Department of


Syllabus for

1 Semester  2019  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
MDS131  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  I  4  4  100 
MDS132  PROBABILITY AND DISTRIBUTION THEORY  4  4  100 
MDS133  PRINCIPLES OF DATA SCIENCE  4  4  100 
MDS134  RESEARCH METHODOLOGY  2  2  50 
MDS161A  INTRODUCTION TO STATISTICS  2  2  50 
MDS161B  INTRODUCTION TO COMPUTERS AND PROGRAMMING  2  2  50 
MDS161C  LINUX ADMINSTRATION  2  2  50 
MDS171  DATA BASE TECHNOLOGIES  6  5  150 
MDS172  INFERENTIAL STATISTICS LAB  6  5  150 
MDS173  PROGRAMMING FOR DATA SCIENCE IN PYTHON  6  4  100 
2 Semester  2019  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
MDS231  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  II  4  4  100 
MDS232  REGRESSION ANALYSIS  4  4  100 
MDS233  DESIGN AND ANALYSIS OF ALGORITHMS  4  4  100 
MDS234  MACHINE LEARNING  4  4  100 
MDS241A  MULTIVARIATE ANALYSIS  4  4  100 
MDS241B  STOCHASTIC PROCESS  4  4  100 
MDS271  PROGRAMMING FOR DATA SCIENCE IN R  6  4  100 
MDS272A  HADOOP  6  5  150 
MDS272B  IMAGE AND VIDEO ANALYTICS  6  5  150 
MDS272C  INTERNET OF THINGS  6  5  150 
MDS281  RESEARCH PROBLEM IDENTIFICATION AND DATA COLLECTION  1  0  0 
 
Assesment Pattern  
CIA  50% ESE  50%  
Examination And Assesments  
CIA  50% ESE  50%  
Department Overview:  
Department of Computer Science of CHRIST (Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation?s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field.  
Mission Statement:  
Vision
The Department of Computer Science endeavours to imbibe the vision of the University ?Excellence and Service?. The department is committed to this philosophy which pervades every aspect and functioning of the department.
Mission
?To develop IT professionals with ethical and human values?. To accomplish our mission, the department encourages students to apply their acquired knowledge and skills towards professional achievements in their career. The department also moulds the st  
Introduction to Program:  
Data Science is popular in all academia, business sectors, and research and development to make effective decision in day to day activities. MSc in Data Science is a two year programme with four semesters. This programme aims to provide opportunity to all candidates to master the skill sets specific to data science with research bent. The curriculum supports the students to obtain adequate knowledge in theory of data science with hands on experience in relevant domains and tools. Candidate gains exposure to research models and industry standard applications in data science through guest lectures, seminars, projects, internships, etc.  
Program Objective:  
Programme Objective
? To acquire indepth understanding of the theoretical concepts in statistics, data analysis, data mining, machine learning and other advanced data science techniques.
? To gain practical experience in programming tools for data sciences, database systems, machine learning and big data tools.
? To strengthen the analytical and problem solving skill through developing real time applications.
? To empower students with tools and techniques for handling, managing, analyzing and interpreting data.
? To imbibe quality research and develop solutions to the social issues.
Programme Specific Outcomes
PSO1: Abstract thinking: Ability to understand the abstract concepts that lead to various data science theories in Mathematics, Statistics and Computer science.
PSO2: Problem Analysis and Design Ability to identify analyze and design solutions for data science problems using fundamental principles of mathematics, Statistics, computing sciences, and relevant domain disciplines.
PSO3: Modern software tool usage: Acquire the skills in handling data science programming tools towards problem solving and solution analysis for domain specific problems.
PSO4: Innovation And Entrepreneurship: Produce innovative IT solutions and services based on global needs and trends.
PSO5: Societal And Environmental Concern: Utilize the data science theories for societal and environmental concerns.
PSO6: Professional Ethics: Understand and commit to professional ethics and  
MDS131  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  I (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Understand the properties of Vector spaces CO2: Use the properties of Linear Maps in solving problems on Linear Algebra CO3: Demonstrate proficiency on the topics Eigenvalues, Eigenvectors and Inner Product Spaces CO4: Apply mathematics for some applications in Data Science. 
Unit1 
Teaching Hours:15 
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:20 
LINEAR MAPS


Definition of Linear Maps  Algebraic Operations on  Null spaces and Injectivity  Range and Surjectivity  Fundamental Theorems of Linear Maps  Representing a Linear Map by a Matrix  Invertible Linear Maps  Isomorphic Vector spaces  Linear Map as Matrix Multiplication  Operators  Products of Vector Spaces  Product of Direct Sum  Quotients of Vector spaces.  
Unit3 
Teaching Hours:10 
EIGENVALUES, EIGENVECTORS, AND INNER PRODUCT SPACES


Eigenvalues and Eigenvectors  Eigenvectors and Upper Triangular matrices  Eigenspaces and Diagonal Matrices  Inner Products and Norms  Linear functionals on Inner Product spaces.  
Unit4 
Teaching Hours:15 
MATHEMATICS APPLIED TO DATA SCIENCE


