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1 Semester - 2024 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MDS131 | RESEARCH METHODS IN DATA SCIENCE | Core Courses | 5 | 4 | 100 |
MDS132 | PROBABILITY AND DISTRIBUTION THEORY | Core Courses | 5 | 4 | 100 |
MDS133 | MATHEMATICAL FOUNDATIONS FOR DATA SCIENCE-I | Core Courses | 4 | 3 | 100 |
MDS151P | APPLIED EXCEL | Core Courses | 3 | 1 | 50 |
MDS161A | PRINCIPLES OF PROGRAMMING | Discipline Specific Elective Courses | 3 | 2 | 50 |
MDS161B | INTRODUCTION TO PROBABILITY AND STATISTICS | Discipline Specific Elective Courses | 3 | 2 | 50 |
MDS161C | LINUX ESSENTIALS | Discipline Specific Elective Courses | 3 | 2 | 50 |
MDS171 | PROGRAMMING USING PYTHON | Core Courses | 7 | 4 | 100 |
2 Semester - 2024 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MDS231 | DESIGN AND ANALYSIS OF ALGORITHMS | Core Courses | 4 | 3 | 100 |
MDS232 | MATHEMATICAL FOUNDATIONS FOR DATA SCIENCE-II | Core Courses | 4 | 3 | 100 |
MDS271 | DATABASE TECHNOLOGIES | Core Courses | 7 | 4 | 100 |
MDS272 | INFERENTIAL STATISTICS USING R | Core Courses | 7 | 4 | 100 |
MDS273 | FULL STACK WEB DEVELOPMENT | Core Courses | 7 | 4 | 100 |
3 Semester - 2024 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MDS311 | CLOUD SERVICES | - | 3 | 2 | 50 |
MDS331 | REGRESSION MODELLING | - | 4 | 3 | 100 |
MDS341A | CATEGORICAL DATA ANALYSIS | - | 4 | 3 | 100 |
MDS341B | MULTIVARIATE ANALYSIS | - | 4 | 3 | 100 |
MDS341C | STOCHASTIC PROCESSES | - | 4 | 3 | 100 |
MDS371 | JAVA PROGRAMMING | - | 7 | 4 | 100 |
MDS372 | MACHINE LEARNING | - | 7 | 4 | 100 |
MDS381 | SEMINAR | - | 3 | 2 | 50 |
4 Semester - 2023 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MDS411 | DATA DRIVEN MODELLING AND VISUALIZATION | Core Courses | 3 | 2 | 50 |
MDS431 | TIME SERIES AND FORECASTING TECHNIQUES | Core Courses | 5 | 4 | 100 |
MDS471 | NEURAL NETWORKS AND DEEP LEARNING | Core Courses | 7 | 4 | 100 |
MDS472A | WEB ANALYTICS | Discipline Specific Elective Courses | 6 | 3 | 100 |
MDS472B | IOT ANALYTICS | Discipline Specific Elective Courses | 6 | 3 | 100 |
MDS472C | NATURAL LANGUAGE PROCESSING | Discipline Specific Elective Courses | 6 | 3 | 100 |
MDS472D | GRAPH ANALYTICS | Discipline Specific Elective Courses | 6 | 3 | 100 |
MDS481 | PROJECT-I (WEB PROJECT WITH DATA SCIENCE CONCEPTS) | Core Courses | 5 | 2 | 100 |
MDS482 | RESEARCH PROBLEM IDENTIFICATION | Core Courses | 3 | 1 | 50 |
5 Semester - 2023 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MDS531A | ECONOMETRICS | Discipline Specific Elective Courses | 5 | 4 | 100 |
MDS531B | BAYESIAN INFERENCE | Discipline Specific Elective Courses | 5 | 4 | 100 |
MDS531C | BIO-STATISTICS | Discipline Specific Elective Courses | 5 | 4 | 100 |
MDS571 | BIG DATA ANALYTICS | Core Courses | 7 | 4 | 100 |
MDS572A | EVOLUTIONARY ALGORITHMS | Discipline Specific Elective Courses | 6 | 3 | 100 |
MDS572B | QUANTUM MACHINE LEARNING | Discipline Specific Elective Courses | 6 | 3 | 100 |
MDS572C | REINFORCEMENT LEARNING | Discipline Specific Elective Courses | 6 | 3 | 100 |
MDS573A | GEOSPATIAL DATA ANALYTICS | Discipline Specific Elective Courses | 6 | 3 | 100 |
MDS573B | BIO-INFORMATICS | Discipline Specific Elective Courses | 6 | 3 | 100 |
MDS573C | IMAGE AND VIDEO ANALYTICS | Discipline Specific Elective Courses | 6 | 3 | 100 |
MDS581 | PROJECT - II (RESEARCH PROJECT_ DATA SCIENCE CAPSTONE PROJECT) | Core Courses | 5 | 2 | 100 |
6 Semester - 2023 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MDS681 | INDUSTRY PROJECT | - | 3 | 10 | 300 |
MDS682 | RESEARCH PUBLICATION | - | 3 | 2 | 50 |
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Introduction to Program: | |
Data Science is popular in all academia, business sectors, and research and development to makeeffective decision in day to day activities. MSc in Data Science is a two year programme with six trimesters. This programme aims to provideopportunity to all candidates to master the skill setsspecific to data science with research bent. The curriculum supports the students to obtain adequateknowledge in theory of data science with hands on experience in relevant domains and tools. Candidategains exposure to research models and industry standard applications in data science through guestlectures,seminars,projects,internships,etc. | |
Programme Outcome/Programme Learning Goals/Programme Learning Outcome: PO1: 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.PO2: Enhance disciplinary competency and employability: Acquire the skills in handling data science programming tools towards problem solving and solution analysis for domain specific problems. PO3: Societal and Environmental Concern: Utilize the data science theories for societal and environmental concerns. PO4: Professional Ethics: Understand and commit to professional ethics and professional computing practices to enhance research culture and uphold the scientific integrity and objectivity. PO5: Individual and Team work: Function effectively as an individual and as a member or leader in diverse teams and in multidisciplinary environments. PO6: Engage in continuous reflective learning in the context of technology advancement: Understand the evolving data and analysis paradigms and apply the same to solve the real life problems in the fields of data science. | |
Assesment Pattern | |
CIA - 50% ESE - 50% | |
Examination And Assesments | |
Evaluation pattern for full CIA courses:
The “Theory and Practical” Type of courses offered in all UG/PG programmes will be considered as Full CIA courses.
For this type of courses, there is no exclusive Mid Semester Examination and End Semester Examination; instead there shall be a continuous evaluation during the semester as,
CAC – Continuous Assessment Component Assessment components such as Hard copy / Soft copy Assignment, Quiz, Presentation, Video Making, MOOC, Project, Demonstration, Service Learning, etc CAT – Continuous Assessment Test A written / Lab test would be conducted on any working day
The total marks for the full CIA courses would vary based on the number of hours allocated in a week for the respective course. Out of the maximum marks allotted to the respective course, 50% marks will be considered as CIA and remaining 50% as ESE based on the combinations of the evaluation components (CAC and CAT) . |
MDS131 - RESEARCH METHODS IN DATA SCIENCE (2024 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:5 |
Max Marks:100 |
Credits:4 |
Course Objectives/Course Description |
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To assist students in planning and carrying out research work in the field of data science. The students are exposed to the basic principles, procedures and techniques of implementing a research project. The course provides a strong foundation for data science and the application area related to it. Students are trained to understand the underlying core concepts and the importance of ethics while handling data and problems in data science. |
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Course Outcome |
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CO1: Understand the essence of research and the importance of research methods and methodology CO2: Explore the fundamental concepts of data science CO3: Understand various machine learning algorithms used in data science process CO4: Learn to think through the ethics surrounding privacy, data sharing and algorithmic decision making CO5: Create scientific reports according to specified standards |
Unit-1 |
Teaching Hours:12 |
Research Methodology
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Introduction: Objectivesof Research, Types of Research,Research Approaches, Significanceof Research, Research Methods versus Methodology. Defining research problem: Selecting the problem, Necessity of defining the problem, Techniques involved in defining a problem, Research Design: Different Research Designs, Basic Principles of Experimental Designs, Developing a Research Plan. | |
Unit-2 |
Teaching Hours:12 |
Introduction to Data Science
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Definition – Big Data and Data Science Hype – Why data science – Getting Past the Hype – The Current Landscape – Who is a Data Scientist? - Data Science Process Overview – Defining goals – Retrieving data – Data preparation – Data exploration – Data modeling – Presentation. | |
Unit-2 |
Teaching Hours:12 |
Sampling, Measurement and Scaling Techniques
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Sampling: Steps in Sampling Design, Different Types of Sample Designs, Measurement and Scaling: Measurement in Research, Measurement Scales, Technique of Developing Measurement Tools, Scaling, Important Scaling Techniques | |
Unit-3 |
Teaching Hours:12 |
Machine Learning
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Machine learning – Modeling Process – Training model – Validating model – Predicting new observations – Supervised learning algorithms–Unsupervised learning algorithms. | |
Unit-4 |
Teaching Hours:12 |
Report Writing
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Working with Literature: Importance, finding literature, Using the resources, Managing the literature, Keep track of references, Literature review. 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. | |
Unit-5 |
Teaching Hours:12 |
Ethics in Research and Data Science
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Research ethics, Data Science ethics – Doing good data science – Owners of the data - Valuing different aspects of privacy - Getting informed consent - The Five Cs – Diversity – Inclusion. | |
Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern CIA - 50% ESE - 50% | |
MDS132 - PROBABILITY AND DISTRIBUTION THEORY (2024 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:5 |
Max Marks:100 |
Credits:4 |
Course Objectives/Course Description |
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Probability and probability distributions play an essential role in modeling data from the realworld phenomenon. This course will equip students with thorough knowledge in probability and various probability distributions and model real-life data sets with an appropriate probability distribution |
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Course Outcome |
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CO1: able to understand the concept of the random variable and expectation for discrete and continuous data CO2: evaluate condition probabilities and conditional expectations CO3: identify the applications of continuous distributions in Data Science CO4: apply Cheby-chevs inequality to verify the convergence of sequence in probability
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Unit-1 |
Teaching Hours:12 |
Random Variables and Expectations
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Random Variables: Definitions and Properties, Distribution Function and its properties, Discrete and Continuous Random Variables; Expectations: Expected value of a random variable, Properties of expectation, Moment Generating Function Use of moments for mean, variance and moments. | |
Unit-2 |
Teaching Hours:12 |
Joint Distributed Random Variables
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Joint probability mass function, joint probability density function, joint distribution functions, marginal functions, conditional distribution functions, conditional pdf, conditional pmf, joint moments, covariance, correlation, conditional expectation. | |
Unit-3 |
Teaching Hours:12 |
Probability Distribution for Discrete Data
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Bernoulli, Binomial, Poisson, Negative Binomial, Hypergeometric Distributions (mean and variance in terms of mgf), their applications in Data Science. | |
Unit-4 |
Teaching Hours:12 |
Probability Distribution for Continuous Data
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Uniform, Normal, Exponential, Gamma distribution, Weibull Distributions (mean and variance in terms of mgf), and their applications in Data Science. | |
Unit-5 |
Teaching Hours:12 |
Limit Theorems
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Chebychev’s inequality - weak law of large numbers (iid): examples - strong law of large numbers (statement only) - central limit theorems (iid case): examples. | |
Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern CIA-50% ESE-50% | |
MDS133 - MATHEMATICAL FOUNDATIONS FOR DATA SCIENCE-I (2024 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
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 and it’s spans and orthogonalization, linear transformation and the use of its matrix bijections in applications to Data Science. |
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Course Outcome |
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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 |
Unit-1 |
Teaching Hours:9 |
INTRODUCTION TO VECTOR SPACES
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Vector Spaces: Definition and properties, Subspaces, Sums of Subspaces, Direct Sums, Span and Linear Independence, Bases, dimension, rank. | |
Unit-2 |
Teaching Hours:9 |
LINEAR TRANSFORMATIONS
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Algebra of Linear Transformations, Null spaces, Column space, Range space, Row space and Injectivity, Surjectivity, Fundamental Theorems of Linear Maps | |
Unit-3 |
Teaching Hours:9 |
EIGENVALUES AND EIGENVECTORS
|
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Invariant Subspaces on real vector Spaces, Eigen values and Eigen vectors – Characteristic equation, Cayley-Hamilton theorem | |
Unit-4 |
Teaching Hours:9 |
INNER PRODUCT SPACES
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Inner Products and Norms – Orthogonality - Orthogonal Bases – Orthogonal Projections – Orthogonal Matrices, Gram Schmidt process - Least square problems – Applications to Linear models | |
Unit-5 |
Teaching Hours:9 |
BASIC MATRIX METHODS FOR APPLICATIONS
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Matrix Norms –Singular value decomposition Householder Transformation and QR decomposition | |
Text Books And Reference Books: 1. David C. Lay, Steven R. Lay, Judi J. McDonald (2016) Linear algebra and its applications. Pearson. 2. S. Axler, Linear algebra done right, Springer, 2017. 3. Strang, G. (2006) Linear Algebra and its Applications: Thomson Brooks. Cole, Belmont, CA, USA. | |
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% | |
MDS151P - APPLIED EXCEL (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:1 |
Course Objectives/Course Description |
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This course is designed to build logical thinking ability and to provide hands-on experience in solving statistical models using MS Excel with Problem based learning. To explore and visualize data using excel formulas and data analysis tools. |
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Course Outcome |
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CO1: Demonstrate the data management using excel
features. CO2: Analyze the given problem and solve using Excel. CO3: Infer the building blocks of excel, excel
shortcuts, sample data creation. |
Unit-1 |
Teaching Hours:10 |
Layout and Properties
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File types - Spreadsheet structure - Menu bar - Quick access toolbar - Mini toolbar - Excel options - Formatting: Format painter - Font - Alignment - Number - Styles - Cells, Clear - Page layout Properties Symbols - Equation - Editing - Link - Filter - Charts - Formula Auditing - Overview of Excel tables and properties - Collecting sample data and arranging in definite format in Excel tables. Lab : 1. Excel Formulas 2. Excel Tables and Properties | |
Unit-2 |
Teaching Hours:10 |
Files and Databases
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Files Importing data from different sources - Exporting data in different formats Database CO1 ,CO2 Creating database with the imported data - Data tools: text to column - identifying and removing duplicates - using format cell options Lab: 5.Import data 6.Export data 7.Creating database 8.Data tools | |
Unit-3 |
Teaching Hours:10 |
Functions
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Functions Application of functions - Concatenate - Upper - Lower - Trim - Repeat - Proper - Clean - Substitute - Convert - Left - Right - Mid - Len - Find - Exact - Replace - Text join - Value - Fixed etc. ,CO2, CO3 Lab: 9.Excel functions. | |
Text Books And Reference Books: [1] Alexander R, Kuselika R and Walkenbach J, Microsoft Excel 2019 Bible, Wiley India Pvt Ltd, New Delhi, 2018.
