CHRIST (Deemed to University), Bangalore

DEPARTMENT OF STATISTICS

School of Sciences

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

 
1 Semester - 2022 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MDS131 MATHEMATICAL FOUNDATION FOR DATA SCIENCE - I Core Courses 4 4 100
MDS132 PROBABILITY AND DISTRIBUTION THEORY Core Courses 4 4 100
MDS133 PRINCIPLES OF DATA SCIENCE Core Courses 4 4 100
MDS134 RESEARCH METHODOLOGY Core Courses 2 2 50
MDS161A INTRODUCTION TO STATISTICS Discipline Specific Elective Courses 2 2 50
MDS161B INTRODUCTION TO COMPUTERS AND PROGRAMMING Discipline Specific Elective Courses 2 2 50
MDS161C LINUX ADMINISTRATION Discipline Specific Elective Courses 2 2 50
MDS171 DATA BASE TECHNOLOGIES Core Courses 6 5 150
MDS171Y DATA BASE TECHNOLOGIES Discipline Specific Elective Courses 6 5 150
MDS172 INFERENTIAL STATISTICS Core Courses 6 5 150
MDS172Y INFERENTIAL STATISTICS Discipline Specific Elective Courses 6 6 150
MDS173 PROGRAMMING FOR DATA SCIENCE IN PYTHON Core Courses 6 4 100
MDS173Y PROGRAMMING FOR DATA SCIENCE IN PYTHON Discipline Specific Elective Courses 6 6 100
2 Semester - 2022 - 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 WEB ANALYTICS 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 JAVA PROGRAMMING Core Courses 5 4 100
MDS281 SEMINAR Core Courses 2 1 50
3 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MDS331 NEURAL NETWORKS AND DEEP LEARNING Core Courses 4 4 100
MDS341A TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES Discipline Specific Elective Courses 4 4 100
MDS341B BAYESIAN INFERENCE Discipline Specific Elective Courses 4 4 100
MDS341C ECONOMETRICS Discipline Specific Elective Courses 4 4 100
MDS341D BIO-STATISTICS Discipline Specific Elective Courses 4 4 100
MDS371 CLOUD ANALYTICS Core Courses 6 5 150
MDS372 JAVA PROGRAMMING Core Courses 5 4 100
MDS372A NATURAL LANGUAGE PROCESSING Discipline Specific Elective 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 - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MDS481 INDUSTRY PROJECT Discipline Specific Elective 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.

Programme Specific Outcome:

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 cyber regulations, responsibilities, and norms of professional computing practices.

PSO7: Conduct Investigations of complex computing problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.

PSO8: Individual and Team work: Function effectively as an individual and as a member or leader in diverse teams and in multidisciplinary environments.

PSO9: Applications in Multi disciplinary domains: Understand the role of statistical approaches and apply the same to solve the real life problems in the fields of data science.

PS10: Project Management: Apply the research-based knowledge to analyse and solve advanced problems in data science.

Assesment Pattern

CIA - 50%

ESE - 50%

Examination And Assesments

CIA - 50%

ESE - 50%

MDS131 - MATHEMATICAL FOUNDATION FOR DATA SCIENCE - I (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand the properties of Vector 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

Unit-1
Teaching Hours:12
INTRODUCTION TO VECTOR SPACES
 

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

Unit-2
Teaching Hours:12
LINEAR MAPS
 

DefinitionofLinearMaps-AlgebraicOperationson L(V,W) - Null spaces and Injectivity-RangeandSurjectivity-FundamentalTheoremsofLinearMaps-Representing aLinearMapbyaMatrix-InvertibleLinearMaps-IsomorphicVectorspaces-LinearMap as Matrix Multiplication - Operators - Products of Vector Spaces - Product of Direct Sum - Quotients of Vector spaces.

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 (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Describe random event and probability of 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

Unit-1
Teaching Hours:12
DESCRIPTIVE STATISTICS AND PROBABILITY
 

Data – types of variables: numeric vs categorical - measures of central tendency – measures of dispersion - random experiment - sample space and random events – probability - probability axioms - finite sample space with equally likely outcomes - conditional probability - independent events - Baye’s theorem

Unit-2
Teaching Hours:12
PROBABILITY DISTRIBUTIONS FOR DISCRETE DATA
 

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

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 McGraw-Hill, 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 (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Explore the fundamental concepts of data 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 decision-making

Unit-1
Teaching Hours:10
INTRODUCTION TO DATA SCIENCE
 

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

Unit-2
Teaching Hours:12
BIG DATA
 

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

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 (2022 Batch)

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

Course Objectives/Course Description

 

This course is intended to assist students in planning and carrying out research work.The students are exposed to the basic principles, procedures and techniques of implementing a research project.