Singular value decomposition  Handwritten digits and simple algorithm  Classification of handwritten digits using SVD bases  Tangent distance  Text Mining.  
Text Books And Reference Books: [1] S. Axler, Linear algebra done right, 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 (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Demonstrate the random variables and its functions CO2: Infer the expectations for random variable functions and generating functions. CO3: Demonstrate various discrete and continuous distributions and their usage 
Unit1 
Teaching Hours:10 
ALGEBRA OF PROBABILITY


Algebra of sets  fields and sigma  fields, Inverse function Measurable function – Probability measure on a sigma field – simple properties  Probability space  Random variables and Random vectors – Induced Probability space – Distribution functions –Decomposition of distribution functions.  
Unit2 
Teaching Hours:10 
EXPECTATION AND MOMENTS OF RANDOM VARIABLES


Definitions and simple properties  Moment inequalities – Holder, Jenson Inequalities – Characteristic function – definition and properties – Inversion formula. Convergence of a sequence of random variables  convergence in distribution  convergence in probability almost sure convergence and convergence in quadratic mean  Weak and Complete convergence of distribution functions – Helly  Bray theorem  
Unit3 
Teaching Hours:10 
LAW OF LARGE NUMBERS


Khintchin's weak law of large numbers, Kolmogorov strong law of large numbers (statement only) – Central Limit Theorem – Lindeberg – Levy theorem, Linderberg – Feller theorem (statement only), Liapounov theorem – Relation between Liapounov and Linderberg –Feller forms – Radon Nikodym theorem and derivative (without proof) – Conditional expectation – definition and simple properties.  
Unit4 
Teaching Hours:10 
DISTRIBUTION THEORY


Distribution of functions of random variables – Laplace, Cauchy, Inverse Gaussian, Lognormal, Logarithmic series and Power series distributions  Multinomial distribution  Bivariate Binomial – Bivariate Poisson – Bivariate Normal  Bivariate Exponential of Marshall and Olkin  Compound, truncated and mixture of distributions, Concept of convolution  Multivariate normal distribution (Definition and Concept only)  
Unit5 
Teaching Hours:10 
SAMPLING DISTRIBUTION


Sampling distributions: Non  central chi  square, t and F distributions and their properties  Distributions of quadratic forms under normality independence of quadratic form and a linear form  Cochran’s theorem.  
Unit6 
Teaching Hours:10 
ORDER STATISTICS


Order statistics, their distributions and properties  Joint and marginal distributions of order statistics  Distribution of range and mid range Extreme values and their asymptotic distributions (concepts only)  Empirical distribution function and its properties – Kolmogorov  Smirnov distributions – Life time distributions Exponential and Weibull distributions  Mills ratio – Distributions classified by hazard rate  
Text Books And Reference Books: [1]. Modern Probability Theory, B.R Bhat, New Age International, 4^{th} Edition, 2014. [2]. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3^{rd} 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, 3^{rd} Edition (Reprint), 2017. [2]. Order Statistics, H.A David and H.N Nagaraja, John Wiley & Sons, 3^{rd} Edition, 2003.  
Evaluation Pattern CIA  50% ESE  50%  
MDS133  PRINCIPLES OF DATA SCIENCE (2019 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 it and understand the underlying core concepts and emerging technologies in data science. 

Learning Outcome 

CO1: Understand the fundamental concepts of data science CO2: Evaluate the data analysis techniques for applications handling large data CO3: Demonstrate the various machine learning algorithms used in data science process CO4: Understand the ethical practices of data science CO4:Visualize and present the inference using various tools CO5:Learn to think through the ethics surrounding privacy, data sharing and algorithmic decisionmaking 
Unit1 
Teaching Hours:10 
INTRODUCTION TO DATA SCIENCE


Definition – Big Data and Data Science Hype – Why data science – Getting Past the Hype – The Current Landscape – Data Scientist  Data Science Process Overview – Defining goals – Retrieving data – Data preparation – Data exploration – Data modeling – Presentation.  
Unit2 
Teaching Hours:10 
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:10 
MACHINE LEARNING


Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms.  
Unit4 
Teaching Hours:10 
DEEP LEARNING


Introduction – Deep Feedforward Networks – Regularization – Optimization of Deep Learning – Convolutional Networks – Recurrent and Recursive Nets – Applications of Deep Learning.  
Unit5 
Teaching Hours:10 
DATA VISUALIZATION


Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools – Creating an interactive dashboard with dc.jssummary.  
Unit6 
Teaching Hours:10 
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., 1^{st} edition, 2016 [2]. An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Springer, 1^{st} edition, 2013 [3]. Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 1^{st} edition, 2016 [4]. Ethics and Data Science, D J Patil, Hilary Mason, Mike Loukides, O’ Reilly, 1^{st} edition, 2018  
Essential Reading / Recommended Reading [1]. Data Science from Scratch: First Principles with Python, Joel Grus, O’Reilly, 1^{st} edition, 2015 [2]. Doing Data Science, Straight Talk from the Frontline, Cathy O'Neil, Rachel Schutt, O’ Reilly, 1^{st} edition, 2013 [3]. Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Cambridge University Press, 2^{nd} edition, 2014  
Evaluation Pattern CIA  50% ESE  50%  
MDS134  RESEARCH METHODOLOGY (2019 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. The students are exposed to the principles, procedures and techniques of implementing a research project. The course starts with an introduction to research and leads through the various methodologies involved in the research process. It focus on finding out the research gap from the literature using computer technology,introduces basic statistics required for research and report the research outcomes scientifically with emphasis on research ethics. 