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Essential Reading / Recommended Reading
[1] Paul M, Microsoft Excel 2019 formulas and functions, Pearson Eduction, 2019 | |
Evaluation Pattern CIA-50% ESE-50% | |
MDS161A - PRINCIPLES OF PROGRAMMING (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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The students shall be able to understand the main principles of programming. The objective also includes indoctrinating the activities of implementation of programming principles. |
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Course Outcome |
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CO1: Understand the fundamentals of programming languages. CO2: Understand the design paradigms of programming languages. CO3: To examine expressions, subprograms and their parameters. |
Unit-1 |
Teaching Hours:10 |
Introduction to Syntax and Grammar
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Introduction, Programming Languages, Syntax, Grammar, Ambiguity, Syntax and Semantics, Data Types (Primitive/Ordinal/Composite data types, Enumeration and sub-range types, Arrays and slices, Records, Unions, Pointers and pointer problems). | |
Unit-2 |
Teaching Hours:10 |
Constructing Expressions
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Expressions, Type conversion, Implicit/Explicit conversion, type systems, expression evaluation, Control Structures, Binding and Types of Binding,Lifetime, Referencing Environment (Visibility, Local/ Nonlocal/ Global variables), Scope (Scope rules, Referencing operations, Static/Dynamic scoping). | |
Unit-3 |
Teaching Hours:10 |
Subprograms and Parameters
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Subprograms, signature, Types of Parameters, Formal/Actual parameters, Subprogram overloading, Parameter Passing Mechanisms, Aliasing, Eager/Normal-order/Lazy evaluation) , Subprogram Implementation (Activation record, Static/Dynamic chain, Staticchain method, Deep/Shallow access, Subprograms as parameters, Labels as parameters, Generic subprograms, Separate/Independent compilation). | |
Text Books And Reference Books: 1. Allen B. Tucker, Robert Noonan, Programming Languages: Principles and Paradigms, Tata McGraw Hill Education, 2006. 2. Bruce J. MacLennan, “Principles of Programming Languages: Design, Evaluation, and Implementation”, Third Edition, Oxford University Press (New York), 1999. | |
Essential Reading / Recommended Reading 1. T. W. Pratt, M. V. Zelkowitz, Programming Languages, Design and Implementation, Prentice Hall, Fourth Edition, 2001. 2. Robert Harper, Practical Foundations for Programming Languages, Second Edition, Cambridge University Press, 2016. | |
Evaluation Pattern CIA - 50% ESE - 50% | |
MDS161B - INTRODUCTION TO PROBABILITY AND STATISTICS (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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This course is designed to introduce the historical development of statistics, presentation of data, descriptive measures and cultivate statistical thinking among students. This course also introduces the concept of probability. |
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Course Outcome |
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CO1: Demonstrate, present and visualize data in various forms, statistically. CO2: Understand and apply descriptive statistics. CO3: Evaluation of probabilities for various kinds of random events |
Unit-1 |
Teaching Hours:8 |
ORGANIZATION AND PRESENTATION OF DATA
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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, ratio and scale - discrete and continuous data - Presentation of data by tables - graphical representation of a frequency distribution by histogram and frequency polygon - cumulative frequency distributions (inclusive and exclusive methods). | |
Unit-2 |
Teaching Hours:6 |
DESCRIPTIVE STATISTICS I
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Measures of location or central tendency: Arithmetic mean - Median - Mode - Geometric mean - Harmonic mean. | |
Unit-3 |
Teaching Hours:6 |
DESCRIPTIVE STATISTICS II
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Partition values: Quartiles - Deciles and Percentiles - Measures of dispersion: Mean deviation - Quartile deviation - Standard deviation - Coefficient of variation - Moments: measures of skewness - kurtosis | |
Unit-4 |
Teaching Hours:10 |
BASICS OF PROBABILITY
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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. David C. Lay, Steven R. Lay, Judi J. McDonald (2016) Linear algebra and its applications. Pearson. 2. S. Axler, Linear algebra done right, Springer, 2017. 2. Strang, G. (2006) Linear Algebra and its Applications: Thomson Brooks. Cole, Belmont, CA, USA. | |
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% | |
MDS161C - LINUX ESSENTIALS (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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This course is designed to introduce Linux working environment to students. This course will enable students to understand the Linux system architecture, File and directory commands and foundations of shell scripting. |
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Course Outcome |
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CO1: Demonstrate the Basic file, directory commands
CO2: Understand the Unix system environment CO3: Apply shell programming concepts to solve given problem |
Unit-1 |
Teaching Hours:10 |
Introduction
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Introduction, Salient features, Unix system architecture,Unix Commands, Directory Related Commands, File Related Commands,Disk related Commands,General utilities,Unix File System,Boot inode, super and data block ,in core structure,Directories, conversion of path name to inode, inode to new file,Disk block Allocation | |
Unit-2 |
Teaching Hours:10 |
Process Management
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Process Management Process state and data structures of a Process,Context of a Process, background processes,User versus Kernel node,Process scheduling commands,. Process scheduling commands,Process terminating and examining commands,Secondary Storage Management: Formatting, making file system, checking disk space, mountable file system, disk partitioning | |
Unit-3 |
Teaching Hours:10 |
shell Programming
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Shell Programming, Vi Editor,.Shell types, Shell command line processing, Shell script & its features, system and user defined variables, Executing shell scripts expr command Shell Screen Interface, read and echo statement,Shell Script arguments Conditional Control Structures – if statement,Case statement,Looping Control Structure – while,for,Jumping Control Structures – break, continue, exit.
| |
Text Books And Reference Books: [1] Linux: The Complete Reference, sixth edition, Richard Petersen, 2017 | |
Essential Reading / Recommended Reading [1] Linux Pocket Guide, Daniel J. Barrett,3rd edition, O’Reilly | |
Evaluation Pattern
CIA 50% ESE 50% | |
MDS171 - PROGRAMMING USING PYTHON (2024 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:100 |
Credits:4 |
Course Objectives/Course Description |
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The objective of this course is to provide comprehensive knowledge of python programming paradigms required for Data Science. |
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Course Outcome |
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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:15 |
INTRODUCTION TO PYTHON
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Python and Computer Programming - Using Python as a calculator - Python memory management - Structure of Python Program - Branching and Looping - Problem-Solving Using Branches and Loops - Lists and Mutability - Functions - Problem-Solving Using Lists and Functions. Lab Exercise
| |
Unit-2 |
Teaching Hours:15 |
SEQUENCE DATATYPES AND OBJECT ORIENTED PROGRAMMING
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Sequences, Mapping and Sets - Dictionaries - Classes: Classes and Instances -Inheritance - Exceptional Handling - Module: Built-in modules & user-defined module - Introduction to Regular Expressions using “re” module Lab Exercises 1. Demonstrate Tuples, Sets, Frozen sets and Dictionaries 2. Demonstrate inheritance and exception handling
3. Demonstrate the use of “re” | |
Unit-3 |
Teaching Hours:15 |
USING NUMPY
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Basics of NumPy - Computation on NumPy - Aggregations - Computation on Arrays- Comparisons, Masks and Boolean Arrays -Sorting Arrays - Structured Data: NumPy’s Structured Array. Lab Exercises
| |
Unit-4 |
Teaching Hours:15 |
DATA MANIPULATION WITH PANDAS
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Introduction to Pandas Objects - Data indexing and Selection - Operating on Data in Pandas - Handling Missing Data - Hierarchical Indexing - Aggregation and Grouping - Pivot Tables - Vectorized String Operations - High-Performance Pandas: and query(). Lab Exercises
| |
Unit-5 |
Teaching Hours:15 |
VISUALIZATION WITH MATPLOTLIB
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Basics of matplotlib - Simple Line Plot and Scatter Plot - Density and Contour Plots - Histograms, Binnings and Density - Customizing Plot Legends - Multiple subplots. Lab Exercises
| |
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.M. Rajagopalan and P. Dhanavanthan- Statistical Inference-1st ed. - PHI Learning (P) Ltd.- New Delhi- 2012. [3] V. K. Rohatgi and E. Saleh- An Introduction to Probability and Statistics- 3rd ed.- John Wiley & Sons Inc- New Jersey- 2015. | |
Evaluation Pattern CIA 50% ESE 50% | |
MDS231 - DESIGN AND ANALYSIS OF ALGORITHMS (2024 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
The course introduces techniques for designing and analyzing algorithms and data structures. It concentrates on techniques for evaluating the performance of algorithms. The objective is to understand different designing approaches like greedy, divide and conquer, dynamic programming etc. for solving different kinds of problems. |
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Course Outcome |
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CO1: Design new algorithms and analyze their asymptotic and absolute runtime and memory demands. CO2: Apply classical sorting, searching, optimization and graph algorithms.
CO3: Understand basic techniques for designing algorithms, including the techniques of recursion, divide-and-conquer, greedy algorithm etc.
CO4: Understand the mathematical criterion for deciding whether an algorithm is efficient and know many practically important problems that do not admit any efficient algorithms. |
Unit-1 |
Teaching Hours:9 |
Introduction
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Algorithms, Analyzing algorithms, Complexity ofalgorithms, Growth of functions, Performancemeasurements, Sorting and order Statistics - Shell sort, Sorting in linear time, Linear Search | |
Unit-2 |
Teaching Hours:9 |
Advanced Data Structures
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Red-Black trees, B – trees, Binomial Heaps, Fibonacci Heaps, Tries, skip list. | |
Unit-3 |
Teaching Hours:9 |
Divide and Conquer
|
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Quick sort, Merge sort,Matrix Multiplication Binary Searching. Greedy methods with examples such as Optimal Reliability Allocation, Knapsack, Minimum Spanning trees – Prim’s and Kruskal’s algorithms, Single source shortest paths - Dijkstra’s algorithms.Optimal merge patterns. | |
Unit-4 |
Teaching Hours:9 |
Dynamic Programming
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Dynamic programming with examples such as Knapsack, All pair shortest paths – Warshal’s andFloyd’s algorithms,Backtracking, n-Queen Problem, Sum of subsets,Graph Coluring, Branch and Bound with examples such as Travelling Salesman Problem . | |
Unit-5 |
Teaching Hours:9 |
Selected Topics
|
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Algebraic Computation, Fast FourierTransform, String Matching, Theory of NP-completeness, Approximation algorithms and Randomized algorithms. | |
Text Books And Reference Books: [1] Coreman, Rivest, Lisserson, “An Introduction to Algorithm”, PHI, 2001
[2] Horowitz & SAHANI,” Fundamental of computer Algoritm”, Galgotia Publications, 2nd Edition. | |
Essential Reading / Recommended Reading [1] Aho, Hopcraft, Ullman, “The Design and Analysis of Computer Algorithms” Pearson Ed9ucation, 2008.
