and semantics in NLP CO2: Apply various methods to discourse, generation, dialogue, and summarization using NLP CO3: Analyze various methodologies used in machine translation, machine learning techniques used in NLP including unsupervised models and to analyze realtime applications 
Unit1 
Teaching Hours:18 
INTRODUCTION


Introduction to NLP Background and overview NLP Applications NLP hard Ambiguity Algorithms and models,Knowledge Bottlenecks in NLPIntroduction to NLTK,Case study. Lab Exercises: 1. Write a program to tokenize text 2. Write a program to count word frequency and toremove stopwords  
Unit2 
Teaching Hours:18 
PARSING AND SYNTAX


WordLevelAnalysis: RegularExpressions,Text Normalization, Edit Distance, Parsing and SyntaxSpelling, Error Detection and correctionWords and Word classesPartof speech Tagging, Naive Bayes and Sentiment Classification: Case study Lab Exercises: 1. Write a program to tokenize NonEnglish Languages 2. Write a program to get synonyms from WordNet  
Unit3 
Teaching Hours:18 
SMOOTHED ESTIMATION AND LANGUAGE MODELLING


Ngram Language Models:NGrams,Evaluating Language ModelsThe language modelling problem  
Unit3 
Teaching Hours:18 
SEMANTIC ANALYSIS AND DISCOURSE PROCESSING


Semantic Analysis: Meaning RepresentationLexical Semantics AmbiguityWord Sense Disambiguation. Discourse Processing: cohesionReference Resolution Discourse Coherence and Structure. Lab Exercises: 1. Write a program to get Antonyms from WordNet 2. Write a program for stemming NonEnglish words
 
Unit4 
Teaching Hours:18 
NATURAL LANGUAGE GENERATION AND MACHINE TRANSLATION


Natural Language Generation: Architecture of NLG Systems, Applications Machine Translation: Problems in Machine Translation Machine Translation Approaches Evaluation of Machine Translation systems.Case study: Characteristics of Indian Languages LabExercises: 1. Write a program for lemmatizing words UsingWordNet 2. Write a program to differentiate stemming and lemmatizing words
 
Unit5 
Teaching Hours:18 
INFORMATION RETRIEVAL AND LEXICAL RESOURCES


Information Retrieval: Design features of Information Retrieval SystemsClassical, Non classical, Alternative Models of Information Retrieval – valuation Lexical Resources: Word Embeddings  Word2vec Glove.  
Unit5 
Teaching Hours:18 
UNSUPERVISED METHODS IN NLP


Graphical Models for Sequence Labelling in NLP Lab Exercises 1. Write a program for POS Tagging or Word Embeddings. 2. Case studybased program (IBM) or Sentiment analysis.  
Text Books And Reference Books: 1. Speech and Language Processing, Daniel Jurafsky and James H., 2nd Edition, Martin PrenticeHall,2013. 2. Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press, 1999.
 
Essential Reading / Recommended Reading 1. Foundations of Computational Linguistics: Humancomputer Communication in Natural Language, Roland R. Hausser, Springer,2014. 2. Steven Bird, Ewan Klein and Edward Loper Natural Language Processing with Python, O’Reilly Media; 1 edition,2009. Web resources: 1. https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf 2. https://nptel.ac.in/courses/106101007/ 3. NLTK – Natural Language Tool Kithttp://www.nltk.org  
Evaluation Pattern CIA:50% ESE:50%  
MDS372B  WEB ANALYTICS (2020 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

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 eCommerce, business research, and market research 
Unit1 
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.
 
Unit2 
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 – ClickPath 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  
Unit3 
Teaching Hours:18 
GOOGLE ANALYTICS


Different analytical tools  Key features and capabilities of Google analytics How Google analytics works  Implementing Google analytics  Getting up and running with Google analytics Navigating Google analytics – Using Google analytics reports Google metrics  Using visitor data to drive website improvement Focusing on key performance indicators Integrating Google analytics with thirdParty applications Lab Exercise 1. Creation of segmentation in web analytics 2. Visualization, acquisition and conversions of web analytics data  
Unit4 
Teaching Hours:18 
OVERVIEW OF QUALITATIVE ANALYSIS


