Masters Degree with Data Science Specialization 1 1 Syllabus Mumbai University

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Master’s and Micro degree Programmes with Data Science specialization
Objective : To produce world class data scientists in diverse domains. An avenue for a
graduate of any discipline to earn MSc in Computer Science with a special training for
making career in the domain of his / her liking by employing innovations and applications
that are based on data science and artific ial intelligence. This Programme is a quality
assured alternative for the employed learners who generally prefer the Distance learning.
Intake : As the Programme shall be offered in blended MOOC model, there shall not be any
limit on the intake; To begin wi th we plan to offer admissions to top 1000 scorers in the
admission entrance test [Ref. Annexure I].
Duration : 2 years, i.e., 4 semester, a specialized Programme including a rigorous training, a
capstone project and one semester internships with WIAI or any such AI industry .
Eligibility : Anybody having passed XII or higher standard examination with minimum 50%
score can appear for the national level entrance examination and take admission if gets
through. The admissions to Master’s degree (MSc in Compu ter Science, specialization data
science) shall be open to the graduates of this University or its equivalents.
The candidates who qualify the entrance test and not having completed their graduation
could take admission to micro degrees (Certificates) and are allowed to accumulate credits
by qualifying discrete Courses of this Programme by following the pre -requisite structure.
The credits earned by such candidates shall be recognized and transferred by this University
and be utilized by the candidates whe rever applicable.
The candidates who qualify the entrance test and have taken admission in any of the
Programmes of This University including a Master’s Degree Programme, can accumulate up
to half of the credits of this Programme while completing the oth er Programme and secure
Master’s in Computer Science with specialization in Data Science by earning the remaining
credits in one year any time after his / her graduation.
The admission procedure to this Programme shall be opened twice a year, i.e., in the
months July and December. The on -line applications for the entrance examination for the
academic year starting in the Second half of 2018 are available till 18th July 2018. Please
visit: www.mahapariksha.gov.in and http://udcs.mu.ac.in/
Since the Programme will be offered in the blended MOOC model the students taking
admission to this Programme would be eligible to take admission simultaneously in some
other Programme of This or any other University subject to the Dual degree facilities at the
respective Institutions.
Mode of dissemination of knowledge : Each Course shall be equivalent of 6 credits, the
teaching -learning spread s over 16 weeks, ideally each week a student is expected to study 6
videos, each of 15 -20 minutes, each ending with an activity that calls for the similar amount
of time, attend s 2 hours of tutoring and 2 hours of practical offered in a flipped classroom
and, contribute s to forum discussions on 2 threads by spending half an hour on each thread.
Evaluation model : For each Course a student has to secure minimum 16% of the total marks
through a Semester -end examination that would carry 40% weight in the total evaluation;
the 60% weig ht would be for the students’ attendance and submissions in response to the
video -based learning and flip ped classroom activities, call it continuous internal evaluation

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(CIE). In order to qualify a Course a student has to earn minimum 40% of the total mar ks in
the same; There shall not be minimum passing requirement for the students score in CIE.
However, a failed student can choose to improve CIE score once by paying only examination
fees and by submitting all the assignments and term -work as prescribed b y the on -going
term. The students failed in the second attempt have to pay 30% of the tuition fees in
addition to the examination fees, i.e., to avail the Course -ware of the year they will wish to
appear for the CIE .
Subject to the availability of the Programme / Course in the University, t here shall be no
limit on the number of attempts a student takes to qualify the same . A syllabus will be valid
only for a year and shall be kept up -to-date by generating a refinement a lmost every year.
The repeaters h ave to follow the syllabus and assignments that are available at the time
they wish to appear for the examination.
The Semester examinations shall be conducted in the months December and May.
Fees :
Fees for the Entrance Examination shall be Rs 500/ - per student per attempt;
If admitted to the Courses from the first three semesters then a student has to pay Tuition
fee Rs 4000 / - and Examination fee Rs 1000 / - per Course plus , Convocation and other fees
like Gymkhana, insurance etc., as prescribed by U oM.
Fees for the Fourth semester will be 10K that includes the Departmental support for
mentoring the students and processing fees that may require to publish or file patents of
their work etc.
The people interested in attending a few of the Courses of the whole Programme in order to
seek micro -degree (Certification) in each Course they complete are welcome to do.
A performance based incentive : A candidate scoring 50% or more shall be awarded up to
50% discount in the fees in his / her further studi es towards the completion of This
Programme. The proposed discount pattern based upon a student’s score is given in the
Table below.
% score Discount: % of fee
paid
50 – 59 10
60 – 74 20
75 – 84 30
85 – 94 40
94 and above 50