| |
Evaluation Pattern
CIA 50% ESE 50% | |
MDS232 - MATHEMATICAL FOUNDATIONS FOR DATA SCIENCE-II (2024 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
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 CO4: Know the about the basic terminologies and properties in Graph Theory |
Unit-1 |
Teaching Hours:9 |
Calculus of Several Variables
|
|
Functions of Several Variables: Functions of two, three variables Limits and continuity in Higher Dimensions: Limits for functions o two variables, Functions of more than two variables - Partia Derivatives: partial derivative of functions of two variables, partia derivatives of functions of more than two variables - The Chain Rule chain rule on functions of two, three variables, chain rule on function defined on surfaces | |
Unit-2 |
Teaching Hours:9 |
Orthogonality
|
|
Perpendicular vectors and Orthogonality - Inner Products and Projections onto lines - Projections of Rank one - Projections and Least Squares Approximations - Projection Matrices | |
Unit-3 |
Teaching Hours:9 |
Introduction to Convex Optimization
|
|
Affine and Convex Sets: Lines and Line segments, affine sets, affin dimension and relative interior, convex sets, cones - Hyperplanes and half-spaces - Euclidean balls and ellipsoids- Norm balls and Norm cones – polyhedral. | |
Unit-4 |
Teaching Hours:9 |
Graph Theory - Basics
|
|
Graph Classes: Definition of a Graph and Graph terminology isomorphism of graphs, Completegraphs, bipartite graphs, complete bipartite graphs-Vertex degree: adjacency and incidence, regular graphs - subgraphs, spanning subgraphs, induced subgraphs | |
Unit-5 |
Teaching Hours:9 |
Graph Theory - More concepts
|
|
Matrix Representation of Graphs, Adjacency matrices, Incidence Matrices, Trees and its properties, Bridges (cut-edges), spanning trees, weighted Graphs, minimal spanning tree problems, Shortest path problems - Applications of Graph Theory | |
Text Books And Reference Books: 1] M D. Weir, J. Hass, and G. B. Thomas, Thomas' calculus. Pearson, 2016. (Unit 1) [2] G Strang, Linear Algebra and its Applications, 4th ed., Cengage, 2006. (Unit 2) [3] S. P. Boyd and L.Vandenberghe, Convex optimization.Cambridge Univ. Pr., 2011.(Unit 3) [4] J Clark, D A Holton, A first look at Graph Theory, Allied Publishers India, 1995. (Unit 4) | |
Essential Reading / Recommended Reading [1] J. Patterson and A. Gibson, Deep learning: a practitioner's approach. O'Reilly Media, 2017 [2] S. Sra, S. Nowozin, and S. J. Wright, Optimization for machine learning. MIT Press, 2012 [3] D. Jungnickel, Graphs, networks and algorithms. Springer, 2014 [4] D Samovici, Mathematical Analysis for Machine Learning and Data Mining, World Scientific Publishing Co. Pte. Ltd, 2018 [5] P. N. Klein, Coding the matrix: linear algebra through applications to computer science. Newtonian Press, 2015 [6] K H Rosen, Discrete Mathematics and its applications, 7th ed., McGraw Hill, 2016 | |
Evaluation Pattern CIA 50% , ESE 50% | |
MDS271 - DATABASE TECHNOLOGIES (2024 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:100 |
Credits:4 |
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:15 |
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 Lab Exercises
| |
Unit-2 |
Teaching Hours:15 |
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, Normal Forms. 1NF, 2 NF, 3NF, BCNF Lab Exercises
| |
Unit-3 |
Teaching Hours:15 |
Data warehouse: the building blocks
|
|
Defining Features, Database and Data Warehouses, Architectural Types, Overview of the Components, Metadata in the Data warehouse, The Star Schema, Star Schema Keys, Advantages of the Star Schema, Star Schema: Examples, Snowflake Schema, Aggregate Fact Tables. Lab Exercises
| |
Unit-4 |
Teaching Hours:15 |
INTRODUCTION TO NOSQL DATABASES
|
|
Overview, and History of NoSQL Databases. Attack of the Clusters, the Emergence of NoSQL, Key Points comparison of relational databases to new NoSQL stores, RDBMS approach, Challenges NoSQL approach, Key-Value Data Model, Document Data Models, Column Family Stores, Graph Databases Lab Exercices
| |
Unit-5 |
Teaching Hours:15 |
DOCUMENT DATABASE-MONGODB
|
|
Distributed Databases- Sharding and Replication, Consistency, The CAP Theorem.Document Data Model: Documents and Collections. Embedded Collection. CRUD (Creating, Reading & Updating Data) -Mongo Shell, Query Operators, Projection Operation, InsertOne, InsertMany Update Operators, and a Few Commands Lab Exercices
| |
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
EVALUATION PATTERN CIA 50% ESE 50% | |
MDS272 - INFERENTIAL STATISTICS USING R (2024 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:100 |
Credits:4 |
Course Objectives/Course Description |
|
Statistical inference plays an important role when analyzing data and making decisions based on real-world phenomena. This course aims to teach students to test hypotheses and estimate 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: Estimate the unknown population parameters using the concepts of point and interval estimations using R. CO4: Test the hypothesis using nonparametric tests for real world problems using R. |
Unit-1 |
Teaching Hours:15 |
ESTIMATION TECHNIQUES
|
|
Population and Sample, Parameter and Statistics, Characteristics of a good estimators, Suffiency -Factorisation Theorem, Unbiased Estimators- Consistency, Efficiency, Different methods of estimation-Moment estimation and MLE Estimation techniques.
Lab Exercises:
| |
Unit-2 |
Teaching Hours:15 |
Testing of Hypothesis I
|
|
Concept of large and small samples, Single sample mean test, Independent sample mean test, Paired sample mean test, Test for Single variance - Test for equality of two variance for normal population. Lab Exercises:
| |
Unit-3 |
Teaching Hours:15 |
Testing of Hypothesis II
|
|
Tests for single proportion, Tests of equality of two proportions for the normal population, Chi square test for independence of attributes, Chi square test for goodness of fit, Chi square tests for attributes. Concept of confidence interval and confidence coefficient, Confidence intervals for the parameters of univariate normal, Lab Exercises:
| |
Unit-4 |
Teaching Hours:15 |
Analysis of Variance
|
|
Meaning and assumptions - Fixed, random and mixed effect models - Analysis of variance of one-way and two-way classified data with and without interaction effects, Multiple comparison tests: Tukey’s method, critical difference. Lab Exercises:
| |
Unit-5 |
Teaching Hours:15 |
Nonparametric Tests
|
|
Concept of Nonparametric tests - Run test for randomness - Sign test and Wilcoxon Signed Rank Test for one and paired samples - Run test - Median test and Mann-Whitney-Wilcoxon tests for two samples. Lab Exercises:
| |
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. Montgomery, D. C., & Runger, G. C. (2010). Applied statistics and probability for engineers. John wiley & sons. 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% | |
MDS273 - FULL STACK WEB DEVELOPMENT (2024 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:100 |
Credits:4 |
Course Objectives/Course Description |
|
On completion of this course, a student will be familiar with full stack and able to develop a web application using advanced technologies and cultivate good web programming style and discipline by solving the real world scenarios. |
|
Course Outcome |
|
CO1: Apply JavaScript, HTML5, and CSS3 effectively to create interactive and dynamic websites. CO2: Describe the main technologies and methods currently used in creating advanced web applications. CO3: Design websites using appropriate security principles, focusing specifically on the vulnerabilities inherent in common web implementations. CO4: Create modern web applications using MEAN. |
Unit-1 |
Teaching Hours:15 |
OVERVIEW OF WEB TECHNOLOGIES AND HTML5
|
|
Internet and web Technologies- Client/Server model - Web Search Engine-Web Crawling-Web Indexing Search Engine Optimization and Limitations-Web Services –Collective Intelligence –Mobile Web – Features of Web 3.0-HTML vs HTML5-Exploring Editors and Browsers Supported by HTML5-New Elements-HTML5 Semantics-Canvas-HTML Media Lab Exercises
| |
Unit-2 |
Teaching Hours:15 |
CLIENT SIDE SCRIPTING
|
|
JavaScript Implementation - Use Javascript to interact with some of the new HTML5 apis -Create and modify Javascript objects- JS Forms - Events and Event handling-JS Navigator-JS Cookies-Introduction to JSON-JSON vs XML-JSON Objects-Importance of Angular JS in web-Angular Expression and Directives-Single Page Application Lab Exercises
| |
Unit-3 |
Teaching Hours:15 |
XML AND AJAX
|
|
XML-Documents and Vocabularies-Versions and Declaration -Namespaces JavaScript and XML: Ajax DOM based XML processing Event-Transforming XML Documents-Selecting XML Data:XPATH Template based Transformations: XSLT-Displaying XML Documents in Browsers - Evolution of AJAX - Web applications with AJAX -AJAX Framework Lab Exercises
| |
Unit-4 |
Teaching Hours:15 |
SERVER SIDE SCRIPTING
|
|
Introduction to Node.js-REPL Terminal-Package Manager(NPM)-Node.js Modules and file system Node.js Events-Debugging Node JS Application-File System and streams-Testing Node JS with jasmine. NODE JS WITH MYSQL Introduction to MySQL- Performing basic database operation (DML) (Insert, Delete, Update, Select). Lab Exercises 1. Implement a single page web application using AngularJS CRUD Operation using AngularJS 2. Demonstrate Node.js file system module
3. Design a web page to demonstrate CRUD operation using MySQL. | |
Unit-5 |
Teaching Hours:15 |
PYTHON WITH MYSQL
|
|
Installing MySQL Connector for Python. Connecting to MySQL database. Executing SQL queries from Python. Fetching and processing results. Error handling. Reading and writing data to/from MySQL. Data manipulation (e.g., sorting, filtering).Data visualization (e.g., using matplotlib or seaborn) Lab Exercises
| |
Text Books And Reference Books: [1] Internet and World Wide Web:How to Program, Paul Deitel , Harvey Deitel & Abbey Deitel, Pearson Education, 5th Edition, 2018. [2] HTML 5 Black Book (Covers CSS3, JavaScript, XML, XHTML, AJAX, PHP, jQuery), DT Editorial Services, Dreamtech Press, 2nd Edition, 2016. | |
Essential Reading / Recommended Reading [1] Chris Northwood, The Full Stack Developer: Your Essential Guide to the Everyday Skills Expected of a Modern Full Stack Web Developer, Apress Publications, 1st Edition, 2018. [2] Laura Lemay, Rafe Colburn & Jennifer Kyrnin, Mastering HTML, CSS & Javascript Web Publishing, BPB Publications, 1st Edition, 2016. [3] Alex Giamas, Mastering MongoDB 3.x, Packt Publishing Limited, First Edition, 2017.
Web Resources:
[2] http://www.php.net/docs.php
| |
Evaluation Pattern CIA - 50% ESE- 50% | |
MDS311 - CLOUD SERVICES (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
|
This on-line course gives students an overview of the field of Cloud Computing, its enabling technologies, main building blocks, and hands-on experience through projects utilizing public cloud infrastructures (Amazon Web Services (AWS) and Microsoft Azure). The student learns the topics of cloud infrastructures, virtualization, software defined networks and storage, cloud storage, and programming models. |
|
Course Outcome |
|
CO1: Understand the core concepts of the cloud computing paradigm CO2: Apply fundamental concepts of cloud infrastructures, cloud storage and in storage systems such as Amazon S3 and HDFS. CO3: Analyze various cloud programming models and apply them to solve problems on the cloud. |
Unit-1 |
Teaching Hours:6 |
Introduction
|
|
Definition and evolution of Cloud Computing, Enabling Technologies, Service and Deployment Models Popular Cloud Stacks and Use Cases Benefits, Risks, and Challenges of Cloud Computing Economic Models and SLAs | |
Unit-2 |
Teaching Hours:6 |
Cloud Infrastructure
|
|
Historical Perspective of Data Centers, Datacenter Components: IT Equipment and Facilities
Design Considerations: Requirements, Power, Efficiency, & Redundancy, Power Calculations, PUE and Challenges in Cloud Data Centers, Cloud Management and Cloud Software Deployment Considerations. | |
Unit-3 |
Teaching Hours:6 |
Virtualization
|
|
Virtualization (CPU, Memory, I/O),Case Study: Amazon EC2,Software Defined Networks (SDN),Software Defined Storage (SDS) | |
Unit-4 |
Teaching Hours:6 |
Cloud Storage
|
|
Introduction to Storage Systems, Cloud Storage Concepts, Distributed File Systems (HDFS, Ceph FS) Cloud Databases (HBase, MongoDB, Cassandra, DynamoDB) ,Cloud Object Storage (Amazon S3, OpenStack Swift, Ceph) | |
Unit-5 |
Teaching Hours:6 |
Programming Models
|
|
Distributed Programming for the Cloud Data-Parallel Analytics with Hadoop MapReduce (YARN) | |
Text Books And Reference Books: Essential Reading:
| |
Essential Reading / Recommended Reading K. Chandrasekaran,Essentials of Cloud Computing,CRC press,2014 | |
Evaluation Pattern CIA-50% ESE- 50% | |
MDS331 - REGRESSION MODELLING (2024 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
This course deals with linear and non-linear regression models with their assumptions, estimation and test of significance of regression coefficients, and overall regression model with various model selection criteria. |
|
Course Outcome |
|
CO1: Formulate the linear regression model and its application to real data. CO2: Understand and identify the various assumptions of linear regression models. CO3: Identify the correct model using model selection and variable selection criteria. CO4: Ability to use and understand generalizations of the linear model to binary and count data. |
Unit-1 |
Teaching Hours:10 |
Simple Linear Regression
|
|
Introduction to regression analysis: overview and applications of regression modelling, major steps in regression modelling. Simple linear regression: assumptions, estimation of regression coefficients using ordinary least squares and maximum likelihood estimation, properties of regression coefficients, significance and confidence intervals of regression coefficients. | |
Unit-2 |
Teaching Hours:9 |
Multiple Linear Regression
|
|
Assumptions, ordinary least square estimation of regression coefficients, properties of the regression coefficients, significance and confidence intervals of regression coefficients with interpretation.
| |
Unit-3 |
Teaching Hours:9 |
Model Adequacy
|
|
Residual analysis; Departures from underlying assumptions: Multicollinearity, Heteroscedasticity, Autocorrelation, Effect of outliers. Diagnostics and remedies. | |
Unit-4 |
Teaching Hours:8 |
Model Selection Criteria
|
|
Model selection criteria: R-Square, Adjusted R-Square, Mean Square error criteria; Variable selection criteria: Forward, Backward and Stepwise procedures. | |
Unit-5 |
Teaching Hours:9 |
Non-Linear Regression
|
|
Introduction to nonlinear regression, Least squares in the nonlinear case and estimation of parameters, Models for binary and count response variable.
| |
Text Books And Reference Books: [1] Montgomery D.C, Peck E.A and Vining G.G, Introduction to Linear Regression Analysis, John Wiley and Sons Inc,. New York, 2012. [2] Chatterjee S and Hadi A, Regression Analysis by Example, 4th edition, John Wiley and Sons Inc, New York, 2015. | |
Essential Reading / Recommended Reading [1] George A.F.S and Lee A.J, Linear Regression Analysis, John Wiley and Sons, Inc, 2012. [2] Pardoe I, Applied Regression Modeling, John Wiley and Sons Inc, New York, 2012 [3] Iain Pardoe, Applied Regression Modeling, John Wiley and Sons, Inc, 2012.
