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Master of Science (M.Sc.) in Big Data Analytics
(In collaboration with TCS)

Programme Objectives:

  • To acquire command in computational techniques and proficiency in data analysis.
  • To gain extensive practical knowledge in Big Data Analytics.
  • To be proficient with the tools and techniques required to work with and analyze today’s increasingly complex data sets in all areas of the sciences.
  • To gain exposure to industry-oriented education in data science and analytics.
  • To collate experiences of trained professionals to hone the ability to meet the demands of the Data Processing and Analytics Industry.

Program Outcomes:

  • Acquire enhanced skills in applied statistics, real analysis and numerical analysis.
  • Apply for data analytics job opportunities in the domain of predictive analytics, descriptive statistics.
  • Acquire skills in Data Mining, Data Infrastructure, Data Visualization, and Decisions Analysis.
  • Deduce cost-effective solutions and improve one’s decision-making power in multiple development areas, including healthcare, manufacturing, education, media, retail, and even real estate.
  • Select job opportunity from a variety of industries which match specific skills and interests.

Key Features:

  • Two years full time post graduate program in collaboration with TCS comprising four semesters with a total of 96 credits.
  • Ideal introduction to knowledge discovery, analysis and assessment of data extracted from structured and unstructured big-data sets, as well as visualization and communication of results with a compulsory core of professional subjects like statistics, machine learning and enabling technologies for data science relevant to all science disciplines.
  • Exposure to practical aspects, application-oriented subjects like business analytics and programming languages.
  • Practical skills developed in courses like computer modeling and, design and analysis of big data sets. TCS supported internships to acquire industry- relevant training in semester IV.
  • Creates plethora of opportunities like Big Data Analyst, Big Data Manager, IT Systems Analyst, Operations Analyst, Data Engineer, Quantitative Analyst, Project Manager, Data Scientist.
  • The USP of this program is that it has an industry-driven curriculum.

Eligibility for Admission:

  • For being eligible to apply for admission to the Program, the learner should have passed either B.Sc.IT. / B.Sc. C.S. /B. Voc. SD/ B.Sc. Mathematics / B.Sc. Statistics / BCA / B.Tech./B.E. degree examination of this University or an equivalent degree of any other University with a minimum of 46 credits or its equivalent (i.e. the minimum credits required for majoring in a subject, and excluding the credits for optional courses) in the subject which he wants to offer for the M.Sc. degree program by papers provided the above candidate undergoes the proposed Bridge Course of 1 credit each in Mathematics, Statistics and IT, each for a duration of 15 hours, satisfactorily.

    OR
  • Students who have graduated majoring in Economics, with Econometrics as one of the Courses or students who have graduated in BAF/BMS/BBA/BFM/BBI/BCom Program of this University or any other University equivalent thereto will also be eligible for admission, provided they had passed Standard XII Board Examination with either Mathematics or Statistics as one of the subjects and undergoes the proposed Bridge Course of 1 credit each in Mathematics, Statistics and IT, each for a duration of 15 hours, satisfactorily.

    AND
  • Provided further the candidates hold a Graduate/Post Graduate Degree with a minimum of 60% marks or CGPA 6.5 on a 10-point scale in the qualifying Degree of BSc/ BCA/ B.Tech./B.E./ BA/ BAF/ BMS/ BBA/ BFM/ BBI/ BCom or equivalent will be eligible for this program provided he/she has scored not less than 60% in aggregate at the other threshold Examinations of Standard X and Standard XII.

    *Note: Candidates from the SC / ST Category will be eligible for a relaxation of 5% in respect of the above requirement.

  • An Entrance Exam will be conducted for admission to the course.
  • Maximum intake for the program per year is 30.

Bridge Course:

  • There shall be a ‘Bridge Course’ in the relevant subjects, of approximately 15 hours each, at the beginning of the academic session which will be offered to the students who are admitted for the MSc Program in Big Data Analytics.
  • It is mandatory to complete the Bridge Course in the relevant subject/s as decided by the Program Coordinator and to the satisfaction of the Course teacher.
    • A student who has not graduated with Statistics as a major subject will be required to undergo the Bridge Course in Statistics.
    • A student who has not graduated with Mathematics as a major subject will be required to undergo the Bridge course in Mathematics.
    • Similarly, a student who has not graduated in either B.Sc. IT or B.C.A will be required to undergo the Bridge Course in Information Technology/Basic Programming.
  • Only on successful completion of the required Bridge Course, will the student be admitted to the M.Sc. Big Data Analytics and be eligible to enroll with the University of Mumbai for the same.

Scheme of Courses & Number of Credits:

Semester

Types of courses

No of Courses

No of Credits

Total Credits

Bridge Courses

Core Courses

3

1

3

I

Core Compulsory Courses

5

3

25

Practical Courses related to Core Courses

2

4

Practical Course related to Core Course

1

2

II

Core Compulsory Courses

4

3

26

Compulsory Course

1

1

Elective Course

1

3

Practical Course related to Core Courses

2

4

Practical Course related to Elective Course

1

2

III

Core Compulsory Courses

3

3

25

Elective Course

2

3

Practical Course related to Core and Elective

2

4

Practical Course related to Elective Course

1

2

IV

Project

1

20

20

 

Total of Semesters

26

 

96

Number of Seats/Total Intake:

The intake is a maximum of 30 students per year.

