Academics
Programme Structure:
The programme offers specializations in :
- Bio-Statistics and Data Analysis.
- Data Science
- Industrial Statistics and Operations Research
- The courses taken in Semester III are based on the choice of specialization and the relevant optional courses offered in that specialization.
- The programme also includes some pre-requisite courses.
- The students will be required to undertake an internship of two months. This is during the month of
April to June after completion of the first two semesters. The students undertake their Dissertation as part of the programme in Semester IV. Other than the regular credit course, Institute will also conduct some tutorial and workshop training, which will be a non-credit course
but forms a part of the programme.
| Course Title | Credits |
| Probability Distributions | 4 |
| Linear Algebra | 4 |
| Sampling Theory | 4 |
| Mathematical Analysis | 4 |
| Statistical Computing | 4 |
| Integrated Disaster Management* | 0 |
| Total | 20 |
* No credit course.
| Course Title | Credits |
| Probability Theory and Applications | 4 |
| Linear Models | 4 |
| Statistical Inference | 4 |
| Stochastic Processes | 4 |
| Multivariate Statistics-1 | 3 |
| Total | 19 |
| Course Title | Credits |
| Generic Core Courses | |
| Design of Experiments | 4 |
| Multivariate Statistical Analysis-2 | 4 |
| Computer Intensive Statistical Methods | 4 |
| Statistical Learning and Data Mining | 4 |
| Internship - Part A | 3 |
| Internship - Part B | 1 |
| Total | 20 |
| Specialization Core Courses : Bio-Statistics and Data Analysis | |
| Demography and Vital Statistics | 3 |
| Survival Analysis | 3 |
| Total | 6 |
| Specialization Core Courses : Data Science | |
| Statistical Simulation | 3 |
| Optimization Techniques | 3 |
| Total | 6 |
| Specialization Core Courses : Industrial Statistics and Operations Research | |
| Optimization Techniques | 3 |
| Time Series Analysis | 3 |
| Total | 6 |
| Total Credits for Sem III | 20 + 6 = 26 |
| Course Title | Credits |
| Generic Core Courses | |
| Statistical Machine Learning | 4 |
| Industry Project In Specialization | 4 |
| Scientific and Report Writing | 2 |
| Seminar | 1 |
| Total | 11 |
| Specialization : Bio-Statistics and Data Analysis | |
| Statistical Methods in Micro-array Data Analysis | 4 |
| Analysis of Clinical Trial Data | 4 |
| Statistical Methods for Bio Computing | 4 |
| Specialization : Data Science | |
| Statistical Methods for Quality Control | 4 |
| Statistical Methods for Bio Computing | 4 |
| Big Data Analytics | 4 |
| Specialization : Industrial Statistics and Operations Research | |
| Stochastic Models in Finance | 4 |
| Statistical Methods for Quality Control | 4 |
| Statistical Methods for Reliability | 4 |
| Total Required Credits | 4 |
| Total Credits for Sem IV | 11 + 4 = 15 |
Note: Theory and practical credits depends upon chosen specialization
- Depending upon the strength of the students we will be offering specialization in Bio-Statistics and Data Analysis,Data Science, Industrial Statistics and Operations Research.
Credits: 80
Specialization
Students have to choose one of the following specializations in the semester III: 5 courses/specialization
| Course code | Specialization in subject |
| S1 | Bio-Statistics and Data Analysis |
| S2 | Data Science |
| S3 | Industrial Statistics and Operations Research |
Note: For semester three, students need to choose any five courses per specialization.
The Institutional committee may decide
(i) To offer two modules, where courses from these five modules will be merged or
(ii) To offer module specific courses as optional/elective courses or
(iii) To offer module specific/optional/elective courses as compulsory courses.
List of elective subjects:
| Course Title | Credits |
| Statistical Methods in Micro-array Data Analysis | 4 |
| Analysis of Clinical Trial Data | 4 |
| Stochastic Models in Finance | 4 |
| Statistical Methods for Reliability | 4 |
| Statistical Methods for Bio-computing | 4 |
| Statistical Methods for Quality Control | 4 |
| Demography and Vital Statistics | 4 |
| Optimization Techniques | 4 |
| Big Data Analytics | 4 |