[4] P. McCullagh, J.A. Nelder, Generalized Linear Models, Chapman & Hall, 1989 | |
Evaluation Pattern CIA - 50% ESE -50% | |
MDS341A - CATEGORICAL DATA ANALYSIS (2024 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
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:9 |
Introduction
|
|
Categorical response data - Probability distributions for categorical data - Statistical inference for discrete data
Teaching /learning Strategy: Lecture /Discussion/Presentation/Problem solving/Class Activity Essential Reading: Agresti, A. (2012). Categorical Data Analysis, 3rd edition. New York, Wiley | |
Unit-2 |
Teaching Hours:9 |
Contingency Tables
|
|
Probability structure for contingency tables - Comparing proportions with 2x2 tables - The odds ratio - Tests for independence - Exact inference
Teaching /learning Strategy: Lecture /Discussion/Presentation/Problem solving/Class Activity Essential Reading: Agresti, A. (2012). Categorical Data Analysis, 3rd edition. New York, Wiley
| |
Unit-3 |
Teaching Hours:9 |
Generalised Linear Model
|
|
Components of a generalised linear model - GLM for binary and count data - Statistical inference and model checking - Fitting GLMs
Teaching /learning Strategy: Lecture /Discussion/Presentation/Problem solving/Class Activity Essential Reading: Agresti, A. (2012). Categorical Data Analysis, 3rd edition. New York, Wiley.
| |
Unit-4 |
Teaching Hours:9 |
Logistic Regression
|
|
Interpreting the logistic regression model - Inference for logistic regression - Logistic regression with categorical predictors - Multiple logistic regression - Summarising effects - Building and applying logistic regression models.
Teaching /Learning Strategy: Lecture /Discussion/Presentation/Problem solving/Class Activity Essential Reading: Agresti, A. (2012). Categorical Data Analysis, 3rd edition. New York, Wiley
| |
Unit-5 |
Teaching Hours:9 |
Log-linear models for Contingency Tables
|
|
Loglinear models for two-way and three-way tables - Inference for Loglinear models - the log-linear-logistic connection - Independence graphs and collapsibility – Models for matched pairs: Comparing dependent proportions
Teaching /Learning Strategy: Lecture /Discussion/Presentation/Problem solving/Class Activity Essential Reading: Agresti, A. (2012). Categorical Data Analysis, 3rd edition. New York, Wiley.
| |
Text Books And Reference Books: 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 edition, 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% | |
MDS341B - MULTIVARIATE ANALYSIS (2024 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
This course lays the foundation of Multivariate data analysis. The exposure provided to the multivariate data structure, multinomial and multivariate normal distribution, estimation and testing of parameters, and 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:9 |
Introduction
|
|
Basic concepts on the multivariate variable. Bivariate normal distribution; an overview. Multivariate normal distribution and its properties, Its expectation, and Variance-Covariance matrix. Conditional distributions and Independence of random vectors. Multinomial distribution.
| |
Unit-2 |
Teaching Hours:9 |
Distribution
|
|
Sample mean vector and its distribution. Likelihood ratio tests: Tests of hypotheses about the mean vectors and covariance matrices for multivariate normal populations.
| |
Unit-3 |
Teaching Hours:9 |
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’ D^2 statistics and their properties.
| |
Unit-4 |
Teaching Hours:9 |
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.
| |
Unit-5 |
Teaching Hours:9 |
Principal Component and Factor Analysis
|
|
Principal components, sample principal components asymptotic properties. Canonical variables and canonical correlations: definition, estimation, computations. Factor analysis: Orthogonal factor model, factor loadings, estimation of factor loadings.
| |
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 EVALUATION PATTERN CIA 50% ESE 50% | |
MDS341C - STOCHASTIC PROCESSES (2024 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
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: Understand and apply the types of stochastic processes in various real-life scenarios. CO2: Demonstrate a discrete space stochastic process in discrete index and estimate the evolving time in a state. CO3: Apply probability arguments to model and estimate the counts in continuous time CO4: Evaluate the extinction probabilities of a generation. CO5: Development of renewal equations in discrete and continuous time. CO6: Understand the stationary process and application in Time Series Modelling |
Unit-1 |
Teaching Hours:9 |
INTRODUCTION TO STOCHASTIC PROCESSES
|
|
Classification of Stochastic Processes, Markov Processes – Markov Chain - Countable State Markov Chain. Transition Probabilities, Chapman - Kolmogorov's Equations, Calculation of n - step Transition Probability and its limit.
| |
Unit-2 |
Teaching Hours:9 |
POISSON PROCESS
|
|
Classification of States, Recurrent and Transient States - Transient Markov Chain, Random Walk. Continuous Time Markov Process: Poisson Processes, Birth and Death Processes, Kolmogorov’s Differential Equations, Applications. | |
Unit-3 |
Teaching Hours:9 |
BRANCHING PROCESS
|
|
Branching Processes – Galton – Watson Branching Process - Properties of Generating Functions – Extinction Probabilities – Distribution of Total Number of Progeny.
| |
Unit-4 |
Teaching Hours:9 |
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. | |
Unit-5 |
Teaching Hours:9 |
STATIONARY PROCESS
|
|
Stationary Processes: Application to Time Series. Auto-covariance and Auto-correlation functions and their properties. Moving Average, Autoregressive, Autoregressive Moving Average. Basic ideas of residual analysis, diagnostic checking, forecasting. | |
Text Books And Reference Books:
| |
Essential Reading / Recommended Reading
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% | |
MDS371 - JAVA PROGRAMMING (2024 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:100 |
Credits:4 |
Course Objectives/Course Description |
|
This course provides a comprehensive understanding of object-oriented programming structures and principles using JAVA programming. It introduces generics and collections frameworks along with java libraries for implementation of data science applications. The course also introduces multi-threaded programming. |
|
Course Outcome |
|
CO1: Apply object-oriented programming structures in Java to solve real world problems CO2: Demonstrate understanding of generics and collections framework CO3: Design programs for multi-threaded environment CO4: Analyze and visualize data using various libraries
|
Unit-1 |
Teaching Hours:15 |
INTRODUCTION
|
|
Overview of JVM Introduction to JVM-JVM Architecture-Java Basics- Structure of Java Program- Data Types – Constants –Variables Operators –Conditional statements- Class and Object : Concept - Method Overloading and Overriding - Constructor - this and static keyword Lab Exercise
| |
Unit-2 |
Teaching Hours:15 |
ARRAYS AND INHERITANCE
|
|
Creation and initialization of arrays, one dimensional and multidimensional arrays Inheritance Basics - Multilevel Hierarchy- Using super - Dynamic Method Dispatch -Abstract keyword- Using final with inheritance – Aggregation and Composition in Java Lab Exercise
| |
Unit-3 |
Teaching Hours:15 |
INTERFACES AND PACKAGES
|
|
Defining Interfaces - Implementing Interfaces - Extending Interfaces- Creating Packages - Importing Packages - Interfaces in a Package. Nested interfaces. Inheritance and interfaces. Use of static in interfaces
Lab Exercise
| |
Unit-4 |
Teaching Hours:15 |
EXCEPTION HANDLING
|
|
Exception Handling in Java-Checked and unchecked exceptions try-catch-finally mechanism - throw statement - throws statement - Built-in-Exceptions – Custom Exceptions-nested try, throw, throws. Introduction to multithreading. – String handling in Java Lab Exercise
| |
Unit-5 |
Teaching Hours:15 |
JAVA I/O Operations
|
|
I/O Basics-Streams-Byte Streams-Input Stream classes-Output Stream Classes-Character Streams-Reader Stream classes. File handling in Java. Overview of Collections framework - Introduction to Data Science Libraries.
Lab Exercise
| |
Text Books And Reference Books:
| |
Essential Reading / Recommended Reading
| |
Evaluation Pattern
CIA 50% ESE 50% | |
MDS372 - MACHINE LEARNING (2024 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:100 |
Credits:4 |
Course Objectives/Course Description |
|
The objective of this course is to provide introduction to the principles and design of machine learning algorithms. The course is aimed at providing foundations for conceptual aspects of machine learning algorithms along with their applications to solve real world problems. |
|
Course Outcome |
|
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:15 |
INRTODUCTION
|
|
Machine Learning-Examples of Machine Applications- Learning Associations-Classification- Regression- Unsupervised Learning-Reinforcement Learning. Supervised Learning: Learning class from examples- Probably Approach Correct (PAC) Learning-Noise- Learning Multiple classes. Regression-Model Selection and Generalization. Lab Exercise
| |
Unit-2 |
Teaching Hours:15 |
DIMENSIONALITY REDUCTION
|
|
Dimensionality Reduction, Dimensionality Reduction: Introduction- Subset Selection-Principal Component Analysis, Feature Embedding-Factor Analysis-Singular Value Decomposition -Multidimensional Scaling
Lab Exercise
| |
Unit-3 |
Teaching Hours:15 |
SUPERVISED LEARNING - I
|
|
Linear Discrimination: Introduction- Generalizing the Linear Model-Geometry of the Linear Discriminant- Pairwise Separation-Gradient Descent-Logistic Discrimination. Kernel Machines: Introduction- optical separating hyperplane- v-SVM, kernel tricks- vertical kernel- vertical kernel- defining kernel- multiclass kernel machines- one-class kernel machines.
Lab Exercise
| |
Unit-4 |
Teaching Hours:15 |
SUPERVISED LEARNING - II
|
|
Multilayer Perceptron: Introduction, training a perceptron- learning Boolean functions- multilayer perceptron- backpropogation algorithm- training procedures. Combining Multiple Learners : Rationale-Generating diverse learners- Model combination schemes- voting, Bagging- Boosting- fine tuning an Ensemble.