Highlights of the Syllabus : Tentatively 75% content is focused to the core data science and
25% is from the interdisciplinary fields of its applications. Theory and practice have been
given equal weights. The scope of the Research methodology Course involves mentoring
stude nts for a research and an industrial project start from problem formulation phase to
publishing the research and securing its IPR. A student alone or in a group is expected to
publish his / her work carried out during this Programme, in a good impact facto r journal or

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file a patent or make its outcome available under Open Source licences etc. The syllabus
emphasizes the project based learning.
The recommended curriculum :
Semester I

CSDC 101: Programming paradigms
UI: Essentials of Algorithms and Data
structures; Introduction to Programming
paradigms – Functional, Imperative and
Object Oriented
UII: Software Engineering – Trends and
techniques

CSDC 102: Database Technologies
UI: Databases and Datawarehousing; Data
preparation
UII: Data Science using R, Excel, Python,
SQL, Tableau

CSDC 103: Data modeling
UI: Descriptive and Inferential statistics,
Data visualization
UII: Exploratory data analytics, Hypothesis
testing
Semester II

CSDC 201: Artificial Intelligence and
Machine Learning
UI: Introduction to Artificial intelligence,
conventional techniques and Logic
programming
UII: Introduction to Machine learning,
regression, classification (ANN, SVM and
Decision tree) and clustering

CSDC 202: Soft computing
UI: Concepts in Soft and Evolutionary
computin g – GA and other nature inspired
search algorithms
UII: Fuzy, Rough and Granular computing

CSDC 203: Data Analytics
UI: Big data, parallel algorithms, Association
rule mining, time series analysis
UII: Managing Big data with Hadoop and
SPARK

Semester III

CSDC 301: Research Methodology

UI: Introduction to Research Methodology,
Literature survey and referencing,
Problem formulation, Data preparation
UII: Design and implementation of
experiment

CSDC 302: Research P ublishing
UI: writing and publishing results
UII: IPR, patent, copyright and Free
knowledge sources

CSDP 303: Project
A mentored Capstone Project in a team of
2-4 (~100 hours efforts by each student) Semester IV

CSDI 401: Internship
A semester long internship with an
Institution that works in the domain of AI
for social good
(~400 hours efforts by each student)

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Compulsory Elective Audit Course
CSDE1 104: Robotics I CSDA1: Robotics I
CSDE2 104: Computer assisted music learning I CSDA2: Computer assisted music learning I
CSDE3 104: Uncertainty and deep learning I CSDA3: Scalability and Optimizations I
CSDE4 104: Mastering SAS

• For each of the papers in Semester I, II and III, UIII will be Case study and Unit IV will be
Practical implementation
• CSDE* 104, 204 and 304: Interdisciplinary studies from the domain selected for Capstone
project; to be completed preferably through MOOC or with a mentoring institution
independently. University may require to give an ancillary affiliation to the Institutions
those would like to enrol our students for these Courses. The choice of the
interdisciplinary domain is totally at the discretion of the students. It can be Science,
Social science, Commerce and Management, Languages or Fine arts or anything that the
student likes to works in.
• Passing at least one of the first three Courses of the previous Semester is necessary in
order to take admission to the semester two or three; Passing minimum two of the *04 is
necessary in order to sub mit the Capstone project.
• The First Semester Courses follow pre -requisite of qualifying the entrance test. CSDC 101
and 102 together form a pre -requisite for CSDC 201 that is the pre -requisite for CSDC
202; CSDC 301 could be opted by by -passing the entran ce test examination and by
providing a recommendation of a designated research mentor. Such students will have to
pay Rs 500/ - admission fees in addition to the Tuition fee and the Examination fee.
• The Capstone project and a Semester long internship is onl y for the students who opt for
the Master’s degree and not for the ones who opt Micro -degrees. However the Master’s
and Micro degree students may plan to appear for NPTEL -IITM certification exams
simultaneously.
• Considering the nature and requirement of th e Programme it has been proposed that
there will be Teaching -learning in blended MOOC model from August to November and
January to April in the respective Semesters. The Semester -end examinations shall start
on the Second Saturday of December and April. P er Course there shall be one Paper that
is to be attempted in two hours. The number of questions may vary depending upon the
nature of its content.
• In addition to this mandatory requirement a student in the interest of enhancing his / her
CV can opt up to 2 audit Courses (each equivalent of 6 credits in F2F model) while
completing this Programme.
Tentative Schedule of Semester End E xamination :
Time 2nd Sat Sun Mon Tue Wed Thr Fri Sat
10 to
12 101 201 102 202 103 203 104 204
3 to 5 301 - 302 - 303 - 304 -
Generally the result will be published on the forth Saturday of the same month.