Lab Usability Testing Heuristic Evaluations Site Visits Surveys (Questionnaires)  Testing and Experimentation: A/B Testing and Multivariate TestingCompetitive 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) AlgorithmsAPI 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  
Unit5 
Teaching Hours:18 
VISUAL ANALYTICS


VISUAL ANALYTICS: Drill down and hierarchiesSortingGrouping 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 networkGraph VisualizationNetwork RelationshipsNetwork structures: equivalenceNetwork EvolutionDiffusion in networks Descriptive ModelingPredictive ModelingCustomer ProfilingNetwork targeting Lab Exercise 1. Assignments and final discussions 2. Web Analytics case studies  
Text Books And Reference Books: 1. Beasley M, (2013), Practical web analytics for user experience: How analytics can help you understand your users. Newnes, 1st edition, Morgan Kaufmann. 2. Sponder M, (2013), Social media analytics: Effective tools for building, interpreting, and using metrics, 1st edition, McGraw Hill Professional. 3. Clifton B, (2012), Advanced Web Metrics with Google Analytics, 3rd edition, John Wiley & Sons..
 
Essential Reading / Recommended Reading 1. Peterson E. T, (2004), Web Analytics Demystified: AMarketer's Guide to Understanding How Your Web Site Affects Your Business. Ingram. 2. Sostre P, LeClaire J, (2007), Web Analytics for dummies, John Wiley & Sons. 3. Burby J, Atchison S, (2007), Actionable web analytics: using data to make smart business decisions, John Wiley & Sons. 4. Dykes B, (2011), Web analytics action hero: Using analysis to gain insight and optimize your business, Adobe Press.  
Evaluation Pattern CIA 50% ESE 50%  
MDS372C  BIO INFORMATICS (2020 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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


Course Outcome 

CO1: To understand the molecular Biology and Bioinformatics applications CO2: Apply the modeling and simulation technologies in Biology and medicine CO3: Evaluate the algorithms to find the similarity between protein and DNA sequences 
Unit1 
Teaching Hours:18 
BIOINFORMATICS


Introduction, Historical Overview and Definition, Applications, Major databases in Bioinformatics, Data management and Analysis, Central Dogma of Molecular Biology. INFORMATION SEARCH AND RETRIEVAL Introduction, Tools for web search, Data retrieval tools, Data mining of Biological databases. Lab Exercise 1. Test and verify the basic Linux commands and Filters. 2. Create the file(s) and verify the file handling commands.  
Unit2 
Teaching Hours:18 
GENOME ANALYSIS AND GENE MAPPING


GENOME ANALYSIS AND GENE MAPPING Introduction, Genome analysis, Genome mapping, Sequence assembly problem, Genetic mapping and linkage analysis, Physical maps, Cloning the entire Genome, Genome sequencing, Applications of Genetic maps, Identification of Genes in Contigs, Human Genome Project. ALIGNMENT OF PAIRS OF SEQUENCES Introduction, Biological motivation of alignment, Methods of sequence alignments, Using score matrices, Measuring sequence detection Lab Exercise 1. Create directories and verify the directory commands. 2. Perform basic mathematical operations using PERL. 3. Write a PERL script to demonstrate the Array operations and Regular expressions.  
Unit3 
Teaching Hours:18 
ALIGNMENT OF MULTIPLE SEQUENCES


ALIGNMENT OF MULTIPLE SEQUENCES Methods of multiple sequence alignment, Evaluating multiple alignments, Applications of multiple alignments, Phylogenetic analysis, Methods of phylogenetic analysis, Tree evaluation, Problems in Phylogenetic analysis. TOOLS FOR SIMILARITY SEARCH AND SEQUENCE ALIGNMENT Introduction, Working with FASTA, Working with BLAST, Filtering and Gapped BLAST, FASTA and BLAST algorithm comparison. Lab Exercise 1. Write a PERL script to concatenate DNA sequences. 2. Write a PERL script to transcribe DNA sequence into RNA sequence 3. Write a PERL script to calculate the reverse complement of a strand of DNA.  
Unit4 
Teaching Hours:18 
PERL FOR BIOINFORMATICS