Lab Exercise
| |
Unit-5 |
Teaching Hours:15 |
UNSUPERVISED LEARNING
|
|
Clustering Introduction-Mixture Densities, K-Means Clustering- Expectation-Maximization algorithm- Mixtures of Latent Varaible Models-Supervised Learning after Clustering - Hierachial Clustering-Clustering- Choosing the number of Clusters. Lab Exercise
| |
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% | |
MDS381 - SEMINAR (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
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 |
PRESENTATIONS
|
|
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% | |
MDS411 - DATA DRIVEN MODELLING AND VISUALIZATION (2023 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
|
This course provides an overview of how to analyse, interpret, and communicate insights from data. A combination of lectures, hands-on exercises, and real-world projects, students will learn how to leverage data effectively to build statistical and machine learning models, uncover patterns. |
|
Course Outcome |
|
CO1: Analyze data to identify trends, patterns, and outliers CO2: Evaluate Charts which could present the insight effectively CO3: Present Data Insights using Charts and Dashboards |
Unit-1 |
Teaching Hours:10 |
Data Processing and Analysis
|
|
Data Processing and Analysis Collection of relevant data from Structured (spreadsheet, database, data warehouse) and Unstructured Data (text, images, sensors). Filtering, Aggregation, Grouping, Pivoting, Scaling, Managing Data Type. Data Profiling, Summarizing the main characteristics of the data, identifying patterns, trends, and outliers. Visualization techniques for data analysis: trend lines, histograms, scatter plots, box plots. | |
Unit-2 |
Teaching Hours:10 |
Data Visualization
|
|
Overview of Visualisation Tools. Understand visualization need. Comparison, Composition, Relation, Distribution. Use of Statistical Functions, searching for insights using Query or Pivot. Insights from Pie Charts, Area Charts, TreeMaps, Correlation Charts, Donut Charts Case Study: Google Trends, Tableau Public Gallery. | |
Unit-3 |
Teaching Hours:10 |
Advanced Data Visualization
|
|
Dashboard, common filters across charts, Animation, Storytelling with Data Visualisations Stock Chart, Candlestick Chart, Sunburst Diagram, Word Clouds, Waterfall chart, Funnel Chart, Polar Graph, GeoSpatial Map, Gantt Chart, Choropleth Map, Parallel Coordinates Plot, Non-Ribbon Chord Diagram. Case Study: MakeOverMonday: A Social Data Project | |
Text Books And Reference Books: 1. O'Connor, Errin. Microsoft Power BI Dashboards Step by Step Microsoft Press, 2018. 2. Milligan, Joshua N, Learning Tableau, Packt Publishing Ltd, 2019 | |
Essential Reading / Recommended Reading 1. Tufte, E., “The Visual display of quantitative information”, Second Edition, 2002 2. Few, Stephen, “Information Dashboard Design.”, 2013 3. Knaflic, Cole Nussbaumer, “Storytelling with Data: Let's Practice!”. John Wiley & Sons,2019 | |
Evaluation Pattern 50% CIA 50% ESE | |
MDS431 - TIME SERIES AND FORECASTING TECHNIQUES (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:5 |
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: Ability 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
|
|
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. | |
Unit-2 |
Teaching Hours:12 |
STATIONARY TIME SERIES MODELS
|
|
Wold representation of linear stationary processes, generalised linear models, Study of linear time series models: Autoregressive, Moving Average and Autoregressive Moving average models and their statistical properties like ACF and PACF function. | |
Unit-3 |
Teaching Hours:12 |
ESTIMATION OF ARMA MODELS
|
|
Estimation of ARMA models: Yule- Walker estimation of AR Processes, Maximum likelihood and least squares estimation for ARMA Processes, MMSE forecast and l-step ahead forecast, Residual analysis and diagnostic checking. | |
Unit-4 |
Teaching Hours:12 |
NON-STATIONARY TIME SERIES MODELS
|
|
Concept of non-stationarity, general unit root tests for testing non stationarity; basic formulation of the ARIMA Model and their statistical properties-ACF and PACF; forecasting using ARIMA models. | |
Unit-5 |
Teaching Hours:12 |
INTRODUCTION TO MULTIVARIATE AND SEASONAL TIME SERIES MODELS
|
|
Stationary Multivariate Time series, Vector AR models, Vector MA models, Vector ARMA models- its stationarity properties, Non stationarity and Cointegration. Seasonal time series models, Introduction to SARIMA models, different representations of SARIMA models and its forecast. | |
Text Books And Reference Books: 1. George E. P. Box, G.M. Jenkins, G.C. Reinsel and G. M. Ljung, Time Series 2. analysis Forecasting and Control, 5th Edition, John Wiley & Sons, Inc., New Jersey, 2016. 3. Montgomery D.C, Jennigs C. L and Kulachi M,Introduction to Time Series 4. analysis and Forecasting, 2nd Edition,John Wiley & Sons, Inc., New Jersey, 2016. | |
Essential Reading / Recommended Reading 1. Brockwell, P.J and Davis R.A. (2006) Time Series: Theory and Methods, 2nd edition, Spinger. 2. Shumway, R. H and Stoffer, D. S. (2006). Time series Analysis and its Applications. Springer. 3. Anderson T.W,Statistical Analysis of Time Series, John Wiley&Sons, Inc., New, Jersey,1971. | |
Evaluation Pattern 50% CIA 50% ESE | |
MDS471 - NEURAL NETWORKS AND DEEP LEARNING (2023 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
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 and its implementation. 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 fundamental concepts of Artificial Neural Networks (ANN) and their
evolution, Analyze the theory and architecture of shallow neural networks,
implementing learning factors in Back-Propagation Networks for effective training. CO2: Apply convolutional operations for image recognition in Convolutional Neural
Networks (CNN) and implement different CNN architectures. CO3: Evaluate the challenges in training Recurrent Neural Networks (RNN) and create an
implementation of Long Short-Term Memory (LSTM) for sequential data analysis.
Understand and apply features of Auto encoders and Restricted Boltzmann Machines
(RBM) for efficient unsupervised feature learning. Apply Neural network models to
solve real time problems. |
Unit-1 |
Teaching Hours:15 |
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. Lab Exercise 1. Create a program to build and train an Artificial Neural Network 2. Create a regression model with Artificial Neural Networks (ANN) by following steps like data preparation and model training for predicting continuous outcomes | |
Unit-2 |
Teaching Hours:15 |
SUPERVISED LEARNING NETWORK
|
|
Shallow neural networks- Perceptron Networks-Theory-Perceptron Learning Rule Architecture- Perceptron Training Algorithm for Single and Multiple Output Classes. Back Propagation Network- Theory-Architecture-Training Algorithm-Learning Factors for Back-Propagation Network. Lab Exercise 1. Develop a shallow neural network emphasizing simplicity and clarity in steps such as data processing, building the network architecture, and training. 2. Create a program to implement learning factors in Backpropagation Neural Networks (BPN), focusing on steps such as data handling, network architecture, and the incorporation of learning factors for improved training. Radial Basis Function Network RBFN: Theory, Architecture and Algorithm. | |
Unit-3 |
Teaching Hours:15 |
CONVOLUTIONAL NEURAL NETWORK
|
|
Introduction - Components of CNN Architecture - Rectified Linear Unit (ReLU) Layer - Exponential Linear Unit (ELU, or SELU), types of CNN Architectures, alexnet, zfnet, googlenet and VGG -Applications of CNN. Lab Exercise 1. Ilustrate the step-by-step process of applying convolutional operations on data for enhanced understanding 2. Develop a Convolutional Neural Network (CNN), guiding through the stages of data preprocessing, model design, and training for effective image recognition | |
Unit-4 |
Teaching Hours:15 |
RECURRENT NEURAL NETWORK
|
|
Introduction- The Architecture of Recurrent Neural Network- The Challenges of Training Recurrent Networks - Long Short-Term Memory (LSTM) - Applications of RNN. Lab Exercise 1. Construct a Recurrent Neural Network (RNN) it includes key steps such as data preprocessing, model architecture design, and training to capture sequential dependencies in data 2. Create a Long Short-Term Memory (LSTM) implementation guiding through essential steps such as data preparation, designing the LSTM model architecture, and training for effective sequential data analysis. | |
Unit-5 |
Teaching Hours:15 |
AUTO ENCODER AND RESTRICTED BOLTZMANN MACHINE
|
|
Introduction - Features of Auto encoder Types of Auto encoder Restricted Boltzmann Machine- Boltzmann Machine - RBM Architecture -Example - Types of RBM. Lab Exercise 1. Develop a Restricted Boltzmann Machine (RBM) implementation using the essential steps of data handling, model construction, and training for unsupervised learning tasks. 2. Build an Autoencoder for efficient unsupervised feature learning | |
Text Books And Reference Books: 1. S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition, 2018. 2. Deep Learning Ian Goodfellow Yoshua Bengio Aaron Courville, MIT PRESS, Ist Edition,2020 | |
Essential Reading / Recommended Reading 1. Charu C. Aggarwal, Neural Networks and Deep Learning, Springer, July 2023. 2. Francois Chollet, Deep Learning with Python, Manning Publications; second edition, 2021 3. John D. Kelleher, Deep Learning (MIT Press Essential Knowledge series), The MIT Press, 2019. | |
Evaluation Pattern FULL CIA | |
MDS472A - WEB ANALYTICS (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
The objective of this course is to provide overview and importance of Web analytics in terms of visualizations. This course also explores the effective of Web analytic strategies and implementation using Google analytics with visual analytics. |
|
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:12 |
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 Create & Hosting of Website, Creation of Google Analytics Account and Adding Property, Container Creation in Google Tag Manager, Create A New Tag and tag the Website Created. 2. Measuring Enhance Metrics | |
Unit-2 |
Teaching Hours:12 |
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. Web Analytics: Log file analysis 2. Explore all the available Metrics for Google Demo Account Data with and without Filter. | |
Unit-3 |
Teaching Hours:12 |
WORKING WITH ANALYTICS
|
|
Different analytical tools - Key features and capabilities of Google analytics- How Google analytics works - Implementing Google analytics - Getting up and running with Google analytics -Navigating Google analytics – Using Google analytics reports -Google metrics - Using visitor data to drive website improvement- Focusing on key performance indicators- Integrating Google analytics with third-Party applications. Lab Exercise 1. Create an Event by using all Built-In Variables of Pages and Scrolling. 2. Create youtube video view event by using all the matching Conditions like equal, contains etc in the trigger. | |
Unit-4 |
Teaching Hours:12 |
OVERVIEW OF QUALITATIVE ANALYSIS
|
|
Lab Usability Testing- Heuristic Evaluations- Site Visits- Surveys (Questionnaires) - Testing and Experimentation: A/B Testing and Multivariate Testing-Competitive Intelligence - Analysis Search Analytics: Performing Internal Site Search Analytics, Search Engine Optimization (SEO) and Pay per Click (PPC)-Website Optimization against KPIs- Content optimization- Funnel/Goal. Lab Exercise 1. Create a Custom Event to trace the Registered Users using Button Click and enable the conversion rate for the same (with all Built-In Variables of click) 2. Create funnel optimization using Google Analytics. | |
Unit-5 |
Teaching Hours:12 |
VISUAL ANALYTICS
|
|
Drill down and hierarchies-Sorting-Grouping- Additional Ways to Group- Creating Sets- Analysis with Cubes and MDX- Filtering for Top and Top N- Using the Filter Shelf- The Formatting Pane- Trend Lines- Forecasting. Lab Exercise 1. Visualization | |
Text Books And Reference Books: 1. Beasley M, Practical web analytics for user experience: How analytics can help you understand your users. Newnes, 1st edition, Morgan Kaufmann, 2013. 2. Sponder M, Social media analytics: Effective tools for building, interpreting, and using metrics, 1st edition, McGraw Hill Professional, 2013. 3. Clifton B, Advanced Web Metrics with Google Analytics, 3rd edition, John Wiley & Sons, 2012. | |
Essential Reading / Recommended Reading 1. Peterson E. T, Web Analytics Demystified: AMarketer's Guide to Understanding How Your Web Site Affects Your Business. Ingram, 2004. 2. Sostre P, LeClaire J, Web Analytics for dummies, John Wiley & Sons,2007. 3. Burby J, Atchison S, Actionable web analytics: using data to make smart business decisions, John Wiley & Sons, 2007. 4. Dykes B, Web analytics action hero: Using analysis to gain insight and optimize your business, Adobe Press, 2011. | |
Evaluation Pattern FULL CIA | |
MDS472B - IOT ANALYTICS (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
This course offers an opportunity to comprehend the principles of big data analytics within the context of the Internet of Things (IoT). Emphasis is placed on comprehending architectural components, protocols for application development, and selecting data analytics and visualization tools tailored to specific problem domains. Through hands-on experiences, students will engage in data collection, storage, and analysis procedures pertaining to IoT data |
|
Course Outcome |
|
CO1: Illustrate the process of constructing a data flow for linking IoT system or device data
to the cloud utilizing particular formats CO2: Describe the utilization of big data tools in distributed computing for processing IoT
data CO3: Employ algorithms to analyze IoT data patterns and extract intelligence |
Unit-1 |
Teaching Hours:12 |
INTRODUCING IOT ANALYTICS
|
|
IoT data and Big data- -IoT Analytics Lifecycle and Techniques- IoT Data Collection, IoT Data Analysis, IoT Data Deployment, Operationalization, and Reuse- Defining IoT analytics and challenges- IoT, Cloud and Big Data Integration for IoT Analytics -Cloud-based IoT Platforms- Requirements of IoT Big Data Analytics Platform- Functional Architecture-Data Analytics for the IoT. | |
Unit-2 |
Teaching Hours:12 |
IOT DEVICES AND NETWORKING PROTOCOLS
|
|
The Wild World of IoT Devices-Sensor Types-Networking Basics- IoT Networking Connectivity Protocols- IoT Networking Data Messaging Protocols-MQTT- HTTP and IoT-REST- CoAP- Analyzing Data to Infer Protocol and Device. | |
Unit-3 |
Teaching Hours:12 |
IOT ANALYTICS FOR THE CLOUD
|
|
Building Elastic Analytics-Cloud Infrastructure- Elastic Analytics Concepts- Introduction to Building an IoT Analytics Pipeline on Google Cloud, AWS, Azure, ThingSpeak | |
Unit-4 |
Teaching Hours:12 |
EXPLORING IOT DATA
|
|
Exploring and Visualizing Data-Techniques to understand Data Quality- Data CompletenessData Validity- Assessing Information Lag-Representativeness- Basic Time Series Analysis-The Basics of Geospatial Analysis. | |
Unit-5 |
Teaching Hours:12 |
DATA SCIENCE FOR IOT ANALYTICS
|
|
Machine Learning- Representation-Evaluation-Optimization-Generalization-Feature Engineering-Dealing with missing Values-Time Series Handling, Validation Methods-Understanding Bias-Variance Tradeoff- Machine Learning Models- Use cases for Deep Learning with IoT Data- Data Analytics in Smart Buildings. | |
Text Books And Reference Books: 1. Andrew Minteer, Analytics for the Internet of Things(IoT), Packt Publishing, First Edition, 2017. 2. Tausifa Jan Saleem and Mohammad Ahsan Chishti, Big Data Analytics for Internet of Things, Wiley, First Edition, 2021 | |
Essential Reading / Recommended Reading 1. John Soldatos, Building Blocks for IoT Analytics, River Publishers, First Edition, 2017. 2. Harry G. Perros, An Introduction to IoT Analytics, CRC Press, First Edition, 2021. | |
Evaluation Pattern CIA : 50% ESE: 50% | |
MDS472C - NATURAL LANGUAGE PROCESSING (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
The course introduces building blocks of Natural Language Processing pipeline. It provides comprehensive understanding on the methods and applications of NLP in the current data analysis paradigms. |
|
Course Outcome |
|
CO1: Understand word and sentence level analysis
CO2: Apply Vector semantics and embeddings for representation of text CO3: Design text based information retrieval systems CO4: Analyze NLP applications for real world data |
Unit-1 |
Teaching Hours:12 |
PARSING AND SYNTAX
|
|
Introduction to NLP- Background and overview- NLP Applications -NLP hard Ambiguity- Algorithms and models, Knowledge Bottlenecks in NLP- Introduction to NLTK Word Level Analysis: Regular Expressions, Text Normalization, Edit Distance, Parsing and Syntax- Spelling, Error Detection and correction | |
Unit-2 |
Teaching Hours:12 |
SEMANTIC ANALYSIS AND DISCOURSE PROCESSING
|
|
Semantic Analysis: Meaning Representation-Lexical Semantics- Ambiguity-Word Sense Disambiguation. Discourse Processing: cohesion-Reference Resolution- Discourse Coherence and Structure. | |
Unit-2 |
Teaching Hours:12 |
SEQUENCE LABELING FOR PARTS OF SPEECH AND NAMED ENTITIES
|
|
Words and Word classes- English Word Classes, Part-of-Speech Tagging, Named Entities and Named Entity Tagging, HMM Part-of-Speech Tagging | |
Unit-3 |
Teaching Hours:12 |
VECTOR SEMANTICS AND EMBEDDINGS
|
|
Lexical Semantics, Vector semantics, words and vectors, Cosine for measuring similarity, TF-IDF: Weighing terms in the vector, Pointwise Mutual Information (PMI), Word2vec. | |
Unit-4 |
Teaching Hours:12 |
QUESTION ANSWERING AND INFORMATION RETRIEVAL
|
|
Information Retrieval, Document Scoring, Term weighting and document scoring, Inverted Index, Evaluation of Information-Retrieval Systems, Using Neural IR for Question Answering, Evaluating Retrieval-based Question Answering | |
Unit-5 |
Teaching Hours:12 |
NLP APPLICATIONS
|
|
Language Divergences and Typology, Machine Translation using Encoder-Decoder, Translating in low-resource situations, MT Evaluation, Chatbots & Dialogue Systems, Properties of Human Conversation, Dialogue Acts and Dialogue State, Dialogue Acts and Dialogue State, Training chatbots, Fine Tuning for Quality and Safety, Learning to perform retrieval as part of responding, RLHF | |
Text Books And Reference Books: 1. Speech and Language Processing, Daniel Jurafsky and James H., 3rd Edition, Martin Prentice Hall,2023. 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 and spacy, O’Reilly Media; 1 edition, 2009 | |
Evaluation Pattern CIA : 50% ESE : 50% | |
MDS472D - GRAPH ANALYTICS (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
The course aims to equip students with a comprehensive understanding of graph theory, algorithms, and their applications in data science. Students will explore fundamental concepts of graph analytics, learn various graph algorithms, develop practical skills in analyzing graph data, understand advanced topics such as community detection and graph-based machine learning, and apply graph analytics techniques to real-world datasets and problems. |
|
Course Outcome |
|
CO1: Understanding of Graph Theory Fundamentals: Students will demonstrate a
solid understanding of fundamental concepts in graph theory. CO2: Proficiency in Graph Algorithms: Students will be proficient in implementing
and applying common graph algorithms. CO3: Application of Community Detection Techniques: Students will be able to
apply community detection algorithms.