Reservation of Seats:

Jai Hind College is a Sindhi Linguistic Minority College, administered under the Provisions of the Indian Constitution, to promote the welfare of the Sindhi Linguistic Minority community and to extend its services to all other communities in India, so as to contribute to the harmony and integration of Indian society. 50% of seats are reserved for Sindhi Linguistic Minority students.

As per the Judgement dated 12th October 2017 of the Honorable Bombay High Court vide Writ Petition No.1726 of 2001, Minority Colleges do not need to set aside seats for Backward Classes. This was reiterated by the University of Mumbai Circular (No.Aff. / Recog.I / Admission (2018-19) / 10 / of 2018) dated 30th May, 2018. Accordingly, the Reservation of seats is as follows:

Category

Percentage of seats reserved

Sindhi Linguistic Minority

50%

Persons with Disability

5%

Special**

3%

** The Special category is for the wards of transferred State / Central Government and Private Sector employees, Defense Personnel, Ex-Servicemen, Freedom Fighters; for students who have met with an accident and also for award winners at the District / State / National Level in Sports or Cultural activities. # Please note, admissions are as per University of Mumbai guidelines.
# 6 seats of the total seats in Sindhi Linguistic Minority and 5 seats in General Category will be reserved for B.Sc (Maths), B.Sc (Statistics) and along with the above graduates, the remaining seats will be available to students who have also graduated with B.Sc(IT), B.Sc (CS) and B.C.A , B.A. Economics, B.Com, BAF. BMS and BBA.

Admission:

Admission of students into M.Sc in Big Data Analytics will be on the basis of the C.G.P.A or aggregate marks in the graduate exams as well as the score of Entrance Test and the interview.

ENTRANCE EXAM DETAILS : Maximum Marks: 100

Distribution of marks:

Subject Weightage in %
Logical Reasoning 20
Mathematics 25
Statistics 25
Computer Science 30

Entrance Exam Syllabus:

The entrance examination will primarily check basic aptitude in mathematics and statistics, understanding of algorithms and programming, and the ability to logically interpret data. Entrance test will consist of multiple choice questions from the following four sections.

Section I - Logical Reasoning and Verbal Ability.

  • Analogy
  • Sequence
  • Blood Relations
  • Predicate and Propositional Logic
  • Puzzle
  • Coding-Decoding
  • Cause-Effect
  • Artificial Language
  • Deductive Reasoning
  • Seating Arrangements

Section II: Mathematics

  • Sequences and Series Of Real Numbers
  • Linear Algebra
  • Vectors and Matrices
  • Numerical Methods
  • Set Theory
  • Relations
  • Logical Compound Statements
  • Permutations and Combinations
  • Differentiation

Section III: Statistics

  • Elementary Statistics
  • Measures Of Central Tendancy
  • Correlation
  • Regression
  • Sampling Theory
  • Probability Distributions
  • Data Visualization
  • Testing Of Hypothesis

Section IV: Computer Science

  • Data Structures
  • Basics Of Programming Concepts And Algorithms
  • Data Communication And Networking
  • Database Management System
  • Software Engineering
  • Information Technology

Master of Science (M.Sc.) in Big Data Analytics: Details of Courses Offered.

S.No.

Course Code

Course Title

Lectures

Credits

1

SBBDA101

Fundamentals of Mathematics

15

1

2

SBBDA102

Fundamentals of Statistics

15

1

3

SBBDA103

Introduction to Python and Scala

15

1

4

SBDA101

Statistical Methods

45

3

5

SBDA102

Probability & Stochastic Process

45

3

6

SBDA103

Linear Algebra & Linear Programming

45

3

7

SBDA104

Database Management

45

3

8

SBDA105

Computing for Data Sciences

45

3

9

SBDA101PR

Practical-I based on SBDA101, SBDA102

40

4

10

SBDA102PR

Practical-II based on SBDA103, SBDA104

40

4

11

SBDA103PR

Practical-III based on SBDA105

20

2

12

SBDA201

Enabling Technologies for Data Science-I

45

3

13

SBDA202

Machine Learning – I

45

3

14

SBDA203

Advanced Statistical Methods

45

3

15

SBDA204

Foundations of Data Science

45

3

16

SBDA205A

Multivariate Statistics

45

3

17

SBDA205B

Operation Research

45

3

18

SBDA205C

Cloud Computing

45

3

19

SBDA206

Value Thinking

30

1

20

SBDA201PR

Practical-I based on SBDA201, SBDA202

40

4

21

SBDA202PR

Practical-II based on SBDA203, SBDA204

40

4

22

SBDA203PR

Practical-III based SBDA205A/ SBDA205B/SBDA205C

20

2

23

SBDA301

Enabling Technologies for Data Science-II

60

3

24

SBDA302

Machine Learning-II

60

3

25

SBDA303

Data Visualization with Tableau & Modelling in Operations Management

60

3

26

SBDA304A

Introduction to Econometrics and Finance

60

3

27

SBDA304B

IPR /Cyber Security/Text mining /Advanced Analytics

60

3

28

SBDA305A

Time series and Forecasting

60

3

29

SBDA305B

Big Data Technologies and Architecture and Introduction to Bioinformatics

60

3

30

SBDA301PR

Practical-I based on SBDA301, SBDA302

40

4

31

SBDA302PR

Practical-II based on SBDA303, SBDA304A/SBDA304B

40

4

32

SBDA303PR

Practical-III based on SBDA305A/ SBDA305B

20

2

33

SBDA401PJ

Internship based project.

20 weeks

20

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