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Home > Academics > Programs > Bsc-applied-statistics-and-analytics >

B.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.

Duration
The rigorous course work is spread over 6 semesters including projects

Number of Seats

Mumbai Campus – 40 seats
Navi Mumbai Campus – 60 seats
Bengaluru Campus – 60 seats

Hyderabad Campus – 60 seats

Eligibility

HSC or equivalent Board with minimum 50% aggregate in any stream with Mathematics/ Statistics/ Mathematics & Statistics (Science / Commerce / Arts).

Admission Criteria

Merit list will be based on performance of the candidate in Mathematics/ Statistics /Mathematics & Statistics marks obtained in 10+2 or Equivalent examination.

Board of Studies

STATISTICS

·Dr. Aparna Khanna (Chairman) – Dean, Sunandan Divatia School of Science, NMIMS

·Dr. M. N. Welling (Co-opted Member) – Advisor to the President-SVKM & to Chancellor-NMIMS

·Dr. Pradnya Khandeparkar - Adjunct Faculty, SDSOS, NMIMS

·Prof. P. G. Patki – Vice Principal, Bhavan’s College

·Mr. Amul Desai – Director, CoE Analytics & Data Sciences, NMIMS University

·Prof. Milan Gholba – Retired, Professor - Statistics, College of Science

·Mr. Udayraj Vichare – Associate Director, Data Science & Advance Analytics (Africa, Middle East and South Asia) at IQVIA

·Prof. Jyotsna Tendulkar – Retired, Head, Dept. of Statistics, K J Somaiya College of Science

·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

·Prof. Prashant Dhamale – Assistant Professor, Sunandan Divatia School of Science, NMIMS

·Dr. Raghunath - Assistant Professor, Sunandan Divatia School of Science, NMIMS

·Prof. Shraddha Sarode – Assistant Professor, Sunandan Divatia School of Science, NMIMS

·Dr. Santosha C D – Assistant Professor (Statistics), Sunandan Divatia School of Science, NMIMS – Bangalore

·Prof. Leena Kulkarni (Convener) – Assistant Professor, Sunandan Divatia School of Science, NMIMS

 
ECONOMICS

·Dr. Rohini Kelkar - Principal, Vidyalankar School of Information Technology (VSIT)

·Prof. Amita Vaidya - Director, Sarla Anil Modi School of Economics, NMIMS

·Dr. Neeraj Hatekar - Professor of Econometrics, Mumbai School of Economics and Public Policy, University of Mumbai

·Dr. Abhay Pethe - Senior Resident Fellow, Mumbai School of Economics and Public Policy, University of Mumbai

 
MATHEMATICS & COMPUTERS

·Dr. Sushil Kulkarni - Associate Professor of Mathematics, Jai Hind College, Mumbai

·Dr. Selby Jose - Associate Professor of Mathematics, Institute of Science, Mumbai

·Dr. Siby Abraham - Head, Department of Maths & Stats, Guru Nanak Khalsa College, Matunga, Mumbai

·Dr. Rajendra Pawale - Professor, Department of Mathematics, University of Mumbai

·Dr. Ajit Kumar - Associate Professor and Head, Department of Mathematics, Institute of Chemical Technology, Mumbai

Value Proposition

·A unique program, designed with inputs from eminent academicians and industry leaders, to focus on building skillsets for the growing requirement of data scientists in the industry.

·Curriculum focuses on applied as well as theoretical aspects of Statistics alongwith subjects from Economics, Mathematics, Computers & Analytics.

·Extensive use of the following soft wares MS Excel, R Studio, Python, SQL, Oracle, SAS Visual Analytics to solve practical problems and projects.

·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.


Program Objectives

·To expose students to the upcoming era of Machine Learning & Data Science

·The programme aims at providing a rigorous training in fundamental concepts of Statistics, Mathematics, Economics & Computers which creates a strong knowledge base in Data Science domain.

·To provide a complete understanding of the subject by introducing projects from the third semester on the relevant statistical subject.

·Introduce two core business areas i.e. Marketing Analytics and Financial Risk Analytics to give a deeper insight of the business domain at the undergraduate level

·Focus on blending theory with practical and industry application to enhance understanding and learning.

·Focus on the overall development of the students to help gain knowledge and skillsets required for further studies as well as for an edge in employability after completion of the course.

 

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 the basic concepts of Statistics, Mathematics, Economics and Computer Programming in the first year. Subsequently moving to the second year, the students are exposed to the core subjects of Statistics along with some papers of Economics and Mathematics. From the third semester student has to do a project in any of the core statistical subject taught in that semester. In the fifth year, students will be exposed to the fundamental papers of Marketing analytics and Financial Risk analytics domain along with the core statistical subjects. Project has to be done in any one of the core statistical paper. In final semester subject of Data Science is introduced with statistical modelling papers of the Marketing Analytics and Risk Analytics. Students are supposed to do a project in Data Science.

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 & future 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.

 

Salient Features

  • Curriculum prepared with inputs from eminent academicians and industry leaders, to focus on building skillsets for the growing requirement of data scientists in the industry
  • Applied Statistics and Analytics form the core of the curriculum
  • Curriculum contains components from Statistics, Economics, Mathematics, Computers and Analytics and emphasis on Experiential learning
  • Focused on blending theory with practical and industry application to enhance understanding and learning.
  • Hands on experience on using software like MS Excel, RStudio, C++, SQL, Oracle, Scilab, SPSS, SAS Visual Analytics, to solve problems involving analytics
  • Regular interaction with Industry leaders to give a holistic picture of importance and application of Analytics in the industry
  • Industry projects are part of the curriculum
  • Faculty are drawn from a pool of experts from Industry/academia thereby ensuring a balanced and continuous interaction with the industry

 

Course Curriculum

PROGRAM OUTCOME

           
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