CO4: Knowledge of Graph-Based Machine Learning: Students will gain
knowledge of graph-based machine learning techniques and understand their
applications. CO5: Practical Application of Graph Analytics: Students will apply graph analytics
techniques to real-world datasets and problems. |
Unit-1 |
Teaching Hours:12 |
Introduction to Graphs and Graph Theory
|
|
Types of graphs: directed, undirected, weighted, etc. Graph representations: adjacency matrix, adjacency list, and edge list, Basic graph properties and terminology, Graph operations and transformations. Lab Exercise 1. Graph Construction and Manipulation: Load a small real-world dataset (social network, citation network) and construct different representations. Implement basic transformations. 2. Graph Visualization: Using libraries like NetworkX, visualize different graph types highlighting their properties. | |
Unit-2 |
Teaching Hours:12 |
Graph Algorithms and Centrality Measures
|
|
Breadth-first search (BFS) and depth-first search (DFS), Shortest path algorithms: Dijkstra's algorithm, Bellman-Ford algorithm, Centrality measures: degree centrality, betweenness centrality, closeness centrality, PageRank algorithm and its applications, Applications of graph algorithms in social networks and recommendation systems. Lab Exercise 1. Traversal and Pathfinding: Implement BFS and DFS. Apply Dijkstra's algorithm to find shortest paths on a transportation or routing dataset. 2. Evaluating Influence: Calculate centrality measures on a social network dataset. Analyze results to identify important nodes. | |
Unit-3 |
Teaching Hours:12 |
Community Detection and Network Analysis
|
|
Introduction to community detection and modularity, Common community detection algorithms: Louvain method, Girvan-Newman algorithm, Network motifs and subgraph analysis, Structural balance theory and triadic closure, Application of community detection in social network analysis and biological networks. Lab Exercise 1. Community Finding: Apply Louvain and Girvan-Newman algorithms on a dataset with known community structure (or a larger social dataset). Compare results and interpretations. 2. Subgraph Patterns: Identify meaningful subgraphs (triangles, etc.) in a network. Analyze their distribution and link them to network properties. | |
Unit-4 |
Teaching Hours:12 |
Graph-Based Machine Learning
|
|
Introduction to graph-based machine learning, Graph convolutional networks (GCNs) and their architecture. Graph embedding techniques: node2vec, DeepWalk, Graph kernels and their applications, Graph neural networks for node classification and link prediction Lab Exercise 1. Graph Embeddings: Use node2vec or DeepWalk to generate embeddings for a dataset. Visualize embeddings to explore relationships. 2. Graph Convolutional Networks with PyTorch Geometric: Build a simple GCN model for node classification on a citation network or similar dataset. | |
Unit-5 |
Teaching Hours:12 |
Applications of Graph Analytics
|
|
Graph databases and querying graph data, Case studies in fraud detection and anomaly detection using graph analytics, Recommendation systems using graph-based algorithms, Graph analytics for biological networks and drug discovery, Ethical considerations and challenges in graph analytics. Lab Exercise 1. Graph Database Exploration: Load a dataset into Neo4j. Learn basic Cypher queries and more advanced path-based queries. 2. Case Study Implementation: Work through significant parts of the fraud detection case study, including visualization, anomaly detection, potentially ML component. | |
Text Books And Reference Books: 1. "Introduction to Graph Theory" by Richard J. Trudeau 2. "Networks, Crowds, and Markets: Reasoning About a Highly Connected World" by David Easley and Jon Kleinberg 3. "Graph Algorithms" by Shimon Even and Guy Even 4. "Mining of Massive Datasets" by Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman 5. "Networks: An Introduction" by Mark Newman | |
Essential Reading / Recommended Reading 1. "Graph Theory and Complex Networks: An Introduction" by Maarten van Steen. 2. "Graph Analytics for Big Data: Applications, Algorithms, and Systems" by Charu C. Aggarwal. 3. "Mining Social Networks and Security Informatics" by Shishir Kumar Shandilya and Suresh Kumar Bodduluru. | |
Evaluation Pattern CIA 50% ESE 50% | |
MDS481 - PROJECT-I (WEB PROJECT WITH DATA SCIENCE CONCEPTS) (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:5 |
Max Marks:100 |
Credits:2 |
Course Objectives/Course Description |
|
This course is designed to provide MSc Data Science students with hands-on experience in integrating data science techniques into web-based projects. In today's digital age, the web serves as a vast repository of data, presenting exciting opportunities for data scientists to extract insights, create impactful visualizations, and develop intelligent applications |
|
Course Outcome |
|
CO1: Demonstrate proficiency in developing web applications CO2: Show proficiency in Integrating Data Science Techniques with Web Development CO3: Perform Effective Problem-solving and Decision-making Skill CO4: Gain Advanced Understanding of Data Ethics and Best Practices |
Unit-1 |
Teaching Hours:30 |
UNIT I
|
|
Identification of application domain to develop a web application/prototype demonstrating data science/ML methods. | |
Unit-2 |
Teaching Hours:30 |
UNIT II
|
|
Requirement analysis, design and development of proposed solution using suitable tools for front end/data design | |
Text Books And Reference Books: 1. Flask Web Development: Developing Web Applications with Python, Second Edition, January 2018, Miguel Grinberg, O'reilly books 2. Django for Beginners: Build Websites with Python and Django, William S Vincent, welcome to code. 3. Interactive Data Visualization for the Web: An Introduction to Designing with D3, Second Edition, Scott Murray, O'reilly books | |
Essential Reading / Recommended Reading 1. Flask Web Development: Developing Web Applications with Python, Second Edition, January 2018, Miguel Grinberg, O'reilly books 2. Django for Beginners: Build Websites with Python and Django, William S Vincent, welcome to code. 3. Interactive Data Visualization for the Web: An Introduction to Designing with D3, Second Edition, Scott Murray, O'reilly books | |
Evaluation Pattern CIA: 50% ESE: 50% | |
MDS482 - RESEARCH PROBLEM IDENTIFICATION (2023 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:1 |
Course Objectives/Course Description |
|
The objective of the course is to provide practical exposure to formal research paradigms in Data Science in various domains. Students apply research methodology principles to identify research based solution in their selected domains after a comprehensive literature review. |
|
Course Outcome |
|
CO1: Understand various data analysis paradigms used in various application domains CO2: Identify gaps to propose research based solution |
Unit-1 |
Teaching Hours:30 |
Literature review
|
|
Literature review in the identified research area, Study of Existing Model and Methodology, Research proposal development with a clearly defined Problem statement and a methodology for the implementation. | |
Text Books And Reference Books: [1] C. R. Kothari, Research Methodology Methods and Techniques, 4th Edition, New Age International Publishers, 2019. [2] Zina O’Leary, The Essential Guide of Doing Research, 3rd Edition, SAGE Publications Ltd, 2017. | |
Essential Reading / Recommended Reading [1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th Edition, SAGE Publications, 2014. | |
Evaluation Pattern CIA 50% ESE 50% | |
MDS531A - ECONOMETRICS (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:5 |
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 CO4: Demonstrate contemporary trends in estimation of econometrics models |
Unit-1 |
Teaching Hours:15 |
Introduction to Econometrics
|
|
Introduction to Econometrics- Meaning and Scope – Methodology of Econometrics – Nature and Sources of Data for Econometric analysis – Types of Econometrics, scope and limitations of econometrics, Generalised Least Squares (GLS) Estimator. | |
Unit-2 |
Teaching Hours:15 |
Econometric Models and Their Inference
|
|
Presence of outliers, omitted variables, nonlinear relationship, correlated disturbances heteroscedasticity. Linear Regression with Stochastic Regressors, Errors in Variable Models and Instrumental Variable Estimation, Independent Stochastic linear Regression, Auto regression, Linear regression, Lag Models | |
Unit-3 |
Teaching Hours:15 |
Linear Equations Model
|
|
Simultaneous Linear Equations Model: Structure of Linear Equations Model, Identification Problem, Rank and Order Conditions, Single Equation and Simultaneous Equations, Methods of Estimation- Indirect Least squares, Least Variance Ratio and Two Stage Least Square, reduced form method or indirect least squares (ILS), the method of instrumental variables (IV), two-stage least squares (2SLS). | |
Unit-4 |
Teaching Hours:15 |
Dummy Variable Regression Models
|
|
Meaning and Nature of dummy variables, ANOVA models, dummy variable alternative to the chow test, interaction effects using dummy variables, the use of dummy variables in seasonal analysis, piecewise linear regression, panel data regression models, dummy variables and heteroscedasticity, dummy variables and autocorrelation. | |
Text Books And Reference Books: 1. Gujarati, D. N. (2021). Essentials of econometrics. Sage Publications. 2. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons. 3. Dinardo, J., Johnston, J., & Johnston, J. (1997). Econometric methods. McGraw-Hill Companies, Inc. | |
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% | |
MDS531B - BAYESIAN INFERENCE (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:5 |
Max Marks:100 |
Credits:4 |
Course Objectives/Course Description |
|
Students who complete this course will gain a solid foundation in how to apply and understand Bayesian statistics and how to understand Bayesian methods vs frequentist methods. Topics covered include: an introduction to Bayesian concepts; Bayesian inference for binomial proportions, and normal means; modelling. |
|
Course Outcome |
|
CO1: Identify Bayesian methods for a binomial proportion CO2: Analyse normal distributed data in the Bayesian framework. CO3: Compare Bayesian methods and frequentist methods |
Unit-1 |
Teaching Hours:12 |
Introduction to Bayesian Thinking
|
|
Basics of minimaxity - subjective and frequentist probability - Bayesian inference - prior distributions - posterior distributions - loss function - the principle of minimum expected posterior loss - quadratic and other common loss functions - advantages of being Bayesian - Improper priors - common problems of Bayesian inference - Point estimators - Bayesian confidence intervals, testing – credible intervals. | |
Unit-2 |
Teaching Hours:12 |
Bayesian Inference for Discrete Random Variables
|
|
Two Equivalent Ways of Using Bayes' Theorem - Bayes' Theorem for Binomial with Discrete Prior-Important Consequences of Bayes' Theorem - and Bayes' Theorem for Poisson with Discrete prior. | |
Unit-3 |
Teaching Hours:12 |
Bayesian Inference for Binomial Proportion
|
|
Using a Uniform Prior - Using a Beta Prior - Choosing Your Prior - Summarizing the Posterior Distribution - Estimating the Proportion - Bayesian Credible Interval - Statistical inference from both frequentist and Bayesian perspectives-Hypothesis Testing - Testing a One-Sided Hypothesis - Testing a Two-Sided Hypothesis. | |
Unit-4 |
Teaching Hours:12 |
Bayesian Inference for Normal Mean
|
|
Bayes' Theorem for Normal Mean with a Discrete Prior - Bayes' Theorem for Normal Mean with a Continuous Prior - Normal Prior, Bayesian Credible Interval for Normal Mean - Predictive Density for Next Observation | |
Unit-5 |
Teaching Hours:12 |