Learning Outcome 

CO1: Understand the essense of research and the necessity of defining a research problem. CO2: Apply research methods and methodology including research design, data 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, 4^{th}ed. 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 (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Demonstrate the history of statistics and present the data in various forms. CO2: Infer the concept of correlation and regression for relating two or more related variables. CO3: Demonstrate the probabilities for various events. 
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 (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Demonstrate the systematic approach for problem solving using computers. CO2: Apply different programming structure with suitable logic for computational problems. 
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 CONCEPTS


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


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


Introduction to programming structure – pointers for structuring a solution – modules and their functions – cohesion and coupling – problem solving with logic structure. Problem solving with decisions – the decision logic structure – straight through logic – positive logic – negative logic – logic conversion – decision tables – case logic structure  examples.  
Text Books And Reference Books: [1] Thomas L.Floyd and R.P.Jain,“Digital Fundamentals”,8th Edition, Pearson Education,2007. [2] Peter Norton “Introduction to Computers”,6th Edition, Tata Mc Graw Hill, New Delhi,2006. [3] Maureen Sprankle and Jim Hubbard, Problem solving and programming concepts, PHI, 9^{th} Edition, 2012  
Essential Reading / Recommended Reading [1]. E Balagurusamy, Fundamentals of Computers, TMH, 2011  
Evaluation Pattern CIA  50% ESE  50%  
MDS161C  LINUX ADMINSTRATION (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

To Enable the students to excel in the Linux Platform 

Learning Outcome 

CO1: Demostrate the systematic approach for configure the Liux environment CO2: Manage the Linux environment to work with open source data science tools 
Unit1 
Teaching Hours:10 
Unit I


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 
UNIT II


Unit3 
Teaching Hours:10 
UNIT  III


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: https://access.redhat.com/documentation/enUS/Red_Hat_Enterprise_Linux/7/  
Essential Reading / Recommended Reading https://access.redhat.com/documentation/enUS/Red_Hat_Enterprise_Linux/7/  
Evaluation Pattern CIA  50% ESE  50%  
MDS171  DATA BASE TECHNOLOGIES (2019 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

The main objective of this course is to fundamental knowledge and practical experience with, database concepts. It includes the concepts and terminologies which facilitate the construction of database tables and write effective queries. Also, to Comprehend Data warehouse and its functions. 

Learning Outcome 

CO1: Design conceptual models of a database using ER modeling CO2: Create and populate a RDBMS for a real life application, with constraints and keys, using SQL CO3: Retrieve any type of information from a data base by formulating complex queries in SQL CO4: Demonstrate various databases CO5: Distinguish database from data warehouse and examine ETL process 
Unit1 
Teaching Hours:16 
INTRODUCTION


Concept & Overview of DBMS, Data Models, Database Languages, Database Administrator, Database Users, Three Schema architecture of DBMS. Basic concepts, Design Issues, Mapping Constraints, Keys, EntityRelationship Diagram, Weak Entity Sets, Extended ER features Lab Exercises 1. Data Definition, 2. Table Creation 3. Specification of Constraints  
Unit2 
Teaching Hours:16 
RELATIONAL MODEL AND DATABASE DESIGN


SQL and Integrity Constraints, Concept of DDL, DML, DCL. Basic Structure, Set operations, Aggregate Functions, Null Values, Domain Constraints, Referential Integrity Constraints, assertions, views, Nested Subqueries, Functional Dependency, Different anomalies in designing a Database, Normalization : using functional dependencies, BoyceCodd Normal Form, 4NF, 5NF Lab Exercises 1. Insert, Select, Update & Delete Commands 2. Nested Queries & Join Queries 3. Views  
Unit3 
Teaching Hours:10 
INTELLIGENT DATABASES


Active databases, Deductive Databases, Knowledge bases, Multimedia Databases, Multidimensional Data Structures, Image Databases, Text/Document Databases, Video Databases, Audio Databases, Multimedia Database Design.  
Unit4 
Teaching Hours:16 
DATA WAREHOUSE: THE BUILDING BLOCKS


Defining Features, Data Warehouses and Data Marts, Architectural Types, Overview of the Components, Metadata in the Data warehouse, Data Design and Data Preparation: Principles of Dimensional Modeling, Dimensional Modeling Advanced Topics From Requirements To Data Design, The Star Schema, Star Schema Keys, Advantages of the Star Schema, Star Schema: Examples, Dimensional Modeling: Advanced Topics, Updates to the Dimension Tables, Miscellaneous Dimensions, The Snowflake Schema, Aggregate Fact Tables, Families Oo Stars  
Unit5 
Teaching Hours:16 
REQUIREMENTS, REALITIES, ARCHITECTURE AND DATA FLOW


