M.Sc. (Applied Statistics & Analytics)

Over the years, Statistics as a subject has shown an immense growth in almost every discipline of Science, Commerce, and Social Science. Recently, many new areas of Statistics are emerging and are showing their significant importance to the age of data analytics such as Big Data and Machine Learning. The classical statistical tools are being modified to form new techniques and methods to deal with the challenges coming from industries like Telecom, Entertainment, Insurance, Banking, Pharmaceutical etc.

By taking the immense need of understanding the data into consideration, the Department of Statistics, under the Sunandan Divatia School of Science, started BSc Applied Statistics and Analytics course in 2015. This course as designed to give the students an insight to applications of Statistics to various fields from the undergraduate level itself. It is very fortunate that this course has got tremendous response from the student fraternity.

Hence, there is a need to take this applied under graduate course to a higher level and introduce the students to more rigorous high performance data mining tools which can enhance their knowledge to apply the statistical techniques to the real world problems. With this objective, the NMIMS (Deemed to be University), under the Sunandan Divatia School of Science, which has always known to be a place to incorporate contemporary changes and flexible in improvising the courses, has introduced M.Sc. Applied Statistics and Analytics programme from academic year 2018-19. This is a more rigorous applied course which focusses on the application of Statistics to almost each and every discipline and is efficient to meet the demands of today’s world be it Big Data, Machine Learning, Data Mining or any other field.

Duration

The rigorous applied course work is spread over 4 semesters, in which the 4 th semester is completely devoted to Industry internship.

Intake

A total of 60 seats are available for the M.Sc. Applied Statistics and Analytics programme.

Eligibility Criterion

Candidate must hold a bachelor’s degree in Statistics with minimum 60% aggregate marks or a minimum CGPA of 3 out of 4. or
Candidate must hold a bachelor’s degree in any stream other than mentioned under (1) above with minimum 60% aggregate marks or a minimum CGPA of 3 out of 4. The candidate must have passed 10 + 2 or equivalent examination with Mathematics / Mathematics & Statistics / Statistics as a compulsory subject with minimum 60% marks in the subject".

Admission Criteria Process

Students who satisfy the requirements of the two stage selection process will be considered for admission to the course

Stage 1 - All eligible candidates will be required to appear for a Written Test to be conducted at the NMIMS campus in Mumbai. The venue will be announced sufficiently in advance of the test date.

Stage 2 - Based on the performance in the written test, candidates will be short listed and called for a personal interview at the NMIMS Campus, Mumbai.

Based on the performance in Stages 1 and 2, a composite merit list will be prepared as per criteria laid down by the University.

BOARD OF STUDIES

Dr. Neetin Desai (Chairman)	Dean, Sunandan Divatia School of Science, NMIMS
Dr. M. N. Welling (Co-opted Member)	Advisor to the President-SVKM & to Chancellor-NMIMS
Dr. S. D. Varde	External Advisor, Warwick University
Dr. T. V. Ramnathan	Professor & Head, Department of Statistics, Savitribai Phule Pune University
Mr. Amul Desai	Director, Myriad Analytics
Mr. Arnab Das	Senior Project Manager - IT, HDFC Ergo
Dr. Sunil Bhardwaj	Certified Data Scientist & Senior Analytics Consultant - Education, SAS Institute (India) Pvt. Ltd
Dr. Vinay Kulkarni	Adjunct Professor, IIT Bombay
Prof. Sunil Shirvaiker	Program Director (Statistics), Sunandan Divatia School of Science, NMIMS
Dr. K S M Rao	Professor (Statistics), Sunandan Divatia School of Science, NMIMS
Dr. Pradnya Khandeparkar	Associate Professor (Statistics), Sunandan Divatia School of Science, NMIMS
Dr. Leena Kulkarni (Convener)	Assistant Professor (Statistics), Sunandan Divatia School of Science, NMIMS
Prof. Prashant Dhamale	Assistant Professor (Statistics), Sunandan Divatia School of Science, NMIMS
Prof. Shraddha Sarode	Assistant Professor (Computer Science), Sunandan Divatia School of Science, NMIMS
Dr. Debasmita Mukherjee	Assistant Professor (Mathematics), Sunandan Divatia School of Science, NMIMS

VALUE PROPOSITION

A distinctive program, focusing on the application of Statistics to the current world problems along with a good understanding of theoretical concepts.
Extensive course work in line with the requirements of Industries, thus adding value to the degree.
Understanding the concepts of other related subjects like Econometrics, Finance, Life Sciences etc.
Development of skills to use statistical and computational techniques as research/data analysis tools like R, SAS, Python, HADOOP, SQL, SPSS.
In built opportunity to improve soft skills as well as scientific writing
Grading system at par with international standards
A system of continuous evaluation through seminars, quizzes and practical assignments.
Guest faculty drawn from a pool of experts from Industry/academia thereby ensuring a balanced and continuous interaction with the industry
Innovative teaching methods involving continuous interaction amongst faculty and students, using a blend of traditional and modern techniques, as well as live projects for better understanding
Focuses on the overall development of the students to help enhance knowledge and skillsets for an edge in employability after completion of the course.

PROGRAM OBJECTIVES

The programme aims at providing high level training in fundamental principles of statistical inference, statistical machine learning, applied and computational statistics through mathematically demanding lectures and case study sessions, hands-on practical sessions in computer laboratory.
This is a specialized and a unique two year (four semesters) full-time program with curricula which includes modern computationally intensive theory and methods.
Teaching theoretical concepts by referring to real life applications of theirs.
Dedicated, qualified faculty to ensure high standard of teaching, learning and evaluation processes.
Periodic review and revision of curricula based on feedback from the industry with quick response to ensure the relevance of the programmes to the changing needs of industry.
Semester system with proper planning to utilize the resources effectively and efficiently.
A complete semester industry based project work as part of the curricula to get recognition and reward to the students in the form of job offer or support for further studies and research.
The program is a full time program which is offered from Monday to Saturday.

PEDAGOGY

The course lays emphasis on the overall development of computational and analytical skills of a student coupled with an expansion of his/her knowledge base through an interdisciplinary course. The course comprises of lectures and practical on some of the fundamental concepts of statistics in first semester. Subsequently moving to the applied statistical tools in Semester II and introducing the high performance data handling tools in Semester III along with specialized elective disciplines. The fourth semester has completely been devoted to industry internships to give students an exposure to understand the real industry problems. The course is designed in consultation with our Board of Studies, which comprises of experts from academia, research institutions and industry. Thus, the course is tailor made to fulfill the requirements needed to keep pace with the current developments in industry.

While the student will have ample opportunity to acquire hands-on training on modern softwares wherever necessary, he/she will also be able to benefit from the expertise of one or more supervisors, wherever needed.

Along with the permanent faculties, the regular lectures are also conducted by visiting faculties who are experts in their respective fields. Apart from the course work, the Faculty also conducts Guest lectures by eminent academicians and statisticians to ensure learner centric environment.

Course Curriculum

Program outcome

ACADEMIC CALENDAR 2020-21

Attendance

The students will be required to have a subject wise minimum attendance of 80%.

Evaluation Criteria

The students will be evaluated through term end exams and through continuous internal assessment. The grade will be awarded at the end of each semester on the basis of Cumulative Grade Point Average.

Admission Open 2022

Important Dates - 2022(Updated)
Instruction Sheet - 2022

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M.Sc. (Applied Statistics & Analytics)