Sequences and Strings: Representing sequence data, Program to store a DNA sequence, Concatenating DNA fragments, Transcription DNA to RNA, Proteins, Files and Arrays, Reading Proteins in Files, Arrays, Scalar and List Context.
Motifs and Loops: Flow control, Code layout, Finding motifs, Counting Nucleotides, Exploding strings and arrays, Operating on strings. Subroutine and Bugs: Subroutines, Scoping and Subroutines, Command line arguments and Arrays, Passing data to Subroutines, Modules and Libraries of Subroutines. Lab Exercise 1. Write a PERL script to read protein sequence data from a file. 2. Write a PERL script to search for a motif in a DNA sequence.  
Unit5 
Teaching Hours:18 
THE GENETIC CODE


Hashes, Data structure and algorithms for Biology, Translating DNA into Proteins, Reading DNA from the files in FASTA format, Reading Frames. GenBank: GenBank files, GenBank Libraries, Separating Sequence and Annotation, Parsing Annotations, Indexing GenBank with DBM. Protein Data Bank: Files and Folders, PDB Files, Parsing PDB Files. 1. Write a PERL script to append ACGT to DNA using a subroutine. 2 . Case Study: a. To retrieve the sequence of the Human keratin protein from UniProt database and to interpret the results. b. To retrieve the sequence of the Human keratin protein from GenBank database and to interpret the results.  
Text Books And Reference Books: [1] Bioinformatics: Methods and Applications, S. C. Rastogi, Namita Mendirata and Parag Rastogi, 4th Edition, PHI Learning, 2013. [2] Beginning Perl for Bioinformatics, Tisdall James, 1st edition, Shroff Publishers (O’Reilly), 2009.  
Essential Reading / Recommended Reading [1] Introduction to Bioinformatics, Arthur M Lesk, 2nd Edition, Oxford University Press,4th edition, 2014. [2] Bioinformatics Technologies, YiPing Phoebe Chen (Ed), 1st edition, Springer, 2005. [3] Bioinformatics Computing, Bryan Bergeron, 2nd Edition, Prentice Hall, 1st edition, 2003. Web resources: [1] http://cac.annauniv.edu/PhpProject1/aidetails/afug_2013_fu/24.%20BIO%20MED.pdf [2] https://www.amrita.edu/school/biotechnology/academics/pg/introductionbioinformaticsbif410 [3] https://canvas.harvard.edu/courses/8084/assignments/syllabus [4] https://www.coursera.org/specializations/bioinformatics [5] http://www.dtc.ox.ac.uk/modules/introductionbioinformaticsbioscientists.html  
Evaluation Pattern CIA 50% ESE 50%  
MDS372D  EVOLUTIONARY ALGORITHMS (2020 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Course Outcome 

CO1: Basic understanding of evolutionary computing concepts and techniques CO2: Classify relevant realtime problems for the applications of evolutionary algorithms CO3: Design solutions using evolutionary algorithms 
Unit1 
Teaching Hours:18 
Lab Program


1. Implementation of single and multiobjectivefunctions 2. Implementation of binaryGA  
Unit1 
Teaching Hours:18 
INTRODUCTION TO EVOLUTIONARY COMPTUTING


Terminologies – Notations – Problems to be solved – Optimization – Modeling – Simulation – Search problems – Optimization constraints  
Unit2 
Teaching Hours:18 
EVOLUTION STRATEGY


One plus one evolution strategy – The 1/5 Rule – (μ+1) evolution strategy – Self adaptive evolution strategy  
Unit2 
Teaching Hours:18 
Lab Program


1. Implementation of continuousGA 2. Implementation of evolutionaryprogramming  
Unit2 
Teaching Hours:18 
EVOLUTIONARY PROGRAMMING


Continuous evolutionary programming – Finite state machine optimization – Discrete evolutionary programming – The Prisoner’s dilemma  
Unit3 
Teaching Hours:18 
GENETIC PROGRAMMING


Fundamentals of genetic programming – Genetic programming for minimal time control  
Unit3 
Teaching Hours:18 
EVOLUTIONARY ALGORITHM VARIATION


Initialization – Convergence – Population diversity – Selection option – Recombination – Mutation  
Unit3 
Teaching Hours:18 
Lab Program


