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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 |
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 |
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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 |
|
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 |
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 |
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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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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 |
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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 |
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 |
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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. |
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 |
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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. |
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. |
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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 |
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. |
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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 |
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 |
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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. |
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. |
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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. |
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 |
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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. |
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Course Outcome |
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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 |
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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 |
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Course Outcome |
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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.
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Course Outcome |
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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 |
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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. |
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Course Outcome |
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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 |
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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. |
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Course Outcome |
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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 |
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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. |
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Course Outcome |
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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 |
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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 |
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Course Outcome |
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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 |
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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. |
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Course Outcome |
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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 |
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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. |
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Course Outcome |
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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 |
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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. |
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Course Outcome |
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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
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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 |
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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. |
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Course Outcome |
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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 |
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The course is designed to provide to enhance the soft skills and technical undetstanding of the students. |
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Course Outcome |
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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. |
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Course Outcome |
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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. |
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Course Outcome |
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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. |
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Course Outcome |
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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. |
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Course Outcome |
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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 |
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Course Outcome |
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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. |
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Course Outcome |
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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. |
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Course Outcome |
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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. |
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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 |
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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 |
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Course Outcome |
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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 |
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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 |
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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. |
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Course Outcome |
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CO1: Understand various data analysis paradigms used in various application domains CO2: Identify gaps to propose research based solution |
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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 |
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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. |
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Course Outcome |
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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 |
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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 |
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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. |
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Course Outcome |
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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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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 |
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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. |
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Course Outcome |
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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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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 |
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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). |
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Course Outcome |
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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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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 |
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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. |
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Course Outcome |
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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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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 |
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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. |
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Course Outcome |
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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 |
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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 |
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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. |
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Course Outcome |
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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 |
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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 |
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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. |
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Course Outcome |
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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.
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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 |
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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 |
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Course Outcome |
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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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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 |
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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. |
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Course Outcome |
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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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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 |
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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 |
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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 |
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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 |
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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 |
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