To introduce the research concept and the various research methodologies is the main objective. It focuses on finding out the research gap from the literature and encourages lateral, strategic and creative thinking. This course also introduces computer technology and basic statistics required for research and reporting the research outcomes scientifically emphasizing on research ethics.

 

Course Outcome

CO1: Understand the essence of research and the necessity of defining a research problem.

CO2: Apply research methods and methodology including research design, data collection, data analysis, and interpretation.

CO3: Create scientific reports according to specified standards.

Unit-1
Teaching Hours:8
RESEARCH METHODOLOGY
 

Defining research problem:Selecting the problem, Necessity of defining the problem ,Techniques involved in defining a problem- Ethics in Research.

Unit-2
Teaching Hours:8
RESEARCH DESIGN
 

Principles of experimental design,Working with Literature: Importance, finding literature, Using your resources, Managing the literature, Keep track of references,Using the literature, Literature review,On-line Searching: Database ,SCIFinder, Scopus, Science Direct ,Searching research articles , Citation Index ,Impact Factor ,H-index.

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 (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Demonstrate the history of statistics and present the data in various forms.

CO2: Infer the concept of correlation and regression for relating two or more related variables.

CO3: Demonstrate the probabilities for various events.

Unit-1
Teaching Hours:8
ORGANIZATION AND PRESENTATION OF DATA
 

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

Unit-2
Teaching Hours:8
DESCRIPTIVE STATISTICS
 

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

Text Books And Reference Books:

[1]. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, John Wiley & Sons Inc., New Jersey, 2015.

[2]. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11th edition, Sultan Chand & Sons, New Delhi, 2014.

Essential Reading / Recommended Reading

[1]. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015.

[2]. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017.

[3]. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013.

[4]. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi, 2008.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS161B - INTRODUCTION TO COMPUTERS AND PROGRAMMING (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

Unit-1
Teaching Hours:10
COMPUTERS AND DIGITAL BASICS
 

Number Representation – Decimal, Binary, Octal, Hexadecimal and BCD numbers – Binary Arithmetic – Binary addition – Unsigned and Signed numbers – one’s and two’s complements of Binary numbers – Arithmetic operations with signed numbers - Number system conversions – Boolean Algebra – Logic gates – Design of Circuits – K - Map

Unit-2
Teaching Hours:5
GENERAL PROBLEM SOLVING CONCEPT
 

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

Text Books And Reference Books:

[1] Thomas L.Floyd and R.P.Jain,“Digital Fundamentals”,8th Edition, Pearson Education,2007.

[2] Peter Norton “Introduction to Computers”,6th Edition, Tata Mc Graw Hill, New Delhi,2006.

[3] Maureen Sprankle and Jim Hubbard, Problem-solving and programming concepts, PHI, 9th Edition, 2012

Essential Reading / Recommended Reading

[1]. E Balagurusamy, Fundamentals of Computers, TMH, 2011

 

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS161C - LINUX ADMINISTRATION (2022 Batch)

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

Course Objectives/Course Description

 

To Enable the students to excel in the Linux Platform

Course Outcome

CO1: Demostrate the systematic approach for configure the Liux environment

CO2: Manage the Linux environment to work with open source data science tools

Unit-1
Teaching Hours:10
Module-1
 

RHEL7.5,breaking root password, Understand and use essential tools for handling files, directories, command-line environments, and documentation - Configure local storage using partitions and logical volumes

Unit-2
Teaching Hours:10
Module-2
 

Swapping, Extend LVM Partitions,LVM Snapshot - Manage users and groups, including use of a centralized directory for authentication

Text Books And Reference Books:

1.    https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/7/

2.    https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/7/

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA:50%

ESE:50%

MDS171 - DATA BASE TECHNOLOGIES (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Demonstrate various databases and Compose effective queries

CO2: Understanding the process of OLAP system construction

CO3: Develop applications using Relational and NoSQL databases.

