M.Sc. (Statistics)

Programme Highlights


  • Career opportunities as a senior statistician, risk analyst, quality analyst, investigator, environmental scientist, reliability analyst, statistical consultant, project manager and an economist.
  • Opportunities in a wide variety of fields such as management science, bio-Informatics, technology, bio-statistics, data mining, data warehousing, healthcare, insurance, finance and agriculture. Also, become employable in international software firms, KPOs and data analytics companies as business analysts and data scientists.
  • Pursue careers in academics & research, government and semi-government undertakings & management of not-for-profit businesses.
  • Entrepreneurship


Foundation Courses -8 Generic Core Courses -14 Generic Elective Courses -4 Specialization Core Courses -6 Specialization Elective Courses -4 Projects -2 Dissertation -2 Audit Courses -8


  • 2 Years – 4 Semesters – Full Time
  • Outcome Based Education (OBE)
  • Choice Based Credit System (CBCS)
  • Credit and Grading system


  • Education that prepares you for life.
  • Enriched programme structure with a fine blend of core, elective, audit and foundation courses designed to provide in-depth knowledge and requisite skills. Experiential and blended learning that enhances effectiveness of classical teaching – learning methods.
  • Expertise – led by in house faculty and augmented by visiting, guest and adjunct faculty as well as accomplished advisory board members.
  • Employability focus integrated through the curriculum, right from the first semester.
  • Enabling environment for nurturing Innovation and Entrepreneurship
  • Ecosystem that enables holistic development of learners through focused curricular, research co-curricular, extra-curricular, industry connect & professional development and extension and CSR initiatives.


“The highest education is that which does not merely give us information, but makes our life in harmony with all existence” - Faculty / Quote by educationist


Facts are stubborn, but statistics are more pliable

Statistics is a discipline that is fundamental in decision making and policy formulation. It’s descriptive and inferential roles not only formulate the basis of growth for almost all disciplines of the contemporary world, but also provide an array of non-traditional employment avenues ranging from that of sport-analysts to business analysts. The MSc. Statistics programme is suitable for students who are interested in advanced learning and to undertake a research project in statistical science and its application to real problems. The programme offers an excellent balance between theory and application and covers traditional theory & methods as well as more modern ideas in statistics. A broad base of training in the important areas of statistical science will allow students to successfully progress into professional employment or research.

PEO1: Prepare graduates for careers in, but not limited to - the financial, health, agriculture, government, business, telecommunication and bio-statistics industry.

PEO2: Familiarize students with computational techniques and software typically used in the statistical arena.

PEO3: Provide a good grounding in the best practice of collating and disseminating information.

PEO4: Prepare students to undertake further study at doctoral level.

PEO5: Teach students to construct practical statistical models for several processes in the real-world.

At the end of the programme the learner will be able to

PO1: Integrate knowledge, skill and attitude that will sustain an environment of learning and creativity.

PO2: Develop an understanding of various statistical tools, techniques and software.

PO3: Apply critical and contextual approaches across wide variety of subject matter.

PO4: Develop logical thinking to comprehend key facts leading to formulation of the solution process.

PO5: Develop self-confidence and awareness of general issues prevailing in the society.


Course CodeCourse TypeCourse NameCredit ValueMarks
101 FOUC Written Analysis and Communication 0.5 25 
102 FOUC Budgeting 0.5  25 
103 GC Statistical Computing -I 100
104 GC Design of Experiments 100 
105 GC Regression Analysis 100 
106 GC Statistical Estimation 100 
107 GC Testing Statistical Hypotheses 100 
108 GE Generic Elective - I 2 50 
109 GE Generic Elective - II 50
110 AUDC Visual Reasoning  
111 AUDC Office Management Tools  


Course CodeCourse TypeCourse NameCredit ValueMarks
201 FOUC Public Speaking Skills 0.5 25 
202 FOUC 7QC 0.5  25 
203 GC Statistical Computing II 100 
204 GC Linear Algebra and Statistical Model 100 
205 GC Time Series Analysis 100 
206 GC Non - parametric Data Analysis 100 
207 GC Multivariate Statistical Analysis 100 
208 GE Generic Elective - III 2 50 
209 GE Generic Elective - IV 50 
210 AUDC Verbal Reasoning 0 0
211 AUDC Web Business Tools


Course CodeCourse TypeCourse NameCredit ValueMarks
301 FOUC Book Review 0.5  25 
302 FOUC Six Sigma 0.5  25 
303 GC Reliability Engineering 100 
304 GC Project - I 100 
305 SC Subject Core -I 100 
306 SC Subject Core -II 100 
307 SC Subject Core -III 100 
308 SE Subject Elective - I 50 
309 SE Subject Elective - II 50 
310 AUDC Logical Reasoning
311 AUDC Website Design and Development


Course CodeCourse TypeCourse NameCredit ValueMarks
401 FOUC Assertiveness Training 0.5  25 
402 FOUC Balanced Scorecard 0.5  25 
403 GC Bayesian Statistics 4 100 
404 GC Project - II 4 100 
405 SC Subject Core -IV 4 100 
406 SC Subject Core -V 4 100 
407 SC Subject Core -VI 100 
408 SE Subject Elective - III 2 50 
409 SE Subject Elective - IV 50 
410 AUDC Critical Reasoning  
411 AUDC Business Analytics  

Generic Electives

Generic Electives Track IGeneric Electives Track II
Probability Theory I Probability Theory II
Data Mining Advanced Design of Experiments
Sampling Theory Portfolio Management
Dissertation - I Dissertation - II

Subject Core

Course CodeSubject CoreCourse NameCredit ValueMarks
305 Subject Core-I Computational Statistics    
306 Subject Core-II R Programming for Statistical Applications    
307 Subject Core-III Stochastic Processes I    
405 Subject Core-IV Actuarial Statistics    
406 Subject Core-V Statistical Process Control    
407 Subject Core-VI Stochastic Processes II    

Subject Elective

SEM III Electives BasketSEM IV Electives Basket
SPSS for Statistical Applications SAS for Statistical Applications
Biostatistics MATLAB for Statistical Applications
C++ for Statistical Applications Mini Tab for Statistical Applications
Fuzzy Theory and Applications Statistical Methods in Manufacturing Design

GA1 :Deep knowledge of Statistics
GA2 :Statistical, Computational, Analytical and Presentational skills
GA3 :Research Skills
GA4 :Independent, Logical and Critical thinking
GA5 :Effective and Confident Communication
GA6 :Ethical and Social Awareness
GA7 :Entrepreneurship & Intraprenuership
GA8 :Life Skills

The method of instruction (pedagogy or teaching-learning processes) shall be determined by the requirements of a course, the learning objectives, learning outcomes & the learner’s context. However, the following methods of instruction shall be commonly used: Lecture Sessions, Practical, Simulations, Field Work, Group Exercises, Massive Open Online Courses, Projects, Self-Learning Materials (SLMs), Self-study, Seminars, Study Tours, Training Programmes, Workshops.

During semester II, students are encouraged to opt for dissertation in lieu of a generic elective course. During semester III and IV, as a part of generic core courses, students are required to take up live projects in an industry to align their theoretical knowledge and its application. Industry based live projects allow students to gain valuable work experience while they’re still studying in college. They pave way for self-empowerment through skill building and hands-on-training.

1. 100 credits to be earned
2. All Audit Courses to be completed with ‘S’ (Satisfactory) grade
3. Minimum CGPA 4.0

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