M.Sc. Data science is one of the in-demand career paths for skilled professionals which prepares students for a career in industry, research, teaching and for limited but attractive jobs in the government sector.
M.Sc. Data science at IAR is a 2-year (4 semesters) long program. The first semester mostly cover the core subjects. Next two semesters offer a range of electives like Introduction to Parallel Computing, Statistical Inference, Big Data and Hadoop, Digital Signal Processing in Machine Learning, Time Series and Forecasting, Artificial Intelligence, Computer Vision, Mathematical Modeling and Numerical Simulation, Cloud Computing, Detection and Estimation Theory, Digital Signal Processing and fourth-semester project, to give students a flavour of current research topics and trends.
This course needs knowledge of mathematics, statistics and computer science. The course contents are designed in such a way that it does not affect any student if students background is either in computer or mathematics or statistics. This course not only prepares students for industries, but also for interdisciplinary research across the country and abroad. Considering the fact that programming is an integral part of the data science program, all program languages C, C++, Python, R included in the laboratory. Further, the special laboratory and advanced courses are offered, aligned with the elective chosen by the students in the second and third semesters. Public lectures, scientific tours, conferences and hands-on workshops are also an integral part of the program.
B.Sc. with Mathematics
B.Sc. with computer science
B.Sc with IT
BTech from any branch
The admission is based on merit.
| Sr. No. | Course Category | Course Code | Course Title | Teaching Scheme | Examination Scheme (Max. Marks) | |||||||
Teaching Hours per week | C | IA | SEE | Total | ||||||||
| L | T | P | MSE | Assi | CA | |||||||
| 1. | CC-1 | MDS101T | Data Structure and Data Analysis | 3 | 0 | 0 | 0 | 20 | 20 | – | 60 | 100 |
| 2 | CC-2 | MDS102T | Linear Algebra and Statistics | 4 | 0 | 0 | 4 | 20 | 20 | – | 60 | 100 |
| 3 | CC-3 | MDS103P | Programming Practical | 0 | 0 | 6 | 2 | – | – | 40 | 60 | 100 |
| 4. | CC-4 | MDS104T | Statistical Inferences | 4 | 0 | 0 | 4 | 20 | 20 | – | 60 | 100 |
| 5. | CC-5 | MDS105P | Data visualization Practical | 0 | 0 | 6 | 2 | – | – | 40 | 60 | 100 |
| 6. | CC-6 | MIT106T | Discrete Optimization | 4 | 0 | 0 | 4 | 20 | 20 | – | 60 | 100 |
| Sr. No. | Course Category | Course Code | Course Title | Teaching Scheme | Examination Scheme (Max. Marks) | |||||||
Teaching Hours per week | C | IA | SEE | Total | ||||||||
| L | T | P | MSE | Assi | CA | |||||||
| 1. | CC-7 | MDS 201P | Natural Language Processing using Big Data Ethics Practical | 0 | 0 | 3 | 3 | 40 | 60 | 100 | ||
| 2 | CC-8 | MDS 202T | Scientific Computing | 4 | 0 | 0 | 4 | 20 | 20 | – | 60 | 100 |
| 3 | CC-9 | MDS 203T | Database Management System | 3 | 0 | 0 | 3 | 20 | 20 | – | 60 | 100 |
| 4. | CC-10 | MDS 203P | Database Management System Practical | 0 | 0 | 3 | 3 | 40 | 60 | 100 | ||
| 5. | CC-11 | MDS 204T | Machine learning | 4 | 0 | 0 | 4 | 20 | 20 | – | 60 | 100 |
| 6. | CC-12 | MIT204P | Machine learning Practical | 0 | 0 | 9 | 3 | 40 | 60 | 100 | ||
| 7. | CC-13 | MDS 205T | Signals and Systems | 2 | 0 | 0 | 2 | 20 | 20 | – | 60 | 100 |
| 8. | DSE-I | 4 | 0 | 0 | 4 | 20 | 20 | – | 60 | 100 | ||
| Sr. No. | Course Category | Course Code | Course Title | Teaching Scheme | Examination Scheme (Max. Marks) | |||||||
Teaching Hours per week | C | IA | SEE | Total | ||||||||
| L | T | P | MSE | Assi | CA | |||||||
| 1. | MDS206T | Introduction to Parallel Computing | 4 | 0 | 0 | 4 | 40 | 60 | 100 | |||
| 2 | MDS207T | Big Data and Hadoob | 4 | 0 | 0 | 4 | 40 | 60 | 100 | |||
| Sr. No. | Course Category | Course Code | Course Title | Teaching Scheme | Examination Scheme (Max. Marks) | |||||||
Teaching Hours per week | C | IA | SEE | Total | ||||||||
| L | T | P | MSE | Assi | CA | |||||||
| 1. | CC-13 | MDS 301T | Stochastic Models | 3 | 0 | 0 | 3 | 20 | 20 | 60 | 100 | |
| 2 | CC-14 | MDS 302P | Computational Statistics using R and Matlab Practical | 0 | 0 | 3 | 1 | 40 | 60 | 100 | ||
| 3 | CC-15 | MDS 303T | Deep Learning | 4 | 0 | 0 | 4 | 20 | 20 | – | 60 | 100 |
| 4. | CC-16 | MDS 303P | Deep Learning Practical | 0 | 0 | 6 | 2 | 40 | 60 | 100 | ||
| 5. | CC-17 | MDS 304T | Time Series and Forcasting | 4 | 0 | 0 | 4 | 20 | 20 | – | 60 | 100 |
| 6. | DSE-II | MDS305T | Multivariate statistical Data Analysis | 3 | 0 | 0 | 3 | 20 | 20 | . | 60 | 100 |
| 7. | MDS 306T | Mathematical Modeling and Numerical Simulation | 3 | 0 | 0 | 3 | 20 | 20 | – | 60 | 100 | |
| 8. | MDS 307T | Advanced Soft computing in Data Science | 3 | 0 | 0 | 3 | 20 | 20 | – | 60 | 100 | |
| 9. | DSE-III | MDS308T | Data Warehousing for Business Intelligence | 3 | 0 | 0 | 3 | 20 | 20 | – | 60 | 100 |
| 10. | MDS309T | Detection and Estimation Theory | 3 | 0 | 0 | 3 | 20 | 20 | – | 60 | 100 | |
| 11. | MDS310T | Trends of IOT and IIOT on Data Science | 3 | 0 | 0 | 3 | 20 | 20 | – | 60 | 100 | |
| Sr. No. | Course Category | Course Code | Course Title | Teaching Scheme | Examination Scheme (Max. Marks) | |||||||
Teaching Hours per week | C | IA | SEE | Total | ||||||||
| L | T | P | MSE | Assi | CA | |||||||
| 1. | CC | MDS401 | Desertation | 24 | 40 | 60 | 100 | |||||