Unit-1
Teaching Hours:18
INTRODUCTION
 

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

 Lab Exercises

1. Data Definition,

2. Table Creation

3. Constraints

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

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

 Lab Exercises

1. Insert, Select, Update & Delete Commands

2. Nested Queries & Join Queries

3. Views

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%

MDS171Y - DATA BASE TECHNOLOGIES (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

Unit-1
Teaching Hours:18
INTRODUCTION
 

INTRODUCTION

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

Lab Exercises

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

RELATIONAL MODEL AND DATABASE DESIGN

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

Lab Exercises

1. Insert, Select, Update & Delete Commands

2. Nested Queries & Join Queries

3. Views

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 (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Demonstrate the concepts of population and samples

CO2: Apply the idea of sampling distribution of different statistics in testing of hypothesis

CO3: Test the hypothesis using nonparametric tests for real world problems

C04: Estimate the unknown population parameters using the concepts of point and interval estimations.

Unit-1
Teaching Hours:18
INTRODUCTION
 

Population and Statistics – Finite and Infinite population – Parameter and Statistics – Types of sampling - Sampling Distribution – Sampling Error - Standard Error – Test of significance –concept of hypothesis – types of hypothesis – Errors in hypothesis-testing – Critical region – level of significance - Power of the test – p-value.

Lab Exercise:

1. Calculation of sampling error and standard error

2. Calculation of probability of critical region using standard distributions

3. Calculation of power of the test using standard distributions.

Unit-2
Teaching Hours:18
TESTING OF HYPOTHESIS I
 

Concept of large and small samples – Tests concerning a single population mean for known σ – equality of two means for known σ – Test for Single variance - Test for equality of two variance for normal population – Tests for single proportion – Tests of equality of two proportions for the normal population.

 

Lab Exercise:

4. Test of the single sample mean for known σ.

5. Test of equality of two means when known σ

6. Tests of single variance and equality of variance for large samples

7. Tests for single proportion and equality of two proportion for large samples.

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%

MDS172Y - INFERENTIAL STATISTICS (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

C1: Demonstrate the concepts of population and samples.

C2: Apply the idea of sampling distribution of different statistics in testing of hypothesis

C3: Test the hypothesis using nonparametric tests for real world problems.

C4: Estimate the unknown population parameters using the concepts of point and interval estimations.

Unit-1
Teaching Hours:18
INTRODUCTION
 

Population and Statistics – Finite and Infinite population – Parameter and Statistics – Types of sampling - Sampling Distribution – Sampling Error - Standard Error – Test of significance –concept of hypothesis – types of hypothesis – Errors in hypothesis-testing – Critical region – level of significance - Power of the test – p-value.

Lab Exercise:

1. Calculation of sampling error and standard error

2. Calculation of probability of critical region using standard distributions

3. Calculation of power of the test using standard distributions.

Unit-2
Teaching Hours:18
TESTING OF HYPOTHESIS I
 

Concept of large and small samples – Tests concerning a single population mean for known σ – equality of two means for known σ – Test for Single variance - Test for equality of two variance for normal population – Tests for single proportion – Tests of equality of two proportions for the normal population.

Lab Exercise:

4. Test of the single sample mean for known σ.

5. Test of equality of two means when known σ

6. Tests of single variance and equality of variance for large samples

7. Tests for single proportion and equality of two proportions for large samples.

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 (2022 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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

Course Outcome

CO1: Demonstrate the use of built-in objects of Python

CO2: Demonstrate significant experience with python program development environment

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

Unit-1
Teaching Hours:17
INTRODUCTION TO PYTHON
 

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

 

Lab Exercises

1.      Demonstrate usage of branching and loopingstatements

2.      Demonstrate Recursivefunctions

3.      DemonstrateLists

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

 

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

Lab Exercises

1.      Demonstrate Tuples andSets

2.      DemonstrateDictionaries

3.      Demonstrate inheritance and exceptionalhandling

4.      Demonstrate use of“re”

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%

 

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

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Demonstrate the use of built-in objects of Python

CO2: Demonstrate significant experience with python program development environment

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

Unit-1
Teaching Hours:17
INTRODUCTION TO PYTHON
 

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

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

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

Unit-2
Teaching Hours:17
Lab Exercises
 
  1. Demonstrate Tuples and Sets 
  2. Demonstrate Dictionaries
  3. Demonstrate inheritance and exception handling
  4. Demonstrate use of “re”
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, Data Science from Scratch First Principles with Python, O’Reilly, Media,2016

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


Evaluation Pattern
CIA:  50%

ESE: 50%

MDS231 - MATHEMATICAL FOUNDATION FOR DATA SCIENCE - II (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Demonstrate the properties of multivariate calculus

