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m.sc. - statistics

Statistics is a rapidly, expanding discipline with openings in practically all industries and service sectors. This is particularly true for the fast growing Insurance sector, which heavily depends on statisticians and actuaries. Unfortunately, the limited availability of seats for the Masters Programme at the Universities level has curtailed academic growth and upward mobility of graduates in Statistics. Moreover, the programmes currently being offered by most Universities lack flexibility and ability to respond to the immediate demands of market forces. This, in general often results in students being qualified but they are not employable.

Considering this scenario and also the fact that there is a need to develop highly skilled manpower which can fulfill the needs as well as demands of an industry which is subject to constantly challenging and evolving market forces, the NMIMS (Deemed to be University),which is known for its dynamism and flexibility in designing courses to suit the need of thehour, now has a unique Master Programme in Statistics, under the Sunandan Divatia School of Science.

Duration

The rigorous course work is spread over 4 semesters, including an Industry Project.

Intake

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

Board Of Studies

  • Dr. Aparna Khanna - Dean, School of Science, NMIMS Deemed to be University
  • Dr. M. N. Welling - Advisor to the President - SVKM & to Chancellor - NMIMS
  • Prof. Sunil Shirvaikar – Program Director (Statistics), Department of Statistics, Sunandan Divatia School of Science
  • Prof. (Mrs.) Anjali Khandeparkar - Head, Department of Statistics, SIES College, Mumbai
  • Prof. (Mrs.) Jyotsna Tendulkar - Head, Department of Statistics, K. J. Somaiya College of Science, Mumbai
  • Mr. Jitendra Tawade - Asst. General Manager, Operations International Business Division, Godfrey Phillips India Ltd
  • Prof. P.G. Patki - Senior Lecturer, Bhavan’s College, Mumbai
  • Dr. Anshula Pandey - Associate Professor, Sunandan Divatia School of Science
  • Prof. Prashant Dhamale - Assistant Professor, Sunandan Divatia School of Science
  • Prof. (Mrs.) Leena Kulkarni - Member Secretary, Assistant Professor, Sunandan Divatia School of Science

Value Proposition

  • A Unique program, blending traditional M. Sc. with practical application skill set building.
  • Extensive course work in line with the requirements of Industry, thus adding value to the degree
  • Development of skills to use statistical and computational techniques as research/data analysis tools like SAS, SPSS, R, etc
  • 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 practicals
  • 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
  • Blending theory with practical using top end statistical tools (MS Excel, RStudio, SPSS, Base SAS, SAS Predictive Modeling)
  • Focuses on the holistic development of the student to help enhance knowledge and skillsets for an edge in employability after completion of the course.

Programme Objectives

  • Modern facilities to provide ambience and support for curricular and extra-curricular activities for the overall development of students.
  • 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.
  • Trimester system with proper planning to utilize the resources effectively and efficiently.
  • 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 “Industry Interface Program” has been initiated to keep the students abreast of the latest trends in the industry/research organizations through industrial visits and guest lectures.
  • The program is a full time program which is offered from Monday to Saturday.
  • Every student will have to complete a research project, preferably industry oriented

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 practicals in the first year and subsequently fine tuning these skills.

The course is reviewed regularly 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.

In the first year, 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.

Eligibility

A student seeking admission to the M.Sc. Statistics program must have passed the B.Sc. Examination in Statistics of any recognized University and scored not less than 60% marks.

Admission Criteria Process

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

  • 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.

Curriculum

First Year

Semester – I

Paper I – Probability Theory,
Paper II – Distribution Theory,
Paper III – Linear Algebra and Numerical Methods,
Paper IV – Sampling Theory and Applications,
Paper V – Parametric Inference-Estimation,
Paper VI – Statistical Computing-I (MS Excel),
Paper VII – Statistical Computing-II (Basic R and Basic Python)

Semester – II

Paper VIII – Design of Experiments,
Paper IX – Testing of Hypothesis,
Paper X – Regression Analysis,
Paper XI – Linear Models
Paper XII – Business Statistics and Project Management,
Paper XIII – Statistical Computing-III (Advanced R and Advanced Python)
Paper XIV – Statistical Computing-IV (Base SAS)

Second Year

Semester – III

Paper XV– Multivariate Analysis,
Paper XVI – Stochastic Processes,
Paper XVII – Statistical Computing-V (SPSS),
Paper XVIII –Statistical Computing-VI (SAS - Predictive Modeling)

Semester - IV

Paper XIX – Non Parametric Inference,
Paper XX – Optimisation Techniques,
Paper XXI– Time Series Analysis,
Paper XXII – Elective I (to be selected from amongst the list given below),
Paper XXIII – Elective II (to be selected from amongst the list given below)

Electives: Survival Analysis, Biostatistics, Financial Statistics, Industrial Statistics

Evaluation Criteria

The students will be required to have a minimum attendance of 80%. The students will be evaluated through trimester end exams and through continuous internal assessment.

Internal assessment will be on the basis of fortnightly tests based on Theory. Practical work will be evaluated each week during the course of the regular practicals. Seminars will be evaluated weekly. The grade will be awarded at the end of the year on the basis of Cumulative Grade Point Average.

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