1. Implementation of geneticprogramming 2. Implementation of Ant ColonyOptimization  
Unit4 
Teaching Hours:18 
Lab Program


1. Implementation of Particle SwarmOptimization 2. Implementation of MultiObjectOptimization  
Unit4 
Teaching Hours:18 
ANT COLONY OPTIMIZATION


Pheromone models – Ant system – Continuous Optimization – Other Ant System  
Unit4 
Teaching Hours:18 
PARTICLE SWARM OPTIMIZATION


Velocity limiting – Inertia weighting – Global Velocity updates – Fully informed Particle Swarm  
Unit5 
Teaching Hours:18 
Lab Program


1. Simulation of EA in Planning problems (routing, scheduling, packing) and Design problems (Circuit, structure,art) 2. Simulation of EA in classification/predictionmodelling  
Unit5 
Teaching Hours:18 
MULTOBJECTIVE OPTIMIATION


Pareto Optimality – Hyper volume – Relative coverage – Nonpareto based EAs – Pareto based EAs – Multiobjective Biogeography based optimization  
Text Books And Reference Books: [1] D. Simon, Evolutionary optimization algorithms: biologically inspired and populationbased approaches to computer intelligence. New Jersey: John Wiley, 2013.  
Essential Reading / Recommended Reading 1. Eiben and J. Smith, Introduction to evolutionary computing. 2nd ed. Berlin: Springer, 2015. 2. D.Goldberg,Geneticalgorithmsinsearch,optimization,andmachinelearning.Boston: AddisonWesley,2012. 3. K. Deb, Multiobjective optimization using evolutionary algorithms. Chichester: John Wiley & Sons,2009. 4. R. Poli, W. Langdon, N. McPhee and J. Koza, A field guide to genetic programming. [S.l.]: Lulu Press,2008. 5. T.Bäck,Evolutionaryalgorithmsintheoryandpractice.NewYork:OxfordUniv.Press, 1996.
Web Resources:
1 E.A.EandS.J.E,"IntroductiontoEvolutionaryComputingTheonline accompaniment to the book Introduction toEvolutionary Computing",Evolutionarycomputation.org,2015.[Online].Available: http://www.evolutionarycomputation.org/. 2 F.Lobo,"EvolutionaryComputation2018/2019",Fernandolobo.info,2018.[Online]. Available:http://www.fernandolobo.info/ec1819. 3 "EClabTools",Cs.gmu.edu,2008.[Online].Available: https://cs.gmu.edu/~eclab/tools.html. 4 "Kanpur Genetic Algorithms Laboratory", Iitk.ac.in, 2008. [Online]. Available: https://www.iitk.ac.in/kangal/codes.shtml. 5 "Course webpage Evolutionary Algorithms", Liacs.leidenuniv.nl, 2017. [Online]. Available:http://liacs.leidenuniv.nl/~csnaco/EA/misc/ga_demo.htm.  
Evaluation Pattern CIA: 50% ESE : 50%  
MDS372E  OPTIMIZATION TECHNIQUE (2020 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

This course will help the students to acquire and demonstrate the implementation of the necessary algorithms for solving advanced level Optimization techniques. 

Course Outcome 

CO1: Apply the notions of linear programming in solving transportation problems CO2: Understand the theory of games for solving simple games CO3: Use linear programming in the formulation of the shortest route problem CO4: Apply algorithmic approach in solving various types of network problems CO5: Create applications using dynamic programming 
Unit1 
Teaching Hours:18 
INTRODUCTION


Operations Research Methods  Solving the OR model  Queuing and Simulation models – Art of modelling – phases of OR study.  
Unit1 
Teaching Hours:18 
MODELLING WITH LINEAR PROGRAMMING


Two variable LP model – Graphical LP solution – Applications. Simplex method and sensitivity analysis – Duality and postoptimal Analysis Formulation of the dual problem. Lab Exercise 1. Simplex Method 2. Dual Simplex Method  
Unit2 
Teaching Hours:18 
TRANSPORTATION MODEL


Determination of the Starting Solution – Iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method – Simplex explanation of the Hungarian Method – The transshipment Model. Lab Exercise 1. Balanced Transportation Problem 2. Unbalanced Transportation Problem 3. Assignment Problems
 