CO2: Use the idea of orthogonality and projections effectively

CO3: Have a clear understanding of Convex Optimization

C04: Know the about the basic terminologies and properties in Graph Theory

Unit-1
Teaching Hours:14
Calculus of Several Variables
 

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

Unit-2
Teaching Hours:10
Orthogonality
 

Perpendicular vectors and Orthogonality - Inner Products and Projections onto lines - Projections of Rank one - Projections and Least Squares Approximations - Projection Matrices - Orthogonal Bases, Orthogonal Matrices and Gram-Schmidt orthogonalization

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 (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Demonstrate deeper understanding of the linear regression model.

CO2: Evaluate R-square criteria for model selection

CO3: Understand the forward, backward and stepwise methods for selecting the variables

CO4: Understand the importance of multicollinearity in regression modelling

CO5: Ability touse and understand generalizations of the linear model to binary and count data

Unit-1
Teaching Hours:13
SIMPLE LINEAR REGRESSION
 

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

Unit-2
Teaching Hours:13
MULTIPLE LINEAR REGRESSION
 

Multiple linear regression model: assumptions, ordinary least square estimation of regression coefficients, interpretation and properties of regression coefficient, significance and confidence intervals of regression coefficients.

Text Books And Reference Books:

[1].D.C Montgomery, E.A Peck and G.G Vining, Introduction to Linear Regression Analysis, John Wiley and Sons,Inc.NY, 2003.

[2]. S. Chatterjee and AHadi, Regression Analysis by Example, 4th Ed., John Wiley and Sons, Inc, 2006

[3].Seber, A.F. and Lee, A.J. (2003) Linear Regression Analysis, John Wiley, Relevant sections from chapters 3, 4, 5, 6, 7, 9, 10.

Essential Reading / Recommended Reading

[1]. Iain Pardoe, Applied Regression Modeling, John Wiley and Sons, Inc, 2012.

[2]. P. McCullagh, J.A. Nelder, Generalized Linear Models, Chapman & Hall, 1989.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS241A - MULTIVARIATE ANALYSIS (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand multivariate data structure, multinomial and multivariate normal distribution

CO2: Apply Multivariate analysis of variance (MANOVA) of one and two-way classified data.

Unit-1
Teaching Hours:12
INTRODUCTION
 

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

Unit-2
Teaching Hours:12
DISTRIBUTION
 

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

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 (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Demonstrate the concepts of point and interval estimation of unknown parameters and their significance using large and small samples.

CO2: Apply the idea of sampling distributions of difference statistics in testing of hypotheses.

CO3: Infer the concept of nonparametric tests for single sample and two samples.

Unit-1
Teaching Hours:12
INTRODUCTION TO STOCHASTIC PROCESSES
 

Classification of Stochastic Processes, Markov Processes – Markov Chain - Countable State Markov Chain. Transition Probabilities, Transition Probability Matrix. Chapman - Kolmogorov's Equations, Calculation of n - step Transition Probability and its limit.

Unit-2
Teaching Hours:12
POISSON PROCESS
 

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

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 (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Describe the categorical response.

CO2: Identify tests for contingency tables.

CO3: Apply regression models for categorical response variables.

CO4: Analyse contingency tables using log-linear models.

Unit-1
Teaching Hours:12
INTRODUCTION
 

Categorical response data - Probability distributions for categorical data - Statistical inference for discrete data

Unit-2
Teaching Hours:12
CONTINGENCY TABLES
 

Probability structure for contingency tables - Comparing proportions with 2x2 tables - The odds ratio - Tests for independence - Exact inference - Extension to three-way and larger tables

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 (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand the basic principles of machine learning techniques.

CO2: Understand how machine learning problems are formulated and solved.

CO3: Apply machine learning algorithms to solve real world problems.

Unit-1
Teaching Hours:18
INRTODUCTION
 

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

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

 Lab Exercise:

1.      Data Exploration using parametric methods

2.      Data Exploration using non-parametric methods

3.      Regression analysis

Unit-2
Teaching Hours:18
DIMENSIONALITY REDUCTION
 

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

Lab Exercise:

1.      Data reduction using Principal ComponentAnalysis

2.      Data reduction using multi-dimensional scaling

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 - WEB ANALYTICS (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1 : Understand the concept and importance of Web analytics in an organization and the role of Web analytic in collecting, analyzing and reporting website traffic.

