M.Sc. Computer Science with specialization in Data Science
About Programme
Data Science is the future of Artificial Intelligence and a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. Data Science is a concept to bring together ideas, data examination, Machine Learning, and their related strategies to comprehend and dissect genuine phenomena with data. The digital transformation deals with the voluminous data that needs to analyse as an asset for the organizational growth. There is growing demand from the industry to analyse their data and discover the knowledge, from the data generated through their operational system. Data Science
is the upcoming technology crawling into all sectors such as Trading, Finance, Production, Manufacturing, Retailing, Medical, Banking, Stock Market, Sports, Logistics etc.
The global Data Science Market is estimated to grow at a CAGR of 30% to reach USD 140 billion by 2024, according to a Markets and Markets report.
The programme
a. Equips learners with the ability to work across disciplinary boundaries in the effective application and deployment of data-intensive solutions in a variety of contexts.
b. Give learners a breadth and depth of knowledge and skills in computational, machine learning, and statistical principles and the capability for insightful large-scale data analysis, so they will be able to specify and implement analytical pipelines for real-world data at scale.
c. Provide learners with a good understanding of the ethical issues in the application of contemporary data science techniques to real-world challenges so they can be conversant with arguments concerning the risks and potential benefits and disadvantages arising from the deployment of these technologies.
d. Enable learners to independently initiate data science projects specified at a high-level perspective, leading them from scoping onward to completion, while exercising appropriate project management methods and maintaining stakeholder engagement.
Programme Specific Outcome
a. Competence in employing principles, techniques and tools of data science for business analytics (BTL: 1 to 4)
b. Curiosity and readiness to deal with small and big data and ability to engage in exploratory research (BSL: 3 to 6)
c. Capability to become a successful trainer in data science, productive decision maker and therefore well-respected personality at work and in life (BTL: 1 to 6)
Eligibility
Any Graduate from recognized University
Intake & Seats Reservation
Ø Intake: 200 Students.
Ø Reservation in Seats: Other University – 6 (3%)
Mumbai University – 194 (97%)
[GEN- 74 (38%); SC- 25 (13%); ST- 13 (7%);
DT (A)- 6 (3%); NT (B)- 5 (2.5%);
NT (C)- 7 (3.5%); NT(D)- 4 (2%); OBC- 37 (19%);
SBC- 4 (2%); EWS-19 (10%)]
Admission Process
Ø Admission to the said course will be made on the basis of Entrance Test conducted by the Department
Ø Documents along with application form:
(1) Entrance Test Fees Rs. 500.00
(2) FY, SY, TY Degree Marksheets
(3) Caste Certificate, if any
Ø Documents along with admission form:
(1) SSC, HSC, FY, SY, TY Degree Marksheets
(2) SSC School Leaving Certificate
(3) Aadhar Card
(4) Caste Certificate & Caste Validity, if any
Subjects and Evaluation
|
Paper Code |
Paper Name |
Credits |
Total Marks |
External (UA) |
Internal (CA) |
|||
|
Max Marks |
Min Marks |
Max Marks |
Min Marks |
Max Marks |
Min Marks |
|||
|
Semester-I |
|
|||||||
|
PSDS101 |
Programme Paradigms |
4 |
100 |
40 |
60 |
24 |
40 |
16 |
|
PSDS102 |
Database Technologies |
4 |
100 |
40 |
60 |
24 |
40 |
16 |
|
PSDS103 |
Fundamentals of Data Science |
4 |
100 |
40 |
60 |
24 |
40 |
16 |
|
PSDS104 |
Statistical Methods for Data Science |
4 |
100 |
40 |
60 |
24 |
40 |
16 |
|
PSDS1P1 |
Practical: Programme Paradigms |
2 |
50 |
20 |
50 |
20 |
- |
- |
|
PSDS1P2 |
Practical: Database Technologies |
2 |
50 |
20 |
50 |
20 |
- |
- |
|
PSDS1P3 |
Practical: Fundamentals of Data Science |
2 |
50 |
20 |
50 |
20 |
- |
- |
|
PSDS1P4 |
Practical: Statistical Methods for Data Science |
2 |
50 |
20 |
50 |
20 |
- |
- |
|
Total |
=SUM(ABOVE) 24 |
=SUM(ABOVE) 600 |
- |
=SUM(ABOVE) 440 |
- |
=SUM(ABOVE) 160 |
- |
|
|
Semester-II |
|
|||||||
|
PSDS201 |
Artificial Intelligence & Machine Learning |
4 |
100 |
40 |
60 |
24 |
40 |
16 |
|
PSDS202 |
Soft Computing |
4 |
100 |
40 |
60 |
24 |
40 |
16 |
|
PSDS203 |
Algorithms for Data Science |
4 |
100 |
40 |
60 |
24 |
40 |
16 |
|
PSDS204 |
Optimization Techniques |
4 |
100 |
40 |
60 |
24 |
40 |
16 |
|
PSDS2P1 |
Practical: Artificial Intelligence & Machine Learning |
2 |
50 |
20 |
50 |
20 |
- |
- |
|
PSDS2P2 |
Practical: Soft Computing |
2 |
50 |
20 |
50 |
20 |
- |
- |
|
PSDS2P3 |
Practical: Algorithms for Data Science |
2 |
50 |
20 |
50 |
20 |
- |
- |
|
PSDS2P4 |
Practical: Optimization Techniques |
2 |
50 |
20 |
50 |
20 |
- |
- |
|
Total |
=SUM(ABOVE) 24 |
=SUM(ABOVE) 600 |
- |
=SUM(ABOVE) 440 |
- |
=SUM(ABOVE) 160 |
- |
|