Tentative Programme Specifications - MS Ramaiah University ...

Tentative Programme Specifications B.Sc. Data Sciences

Programme: B.Sc. Data Sciences Department: Data Sciences

Faculty of Social Sciences M. S. Ramaiah University of Applied Sciences University House, New BEL Road, MSR Nagar, Bengaluru – 560054

www.msruas.ac.in

http://www.msruas.ac.in/

1. Title of the Award

B.Sc. Data Sciences

2. Modes of study

Full-Time

3. Awarding Institution / Body

M.S. Ramaiah University of Applied Sciences – Bengaluru, India

4. Joint Award

--

5. Teaching Institution

Faculty of Social Sciences

6. Date of Programme Specifications

January 2020

7. Date of Programme Approval by the Academic Council of MSRUAS

8. Next Review Date

9. Programme Approving Regulatory Body and Date of Approval

--

10. Programme Accrediting Body and Date of Accreditation

--

11. Grade Awarded by the Accreditation Body

--

12. Programme Accreditation Validity

--

Tentative Programme Specifications: B.Sc. Data Sciences

Faculty Faculty of Social Sciences

Department Data Analytics

Programme B.Sc. Data Sciences

Dean of Faculty

13. Programme Benchmark --

14. Rationale for the Programme

B.Sc.Data Sciences is an undergraduate degree programme designed to create motivated, energetic, thinking and creative graduates to fill the roles as data analysts, programmers, researchers and professionals like data scientists, data experts, process analyst, statistical analyst, teachers, professors and researchers with additional qualification and training. With the growing economy, there are several problems and challenges faced by people from all walks of life. While there is availability of data everywhere, at the same time there is a need for proper utilization of data; thus, there is further need for qualified and efficient data analysts in the present scenario. There is a greater demand for qualified graduates in data sciences can work in wide areas of human activities like multinational companies, industries, education, health and research sectors. The B.Sc in Data Analytics has been created to meet the objectives of access and quality in higher education. The Ministry of Human Resources and Development (MHRD), GOI is bringing in many policies to improve the quality of higher education including establishment of institutions of eminence both in government and private sectors in order to compete for the position of world class universities and new education policy. Thus, at present there is a need for quality academic programmes at degree level and the proposed the B.Sc (Hons) in Data Analytics programme is a step in that direction. The B.Sc Data Sciences programme at Faculty of Social Sciences, RUAS has been developed by the members of the faculty based on interactions with various universities, financial institutions and industries. The curriculum is outcome based and it imbibes required theoretical concepts and practical skills in the domain. By undergoing this programme, students develop application-oriented learning skills, critical, analytical thinking and problem-solving abilities for a smooth transition from academic to real-life work environment. Students are trained in communication skills and interdisciplinary topics to enhance their scope. In addition, the students also can enroll into one-year Ramaiah Advanced Post Graduate Diploma which will give them a leading edged and make the students more versatile, generating wide range of opportunities including registering for Master’s and Ph.D. programme in a chosen subject area, if one wishes to be considering teaching in a university. The above-mentioned features of the programme, advanced teaching and learning resources, and experience of the faculty members with their strong connections with industry and business organizations makes this programme unique.

15. Programme Mission

The purpose of the programme is creation of knowledgeable human resources to work in Private and

Public sector organization. With further progression in education, graduates should be able to

undertake teaching and counselling in schools, colleges and universities and become independent

professional practitioners and social work experts.

16. Graduate Attributes

1. Ability to apply fundamental knowledge of data science to solve real-life problems and address

issues

2. Ability to analyze problems, process and issues of society and individuals

3. Ability to apply appropriate tools and techniques to analyses data across several domains

4. Ability to exhibit the competency and skills to deal with the data and comprehend problems and

issues with suitable solutions

5. Ability to apply ethical principles to data science practices and professional responsibilities

6. Ability to work as a member of a team, to plan and to integrate knowledge of various aligned

disciplines

7. Ability to make effective oral presentations and communicate technical ideas to a broad audience

using written and oral means

8. Ability to develop sustainable solutions and understand their effect on society and environment

9. Ability to lead and manage multidisciplinary teams by applying data science principles and

knowledge

10. Ability to adapt to the changes and advancements in technology and engage in independent and

life-long learning

17. Programme Goal

The programme goal is to produce graduates with application-oriented learners, critical, analytical and problem-solving skills, and ability to think independently, to pursue a career in data analytics.