Requirements, ETL Data Structures, Extracting, Cleaning and Conforming, Delivering Dimension Tables, Delivering Fact Tables (CH:1,2,3,4,5,6) Lab Exercises: 1. Importing source data structures 2. Design Target Data Structures 3. Create target structure 4. Design and build the ETL mapping  
Unit6 
Teaching Hours:16 
IMPLEMENTATION, OPERATIONS AND ETL SYSTEMS:


Development, Operations, Metadata, RealTime ETL Systems. (CH:7,8,9,11)
Lab Exercises: 1. Perform the ETL process and transform into data map 2. Create the cube and process it 3. Generating Reports 4. Creating the Pivot table and pivot chart using some existing data  
Text Books And Reference Books: [1]. Henry F. Korth and Silberschatz Abraham, “Database System Concepts”, Mc.Graw Hill. [2]. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education, 2007. [3]. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd John Wiley & Sons, Inc. New York, USA, 2002  
Essential Reading / Recommended Reading [1] LiorRokach and OdedMaimon, Data Mining and Knowledge Discovery Handbook, Springer, 2nd edition, 2010.  
Evaluation Pattern CIA  50% ESE  50%  
MDS172  INFERENTIAL STATISTICS LAB (2019 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Demonstrate the concepts of point and interval estimation of unknown parameters and their significance using large and small samples. CO2: Apply the idea of sampling distributions of difference statistics in testing of hypotheses. CO3: Infer the concept of nonparametric tests for single sample and two samples. 
Unit1 
Teaching Hours:15 
SUFFICIENT STATISTICS


Neyman  Fisher Factorisation theorem  the existence and construction of minimal sufficient statistics  Minimal sufficient statistics and exponential family  sufficiency and completeness  sufficiency and invariance. Lab Exercise
 
Unit2 
Teaching Hours:15 
UNBIASED ESTIMATION


Minimum variance unbiased estimation  locally minimum variance unbiased estimators  Rao Blackwell – theorem – Completeness: Lehmann Scheffe theorems  Necessary and sufficient condition for unbiased estimators  Cramer Rao lower bound  Bhattacharya system of lower bounds in the 1parameter regular case  Chapman Robbins inequality Lab Exercise
 
Unit3 
Teaching Hours:15 
MAXIMUM LIKELIHOOD ESTIMATION


Computational routines  strong consistency of maximum likelihood estimators  Asymptotic Efficiency of maximum likelihood estimators  Best Asymptotically Normal estimators  Method of moments  Bayes’ and minimax estimation: The structure of Bayes’ rules  Bayes’ estimators for quadratic and convex loss functions  minimax estimation  interval estimation. Lab Exercise
 
Unit4 
Teaching Hours:15 
HYPOTHESIS TESTING


Uniformly most powerful tests  the NeymanPearson fundamental Lemma  Distributions with monotone likelihood ratio  Problems  Generalization of the fundamental lemma, two sided hypotheses  testing the mean and variance of a normal distribution. Lab Exercise
 
Unit5 
Teaching Hours:15 
MEAN TESTS


Unbiasedness for hypotheses testing  similarity and completeness  UMP unbiased tests for multi parameter exponential families  comparing two Poisson or Binomial populations  testing the parameters of a normal distribution (unbiased tests)  comparing the mean and variance of two normal distributions  Symmetry and invariance  maximal invariance  most powerful invariant tests. Lab Exercise
 
Unit6 
Teaching Hours:15 
SEQUENTIAL TESTS


SPRT procedures  likelihood ratio tests  locally most powerful tests  the concept of confidence sets  non parametric tests. Lab Exercise
 
Text Books And Reference Books: [1]. Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd, New Delhi, 2012. [2]. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3rd Edition, 2015.  
Essential Reading / Recommended Reading [1]. Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGrawHill, 3rd Edition (Reprint), 2017. [2]. Linear Statistical Inference and its Applications, Rao C.R, Willy Publications, 2nd Edition, 2001.  
Evaluation Pattern CIA  50% ESE  50%  
MDS173  PROGRAMMING FOR DATA SCIENCE IN PYTHON (2019 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Demonstrate the usage of builtin objects in Python CO2: Analyze the significance of python program development environment by working on real world examples 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 Exercises 1. Demonstrate usage of branching and looping statements 2. Demonstrate Recursive functions 3. Demonstrate Lists  
Unit2 
Teaching Hours:17 
SEQUENCE DATATYPES AND OBJECTORIENTED PROGRAMMING


Sequences, Mapping and Sets Dictionaries Classes: Classes and InstancesInheritanceExceptional HandlingIntroduction to Regular Expressions using “re” module. Lab Exercises 1. Demonstrate Tuples and Sets 2. Demonstrate Dictionaries 3. Demonstrate inheritance and exceptional handling 4. Demonstrate use of “re”.  
Unit3 
Teaching Hours:13 
USING NUMPY