CO2: Identify key tools and diagnostics associated with Web analytics.

CO3: Explore effective Web analytics strategies and implementation and Understand the importance of web analytic as a tool for e-Commerce, business research, and market research.

Unit-1
Teaching Hours:18
INTRODUCTION TO WEB ANALYTICS
 

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

Lab Exercise

1. Working concept of web analytics

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

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

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

Lab Exercise

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

2. Delivering reports based on collected data

3. Implement the concept of web analytics ecosystem

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%

MDS272B - IMAGE AND VIDEO ANALYTICS (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand the fundamental principles of image and video analysis

CO2: Apply the image and video analysis approaches to solve real world problems

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

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.

Gray-Level Processing: Image Histogram, Linear and Non-linear 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, Gray-scale and color images using various methods.

2. Program to implement contrast stretching.

 

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

Spatial domain-Linear and Non-linear 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 Built-in and user defined functions.

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

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 (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand the concepts of IoT and IoT enabling technologies

CO2: Gain knowledge on IoT programming and able to develop IoT applications

CO3: Identify different issues in wireless ad hoc and sensor networks

CO4: Develop an understanding of sensor network architectures from a design and performance perspective

CO5: Understand the layered approach in sensor networks and WSN protocols

Unit-1
Teaching Hours:18
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.

Unit-1
Teaching Hours:18
Introduction to IOT
 

Introduction to IoT - Definition and Characteristics, Physical Design Things- Protocols, Logical Design- Functional Blocks, Communication Models- Communication APIs- Introductiontomeasurethephysicalquantities,IoTEnablingTechnologies-WirelessSensor Networks, Cloud Computing Big Data Analytics, Communication Protocols- Embedded System- IoT Levels and DeploymentTemplates.

Unit-2
Teaching Hours:18
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.

Unit-2
Teaching Hours:18
IOT Programming
 

Introduction to Smart Systems using IoT - IoT Design Methodology- IoT Boards (Raspberry Pi,Arduino)andIDE-CaseStudy:WeatherMonitoring-LogicalDesignusingPython, Data types & Data Structures- Control Flow, Functions- Modules- Packages, File Handling - Date/Time Operations, Classes- Python Packages of Interest forIoT.

Text Books And Reference Books:

[1]   Arshdeep Bahgaand, Vijay Madisetti, Internet of Things: Hands-on Approach, Hyderabad University Press, 2015.

[2]   Kazem Sohraby, Daniel Minoli and TaiebZnati, Wireless Sensor Networks: Technology. Protocols and Application, Wiley Publications, 2010.

[3]    Waltenegus Dargie and Christian Poellabauer, Fundamentals of Wireless Sensor Networks: Theory and Practice, A John Wiley and Sons Ltd., 2010.

Essential Reading / Recommended Reading

[1]    Edgar Callaway, Wireless Sensor Networks: Architecture and Protocols, Auerbach Publications, 2003.

[2]   Michael Miller, The Internet of Things, Pearson Education, 2015.

[3]    Holger Karl and Andreas Willig, Protocols and Architectures for Wireless Sensor Networks, John Wiley & Sons Inc., 2005.

[4]   Erdal Çayırcı and Chunming Rong, SecurityinWirelessAdHocandSensorNetworks,John Wiley and Sons, 2009.

[5]   Carlos De MoraisCordeiro and Dharma Prakash Agrawal, Ad Hoc and Sensor Networks: Theory and Applications, World Scientific Publishing, 2011.


[6]   Adrian Perrig and J.D.Tygar, Secure Broadcast Communication: In Wired and Wireless Networks, Springer, 2006.

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS273 - JAVA PROGRAMMING (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

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

Unit-1
Teaching Hours:9
INTRODUCTION-Overview of JVM & JAVA Basics
 

Overview of JVM

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

Java Basics

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

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

Inheritance in Java

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

Interfaces and Packages

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

Exception Handling in Java

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

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

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

Online Resources:

1.      www.w3cschools.com

2.      www.javatpoint.com

3.      http://stackoverflow.com/

Evaluation Pattern

CIA-50%

ESE-50%

MDS281 - SEMINAR (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1 : Understand new and latest trends in data science.

CO2: Demonstrate the professional presentation abilities.

CO3: Apply the acquired knowledge in their Research.