18. Programme Objectives

The main objective of this programme is to impart knowledge, understanding and cognitive abilities in data analytics, research, practice and interdisciplinary courses. The programme further focuses on development of competencies and practical skills required for effective problem solving and right decision making in different activities relevant to social groups, individuals, institutions and research. The programme also embeds courses to train students on personal development and interactive skills with a feel for society.

The objectives of the programme are to enable the students to:

1. Impart knowledge on contemporary trends in data science 2. Enhance knowledge regarding data analytics principles and processing, and other allied

activities 3. Apply statistical methods and techniques for modelling and analysis of data 4. Apply skills to analyse and evaluate problems, issues and address with suitable solutions 5. Exhibit the statistical and programming competency and skills to deal with various issues

across varied settings and modalities 6. Educate on professional ethics, and interpersonal skills relevant to professional practice 7. Provide a general perspective on lifelong learning and opportunities for a career in

industry, academia and government

19. Intended Learning Outcomes of the Programme

The Intended Learning Outcomes (ILOs) are listed under four headings: 1. Knowledge and Understanding, 2. Cognitive Skills 3. Practical Skills and 4. Capability / Transferable Skills.

1. Knowledge and Understanding

After undergoing this programme, a student will be able to:

KU1: Explain the principles and practices of data analytics

KU2: Discuss the concepts in data analytics

KU3: Describe the concepts in data and data analytics

2. Cognitive Skills


CS1: Apply statistical methods to model and interpret data

CS2: Analyse problems and issues; to arrive at appropriate solutions

CS3: Analyse the effect and relationships amongst the available data and information

CS4: Examine implications of data analysis in terms of interventions and solutions

3. Practical Skills


PS1: Use statistical and management tools

PS2: Use appropriate data science knowledge

4. Capability / Transferable Skills


TS1: Manage information, develop technical reports and make presentations

TS2: Build, manage and lead a team to successfully complete a project and communicate across teams and organizations to achieve professional objectives

TS3: Work under various constraints to meet project targets

TS4: Adopt to the chosen profession by continues upgrading his/her knowledge and understanding through lifelong philosophy

20. Programme Structure

SEMESTER 1

Sl.No. Code Course Title Theory (h/W/S)

Tutorials (h/W/S)

Practical (h/W/S)

Total Credits

Max. Marks

1 DA101D Databases and Data

management systems 4 100

2 DA102D Probability theory and

theoretical distribution 4

100

3 DA103D Basic Math 4 2 100 4 DA104D Basic Statistics 4 2 100 5 DA105D Skill Enhancement Course 1 2 100

Total 18 00 04 500

Total number of contact hours per week 22 hours

SEMESTER 2


Tutorials (h/W/S)

Practical (h/W/S)

Total Credits

Max. Marks

1 DA106D Design of experiment

and multivariate analysis 4 100

2 DA107D 3 100

3 DA108D Advanced Maths 3 2 100

4 DA109D Advanced Statistics 3 2 100

5 DA110D English Communication 3 100

Total 16 00 04 850


SEMESTER 3


Tutorials (h/W/S)

Practical (h/W/S)

Total Credits

Max. Marks

1 DA201D Management concepts and

marketing management 3

3 100

2 DA202D Structural Query Language 4 3 100

3 DA203D Sample distributions and

Statistical inference 3

3 100

4 DA204D Skill Enhancement Course 2 3 2 100

Total 13 00 00 11 400


SEMESTER 4


Tutorials (h/W/S)

Practical (h/W/S)

Total Credits

Max. Marks

1 DA205D Data Structures and

Algorithms 3

3 100

2 DA206D Data manipulation and

cleaning using R 3 3 100

3 DA207D Cyber law 3 3 100

4 DA208D Environmental Science 3 1 2 100

Total 18 00 06 11 400

Total number of contact hours per week hours

SEMESTER 5


Tutorials (h/W/S)

Practical (h/W/S)

Total Credits

Max. Marks

1 DA301D Natural language

processing 4 4 3 100

2 DA302D Machine Learning 3 3 100

3 DA303D Generic Elective -1 3 3 100

4 DA304D Generic Elective-2 3 3 100

Total 13 00 4 12 400


SEMESTER 6


Tutorials (h/W/S)

Practical (h/W/S)

Total Credits

Max. Marks

1 DA305D Deep learning using

Python and TensorFlow 4 3 100

2 DA306D Introduction to Actuarial

Science 4 3 100



Total 12 00 12 400


Generic Electives: A number of electives from faculty of social sciences will be announced one semester prior to the scheduled semester.

