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M.Sc. (Data Science)

Program OverviewData science is a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data. In other words, the detailed study of the flow of information from structured and unstructured data available with an organization is called data science. It primarily involves obtaining the meaningful insights from the data which is processed through analytical study. The current era is becoming a digital space where each organization deals with large amount of structured and unstructured data on a daily basis. Evolving technologies are leading to cost saving solutions for storage and analysis of such large data. In the current era, for the career progression, one needs to understand the language of data through analytical skill. Hence, it is absolutely necessary nowadays, to develop manpower with a skill to perform data analysis to get meaningful information from the data of different domains such as banking and finance, insurance, agriculture, healthcare, retail, education, social media, manufacturing, transportation, entertainment and so on. As reported recently, with nearly 100,000 vacancies, India is the second biggest data analytics jobs hub after the US and demand for data science skill sets is increasing at a very fast pace.

The field of data science has witnessed an immense growth in recent years particularly due to the rise of internet and social media. The exploration of data science by the business world initially started with analysis of business data and hence emphasis was given for financial data analytics. With the increase of multimedia data such as image, video, audio and text, each domain as mentioned above, many a times needs to perform analysis of such multimedia big data. Hence the study of data science includes analysis of multimedia data along with other types of data such as business data and unstructured social media data. In our daily life, now we are capturing data from sources such as i) sensors used in various places like agricultural fields, shopping malls, ii) posts on social media, iii) digital images and videos captured in cell phones and iv) purchase transactions made through e-commerce. Analysis of such big data which could be multimodal in nature is a huge challenge. Modern technologies in the areas of artificial intelligence (AI) and machine learning (ML) are now extensively used to get insights of such big data.

With the availability of modern technologies of data storage, cleaning and computing, the study of data science expanded beyond the boundaries of mathematics and statistics. In modern days the study of data science is constituted with the knowledge of mathematics, statistics and computer science. Data science brings together a lot of skills of these disciplines with adequate domain knowledge to help any organization find ways to i) take major business decisions, ii) reduce costs, iii) get in to new markets, iv) launch a new product or service, v) find the sentiment of the customers, vi) recruiting the best talent and so on.

With all these in mind, our new master’s program in Data Science (launched in 2020), not only includes traditional data analysis skills but also incorporates other crucial skills to perform multimedia and big data analysis. The courses focus on acquiring fundamental knowledge of mathematics, statistics, computer science and machine learning. The curriculum also includes domain specific knowledge by incorporating courses in multimedia, business and finance. Techniques such as data processing, database management, deep learning, data visualization along with tools such as Python, R, and Tableau are also included to enhance the technical and analytical skills. Value Added Courses are offered during the program to make the students hands-on with the challenges of data science and to enable students with industry ready skills. In essence, MSc. in Data Science program has been designed to provide students with a strong foundation in data management and analysis, and the necessary skills to succeed in data science and data-analytics related job.

MSc_Data_Science_Academics_2022

Based on student requests, optional Value Added Courses such as comprehensive SAS training which has variety of tools and applications may be also offered during summer/winter breaks. The SAS based training will also enable the students to obtain SAS global certification in many fields and the skills can be ratified and showcased through SAS international certification badges.

Degree Name: M.Sc. in Data Science

Duration: 2 Years (Four Semesters)

Characterization of the Program: Intersection of Mathematics, Statistics, Programming, Big-Data and Machine Learning

Uniqueness of the Program: Hands on and Case Study based Program

The program primarily aims to cater to the following audience:

  • Traditional Science/ Economics/ Engineering Graduates with good mathematical aptitude, basic programming skills and inclination towards data science.
  • Professionals in the workplace who wish to improve their skills for the emerging jobs in data-science related fields.

Program Objective

The primary objective of the M.Sc. in Data Science program is to develop skilled professional workforce that is prepared to address the increasing needs in the rapidly expanding area of big data analytics. The program aims to provide skills in quantitative data analysis, data mining, data modeling and prediction, data storage and management, machine learning, big data processing, data visualization, multimedia big data, programming and communication skills. Value Added Course/ training and a large number of practical case studies have been integrated in the program to boost the learner confidence and market acceptability.

Pedagogy

The program relies on a wide range of teaching methods including lectures, tutorials, case studies, lab exercises, and projects throughout the year. The program’s emphasis is on learning by doing and this is imparted in the form of mini-projects and case-studies.

