One account
Many possibilites
M.Phil. (Statistics) is a postgraduate level master’s degree program offering core and advanced statistical concepts and research methods training. The normal span of M.Phil. is two years for a full-time program, and three years for a half-time program. The program duration of Master’s Degree in Statistics is 2 years which includes 4 semesters. The program offers enhanced specialization in statistical theory, modelling, data analysis, computing and research methods for those seeking careers in statistical or quantitative research, data science or analytics.
PMT and other important entrance tests are only accepted for the admission to the course of M.Phil. in Statistics include CSIR UGC NET in Mathematical Sciences (Statistics), UGC NET in Statistics, GATE in Statistics, University written entrance tests like ISI Admission test, CUCET, Mumbai University CET and other exams in the central and state University level. Other institutes may also require the candidates to have a valid GATE or NET score in Mathematics or Computer Science for M.Phil. Statistics admissions. Performance in an interview and academic records as measured by grade point average and test scores are other factors used in short listing candidates in many institutes.
| Feature | Details |
|---|---|
| Full Form | Master of Philosophy in Statistics |
| Duration | Typically 1-2 years |
| Course Level | Postgraduate |
| Eligibility | Master's degree in Statistics or a related field with a minimum percentage (varies by college) |
| Top Colleges (India) | Indian Statistical Institute (ISI), Delhi University, University of Mumbai, University of Pune, IITs (Indian Institutes of Technology) |
| Entrance Exam | ISI Entrance Exam, University-specific entrance exams, NET/JRF (National Eligibility Test/Junior Research Fellowship) |
1. Graduation: A candidate must be a holder of a first degree in Statistics or Mathematics with not less than an aggregate of fifty-five (55) per cent.
2. CUCET or university own entrance and qualifying score.
3. Knowledge of basic statistical concepts (probability distributions, inference, and sampling methods).
4. Strong analytical and quantative abilities, good knowledge of statistics and experience in statistical packages like R, SPSS, SAS.
5. Has a firm grasp on core computer competencies, development tools and utilities like MS Excel and Database.
6. Academic training should show good academic performance, statistical data analysis experience, and research aptitude.
| Subject | Important Topics |
|---|---|
| Advanced Probability Theory | Probability spaces, random variables, distribution functions, characteristic functions, limit theorems |
| Statistical Inference | Estimation theory, hypothesis testing, confidence intervals, Bayesian inference |
| Linear Models | Regression analysis, analysis of variance (ANOVA), experimental design |
| Sampling Theory | Sampling methods, estimation, sample surveys |
| Stochastic Processes | Markov chains, time series analysis, stochastic differential equations |
| Multivariate Analysis | Principal component analysis, factor analysis, discriminant analysis, cluster analysis |
| Operations Research | Linear programming, optimization techniques, queuing theory |
| Computational Statistics | Statistical software (R, SAS, SPSS), simulation, data mining |
| Research Methodology | Research design, data collection, data analysis, report writing |
| Dissertation | Original research in a specific area of statistics |
| Job Profile | Specialization | Average Salary (INR LPA) |
|---|---|---|
| Statistician | Statistical analysis, data modeling | 6-12 LPA |
| Data Analyst | Data cleaning, visualization, reporting | 5-8 LPA |
| Data Scientist | Data mining, machine learning, predictive modeling | 8-15 LPA |
| Operations Research Analyst | Optimization, modeling, decision analysis | 6-10 LPA |
| Actuary | Risk assessment, financial modeling | 8-15 LPA |
| Market Research Analyst | Data collection, analysis, reporting | 5-8 LPA |
