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Home > Academics > Programs > Msc > Statistics >

M.SC. STATISTICS & DATA SCIENCE

 Statistics is a rapidly expanding discipline in the era of data clouds and data lakes. Statistical methods can be used to derive meaningful insights from data. Statistics plays a crucial role in decision making under uncertainty. Statistics deals with many aspects of data and is an essential pillar in data science. Statistics and Data Science can provide insights into complex data in scientific discovery and industrial problems. Department Statistics Sunanadan Divaitia School of Science, NMIMS (Deemed to be University) has merged traditional Maters course in Statistics with Data Science courses: Data Management, Machine Learning, Deep Learning, and Big Data Technology. The need for data analysis in the industry in this computer age has made Statistics a critical application. There are openings for Statisticians in practically all enterprises and service sectors. However, there is a lack of Statisticians. There is a need to develop highly skilled Statisticians or Data scientists to fulfil the industry's demands.

Considering this scenario NMIMS (Deemed to be University), which is known for its dynamism and flexibility in designing courses to suit the need of the hour, now has a unique Masters Programme in Statistics & Data Science, under the Department of Statistics, Sunanadan Divaitia School of Science.

Duration

M.Sc. Statistics & Data Science is a two-year full-time program spread over four semesters, including an Industry Project in the last semester.

Intake

A total of 40 seats are available for the M.Sc. program in Statistics & Data Science

Eligibility Criterion

Candidate must hold a Bachelor of Science degree in Statistics with a minimum of 60% aggregate marks or a minimum CGPA of 3 out of 4 or equivalent.

Admission Process

Students satisfying the eligibility criterion must undergo a two-stage selection procedure.

Stage 1- The eligible candidates will appear for a written test conducted by the Department of Statistics at NMIMS Campus, Mumbai.

Stage 2- The candidates will be shortlisted for the personal interview based on the written test's performance. The personal interview will be conducted at NMIMS Campus Mumbai.

The merit list of the candidates eligible for admission, based on the candidates' two-stage performance, will be displayed as per criteria laid down by the University.

Board of Studies

o Dr Neetin Desai- Dean SDSOS, NMIMS Deemed to be University, Mumbai

o Dr M. N. Welling - Advisor to the President - SVKM & to Chancellor – NMIMS Deemed to be University, Mumbai

o Dr R. N. Rattihalli, Professor & Former HOD Statistics, Shivaji University

o Prof. P.G. Patki - Vice-Principal, Bhavan's College, Mumbai

o Amul Desai- Founder & Director Myriad Analytics

o C. G. Venkatesh - Global Head- Senior Principal Data Scientist- L &T Infotech

o Prof. Sunil Shirvaikar – Program Director (Statistics), Department of Statistics, Sunandan Divatia School of Science.

o Dr K. S. Madhava Rao, Professor, Department of Statistics, Sunandan Divatia School of Science.

o Dr Pradnya Khandeparkar – Associate Professor, Department of Statistics, Sunandan Divatia School of Science.

o Dr Leena Kulkarni - Assistant Professor, Department of Statistics, Sunandan Divatia School of Science

o Prof. Prashant Dhamale - Assistant Professor, Department of Statistics, Sunandan Divatia School of Science

o Prof. Shraddha Sarode - Assistant Professor, Department of Statistics, Sunandan Divatia School of Science

o Dr Debasmita Mukherjee- Assistant Professor, Department of Statistics, Sunandan Divatia School of Science

Value Proposition

o A Unique program, blending traditional M. Sc. Statistics program with courses on Data Science.

o Extensive course works in line with the requirements of industry, thus adding value to the degree.

o Innovative teaching methods involving continuous interaction amongst faculty and students, using a blend of traditional and modern techniques, and live projects for better understanding.

o Blending theory with practical using top end statistical tools (MS Excel, R Studio, Python, SPSS, Base SAS, SAS Predictive Modelling)

o Development of scientific writing skills required in research.

o In built opportunity to improve soft skills.

o A system of continuous evaluation through seminars, quizzes and practicals

o Grading system at par with international standards

o Guest faculty are drawn from a pool of experts from Industry/academia worldwide, thereby ensuring a balanced and continuous interaction with the industry and academia.

