CHRIST (Deemed to University), Bangalore

DEPARTMENT OF STATISTICS_AND _DATA_SCIENCE

School of Business and Management

Syllabus for
Master of Science (Data Science)
Academic Year  (2024)

 
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
MDS151 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 - 4 3 100
MDS232 MATHEMATICAL FOUNDATIONS FOR DATA SCIENCE-II - 4 3 100
MDS271 DATABASE TECHNOLOGIES - 7 4 100
MDS272 INFERENTIAL STATISTICS USING R - 7 4 100
MDS273 FULL STACK WEB DEVELOPMENT - 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 - 5 4 100
MDS531B BAYESIAN INFERENCE - 5 4 100
MDS531C BIO-STATISTICS - 5 4 100
MDS571 BIG DATA ANALYTICS - 7 4 100
MDS572A EVOLUTIONARY ALGORITHMS - 6 3 100
MDS572B QUANTUM MACHINE LEARNING - 6 3 100
MDS572C REINFORCEMENT LEARNING - 6 3 100
MDS573A GEOSPATIAL DATA ANALYTICS - 6 3 100
MDS573B BIO-INFORMATICS - 6 3 100
MDS573C IMAGE AND VIDEO ANALYTICS - 6 3 100
MDS581 PROJECT - II (RESEARCH PROJECT_ DATA SCIENCE CAPSTONE PROJECT) - 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
    

    

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

 

 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.

Course Outcome

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

 

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

Course Outcome

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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.

Course Outcome

CO1: Understand the properties of Vector spaces

CO2: Use the properties of Linear Maps in solving problems on Linear Algebra

CO3: Demonstrate proficiency on the topics Eigenvalues, Eigenvectors and Inner Product Spaces

CO4: Apply mathematics for some applications in Data Science

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

MDS151 - 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

 

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.

Course Outcome

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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

 

The students shall be able to understand the main principles of programming. The objective also includes indoctrinating the activities of implementation of programming principles.

Course Outcome

CO1: Understand the fundamentals of programming languages.

CO2: Understand the design paradigms of programming languages.

CO3: To examine expressions, subprograms and their parameters.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

 

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. 

Course Outcome

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

 

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.

Course Outcome

CO1: Demonstrate the Basic file, directory commands

CO2: Understand the Unix system environment

CO3: Apply shell programming concepts to solve given problem

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

 

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

Course Outcome

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

CO2: Demonstrate significant experience with python program development environment

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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.

Course Outcome

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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

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

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Essential Reading / Recommended Reading
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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

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Essential Reading / Recommended Reading
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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

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Essential Reading / Recommended Reading
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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.

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Essential Reading / Recommended Reading
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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

Text Books And Reference Books:
Essential Reading / Recommended Reading
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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

Text Books And Reference Books:
Essential Reading / Recommended Reading
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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.

Text Books And Reference Books:
Essential Reading / Recommended Reading
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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

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Essential Reading / Recommended Reading
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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

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Essential Reading / Recommended Reading
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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

 

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.

Course Outcome

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

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Essential Reading / Recommended Reading
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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

 

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.

Course Outcome

CO1: Develop Real time Projects.

CO2: Practice data science principles and strategies in the project development

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Essential Reading / Recommended Reading
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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

 

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.

 

Course Outcome

CO1: Analyze various data science paradigms

CO2: Build a data science model to provide solution to the identified problem

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Essential Reading / Recommended Reading
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