Basics of NumPyComputation on NumPyAggregationsComputation on ArraysComparisons, Masks and Boolean ArraysFancy IndexingSorting ArraysStructured Data: NumPy’s Structured Array. Lab Exercises 1. Demonstrate Aggregation 2. Demonstrate Indexing and Sorting  
Unit4 
Teaching Hours:13 
DATA MANIPULATION WITH PANDAS I


Introduction to Pandas ObjectsData indexing and SelectionOperating on Data in PandasHandling Missing DataHierarchical Indexing  Combining Data Sets Lab Exercises 1. Demonstrate handling of missing data 2. Demonstrate hierarchical indexing  
Unit5 
Teaching Hours:17 
DATA MANIPULATION WITH PANDAS II


Aggregation and GroupingPivot TablesVectorized String Operations Working with Time SeriesHigh Performance Pandas and query() Lab Exercises 1. Demonstrate usage of Pivot table 2. Demonstrate use of and query()  
Unit6 
Teaching Hours:13 
VISUALIZATION AND MATPLOTLIB


Basic functions of matplotlibSimple Line Plot, Scatter PlotDensity and Contour PlotsHistograms, Binnings and DensityCustomizing Plot Legends, Colour BarsThreeDimensional Plotting in Matplotlib. Lab Exercises 1. Demonstrate Scatter Plot 2. Demonstrate 3D plotting  
Text Books And Reference Books: [1]. Jake VanderPlas ,Python Data Science Handbook  Essential Tools for Working with Data, O’Reily Media,Inc, 2016 [2]. Zhang.Y ,An Introduction to Python and Computer Programming, Springer Publications,2016  
Essential Reading / Recommended Reading [1]. Joel Grus ,Data Science from Scratch First Principles with Python, O’Reilly Media,2016 [2]. T.R.Padmanabhan, Programming with Python,Springer Publications,2016  
Evaluation Pattern CIA 100%  
MDS231  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  II (2019 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 the basic notions of multivariable calculus and graph theory in applications to Data Science. 

Learning Outcome 

CO1: Understand the properties of multivariable calculus CO2: Understand the properties of graphs CO2: Apply mathematics for some applications in Data Science 
Unit1 
Teaching Hours:15 
CALCULUS OF SEVERAL VARIABLES


Functions of Several Variables  Limits and continuity in HIgher Dimensions  Partial Derivatives  The Chain Rule  Directional Derivative and Gradient vectors  Tangent Planes and Differentials  Extreme Values and Saddle Points  Lagrange Multipliers.  
Unit2 
Teaching Hours:10 
INTRODUCTION TO CONVEX OPTIMIZATION


Affine and Convex Sets  Hyperplanes and halfspaces  Euclidean balls and ellipsoids  Norm balls and Norm cones  polyhedra  simplexs  The positive definite cone. separating and supporting hyperplanes.  
Unit3 
Teaching Hours:10 
NORMS AND INNER PRODUCT SPACES


Introduction  Inequalities on Linear Spaces  Norms on Linear Spaces  Inner products  Orthogonality  Unitary and Orthogonal Matrices  norms for matrices  
Unit4 
Teaching Hours:13 
BASIC GRAPH THEORY


Graphs  subgraphs  factors  Paths  cycles  connectedness  trees  Euler tours  Hamiltonian cycles  Planar Graphs  Digraphs.  
Unit5 
Teaching Hours:12 
ALGORITHMS AND COMPLEXITY


Algorithms  Representing Graphs  The algorithm of Hierholzer  Writing algorithms  Complexity of Algorithms.  
Text Books And Reference Books: [1]. M. D. Weir, J. Hass, and G. B. Thomas, Thomas' calculus. Pearson, 2016. [2]. S. P. Boyd and L. Vandenberghe, Convex optimization. Cambridge Univ. Pr., 2011. [3]. D. Jungnickel, Graphs, networks and algorithms. Springer, 2014.  
Essential Reading / Recommended Reading [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.  
Evaluation Pattern CIA  50% ESE  50%  
MDS232  REGRESSION ANALYSIS (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Demonstrate deeper understanding of the linear regression model. CO2: Evaluate 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:15 
SIMPLE LINEAR REGRESSION


Introduction to regression analysis: Modelling a response, overview and applications of regression analysis, major steps in regression analysis. Simple linear regression (Two variables): assumptions, estimation and properties of regression coefficients, significance and confidence intervals of regression coefficients, measuring the quality of the fit.  
Unit2 
Teaching Hours:15 
MULTIPLE LINEAR REGRESSION


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:10 
CRITERIA FOR MODEL SELECTION


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


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


Introduction to nonlinear regression, Least squares in the nonlinear case and estimation of parameters, Models for binary response variables, estimation and diagnosis methods for logistic and Poisson regressions. Prediction and residual analysis.  
Text Books And Reference Books: [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%  
MDS233  DESIGN AND ANALYSIS OF ALGORITHMS (2019 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 introduce the methods to analyze and evaluate the performance of an algorithm. It introduces the different design techniques for designing efficient algorithms. 