Unit-1
Teaching Hours:30
Seminar
 

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

Text Books And Reference Books:

Research Articles / Books / Web resources related to data science domain

Essential Reading / Recommended Reading

Recommended References

Evaluation Pattern

CIA-100%

MDS331 - NEURAL NETWORKS AND DEEP LEARNING (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand the major technology trends in neural networks and deep learning

CO2: Build, train and apply neural networks and fully connected deep neural networks

CO3: Implement efficient (vectorized) neural networks for real time application

Unit-1
Teaching Hours:12
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
 

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

Unit-2
Teaching Hours:12
SUPERVISED LEARNING NETWORK
 

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

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

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

Text Books And Reference Books:

1. S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition, 2018.

2. Dr. S Lovelyn Rose, Dr. L Ashok Kumar, Dr. D Karthika Renuka, Deep Learning Using Python, Wiley-India, 1st Edition, 2019. 

Essential Reading / Recommended 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 (2021 Batch)

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

Course Objectives/Course Description

 

This course covers applied statistical methods pertaining to time series and forecasting techniques. Moving average models like simple, weighted and exponential are dealt with. Stationary time series models and non-stationary time series models like AR, MA, ARMA and ARIMA are introduced to analyse time series data.

Course Outcome

CO1: Ability to approach and analyze univariate time series

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

CO3: Evaluate stationary and non-stationary time series models

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

Unit-1
Teaching Hours:12
INTRODUCTION TO TIME SERIES AND STOCHASTIC PROCESS
 

Introduction to time series and stochastic process, graphical representation, components and classical decomposition of time series data.Auto-covariance and auto-correlation functions, Exploratory time series analysis, Test for trend and seasonality, Smoothing techniques such as Exponential and moving average smoothing, Holt- Winter smoothing, Forecasting based on smoothing.

Unit-2
Teaching Hours:12
STATIONARY TIME SERIES MODELS
 

Wold representation of linear stationary processes, Study of linear time series models: Autoregressive, Moving Average and Autoregressive Moving average models and their statistical properties like ACF and PACF function.

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, Springer-Verlag, 2009. 

4.      S.C. Gupta and V.K. Kapoor, Fundamentals of Applied Statistics, 4th Edition, Sultan Chand and Sons, 2008.

Evaluation Pattern

CIA: 50%

ESE: 50%

 

MDS341B - BAYESIAN INFERENCE (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand Bayesian models and their specific model assumptions.

CO2: Identify suitable informative and non-informative prior distributions to derive posterior distributions

CO3: Apply computer intensive methods like MCMC for approximating the posterior distribution.

CO4: Analyse the results obtained by Bayesian methods.

Unit-1
Teaching Hours:12
INTRODUCTION
 

Basics on minimaxity: subjective and frequents probability, Bayesian inference, Bayesian estimation , prior distributions, posterior distribution, loss function, principle of minimum expected posterior loss, quadratic and other common loss functions, Advantages of being a Bayesian HPD confidence intervals, testing, credible intervals, prediction of a future observation.

Unit-2
Teaching Hours:12
BAYESIAN ANALYSIS WITH PRIOR INFORMATION
 

Robustness and sensitivity, classes of priors, conjugate class, neighbourhood class, density ratio class different methods of objective priors: Jeffrey’s prior, probability matching prior, conjugate priors and mixtures, posterior robustness: measures and techniques

Text Books And Reference Books:

1. Albert Jim (2009) Bayesian Computation with R, second edition, Springer, New York

2. Bolstad W. M. and Curran, J.M. (2016) Introduction to Bayesian Statistics 3rd Ed. Wiley, New York

3. Christensen R. Johnson, W. Branscum A. and Hanson T.E. (2011) Bayesian Ideas and data analysis : A introduction for scientist and Statisticians, Chapman and Hall, London 

4. A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin (2004). Bayesian Data Analysis, 2nd Ed. Chapman & Hall

Essential Reading / Recommended Reading

 1. Congdon P. (2006) Bayesian Statistical Modeling, Wiley, New York.

2. Ghosh, J.K. Delampady M. and T. Samantha (2006). An Introduction to Bayesian Analysis: Theory and Methods, Springer, New York.

3. Lee P.M. (2012) Bayesian Statistics: An Introduction-4th Ed. Hodder Arnold, New York.

4. Rao C.R. Day D. (2006) Bayesian Thinking, Modeling and Computation, Handbook of Statistics, Vol.25.

Evaluation Pattern

CIA: 50%

ESE: 50%

MDS341C - ECONOMETRICS (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Demonstrate Simple and multiple Econometric models

CO2: Interpret the models adequacy through various methods

CO3: Demonstrate simultaneous Linear Equations model.