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Annexure I

November 15, 2017: The first meeting of CM War room interns with the University Officials
[Attached the presentation copy and a draft proposed MOU]

November 26, 2017: The detail draft of agreement submitted; Dr Anandan added a note that initially
they will fund the Teaching positions for DS programme for three years and depending upon the
performance of the system they would extend the support for another 2 years. A review after 5
years will tell if we want to continue with DS or any other new thing of the then contemporary
requirement; Agreed by all the three parties.

January 18, 2018: The WIAI Team visited campus and met University Engineer for the selection of
venue for the inauguration of WIAI

February 2, 2018: Agreement revision submitted by WIAI, annexure regarding DS programme is
pending.
[The drafts attached]
Syllabus for Entrance Examination
(5 Questions in English and 10 in each of the other 5)
1 Section name – English
Three questions to test English comprehension of class XII level, Two questions
to test the vocabulary knowledge / an appropriate usage of a word / correct
forms of words
2. Section name - General knowledge
Questions on History, Culture, Literature, Civics & Politics, Sports, Environmental
science, Technology, Behavioural science & Psychology, knowledge of current
affair from news broadcast
3. Section Name - Logic
Analogy, Sequence, Blood relations. Predicate and P ropositional logic
4. Section name -Mathema tics
Matrices and Determinants, Lattice theory and linear algebra, Differentiation
and integration, 2-D and 3-D geometry, Numerical methods
5. Section name -Statistics
Elementary statistics: Aggregation and measure of central tendency, Probability
and Random numbers, Discrete and continuous probability distributions,
Hypothesis testing, Operations research
6 Section name -Computer Science
Algorithms and data structures, Operating environments and operating systems,
Programming and software engineering, Finite state machines and Artificial
intelligence, Networking and databases

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February 10, 2018: WIAI communicated a list of requirement at Green -Tech -Audi for the
inauguration of WIAI in the hands of Hon PM on February 18, 2018 (They co -ordinated with Team
UoM on everyday basis till 17 and we had a successful event on 18)

February 16, 2018: A long meeting of all parties took place in the Hon Vice -Chancellor’s Office. The
Registrar, The Legal Advisor and HoD UDCS were present. A revised draft was prepared. [Copy
attached].

May 15, 2018: The gist of the outcome of our meeti ngs after the Inaururation and progress on the
front of the DS Programme was communicated to all parties by Dr Ambuja of UoM and Er Kirtee of
WIAI with an assurance that we shall launch the DS Programme in July 2018.

Jun 8, 2018: University formulated a Committee for formalizing the requirement of launching the
proposed DS Programme w.e.f. the academic year 2018 -19. WIAI was notified it.
June 19, 2018: We have joint meeting of all the Parties (the first formal introduction of Hon Vice -
Chancellor, Professo r Pednekar, to this Project) in which only the DS Programme plans were
discussed. No discussion happened on WIAI; In the end Dr Anandan indicated that they have started
work in some place and therefore they do not require place in Vidyanagari campus for th e Institute
of Artificial Intelligence.

Jul 28, 2018: With the active support of CMO we conducted a national level entrance test for the DS
Programme

Aug 22, 2018: WIAI sponsored Professor Lakshminarayana Subramaniyam of New York University as
the Chie f speaker of the formal inauguration session of the Data Science Programme.

Aug 26, 2018: CMO communicated approval to UoM draft to WIAI with the modification that now
the agreement would be only regarding the DS Programme. WIAI in the Vidyanagari campus is no
longer a valid proposal [copy attached].

Aug 28, 2018: WIAI requested to replace the name, Dr Rahul Panikar from their team with Professor
Subhashis and that was acceptable to UoM.