O The course is reviewed regularly in consultation with the Board of Studies, which comprises experts from academia, research institutions, and industry. Thus, the program is tailor-made to fulfil the requirements needed to keep pace with industry developments.

o Focuses on the student's holistic development to enhance knowledge and skillsets for an edge in employability after completion of the course.

Programme Objectives

o M.Sc. Statistics & Data Science is a two-year full-time program with rigorous course work in Statistics & Data Science.

O Students will learn statistical methods and applications in real-world settings.

O Students will understand techniques required for managing data in the workplace environment with the help of well-equipped modern facilities available at the campus.

O The course emphasizes the Development of computational and analytical skills of a student.

O The "Industry Interface Program" has been initiated to keep the students abreast of the latest industry/research organizations' latest trends through industrial visits and guest lectures.

O industry-based project work as part of the curricula to get recognition and reward to the students in a job offer or support for further studies and research.

O The curricular and extra-curricular activities are conducted for the overall development of students.

Pedagogy

o M.Sc. Statistics & Data Science is a full-time program. The lectures and practicals are conducted from Monday to Saturday.

o The course comprises Core Statistics courses, Applied Statistics courses, Data Science courses and Programming language courses in the first three semesters. Subsequently, it aims to fine-tune these skills for industry Internship Project work to be carried out in the fourth semester.

o The regular lectures are also conducted by visiting faculties who are experts in their respective field and the permanent faculties.

o Students have ample opportunity to acquire hands-on training on modern software and will be able to benefit from the expertise of one or more supervisors, wherever needed.

o Students are required to carry out mini-project in each semester related to the courses taught in that semester.

o Apart from the course work, Guest lectures of eminent academicians and statisticians are arranged to ensure a learner-centric environment.

Course Curriculum

First Year

Semester – I

Paper I – Probability Theory
Paper II – Distribution Theory
Paper III – Estimation Theory
Paper IV –
Linear Models & Design of Experiments

Paper V – Real Analysis and Linear Algebra
Paper VI –
Data Management

Paper VII – Programming Analytics

Paper VIII-Statistical Computing I

Paper IX - Research Treatise-I

Semester – II

Paper X – Regression Analysis
Paper XI –
Testing of Hypothesis

Paper XII- Applied Multivariate Analysis
Paper XIII –
Machine Learning Techniques

Paper XIV – Python for Data Analysis
Paper XV –
Reporting & Correspondence in Data Science
Paper XVI –
Statistical Computing II

Paper XVII - Project Management

Paper XVIII - Research Treatise – II

Second Year

Semester – III

Paper XIX– Stochastic Processes
Paper XX –
Time Series Analysis

Paper XXI- Elective I

Paper XXII- Elective II

Paper XXIII- Introduction to Big Data Technologies & its applications
Paper XXIV –
Predictive Modelling
Paper XXV –
Statistical Computing III

Paper XXVI- Organizational Behaviour

Paper XXVII- Research Treatise – III

Semester - IV

Industry Internship

List of Electives

a. Deep Learning Techniques

b. Pricing & Revenue Optimization

c. Stochastic Finance

d. Survival Analysis

e. Quality Management

f. Nonparametric Inference

Program outcome

Academic Calendar 2021-22

Evaluation Criteria

Students will be required to have a minimum attendance of 80%.

The students will be evaluated through

1.Continuous internal assessment: Internal assessment will be based on tests, seminars, quizzes etc. Practical work will be evaluated each week during the regular practicals.

2.Term End Examination

The grade will be awarded at the end of the year based on the Cumulative Grade Point Average.

 

           
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