Bayesian Computations
|
|
Analytic approximation - E-M Algorithm - Monte Carlo sampling - Markov Chain Monte Carlo Methods - Metropolis-Hastings Algorithm - Gibbs sampling: examples and convergence issues - Bayesian linear regression. | |
Text Books And Reference Books: 1. Bolstad W. M. and Curran, J.M. (2016) Introduction to Bayesian Statistics 3rd Edition. Wiley, New York 2. Jim, A. (2009). Bayesian Computation with R, 2nd Edition, Springer | |
Essential Reading / Recommended Reading 1. Berger, J.O. (1985a). Statistical Decision Theory and Bayesian Analysis, 2nd Ed. Springer-Verlag, New York. 2. Christensen R, Johnson, W., Branscum, A. and Hanson T. E. (2011). Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians, Chapman & Hall. 3. Congdon, P. (2006). Bayesian Statistical Modeling, Wiley 4. Ghosh, J. K., Delampady M. and T. Samantha (2006). An Introduction to Bayesian Analysis: Theory & Methods, Springer. 5. Rao. C.R. and Day. D. (2006). Bayesian Thinking, Modeling & Computation, Handbook of Statistics, Vol. 25. Elsevier. | |
Evaluation Pattern CIA 50% ESE 50% | |
MDS531C - BIO-STATISTICS (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:5 |
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: Analyze and interpret the data based on the discrete and continuous probability
distributions. Apply parametric and non-parametric methods of statistical
data analysis. CO4: Understand the concepts of Epidemiology and Demography |
Unit-1 |
Teaching Hours:12 |
Introduction to Biostatistics
|
|
Types of variables. Measurement and measurement levels of the variables in biological science. Visualization and descriptive analysis of biological data. Sensitivity, Specificity, Positive predictive value, Negative predictive value. ROC Curves. | |
Unit-2 |
Teaching Hours:12 |
Parametric and Non - Parametric Methods
|
|
Parametric methods in biological data analysis: 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, Post Hoc Analysis for ANOVA, Pearson correlation coefficient: Introduction to non- parametric methods and its use in biological data. | |
Unit-3 |
Teaching Hours:12 |
Generalized Linear Models
|
|
Review of simple and multiple linear regression - introduction to generalized linear models - parameter estimation of generalized linear models - models with different link functions - binary (logistic) regression - estimation and model fitting - Poisson regression for count data - mixed effect models and hierarchical models with practical examples. | |
Unit-4 |
Teaching Hours:12 |
Basics of Epidemiology
|
|
Introduction to epidemiology, measures of epidemiology, observational study designs: case report, case series correlational studies, cross-sectional studies, retrospective and prospective studies, analytical epidemiological studies-case control study and cohort study, odds ratio, relative risk, the bias in epidemiological studies. | |
Unit-5 |
Teaching Hours:12 |
Demography
|
|
Introduction to demography, mortality and life tables, infant mortality rate, standardized death rates, life tables, fertility, crude and specific rates, migration-definition and concepts population growth, measurement of population growth-arithmetic, geometric and exponential, population projection and estimation, different methods of population projection, logistic curve, urban population growth, components of urban population growth. | |
Text Books And Reference Books: 1. Rosner, B. A. (2011). Fundamentals of Biostatistics. Austria: Brooks/Cole 2. Leon Gordis, Epidemiology | |
Essential Reading / Recommended Reading 1. Marcello Pagano and Kimberlee Gauvreau (2018), Principles of Biostatistics, 2 nd Edition, Chapman and Hall/CRC press. 2. Park K., (2019), Park's Text Book of Preventive and Social Medicine, Banarsidas Bhanot, Jabalpur | |
Evaluation Pattern ESE 50% CIA 50% | |
MDS571 - BIG DATA ANALYTICS (2023 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:100 |
Credits:4 |
Course Objectives/Course Description |
|
The subject is intended to give the knowledge of Big Data evolving in every real-time application 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). |
|
Course Outcome |
|
CO1: Understand the Big Data concepts in real time scenario CO2: Identify different types of Hadoop architecture CO3: Demonstrate an ability to use Hadoop framework for processing Big Data for Analytics CO4: Analyze the Big data under Spark architecture CO5: Demonstrate the programming of Big data using Hive and Pig environments |
Unit-1 |
Teaching Hours:15 |
Introduction
|
|
Concepts of Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive analytics - Big Data characteristics: Volume, Velocity, Variety, Veracity of data - Types of data: Structured, Unstructured, Semi-Structured, Metadata - Introduction to Hadoop Scaling - Distributed Framework -Hadoop v/s RDBMS-Brief history of Hadoop. | |
Unit-1 |
Teaching Hours:15 |
Lab Exercise
|
|
1. Installing and Configuring Hadoop 2. Case study for identifying Data Characteristics | |
Unit-2 |
Teaching Hours:15 |
Big Data Architecture
|
|
Standard Big data architecture - Big data application - Hadoop framework - HDFS Design goal - Master Slave architecture - Block System - Read-write Process for data - Installing HDFS - Executing in HDFS: Reading and writing Local files and Data streams into HDFS - Types of files in HDFS - Strengths and alternatives of HDFS - Concept of YARN. Apache Hadoop Moving Data in and out of Hadoop Understanding inputs and outputs of MapReduce - Problems with traditional large-scale systems-Requirements for a new approach. | |
Unit-2 |
Teaching Hours:15 |
Lab Exercise
|
|
1. Exercise on Reading and Writing Local files into HDFS 2. Exercise on Reading and Writing Data streams into HDFS | |
Unit-3 |
Teaching Hours:15 |
Parallel Processing with MapReduce
|
|
Introduction to MapReduce - Sample MapReduce application: Wordcount - MapReduce Data types and Formats - Writing MapReduce Programming - Testing MapReduce Programs - MapReduce Job Execution - Shuffle and Sort - Managing Failures - Progress and Status Updates. MapReduce Programs: Using languages other than Java with Hadoop, Analyzing a large dataset. | |
Unit-3 |
Teaching Hours:15 |
Lab Exercise
|
|
1. Exercise on MapReduce applications 2. Exercise on writing and testing MapReduce Programs 3. Exercise on Shuffle and Sort 4. Exercise on Managing Failures | |
Unit-4 |
Teaching Hours:15 |
Lab Exercise
|
|
1. Exercise on Hive Architecture 2. Exercise on Pig Architecture | |
Unit-4 |
Teaching Hours:15 |
Hive and Pig
|
|
Hive Architecture - Components - Data Definition - Partitioning - Data Manipulation - Joins, Views and Indexes - Hive Execution - Pig Architecture - Pig Latin Data Model - Latin Operators - Loading Data - Diagnostic Operators - Group Operators - Pig Joins - Row Level Operators - Pig Built-in function - User defined functions - Pig Scripts | |
Unit-5 |
Teaching Hours:15 |
Stream Processing with Spark
|
|
Stream processing Models and Tools - Apache Spark - Spark Architecture: Resilient Distributed Datasets, Directed Acyclic Graph - Spark Ecosystem - Spark for Big Data Processing: MLlib, Spark GraphX, SparkR, SparkSQL, Spark Streaming - Spark versus Hadoop . PySpark + NumPy + SciPy, Code Optimization | |
Unit-5 |
Teaching Hours:15 |
Lab Exercise
|
|
1. Exercise on installing Spark 2. Exercise on Directed Acyclic Graph 3. Exercise on Spark using MLlib, Spark GraphX 4. Exercise on Spark using SparkR, Spark Streaming | |
Text Books And Reference Books: [1]. Anil Maheshwari (2020). Big Data. 2nd Edition. McGraw Hill Education Pvt Ltd. [2]. S Chandramouli, Asha A George, C R Rene Robin,,D Doreen H Miriam,J JasmineC M, Big Data Analytics,University Press India Ltd., 2024 | |
Essential Reading / Recommended Reading [1]. Thomas Erl, Wajid Khattak and Paul Buhler (2016). Big Data Fundamentals: Concepts, Drivers and Techniques. Service Tech Press. [2]. Julián Luengo, Diego García-Gil, Sergio Ramírez-Gallego, Salvador García, Francisco Herrera (2020). Big Data Preprocessing: Enabling Smart Data. Springer Nature Publishing. [3]. Seema Acharya, Subhasini Chellappan (2019), Big Data and Analytics. 2nd Edition, Wiley India Pvt Ltd | |
Evaluation Pattern CIA 50% ESE 50% | |
MDS572A - EVOLUTIONARY ALGORITHMS (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:3 |
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:12 |
Introduction to Evolutionary Computing
|
|
Terminologies – Notations – Problems to be solved – Optimization – Modeling – Simulation – Search problems – Optimization constraints Lab Exercise 1. Implementation of single and multi-objective functions 2. Implementation of binaryGA | |
Unit-2 |
Teaching Hours:12 |
Evolutionary Programming
|
|
Continuous evolutionary programming – Finite state machine optimization – Discrete evolutionary programming – The Prisoner’s dilemma 1. Implementation of continuousGA. 2. Implementation of evolutionary programming | |
Unit-3 |
Teaching Hours:12 |
GENETIC PROGRAMMING
|
|
BASICS: Fundamentals of genetic programming – Genetic programming for minimal time control EVOLUTIONARY ALGORITHM VARIATION: Initialization – Convergence – Population diversity – Selection option – Recombination – Mutation Lab Programs 1. Implementation of geneticprogramming
2. Implementation of Ant ColonyOptimization | |
Unit-4 |
Teaching Hours:12 |
OPTIMIZATION MODELS
|
|
ANT COLONY OPTIMIZATION: Pheromone models – Ant system – Continuous Optimization – Other Ant System PARTICLE SWARM OPTIMIZATION: Velocity limiting – Inertia weighting – Global Velocity updates – Fully informed Particle Swarm Lab Programs 1. Implementation of Particle SwarmOptimization
2. Implementation of Multi-ObjectOptimization | |
Unit-5 |
Teaching Hours:12 |
MULT-OBJECTIVE OPTIMIATION
|
|
Pareto Optimality – Hyper volume – Relative coverage – Non-pareto based EAs – Pareto based EAs – Multi-objective Biogeography based optimization Lab Programs 1. Simulation of EA in Planning problems (routing, scheduling, packing) and Design problems (Circuit, structure,art)
2. Simulation of EA in classification/predictionmodelling | |
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. 2. Eiben and J. Smith, Introduction to evolutionary computing. 2nd ed. Berlin: Springer, 2015. | |
Essential Reading / Recommended Reading
| |
Evaluation Pattern ESE 50 % CIA 50% | |
MDS572B - QUANTUM MACHINE LEARNING (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
This course explores the intersection of quantum computing and machine learning, introducing students to the fundamental principles of quantum mechanics and their application in designing quantum algorithms for machine learning tasks. Students will gain hands-on experience in implementing quantum machine learning algorithms using relevant programming frameworks. The course aims to equip students with the knowledge and skills necessary to navigate the rapidly evolving field of quantum machine learning. |
|
Course Outcome |
|
CO1: Understand the basics of quantum mechanics and quantum computing. CO2: Implement and analyze quantum machine learning algorithms using Qiskit CO3: Apply quantum algorithms to solve machine learning problems. CO4: Critically evaluate the advantages and limitations of quantum machine learning approaches |
Unit-1 |
Teaching Hours:12 |
Introduction to Quantum Mechanics
|
|
Introduction and overview, Global perspectives, Quantum bits, Quantum computation, Quantum algorithms, Quantum information processing. Introduction to Quantum Mechanics - The postulates of Quantum Mechanics, Application: superdense coding, The density operator.
LAB Exercise
| |
Unit-2 |
Teaching Hours:12 |
Introduction to Quantum Computation
|
|
Quantum Circuits - Quantum algorithms, Single Qubit operations, Controlled operations, Measurement, Universal Quantum gates. Simulation of Quantum systems.
