The two-year M.Sc. Program on Applied Statistics and Data Analytics is intended for the students who aspire to excel in data analytics. It provides a spectrum of basics to advanced statistical methods and their applications to both conventional and IT industries that enable to tackle emerging problems. The curriculum covers emerging topics such as Big Data Analytics, Pattern Recognition and Marketing Analytics to deal with vast data from business, healthcare, sensors, Internet, surveys, social media, astronomy, human space exploration and operations, and aeronautics. In addition to the regular curriculum, students are encouraged to take up summer/winter In-plant training/Internship. This will help students to learn about using data mining techniques to analyze data, discover patterns, suggest analytical models to identify and respond to various patterns such as structural pattern, creational pattern and behavioral pattern, and then enrich the enactment of business, technological, industrial and healthcare processes by implanting the analytical models within the consistent operational applications. A full length project is included in the curriculum to be completed in the final semester. Students are expected to take up such projects in industries. This helps students to gain knowledge on working conditions and industrial requirements when they are employed.
| Course code | Course | L | T | P | Credit | ES |
|---|---|---|---|---|---|---|
| 20MAT505 | Linear Algebra | 3 | 1 | 0 | 4 | A |
| 20MAT506 | Probability Theory and Estimation | 3 | 1 | 0 | 4 | B |
| 20MAT507 | Data Structures and Algorithms | 3 | 0 | 2 | 4 | C |
| 20MAT508 | Optimization Techniques | 3 | 1 | 0 | 4 | D |
| 20MAT509 | Introduction to Data Science | 3 | 0 | 2 | 4 | E |
| 20MAT510 | Python Programming | 3 | 0 | 2 | 4 | F |
| 18CUL501 | Cultural Education | 2 | 0 | 0 | P/F | G |
| Total | 24 | |||||
| Course code | Course | L | T | P | Credit | ES |
|---|---|---|---|---|---|---|
| 20MAT515 | Statistical Inference and Design of Experiments | 3 | 1 | 0 | 4 | A |
| 20MAT516 | Multivariate Statistics and Regression Analysis | 3 | 1 | 0 | 4 | B |
| 20MAT517 | Machine Learning | 3 | 0 | 2 | 4 | C |
| 20MAT518 | Big Data Analytics and Hadoop | 3 | 0 | 2 | 4 | D |
| 20MAT519 | Data Mining | 3 | 1 | 0 | 4 | E |
| 20MAT520 | Data Security | 3 | 0 | 0 | 3 | F |
| 18AVP501 | Amrita Value Programme | 1 | 0 | 0 | 1 | G |
| Total | 24 | |||||
| Course code | Course | L | T | P | Credit | ES |
|---|---|---|---|---|---|---|
| 20MAT606 | Statistical Quality Control and Reliability | 3 | 1 | 0 | 4 | A |
| 20MAT607 | Introduction to Deep Learning | 3 | 0 | 2 | 4 | B |
| Elective I | 3 | 0 | 0 | 3 | D | |
| Elective II | 3 | 0 | 0 | 3 | E | |
| Elective II | 3 | 0 | 0 | 3 | F | |
| 20MAT690 | Live-in-Lab.@/ Open Elective* | 2 | 0 | 0 | 2 | J |
| Total | 19 | |||||
| Semester 4 | ||||||
| Course code | Course | L | T | P | Credit | ES |
|---|---|---|---|---|---|---|
| 20MAT696 | Dissertation | 10 | P | |||
| Total | 10 | |||||
Total credits for the programme: 77
| Course code | Course | L | T | P | Credit | ES |
|---|---|---|---|---|---|---|
| 20MAT651 | Taugchi Techniques | 3 | 0 | 0 | 3 | D/E |
| 20MAT652 | Special Distribution Functions | 3 | 0 | 0 | 3 | D/E |
| 20MAT653 | Pattern Recognition | 3 | 0 | 0 | 3 | D/E |
| 20MAT654 | Stochastic Process | 3 | 0 | 0 | 3 | D/E |
| 20MAT655 | Queuing Theory | 3 | 0 | 0 | 3 | D/E |
| 20MAT656 | Market Analytics | 3 | 0 | 0 | 3 | D/E |
| 20MAT657 | Survival Analysis | 3 | 0 | 0 | 3 | D/E |
| 20MAT658 | Sampling Techniques | 3 | 0 | 0 | 3 | D/E |
| 20MAT659 | Demography and Actuarial Statistics | 3 | 0 | 0 | 3 | D/E |
| Course code | Course | L | T | P | Credit | ES |
|---|---|---|---|---|---|---|
| 20MAT660 | Official Statistics | 3 | 0 | 0 | 3 | D/E |
| 20MAT661 | Healthcare Analytics | 3 | 0 | 0 | 3 | D/E |
| 20MAT662 | Computational Biology | 3 | 0 | 0 | 3 | D/E |
| 20MAT663 | Computer aided drug designing | 3 | 0 | 0 | 3 | D/E |
| 20MAT664 | Reinforcement Learning | 3 | 0 | 0 | 3 | D/E |
| 20MAT665 | Social Network Analytics | 3 | 0 | 0 | 3 | D/E |
| 20MAT666 | Mining of Massive Datasets | 3 | 0 | 0 | 3 | D/E |
| 20MAT667 | Parallel and Distributed Systems | 3 | 0 | 0 | 3 | D/E |
A pass in B. Sc. in Mathematics / Statistics / Computer Science/ B. Tech. Computer Science with minimum 60% marks.
Based on the marks obtained in the qualifying examination and interview.
The curriculum and syllabi for this program are on par with any reputed educational institution in India and abroad. Syllabus is framed in such a way that the candidates will be competent enough to take up the tests like NET, SLET, GATE etc. The Program offers comprehensive instruction in the theory, methods and application of Statistics. The courses include computer-intensive classes as a tool to support the analysis and interpretation of statistical data.
Employment opportunities for people qualified with M.Sc. in Applied Statistics and Data analytics are available in the form of teaching positions in various educational institutions all over India and abroad. They are also eligible to work as Scientists and Research Associates in highly reputed organizations such as ISRO, DRDO and CSIR. In addition, lucrative jobs are available in software industries. In our integrated mathematics batch, while some of the students have got jobs in both public and private sector banks, many current final year students have already secured internships/jobs in industries (manufacturing/ software/insurance). The prospective recruiters for the students of Applied Statistics and Data Analytics are:
The department always focuses on evolving with the changing needs of our stakeholders (students/parents/recruiters). The feedback from our stakeholders reveals that the curriculum and syllabi for Applied Statistics and Data Analytics are on par with any reputed educational institution in India and abroad. Students are finding it more interesting as the program is a combination of both Statistics and Data Analytics. They learn advanced programming languages and application of statistical software which are useful in industries and IT companies. The intersection of Statistics and Data Analytics has enabled more sophisticated ways in learning. They have learnt to implement, and do experiments with data analysis techniques and algorithms which will lead them to be effective practitioners in data handling at the end of second year. Students can identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making. They can also analyze big data and make data-driven predictions through probabilistic modeling and statistical inference.
To be Updated
| Program Fees for the year 2019-2021 | ||||
| Campus | Tuition Fees per sem |
Tuition Fees Total |
Hostel & Mess Per Year |
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| Updating Soon | * | # | ||
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