Learning Outcome 

CO1: Demonstrate their ability to apply appropriate Data Structures to solve problems. CO2: Design and develop algorithms using various design techniques. CO3: Evaluate the efficiency of Algorithms by analyzing the running time of algorithms for problems in various domain 
Unit1 
Teaching Hours:10 
INTRODUCTION


Algorithm Specification – Analysis of Insertion sort – Performance Analysis  Space complexity – Time Complexity – Asymptotic notations – Amortized Analysis  
Unit2 
Teaching Hours:10 
DIVIDE & CONQUER and GREEDY APPROACH


Divide and Conquer – Binary search – Quick sort – Strassen’s Matrix Multiplication Greedy Approach – Knapsack problem – Minimum cost spanning tree – PRIM’s and Kruskal’s Algorithm – single source shortest path.  
Unit3 
Teaching Hours:10 
DYNAMIC PROGRAMMING AND BACKTRACKING


– Dynamic Programming – All pairs shortest path – longest common sequence  The general method – 8 Queens problem – Sum of subsets  
Unit4 
Teaching Hours:10 
BRANCH AND BOUND TECHNIQUES


Branch and Bound – 0/1 knapsack problem – Travelling salesperson problem  
Unit5 
Teaching Hours:10 
NP HARD and NP COMPLETE PROBLEMS


Basic Concepts – NP hard Graph problems  NP Hard Scheduling problems – NP hard code generation problems  
Unit6 
Teaching Hours:10 
ADVANCED TECHNIQUES


Approximation Algorithms – Polynomial time approximation schemes – PRAM Algorithms – Computational model – merge sort – Mesh Algorithms – Computational model – odd–even merge in a mesh – Hypercube Algorithms – Computational model – merge sort  
Text Books And Reference Books: [1]. Horowitz, Sahni, Rajasekaran, Fundamentals of Computer Algorithms, Universities Press Pvt Ltd, second edition , 2010. [2]. Coremen T H, Leiserson C E, Rivest R L and Stein, Clifford, Introduction to Algorithms, PHI, Third Edition, 2010.  
Essential Reading / Recommended Reading [1]. Donald E. Knuth, The Art of Computer Programming Volume 3, Sorting and Searching, 2^{nd} Edition, Pearson Education, AddisonWesley, 1997. [2]. GAV PAI, Data structures and Algorithms, Tata McGraw Hill, Jan 2008  
Evaluation Pattern CIA  50% ESE  50%  
MDS234  MACHINE LEARNING (2019 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 sound foundation to fundamental concepts of machine learning and its application and prepare students for advanced research and real time problem solving in machine learning and related fields. 

Learning Outcome 

CO1: Understand a wide variety of learning algorithms. CO2: Apply a variety of learning algorithms to different domain data. CO3: Analyse and perform evaluation of learning algorithms and model selection. 
Unit1 
Teaching Hours:8 
INTRODUCTION


Machine LearningExamples of Machine ApplicationsLearning AssociationsClassificationRegressionUnsupervised LearningReinforcement Learning. Supervised Learning: Learning class from examplesVapnikChervonenkis DimensionProbably Approach Corre(PAC)LearningNoiseLearning Multiple classes.RegressionModel Selection and Generalization.  
Unit2 
Teaching Hours:8 
PARAMETRIC METHODS


Introduction to Parametric methodsMaximum Likelihood Estimation:Bernoulli DensityMultinomial DensityGaussian Density.Evaluating an Estimator:Bias and VarianceThe Bayes EstimatorParametric Classification.  
Unit3 
Teaching Hours:8 
NONPARAMETRIC METHODS


IntroductionNonparametric Density Estimation: Histogram EstimatorKernel EstimatorKNearest Neighbour EstimatorGeneralization to Multivariate DataNonparametric ClassificationDistance Based ClassificationOutlier Detection.  
Unit4 
Teaching Hours:12 
MULTIVARIATE METHODS & DIMENSIONALITY REDUCTION


Multivariate DataParameter EstimationEstimation of Missing ValuesMultivariate Normal Distribution Multivariate ClassificationTuning ComplexityDiscrete Features. Dimensionality Reduction: Introduction Subset SelectionPrincipal Component Analysis, Feature EmbeddingFactor AnalysisSingular Value DecompositionMultidimensional ScalingLinear Discriminant AnalysisCanonical Correlation AnalysisLaplacian Eigenmaps  
Unit5 
Teaching Hours:12 
SUPERVISED LEARNING


Linear Discrimination:Introduction Generalizing the Linear ModelGeometry of the Linear Discriminant Pairwise SeparationGradient DescentLogistic Discrimination. Bayesian Estimation:IntroductionEstimating the Parameter of a Discrete DistributionBayesian Estimation of the Parameters of a Gaussian DistributionBayesian Estiamtion of the Parameters of a FunctionBayesian ClassificationBayesian Models Comparison.  
Unit6 
Teaching Hours:12 
UNSUPERVISED LEARNING