Unit-1
Teaching Hours:15
INTRODUCTION
 

Introduction to Econometrics- Meaning and Scope – Methodology of Econometrics – Nature and Sources of Data for Econometric analysis – Types of Econometrics

Unit-2
Teaching Hours:15
CORRELATION
 

Aitken’s Generalised Least Squares(GLS) Estimator, Heteroscedasticity, Auto-correlation, Multicollinearity, Auto-Correlation, Test of Auto-correlation, Multicollinearity, Tools for Handling Multicollinearity

Text Books And Reference Books:

1.  Johnston, J. (1997). Econometric Methods, Fourth Edition, McGraw Hill

2.  Gujarathi, D., and Porter, D. (2008). Basic Econometrics, Fifth Edition, McGraw-Hill

Essential Reading / Recommended Reading

1.   Intriligator, M. D. (1980). Econometric Models-Techniques and Applications, Prentice Hall.

2.  Theil, H. (1971). Principles of Econometrics, John Wiley.

3.  Walters, A. (1970). An Introduction to Econometrics, McMillan and Co.

Evaluation Pattern

CIA : 50%

ESE : 50%

MDS341D - BIO-STATISTICS (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Demonstrate the understanding of basic concepts of biostatistics and the process involved in the scientific method of research.

CO2: Identify how the data can be appropriately organized and displayed.

CO3: Interpret the measures of central tendency and measures of dispersion.

CO4: Interpret the data based on the discrete and continuous probability distributions.

CO5: Apply parametric and non-parametric methods of statistical data analysis.

Unit-1
Teaching Hours:12
INTRODUCTION TO BIOSTATISTICS
 

Presentation of data - graphical and numerical representations of data - Types of variables, measures of location - dispersion and correlation - inferential statistics - probability and distributions - Binomial, Poisson, Negative Binomial, Hyper geometric and normal distribution.

Unit-2
Teaching Hours:12
PARAMETRIC AND NON - PARAMETRIC METHODS
 

Parametric methods - one sample t-test - independent sample t-test - paired sample t-test - one-way analysis of variance - two-way analysis of variance - analysis of covariance - repeated measures of analysis of variance - Pearson correlation coefficient - Non-parametric methods: Chi-square test of independence and goodness of fit - Mann Whitney U test - Wilcoxon signed-rank test - Kruskal Wallis test - Friedman’s test - Spearman’s correlation test.

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 (2021 Batch)

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

Course Objectives/Course Description

 

The objective of this course is to explore the basics of cloud analytics and the major cloud solutions. Students will learn how to analyze extremely large data sets, and to create visual representations of that data. Also aim to provide students with hands-on experience working with data at scale.

Course Outcome

CO1: Interpret the deployment and service models of cloud applications.

CO2: Describe big data analytical concepts.

CO3: Ingest, store, and secure data.

CO4: Process and Visualize structured and unstructured data.

Unit-1
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

Unit-2
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

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 Hands-On 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%

MDS372 - JAVA PROGRAMMING (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

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

Unit-1
Teaching Hours:15
Lab Exercises
 

1.  Implement the concept of class, data members, member functions and access specifiers.

2. Implement the concept of function overloading & Constructor overloading

3. Implement the static keyword – static variable, static block, static function and static class

4. Implement String and String Buffer classes.

Unit-1
Teaching Hours:15
OVERVIEW OF JVM AND JAVA BASICS
 

Overview of JVM

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

Java Basics

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

Unit-2
Teaching Hours:15
INHERITANCE, INTERFACES & PACKAGES AND EXCEPTION HANDLING IN JAVA
 

Inheritance in Java

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

Interfaces and Packages

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

Exception Handling in Java

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

Unit-2
Teaching Hours:15
Lab Exercises
 

 1.         Implement this keyword and command line arguments.

 

2.         Implement the concept of inheritance, super, abstract and final keywords

3.         Implement package and interface

4.         Implement Exception Handing in java

 

 

 

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

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

1.      www.w3cschools.com

2.      www.javatpoint.com

3.      http://stackoverflow.com/

Evaluation Pattern

CIA - 50%

ESE - 50%

MDS372A - NATURAL LANGUAGE PROCESSING (2021 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 real-time applications

Unit-1
Teaching Hours:18
INTRODUCTION
 

Introduction to NLP- Background and overview- NLP Applications -NLP hard Ambiguity- Algorithms and models,Knowledge Bottlenecks in NLP-Introduction to NLTK,Case study.