21. Programme Delivery As per Time Table

22. Teaching and Learning Methods

The module delivery comprises of a combination of few or all the following:

1. Face to face lectures using audio-visuals 2. Workshops-group discussions, debates, presentations 3. Demonstrations 4. Guest lectures 5. Laboratory-work/Field work/Workshop 6. Industry visit 7. Seminars 8. Group Exercises 9. Project Work 10. Project Exhibitions 11. Technical Events

23. Assessment and Grading

1. Every course will be assessed for a weight of 100% 2. There are two components-Component-1 and Component-2 3. Component-1 carries a weight of 50% and Component -2 carries a weight of 50% 4. Component -1 (CE) is subdivided into Term Tests and one Assignment, tests carry 25%

weight and assignment carry 25% weight. 5. Component -2 (SEE) is Written Examination 6. Laboratory Examination will have two components

Component -1(CE): Conduction of Laboratory Exercises and Submission of Report: 50% weight Component -2: SEE (Semester End Laboratory Examination): 50% weight

7. A minimum of overall 40% is required for a pass with 40% in each of the Components 8. The marks distribution for each course is given in the programme structure-section 20 9. Other flexibilities(exceptions) as per the programme regulations

24. Attendance A minimum of 80% attendance compulsory to appear for semester end examinations. Any condoning is as per the programme regulations.

25. Award of Class

As per the Academic Regulations for B.Sc. (Hons) Data Analytics

26. Student Support for Learning

Students are given the following support:

1. Course notes

2. Reference books in the library

3. Magazines and Journals

4. Internet facility

5. Computing facility

6. Laboratory facility

7. Workshop facility

8. Staff support

9. Lounges for discussions

10. Any other support that enhances their learning

27. Quality Control Measures

Following are the Quality Control Measures:

1. Review of course notes

2. Review of question papers and assignment questions

3. Student Feedback

4. Moderation of assessed work

5. Opportunities for the students to see their assessed work

6. Review by external examiners and external examiners reports

7. Staff Student Consultative Committee meetings

8. Student exit feedback

9. Subject Assessment Board (SAB) 10. Programme Assessment Board (PAB)

28. Curriculum Map

Intended Learning outcomes

Sl.no COURSE CODE Knowledge and understanding

Cognitive Skills Practical Skills

KU1 KU2 KU3 CS1 CS2 CS3 CS4 PS1 PS2

1 DA101D x x X X X

2 DA102D X X

3 DA103D X X X X X

4 DA104D X X X x

5 DA105D X X

6 DA106D X X X X X X X


8 DA108D X X X X X X X x

9 DA109D X X X X X


11 DA201D X

12 DA202D X X X X

13 DA203D X X X

14 DA204D X X X X x

15 DA205D X X X X X X x X

16 DA206D X X X X X x x

17 DA207D X X

18 DA208D X X X X

19 DA301D X X X x X

20 DA302D X X X X X X x

21 DA303D X X X X X x

22 DA304D X X

23 DA305D X X X x

24 DA306D X X X X

25 DA308D x X X X

26 DA309D X X X X X

29. Capability / Transferable Skills Map

Course Code

Skills

GK SL WC OC P B IM PM L

DA101D X X X X X

DA102D X X X X X X

DA103D X X X X X X X X


DA105D X X X X X

DA106D X X X X




DA110D X X X X X

DA201D X X X X X

DA202D X X X X X X

DA203D X X X X X

DA204D X X X X X

DA205D X X X X

DA206D X X X X



DA301D X X X X X

DA302D X X X X X X

DA303D x X x X

DA304D X X X X X X X

DA305D X X X X X X X X X

DA306D X X X X

DA308D X X X X X X

DA309D X X X X

GK: Group Work; SL: Self Learning; WC: Written Communication; OC: Oral Communication P: Presentation; B:

Behavioural; IM: Information Management; PM: Personal Management L: Leadership

29. Co-curricular Activities

Students are encouraged to take part in co-curricular activities like seminars, conferences, symposium, paper writing, attending industry exhibitions, project competitions and related activities to enhance their knowledge and network.

30. Cultural and Literary Activities

To remind and ignite the creative endeavours annual cultural festivals held and the students are made to plan and organize the activities.

31. Sports and Athletics

Students are encouraged to develop a habit of taking part in outdoor and indoor games on regular basis.

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