MS Data Science

Details & Semester Outlines for MSDS

The MS (Data Science) program is of 2-years duration offered in the evening. It requires 30 credit hours including 3 core courses, 2 specialized data science courses and a Thesis of 6 credit hours is mandatory.
The maximum time limit to complete the MS (Data Science) degree is 4 years.

Why Study Data Science?

The amount of data is growing so rapidly as well as its significance in the emerging societal setups such as the pervasive Internet of Things. The way one imagines data is going to change in the coming years. Both Big Data Analytics and pervasive computing hinge on the principle axis of data analytics. MS (Data Science) program is going to be relevant in terms of job creation and artisanal smart business generation. Graduates from this program would definitely avail the early-bird advantage.

Program Objectives

The MS (Data Science) program has been designed to give students the option to be part of a data science endeavour that begins with the identification of business processes, determination of data provenance and ownership, understanding the ecosystem of the business decisions, skill sets and tools that shape the data, making data amenable to analytics, identifying sub-problems, recognizing the technology matrix required for problem resolution, creating incrementally-complex data-driven models and then maintaining them to ultimately leverage them for business growth. Individual objectives include:

• To equip students to transform data into actionable insights to make complex decisions.
• To enable students to understand and analyze problems and arrive at computable solutions.
• To expose students to the set of technologies that match those solutions.
• To gain hands-on experience on data-centric tools for statistical analysis, visualization and big data applications at the same rigorous scale as in a practical data science project.
• To understand the implications of handling data in terms of data security and business ethics.

Master of Science in Data Science

First Year

First Semester	Second Semester
DSC xxxx Statistical and Mathematical Methods for Data Science	DSC xxxx Machine Learning
DSC xxxx Tools and Techniques in Data Science	DSC xxxx Specialization-Elective-I
DSC xxxx Elective-I	DSC xxxx Specialization-Elective-II

Second Year

Third Semester	Fourth Semester
DSC xxxx Thesis (Part-I)	DSC xxxx Elective-III
DSC xxxx Elective-II	DSC xxxx Thesis (Part-II)

Course types	Cumulative Credits
Core courses (3)	9
Specialization Requirement Courses (2)	6
Electives (3)	9
Thesis (Part-I & Part-II)	6
Total	30

Three Core Courses	Cr.Hrs
Statistical and Mathematical Methods for Data Science	3
Tools and Techniques in Data Science	2 + 1*
Machine Learning	3

Specialization Courses	Cr.Hrs
Big Data Analytics	3
Deep Learning	3
Natural Language Processing	3
Distributed Data Processing	3

Elective Courses

• Advanced Computer Vision
• Algorithmic trading
• Bayesian Data Analysis
• Big Data Analytics
• Bioinformatics
• Cloud computing
• Computational Genomics
• Data Visualization
• Deep Learning
• Deep Reinforcement Learning
• Distributed Data Processing and Machine Learning
• Distributed Machine Learning in Apache Spark
• High-performance computing
• Inference & Representation
• Natural Language Processing
• Optimization Methods for Data Science and Machine Learning
• Probabilistic Graphical Models
• Scientific Computing in Finance
• Social network analysis
• Time-series Analysis and Prediction

-All courses may not be offered in every semester. Elective courses may vary from time to time.
-Alternative courses may be substituted as and when required.
-The MS Data Science Program is subjected to 20 students.

Deficiency Courses

DSC xxxx Programming Fundamentals (Core Programming Course)
DSC xxxx Data Structures & Algorithms OR Design & Analysis of Algorithms
DSC xxxx Database Systems