LAB Exercise
| |
Unit-3 |
Teaching Hours:12 |
Clustering Structure and Quantum Computing
|
|
Quantum Random Access Memory, Quantum Principal Component Analysis, Quantum K-Means, Quantum Hierarchical Clustering. LAB Exercise
| |
Unit-4 |
Teaching Hours:12 |
Quantum Classification
|
|
Nearest Neighbors, Support Vector Machines with Grover’s Search, Support Vector Machines with Exponential Speedup, Computational Complexity LAB Exercise
| |
Unit-5 |
Teaching Hours:12 |
Quantum Pattern Recognition
|
|
Quantum Associative Memory, The Quantum Perceptron, Quantum Neural Networks, Physical Realizations. Variational quantum algorithms and their applications LAB Exercise
| |
Text Books And Reference Books: [1] Quantum Computation and Quantum Information by Michael Nielsen and Isaac Chuang
[2] Quantum Machine Learning: What Quantum Computing Means to Data Mining by Peter Wittek | |
Essential Reading / Recommended Reading [1] Quantum Computing for Computer Scientists by Noson S. Yanofsky and Mirco A. Mannucci [2] Quantum Machine Learning: A Gentle Introduction by Jacob Biamonte, Peter Wittek, and Nicola Pancotti [3] Quantum Machine Learning: Theory and Experiments" by Maria Schuld and Francesco Petruccione
[4] Learn Quantum Computing with Python and Q# by Sarah C. Kaiser and Christopher Granade" | |
Evaluation Pattern ESE 50% CIA 50% | |
MDS572C - REINFORCEMENT LEARNING (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
The main objective of this course is to teach students how to define reinforcement learning problems and apply algorithms such as dynamic programming, Monte Carlo, and temporal-difference learning to solve them. Students will advance towards more complex state space environments by employing function approximation, deep Q-networks, and cutting-edge policy gradient techniques. We will also discuss current approaches rooted in reinforcement learning, including imitation learning, meta learning, and more intricate environment formulations. |
|
Course Outcome |
|
CO1: Grasp the fundamental concepts of Reinforcement Learning, including Markov Decision Processes, states, actions, rewards, and key components of RL System.
CO2: Able to apply dynamic programming methods
CO3: Develop skills in model-free prediction using Monte Carlo methods.
CO4: Comprehend various exploration strategies such as epsilon-greedy, softmax exploration, and Upper Confidence Bound (UCB) CO5: Apply and understand Policy Gradient method in Reinforcement Learning Environment |
Unit-1 |
Teaching Hours:12 |
Introduction to Reinforcement Learning
|
|
Overview of Reinforcement Learning (RL)-Definition and key concepts - Contrasting RL with supervised and unsupervised learning, Markov Decision Processes (MDPs) - States, actions, and rewards - Transition probabilities and dynamics - Bellman equation and optimality Value Functions - State-value and action-value functions
LAB Exercise
| |
Unit-2 |
Teaching Hours:12 |
Dynamic Programming Approaches
|
|
Policy Evaluation,Policy Improvement,Policy iteration,Value iteration,Asynchronous Dynamic Programming Lab Exercises
| |
Unit-3 |
Teaching Hours:12 |
Model-Free Prediction
|
|
Monte Carlo methods,Temporal Difference (TD) learning, Eligibility traces Lab Exercise
| |
Unit-4 |
Teaching Hours:12 |
Exploration and Exploitation
|
|
Exploration Strategies-Epsilon-greedy- Softmax exploration - Upper Confidence Bound (UCB), Multi-Armed Bandits
Lab Exercise
| |
Unit-5 |
Teaching Hours:12 |
Policy Gradient Method
|
|
Policy Approximation and its advantages,Actor-Critic Methods - Advantage functions- A3C (Asynchronous Advantage Actor-Critic),Policy Gradient for continuous problem LAB Exercises
| |
Text Books And Reference Books: [1] Richard S. Sutton and Andrew G. Barto, "Reinforcement learning: An introduction", Second Edition, MIT Press, 2019
[2]Dimitri Bertsekas and John G. Tsitsiklis, Neuro Dynamic Programming, Athena Scientific. 1996. ISBN-13: 978-1886529106 | |
Essential Reading / Recommended Reading [1]. V. S. Borkar, Stochastic Approximation: A Dynamical Systems Viewpoint, Hindustan Book Agency, 2009. ISBN-13: 978-0521515924
[2]. Deep Learning. Ian Goodfellow and Yoshua Bengio and Aaron Courville. MIT Press. 2016.ISBN-13: 978-0262035613. | |
Evaluation Pattern ESE 50% CIA 50% | |
MDS573A - GEOSPATIAL DATA ANALYTICS (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
This course aims to provide students with a comprehensive understanding of geospatial data analytics techniques, tools and applications. Students will learn to analyze, interpret, and visualize spatial data to derive meaningful insights. |
|
Course Outcome |
|
CO1: Understand fundamental geospatial data analysis techniques
CO2: Apply geospatial data visualization methods to represent spatial patterns and trends CO3: Apply different geospatial analysis techniques CO4: Implement geospatial data analytics workflows using relevant software tools.
|
Unit-1 |
Teaching Hours:12 |
Introduction to Geospatial Data Analytics
|
|
Overview of geospatial data and its sources, Introduction to GIS (Geographic Information Systems), spatial data structures, coordinate systems and data format. Basic spatial data analysis techniques, spatial querying, buffering and overlay operations.
Lab Exercise Introduction to ArcGIS or QGIS to load spatial data layers and perform basic spatial analysis tasks. | |
Unit-2 |
Teaching Hours:12 |
Spatial Data Visualization
|
|
Principles and techniques of spatial data visualization , Cartography and map design.Visualization techniques for spatial data : choropleth maps, proportional symbol maps and heatmaps. interactive mapping tools and platforms for creation of dynamic, web-based maps. | |
Unit-3 |
Teaching Hours:12 |
Spatial Analysis Techniques Teaching
|
|
Introduction to Spatial interpolation methods: inverse distance weighting and kriging. Spatial clustering and pattern analysis. Understanding Geostatistics and spatial regression | |
Unit-4 |
Teaching Hours:12 |
Geospatial Data Mining and Machine Learning
|
|
Introduction to geospatial data mining. Understanding Machine learning algorithms like decision trees, random forests, and support vector machines for spatial data analysis Land cover classification, spatial prediction and anomaly detection. Lab Exercise: Implementing machine learning models for spatial prediction tasks.
| |
Unit-5 |
Teaching Hours:12 |
Advanced Topics in Geospatial Data Analytics
|
|
Understanding Big data analytics for geospatial data, Web mapping and spatial data APIs Introduction to Spatial data integration and interoperability
Lab Exercise: Building a web-based geospatial application using Leaflet.js | |
Text Books And Reference Books:
| |
Essential Reading / Recommended Reading
Web Links
| |
Evaluation Pattern CIA-50% ESE 50% | |
MDS573B - BIO-INFORMATICS (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
1. Provide an overview of the Machine Learning concepts and practices in Bioinformatics 2. Gain experience in applications and limitations of Machine Learning
3. To encompass a broad range of approaches to data analysis across the biological sciences |
|
Course Outcome |
|
CO1: Understand how to evaluate models generated from data CO2: Understand public-domain biological datasets CO3: Analyze genomics using decision trees, and random forests CO4: Design computational experiments for training and evaluating machine learning methods for solving bioinformatics problems |
Unit-1 |
Teaching Hours:12 |
Introduction to Bio-Informatics Data and Databases
|
|
Types of Biological data:-genomic DNA, Complementary DNA, Recombinant DNA, Expressed sequence tags, Sequence -Tagged sites. Lab Exercise
| |
Unit-2 |
Teaching Hours:12 |
Gene Selection using Omics Data
|
|
Approaches for Gene selection - multi-level omics data integration, Machine learning approaches for multi-level data integration, Random Forest algorithm in imbalance genomics classification Lab Exercise:
| |
Unit-3 |
Teaching Hours:12 |
Microarray Data Optimization
|
|
Microarray data, Grey Wolf Optimization (GWO) Algorithm, Studies on GWO variants, Application of GWO in medical domain, Application of GWO in Microarray data. Case study, Using AI to detect Coronavirus. Healthcare Solutions: Using machine learning approaches for different purposes, Various resources of medical data set for research, Deep learning in Health care, Projects in medical imaging and diagnostics. Lab Exercise:
| |
Unit-4 |
Teaching Hours:12 |
Python for Bioinformatics working with BioPython
|
|
Representing sequence data: Storing DNA sequence, Concatenating DNA fragments, Transcription DNA to RNA, Proteins,Files and Arrays, Reading Proteins in Files, Arrays, dictionary and List Context. Lab Exercise
| |
Unit-5 |
Teaching Hours:12 |
The Genetic Code
|
|
GenBank: GenBank files, GenBank Libraries, Separating Sequence and Annotation, AParsing Annotations, Indexing GenBank with DBM. Protein Data Bank: Files and Folders, PDB Files, Parsing PDB Files. Lab Exercise 1. Case Study:
| |
Text Books And Reference Books:
| |
Essential Reading / Recommended Reading
Web resources: [1] https://canvas.harvard.edu/courses/8084/assignments/syllabus [2] https://www.coursera.org/specializations/bioinformatics
[3] http://www.dtc.ox.ac.uk/modules/introduction-bioinformatics-bioscientists.html | |
Evaluation Pattern CIA-50% ESE-50% | |
MDS573C - IMAGE AND VIDEO ANALYTICS (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
Course Description : This course will provide a basic foundation towards digital image processing and video analysis. This course will also provide a 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: Develop proficiency in image enhancement and segmentation CO3: Develop skills in object detection and recognition CO4: Apply the image and video analysis approaches to solve real world problems |
Unit-1 |
Teaching Hours:12 |
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, Introduction to Digital Video, Sampled Video, Video Transmission. Gray-Level Processing: Image Histogram, Linear and Non-linear point operations on Images, Image Thresholding, Region labelling, Binary Image Morphology.
| |
Unit-2 |
Teaching Hours:12 |
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, A model of The Image Degradation /Restoration, Noise Models and basic methods for image restoration. | |
Unit-3 |
Teaching Hours:12 |
Image and Video Compression
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Fundamentals of Image Compression: Huffman Coding, Run length Coding, LZW Coding, Bit plane coding. Video Compression: Basic Concepts and Techniques of Video compression, MPEG-1 and MPEG-2 Video Standards. | |
Unit-4 |
Teaching Hours:12 |
Feature Detection and Description
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Introduction to feature detectors, Point, line and edge detection, Image Segmentation - Region Based Segmentation – Region Growing and Region Splitting and Merging, Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method. | |
Unit-5 |
Teaching Hours:12 |
Object Detection and Recognition
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Descriptors: Boundary descriptors - Fourier descriptors - Regional descriptors Object detection and recognition in image and video: Minimum distance classifier, Applications in image and video analysis, object tracking in videos | |
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. Richard Szeliski, Computer Vision Algorithms and Applications, 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% | |
MDS581 - PROJECT - II (RESEARCH PROJECT_ DATA SCIENCE CAPSTONE PROJECT) (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:5 |
Max Marks:100 |
Credits:2 |
Course Objectives/Course Description |
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The Capstone/Research Project in Data Science provide students with the opportunity to integrate and apply the knowledge and skills acquired throughout their coursework to address real-world data science challenges. This course emphasizes advanced research methodologies, data analysis techniques, and effective communication of findings. Students will work individually or in teams of 2 under the supervision of faculty advisors to complete a substantial research project. Projects may focus on a wide range of topics within the field of data science, including but not limited to machine learning, data mining, natural language processing, computer vision, predictive modelling big data analytics etc. |
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Course Outcome |
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CO1: To demonstrate advanced proficiency in conducting independent research in the field of data
science CO2: To apply advanced statistical and machine learning techniques to analyze complex datasets CO3: Students will develop and apply creative problem-solving skills to address data science challenges CO4: Students will develop proficiency in project management skills |
Unit-1 |
Teaching Hours:30 |
UNIT I
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Identifying a research question or problem statement relevant to the field of data science, Conducting a comprehensive literature review to understand the existing research and methodologies related to the chosen topic, designing and implementing appropriate data collection and pre-processing techniques, | |
Text Books And Reference Books: Research papers related to selected domain of study | |
Essential Reading / Recommended Reading Research papers related to selected domain of study | |
Evaluation Pattern 50% CIA 50% ESE | |
MDS681 - INDUSTRY PROJECT (2023 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:300 |
Credits:10 |
Course Objectives/Course Description |
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This course helps the student to gain practical knowledge of data science project pipelines through real time industry experience and become globally competent. The course also helps in developing Entrepreneurial skills among students. |
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Course Outcome |
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CO1: Develop Real time Projects. CO2: Practice data science principles and strategies in the project
development |
Unit-1 |
Teaching Hours:30 |
UNIT 1
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Implementation of proposed solution with relevant tools. Writing a research paper to present the findings and communication to identified journal. | |
Text Books And Reference Books: Relevant online/offline resources | |
Essential Reading / Recommended Reading Relevant online/offline resources | |
Evaluation Pattern 50% CIA 50% ESE | |
MDS682 - RESEARCH PUBLICATION (2023 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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The objective of the course is to provide practical exposure to major data analysis paradigms in various application domains for performing research. Students complete the implementation of identified research problem and present the finding through a research paper.
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Course Outcome |
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CO1: Analyze various data science paradigms CO2: Build a data science model to provide solution to the identified problem |
Unit-1 |
Teaching Hours:30 |
UNIT 1
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Implementation of proposed solution with relevant tools. Writing a research paper to present the findings and communication to identified journal. | |
Text Books And Reference Books: Research Papers Relevant to selected problem of study | |
Essential Reading / Recommended Reading Research Papers Relevant to selected problem of study | |
Evaluation Pattern CIA 50 Marks |