 Lab Exercises:

1.     Write a program to tokenize text

2.     Write a program to count word frequency and toremove stopwords

Unit-2
Teaching Hours:18
PARSING AND SYNTAX
 

WordLevelAnalysis: RegularExpressions,Text Normalization, Edit Distance, Parsing and Syntax-Spelling, Error Detection and correction-Words and Word classes-Part-of speech Tagging, Naive Bayes and Sentiment Classification: Case study

 Lab Exercises:

 1.     Write a program to tokenize Non-English Languages

 2.     Write a program to get synonyms from WordNet

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: Human-computer 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 Kit-http://www.nltk.org

Evaluation Pattern

CIA:50%

ESE:50%

MDS372B - WEB ANALYTICS (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand the concept and importance of Web analytics in an organization and the role of Web analytic in collecting, analyzing and reporting website traffic.

CO2: Identify key tools and diagnostics associated with Web analytics.

CO3: Explore effective Web analytics strategies and implementation and Understand the importance of web analytic as a tool for e-Commerce, business research, and market research.

Unit-1
Teaching Hours:18
INTRODUCTION TO WEB ANALYTICS
 

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

Lab Exercise

1. Working concept of web analytics

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

 

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

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

Lab Exercise

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

2. Delivering reports based on collected data

3. Implement the concept of web analytics ecosystem

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 (2021 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.

Unit-1
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.

Unit-2
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.

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, Yi-Ping 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/introduction-bioinformaticsbif410

[3] https://canvas.harvard.edu/courses/8084/assignments/syllabus

[4] https://www.coursera.org/specializations/bioinformatics

[5] http://www.dtc.ox.ac.uk/modules/introduction-bioinformatics-bioscientists.html 

Evaluation Pattern

CIA 50%

ESE 50%

MDS372D - EVOLUTIONARY ALGORITHMS (2021 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 real-time problems applicable with evolutionary computing.

Course Outcome

CO1: Basic understanding of evolutionary computing concepts and techniques

CO2: Classify relevant real-time problems for the applications of evolutionary algorithms.

CO3: Design solutions using evolutionary algorithms

Unit-1
Teaching Hours:18
Lab Program
 

1.     Implementation of single and multi-objectivefunctions

2.     Implementation of binaryGA

Unit-1
Teaching Hours:18
INTRODUCTION TO EVOLUTIONARY COMPTUTING
 

Terminologies – Notations – Problems to be solved – Optimization – Modeling – Simulation

– Search problems – Optimization constraints

Unit-2
Teaching Hours:18
EVOLUTIONARY PROGRAMMING
 

Continuous evolutionary programming – Finite state machine optimization – Discrete evolutionary programming – The Prisoner’s dilemma

Unit-2
Teaching Hours:18
Lab Program
 

1.     Implementation of continuousGA

2.     Implementation of evolutionaryprogramming

Unit-2
Teaching Hours:18
EVOLUTION STRATEGY
 

One plus one evolution strategy – The 1/5 Rule – (μ+1) evolution strategy – Self adaptive evolution strategy

Text Books And Reference Books:

[1] D. Simon, Evolutionary optimization algorithms: biologically inspired and population-based 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: Addison-Wesley,2012.

3.     K. Deb, Multi-objective 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,"IntroductiontoEvolutionaryComputing|Theon-line 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 (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 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.

Unit-1
Teaching Hours:18
MODELLING WITH LINEAR PROGRAMMING
 

Two variable LP model – Graphical LP solution – Applications. Simplex method and sensitivity analysis – Duality and post-optimal Analysis- Formulation of the dual problem.

Lab Exercise

 1.    Simplex Method 

 2.   Dual Simplex Method

Unit-1
Teaching Hours:18
INTRODUCTION
 

Operations Research Methods - Solving the OR model - Queuing and Simulation models – Art of modelling – phases of OR study.

Unit-2
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 trans-shipment Model. 

Lab Exercise

1.   Balanced Transportation Problem

2.   Unbalanced Transportation Problem

3.   Assignment Problems

 

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., McGraw-Hill 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 (2021 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 real-world 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.

Unit-1
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 (2021 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

Unit-1
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 (2021 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.

Unit-1
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%