Clustering:IntroductionMixture Densities, KMeans Clustering ExpectationMaximization algorithm Mixtures of Latent Varaible ModelsSupervised Learning after ClusteringSpectral ClusteringHierachial ClusteringClustering, Choosing the number of Clusters.  
Text Books And Reference Books: [1]. E. Alpaydin, Introduction to Machine Learning, 3^{rd} Edition, MIT Press, 2014.  
Essential Reading / Recommended Reading [1]. C.M. Bishop, Pattern Recognition and Machine Learning, 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, Machine Learning:A Probabilistic Perspective, MIT Press, 2012.  
Evaluation Pattern CIA  50% ESE  50%  
MDS241A  MULTIVARIATE ANALYSIS (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Understand multivariate data structure, multinomial and multivariate normal distribution CO2: Apply Multivariate analysis of variance (MANOVA) of one and 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 (2019 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. 

Learning Outcome 

CO1: Demonstrate the concepts of point and interval estimation of unknown parameters and their significance using large and small samples. CO2: Apply the idea of sampling distributions of difference statistics in testing of hypotheses. CO3: Infer the concept of nonparametric tests for single sample and two samples. 
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%  
MDS271  PROGRAMMING FOR DATA SCIENCE IN R (2019 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Learning Outcome 

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


Download and install R – R IDE environments – Why R – Getting started with R – Vectors and Data Frames – Loading Data Frames – Data analysis with summary statistics and scatter plots – Summary tables  Working with Script Files Linear Regression – Introduction – Regression model for one variable regression – Selecting best model – Error measures SSE, SST, RMSE, R^{2} – Interpreting R^{2 }– Multiple linear regression – Lasso and ridge regression – Correlation – Recitation – A minimum of 3 data sets for practice  
Unit2 
Teaching Hours:18 
LOGISTIC REGRESSION


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


Using text as data – Text analytics – Natural language processing – Bag of words – Stemming – word clouds – Recitation – min 3 data sets for practice – Time series analysis – Clustering – kmean clustering – Random forest with clustering – Understanding cluster patterns – Impact of clustering – Heatmaps – Recitation – min 3 data sets for practice  
Unit5 
Teaching Hours:18 
ENSEMBLE MODELING


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].Handson programming with R, Garrett Grolemund, O’Reilley, 1^{st} Edition, 2014 [2]. R for everyone, Jared Lander, Pearson, 1^{st} Edition, 2014  
Essential Reading / Recommended Reading [1]. Statistics : An Introduction Using R, Michael J. Crawley, WILEY, Second Edition, 2015.  
Evaluation Pattern CIA  50% ESE  50%  
MDS272A  HADOOP (2019 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. 

Learning Outcome 

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

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 (2019 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. 

Learning Outcome 

CO1: Understand the fundamental principles of image and video analysis CO2: Apply the image and video analysis approaches to solve real world problems 
Unit1 
Teaching Hours:15 
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. Binary Image Processing: Image Thresholding, Region labeling, Binary Image Morphology. Lab Exercise: 1. Implement basic grayscale and binary processing  image histogram, image labeling, image thresholding 2. Implementing Image Database Analysis  
Unit2 
Teaching Hours:15 
IMAGE AND VIDEO ENHANCEMENT AND RESTORATION


Spatial domain  Linear and Nonlinear Filtering, Morphological filtering, Frequency domain – Homomorphic Filtering, Blotch Detection and Removal  Blotch Detection, Motion Vector Repair and Interpolating Corrupted Intensities, Intensity Flicker Correction  Flicker Parameter Estimation, Brief introduction towards Wavelets, Wavelet based image denoising, Basic methods for image restoration using deconvolution filters. Lab Exercise: 1. Extraction of frames from videos and analyzing frames 2. Implement spatial domain  linear and nonlinear filtering  
Unit3 
Teaching Hours:15 
IMAGE ANALYSIS


Image Compression: Huffman coding, Run length coding, LZW coding, Lossless Coding, Wavelets based image compression. Lab Exercise 1. 1. Frequency domain – homomorphic filtering on gray scale and color images 2. 2. mplement image restoration methods on images  
Unit4 
Teaching Hours:15 
VIDEO ANALYSIS


Video Compression: Basic Concepts and Techniques of Video Coding and the H.264 Standard, MPEG1 and MPEG2 Video Standards Lab Exercise: 1. 1. Implement flicker correction on video datasets 2. 2. mplement multiresolution image decomposition and reconstruction using wavelet  
Unit5 
Teaching Hours:15 
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 Exercise: 1. Implement image compression using wavelets 2. Implement image segmentation using thresholding  
Unit6 
Teaching Hours:15 
OBJECT DETECTION AND RECOGNITION