Participants

Science (Statistics, Mathematics, Physics)/ IT/ Computer Science/ Data Science/ Economics/ Engineering Graduates or its equivalent with good mathematical aptitude, basic programming skills and inclination to pursue a career in data science.
Professionals who are interested in upskilling in the field of data science.

Outcome of The Program

On completion of the program the participants would

  • Acquire a strong foundation in data management and data analysis
  • Be well-versed with state of the art Data Science tools & techniques
  • Demonstrate skills to formulate and solve real-life problems using data
  • Develop data driven decision making skills and be equipped to apply technology in Business
  • Demonstrate a critical awareness of the current areas of technology and business where data science is applied

Semester-wise Program Structure

Autumn Semester (Semester 1)
Course Name Credits (L-T-P-C)
Mathematical Foundation for Data Science 4 Credits (3-1-0-4)
Data Structures and Algorithms (Lab:Python) 4 Credits (3-0-2-4)
Statistical Methods (Lab:R) 4 Credits (3-0-2-4)
Programming Lab 2 Credits (0-0-4-2)
Introduction to Database Management 4 Credits (3-0-2-4)
Total 18 Credits
Winter Semester (Semester 2)
Course Name Credits (L-T-P-C)
Machine Learning 4 Credits (3-0-2-4)
Numerical Methods for Data Science 4 Credits (3-0-2-4)
Big-Data Processing 3 Credits (2-0-2-3)
Mini Project - 1* 3 Credits (0-0-6-3)
Optimization 3 Credits (2-0-2-3)
Technical Elective - 1 4 Credits (3-0-2-4)
Total 21 Credits
SUMMER SEMESTER

Value Added Courses (Pass/ Fail)

Autumn Semester (Semester 3)
Course Name Credits (L-T-P-C)
Deep Learning 4 Credits (3-0-2-4)
Interactive Data Visualization 4 Credits (3-0-2-4)
Open Elective - 1 3 Credits (3-0-0-3)
Technical Elective-2 3/ 4 Credits
Mini Project - 2* 3 Credits (0-0-6-3)
Total 17/18 Credits

*Mini Project (to be executed in two phases 1 and 2) will start in second semester and will continue till the end of third semester. Students are expected to work on Mini Project during summer semester also.

Winter Semester (Semester 4)

Full time On-Campus Projects / Industry Internships

Total 16 Credits (Pass/ Fail)

List of Technical Electives

  • Image Processing
  • Information Retrieval
  • Computational Finance
  • No SQL Databases
  • Cloud Computing
  • Information Systems Security
  • Natural Language Processing
  • Computer Vision
  • Financial/ Business Data Analysis
  • Data Warehousing and Data Mining
  • Statistical Foundation for Data Science
  • Speech Processing

SAS Training (optional) – to be imparted during winter/summer breaks

  • PROGRAMMING FOR DATA SCIENCE IN SAS -ESSENTIALS & MANIPULATION TECHNIQUES
  • MACRO & SQL PROGRAMMING FOR DATA SCIENCE IN SAS INTRODUCTION TO SAS AND HADOOP

SAS Certification Exam – I

(SAS Certified Specialist: Base Programming Using SAS 9.4)

  • DATA INTEGRATION FOR MANAGERS
  • BIG DATA VISUALIZATION – ESSENTIALS AND ADVANCED
  • STATISTICAL INFERENCE AND MODELING USING SAS
  • APPLIED MACHINE LEARNING USING SAS

SAS Certification Exam – II

( SAS Certified Data Integration Developer for SAS 9)

SAS Certification Exam - III

( SAS Certified Specialist: Machine Learning Using SAS VIYA 3.4)

  • NEURAL NETWORK ESSENTIALS
  • DEEP LEARNING USING SAS
  • VISUAL TEXT ANALYTICS USING SAS
  • VISUAL FORECASTING USING SAS
  • OPTIMIZATION CONCEPTS FOR DATA SCIENCE AND ARTIFICAL INTELLIGENCE

SAS Certification Exam - IV

(SAS Certified Specialist: Natural Language Processing and Computer Vision Using SAS VIYA 3.4)

SAS Certification Exam – V

(SAS Certified Specialist: Forecasting and Optimization Using SAS VIYA 3.4)

Admission Process

Details on the application process, admission criteria, fee structure and financial assistance can be found here

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