Unit3 
Teaching Hours:18 
CPM and PERT


Network Representation – Critical Path Computations – Construction of the time Schedule – Linear Programming formulation of CPM – PERT networks. Lab Exercise: 1. Shortest path computations in a network 2.Maximum flow problem
 
Unit3 
Teaching Hours:18 
NETWORK MODELS


Minimal Spanning tree Algorithm – Linear Programming formulation of the shortestroute problem. Maximal Flow Model: Enumeration of cuts – Maximal Flow Diagram – Linear Programming Formulation of Maximal Flow Model.  
Unit4 
Teaching Hours:18 
GOAL PROGRAMMING


Formulation – Tax Planning Problem – Goal Programming algorithms – Weights method – Preemptive method. Lab Exercise: 1. Critical path Computations 2. Game Programming  
Unit4 
Teaching Hours:18 
GAME THEORY


Strategic Games and examples  Nash equilibrium and examples  Optimal Solution of two person zero sum games  Solution of Mixed strategy games  Mixed strategy Nash equilibrium  Dominated action with example.  
Unit5 
Teaching Hours:18 
DYNAMIC PROGRAMMING


Recursive nature of computation in Dynamic Programming – Forward and Backward Recursion – Knapsack / Fly Away / CargoLoading Model – Equipment Replacement Model. Lab Exercise: 1. Goal Programming 2. Dynamic Programming  
Unit5 
Teaching Hours:18 
MARKOV CHAINS


Definition – Absolute and nstep Transition Probability – Classification of states.  
Text Books And Reference Books: 1. Hamdy A Taha, Operations Research, 9th Edition, Pearson Education, 2012. 2. Garrido José M. Introduction to Computational Models with Python. CRC Press, 2016.  
Essential Reading / Recommended Reading 1. Rathindra P Sen, Operations Research – Algorithms and Applications, PHI Learning Pvt. Limited, 2011 2. R. Ravindran, D. T. Philips and J. J. Solberg, Operations Research: Principles and Practice, 2nd ed., John Wiley & Sons, 2007. 3. F. S. Hillier and G. J. Lieberman, Introduction to operations research, 8th ed., McGrawHill Higher Education, 2004. 4. K.C. Rao and S. L. Mishra, Operations research, Alpha Science International, 2005. 5. Hart, William E. Pyomo: Optimization Modeling in Python. Springer, 2012. 6. Martin J. Osborne, An introduction to Game theory, Oxford University Press, 2008  
Evaluation Pattern CIA: 50% ESE: 50%  
MDS381  SPECIALIZATION PROJECT (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

The course is designed to provide a realworld project development and deployment environment for the students. 

Course Outcome 

CO1: Identify the problem and relevant analytics for the selected domain CO2: Apply appropriate design/development strategy and tools 
Unit1 
Teaching Hours:60 
Specialization Project


Project will be based on the specialization domains which students are opted for during this semester.  
Text Books And Reference Books:   
Essential Reading / Recommended Reading   
Evaluation Pattern CIA: 50% ESE: 50%  
MDS382  SEMINAR (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:1 
Course Objectives/Course Description 

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


Course Outcome 

CO1: Understand new and latest trends in data science CO2: Demonstrate the professional presentation abilities CO3: Apply the acquired knowledge in their Research 
Unit1 
Teaching Hours:30 
Students will be giving presentations on any advanced concepts and technologies in data science and submit the report


  
Text Books And Reference Books: Research Articles / Books / Web resources related to data science domain  
Essential Reading / Recommended Reading Recommended References  
Evaluation Pattern CIA 100%
 
MDS481  INDUSTRY PROJECT (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:300 
Credits:12 
Course Objectives/Course Description 

This course helps the student to develop students to become globally competent and to inculcate Entrepreneurial skills among students. 

Course Outcome 

CO1: Develop Real time Projects CO2: Practices different data science principles and strategies in the project 
Unit1 
Teaching Hours:30 
Project Work


It is a full time project to be taken up either in the industry or in an R&D organization  
Text Books And Reference Books:   
Essential Reading / Recommended Reading   
Evaluation Pattern CIA: 50% ESE: 50% 