
Program Outline
This is unique Two-year full-time Master of Science in Data Science and Analytics degree program enables students to develop interdisciplinary skills, and gain a deep understanding of technical and applied knowledge in data science and analytics. The Graduates are highly trained, qualified data scientists and Analysts who can go on to pursue careers in industry, government or research.
Now when Hadoop and other frameworks have successfully solved the problem of huge storage requirements, the focus has now shifted to the processing of this myriad of data. Data Science is the secret sauce here which helps in extracting meaningful insights from the complex and large sets of data all around us.
Students will also get the opportunity to work on live projects and explore their academic learning with practical and real-life industry experience.
Eligibility
Any Bachelor’s Degree in Science or graduate of Engineering and technology of this university or any other recognized university as equivalent with minimum 50% marks (45% for reserved category) can apply.
Fees Structure
M.Sc.(Data Science) Rs.100000/- Per Year + University Fees as Listed Below
Sr.No. |
Details of Fees |
Amount |
Period |
Remark |
1 |
Application Form & CET |
Rs.1500/- |
One Time |
For UG/PG |
2 |
Caution Money Deposit |
Rs.5000/- |
One Time |
For UG/PG
|
3 |
Eligibility Fees |
Rs.5000/- |
One Time |
For UG/PG & Diploma only |
4 |
Examination Fees UG/PG |
Rs.5000/- |
Yearly |
For UG/PG
|
5 |
Examination Fees for Diploma |
Rs.2500/- |
Semester Wise |
______ |
6 |
Examination Fees for Certificate |
Rs.500/- |
Semester Wise |
______ |
7 |
Student Activities Fees |
Rs.1000/- |
Yearly |
______ |
8 |
UMIS & Student Insurance |
Rs.3000/- |
One Time |
______ |
9 |
Supplementary Examination Fees upto 3 Subjects |
Rs.1500/- |
Yearly |
______ |
10 |
Supplementary Examination Fees more than 3 Subjects |
Rs.2500/- |
Yearly |
______ |
11 |
Alumni Association Membership Fees for UG/PG(To be paid in the final year) |
Rs.1000/- |
One Time |
______ |
Key Features
- Prospective students are expected to demonstrate working knowledge of statistics, data structures and algorithms, Python and R software packages
- Learn to make Data-driven decision
- Focuses on developing your skills like problem-solving, communication, teamwork, creativity and business intelligence
- Students get Campus Recruitment Training program to enhance and improve their skills required for getting placement
- We provide students with Placement Assistance and Support for placement at highly reputed Multi-National Companies with good package
Program Structure
For M.Sc.in Data Science and Analytics Two years Program
Program structure includes basic and advanced skills on Current trends like Machine Learning Algorithms, Artificial intelligence, Big Data Analytics, Deep Learning, Statistical Analysis, Data mining and data warehousing techniques using Python and R Programming languages.
Semester-I
Sr. No | Code /Name | Type | Weekly Workload | Credits | Assessment | |||||
Theory | Practical | Tutorial | Theory | Practical | Internal | External | Total | |||
1 | CC-01 Introduction to Data Science | CC | 3 | – | 1 | 4 | – | 40 | 60 | 100 |
2 | CC-02 Statistical Methods | CC | 3 | – | 1 | 4 | – | 40 | 60 | 100 |
3 | DSE-01 Data Mining | DSE | 3 | – | 1 | 4 | – | 40 | 60 | 100 |
4 | DSE-02 Programming for Data Science | DSE | 3 | – | 1 | 4 | 40 | 60 | 100 | |
5 | LAB-01 Based on CC-01 | CC | – | 4 | – | 2 | 40 | 60 | 100 | |
6 | LAB-02 Based on CC-02 | CC | – | 4 | – | – | 2 | 40 | 60 | 100 |
7 | LAB-03 Based on DSE-01 | DSE | – | 4 | – | – | 2 | 40 | 60 | 100 |
8 | LAB-04 (Project Work) | DSE | – | 4 | – | – | 2 | 40 | 60 | 100 |
12 | 16 | 4 | 16 | 08 | 320 | 480 | 800 |
Semester-II
Sr. No | Code /Name | Type | Weekly Workload | Credits | Assessment | |||||
Theory | Practical | Tutorial | Theory | Practical | Internal | External | Total | |||
1 | CC-01 Fundamentals of Analytics | CC | 3 | – | 1 | 4 | – | 40 | 60 | 100 |
2 | CC-02 Time Series Analysis and Forecasting | CC | 3 | – | 1 | 4 | – | 40 | 60 | 100 |
3 | DSE-01 Social Media and Web Analytics | DSE | 3 | – | 1 | 4 | – | 40 | 60 | 100 |
4 | DSE-02 Research Methodology | DSE | 3 | – | 1 | 4 | 40 | 60 | 100 | |
5 | LAB-01 Based on CC-01 | CC | – | 4 | – | 2 | 40 | 60 | 100 | |
6 | LAB-02 Based on CC-02 | CC | – | 4 | – | – | 2 | 40 | 60 | 100 |
7 | LAB-03 Based on DSE-01 | DSE | – | 4 | – | – | 2 | 40 | 60 | 100 |
8 | LAB-04 (Project Work) | DSE | – | 4 | – | – | 2 | 40 | 60 | 100 |
12 | 16 | 4 | 16 | 08 | 320 | 480 | 800 |
Semester-III
Sr.No | Code /Name | Type | Weekly Workload | Credits | Assessment | |||||
Theory | Practical | Tutorial | Theory | Practical | Internal | External | Total | |||
1 | CC-01 Machine Learning | CC | 3 | – | 1 | 4 | – | 40 | 60 | 100 |
2 | CC-02 Business Intelligence | CC | 3 | – | 1 | 4 | – | 40 | 60 | 100 |
3 | DSE-01 Big Data Analytics | DSE | 3 | – | 1 | 4 | – | 40 | 60 | 100 |
4 | DSE-02 Healthcare Analytics | DSE | 3 | – | 1 | 4 | 40 | 60 | 100 | |
5 | LAB-01 Based on CC-01 | CC | – | 4 | – | 2 | 40 | 60 | 100 | |
6 | LAB-02 Based on CC-02 | CC | – | 4 | – | – | 2 | 40 | 60 | 100 |
7 | LAB-03 Based on DSE-01 | DSE | – | 4 | – | – | 2 | 40 | 60 | 100 |
8 | LAB-04 (Project Work) | DSE | – | 4 | – | – | 2 | 40 | 60 | 100 |
12 | 16 | 4 | 16 | 08 | 320 | 480 | 800 |
Semester-IV
Sr.No | Code /Name | Type | Weekly Workload | Credits | Assessment | |||||
Theory | Practical | Tutorial | Theory | Practical | Internal | External | Total | |||
1 | Ind. Training | OEC/AEC | – | – | – | – | 24 | 300 | 500 | 800 |
Job Opportunities
Data Scientist was the most promising career from 2019. As the size of cloud is keep on increasing, the demand for Data scientist will also be in huge demand in coming days.
You can work in the below roles:
Data Scientist, Data Analyst, Data Architect, Statistical Officer, Data Mining and Statistical Analysis, Cloud and Distributed Computing, Database Management and Architecture, Business Intelligence and Strategy
Machine Learning, Cognitive Computing Development, Data Visualization and Presentation, Operations-Related Data Analytics Market-Related Data Analytics.