Object detection and recognition in image and video, basic texture descriptors –GLCM, LBP and its applications in image and video analysis, object tracking in videos. Lab Exercise 1. Implement Local Binary Pattern texture descriptor  
Text Books And Reference Books: [1] Alan Bovik, Handbook of Image and Video Processing, Second Edition, Academic Press, 2005. [2] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Third Edition, Pearson Education, 2008. [3] Richard Szeliski, Computer Vision – Algorithms and Applications, Springer, 2011.  
Essential Reading / Recommended Reading [1] Anil K Jain, Fundamentals of Digital Image Processing, PHI, 2011. [2] Oge Marques, Practical Image and Video Processing Using MatLab, Wiley, 2011. [3] John W. Woods, Multidimensional Signal, Image, Video Processing and Coding, Academic Press, 2006.  
Evaluation Pattern CIA  50% ESE  50%  
MDS272C  INTERNET OF THINGS (2019 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Learning Outcome 

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


Introduction to IoT  Definition and Characteristics, Physical Design Things Protocols, Logical Design Functional Blocks, Communication Models Communication APIs Introduction to measure the physical quantities, IoT Enabling Technologies  Wireless Sensor Networks, Cloud Computing Big Data Analytics, Communication Protocols Embedded System IoT Levels and Deployment Templates. lab Exercise: 1. Introduction to ICs and Sensors. A basic program can be shown which makes use of logic gates ICs for understanding the basics of sensor nodes. Different sensors which find application in IoT projects can be shown, their working explained. 2. Introduction to Arduino/Raspberry Pi. Sample sketches or code can be selected from the Arduino software and executed, making use of different sensors.  
Unit2 
Teaching Hours:18 
IoT PROGRAMMING


Introduction to Smart Systems using IoT  IoT Design Methodology IoT Boards (Rasberry Pi, Arduino) and IDE  Case Study: Weather Monitoring Logical Design using Python, Data types & Data Structures Control Flow, Functions Modules Packages, File Handling  Date/Time Operations, Classes Python Packages of Interest for IoT. Lab Exercise 1. 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. 2. 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. Lab Exercise: 1. 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. 2. 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.  
Unit4 
Teaching Hours:18 
NETWORK OF WIRELESS SENSOR NODES


Sensing and Sensors  Wireless Sensor Networks, Challenges and Constraints  Applications: Structural Health Monitoring, Traffic Control, Health Care  Node Architecture  Operating system. Lab Exercise: 1. A basic obstacle avoiding robot by making use of Ultrasonic sensors, dc motors, and the chassis kit for robotic car. 2. 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. 3. 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.  
Unit5 
Teaching Hours:18 
MAC, ROUTING AND TRANSPORT CONTROL IN WSN


Introduction – Fundamentals of MAC Protocols – MAC protocols for WSN – Sensor MAC Case Study – Routing Challenges and Design Issues – Routing Strategies – Transport Control Protocols – Transport Protocol Design Issues – Performance of Transport Protocols Lab Exercise: 1. 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. 2. 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. 3. Implement 3bit Binary Counter using 3 LED Module.
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] ArshdeepBahga and Vijay Madisetti, Internet of Things: Handson Approach, Hyderabad University Press, 2015. [2] KazemSohraby, Daniel Minoli and TaiebZnati, Wireless Sensor Networks: Technology. Protocols and Application, Wiley Publications, 2010. [3] WaltenegusDargie and Christian Poellabauer, Fundamentals of Wireless Sensor Networks: Theory and Practice, AJohn 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] ErdalÇayırcıandChunmingRong, Security in Wireless Ad Hoc and Sensor Networks, John Wiley and Sons, 2009. [5] Carlos De MoraisCordeiro and Dharma PrakashAgrawal, Ad Hoc and Sensor Networks: Theory and Applications, World Scientific Publishing, 2011. [6] WaltenegusDargie and Christian Poellabauer, Fundamentals of Wireless Sensor Networks Theory and Practice, John Wiley and Sons, 2010 [7] Adrian Perrig and J. D. Tygar, Secure Broadcast Communication: In Wired and Wireless Networks, Springer, 2006.  
Evaluation Pattern CIA  50% ESE  50%  
MDS281  RESEARCH PROBLEM IDENTIFICATION AND DATA COLLECTION (2019 Batch)  
Total Teaching Hours for Semester:15 
No of Lecture Hours/Week:1 
Max Marks:0 
Credits:0 
Course Objectives/Course Description 

This research inclusive curriculum is designed with two main objectives: 1. Inculcating research culture among the post graduate students. 2.Enhancing employability skills of students by providing necessary research foundation 

Learning Outcome 

CO1: Carry out research work with data collection and result validations CO2: Understand the basics of research data collection and research paper writing. 
Unit1 
Teaching Hours:15 
ResearchProblem Identification


There is only CIA for this course. Students should do a thorough literature review in their research area. They should give a presentation and submit a document containing the following: Introduction to topic, existing scenario and applications (5 marks) Literature review (Minimum 25 references) (15 marks) Existing Model and Methodology (5 marks) Concrete problem statement definition (10 marks)  
Text Books And Reference Books:   
Essential Reading / Recommended Reading   
Evaluation Pattern CIA  50% ESE  50% 