The Five-year Integrated M.Sc. Program on Data Science is intended for the students who aspire to excel in data science. It provides a spectrum of basics to advanced mathematical methods and their applications to both conventional and IT industries that enable to tackle emerging problems. The curriculum covers the necessary basics in Mathematics, Statistics, Computer programming and Business analytics and it also includes the emerging topics such as Big Data Analytics, Neural Networks, Machine Learning, Artificial Intelligence, Data Visualization, and Data Modeling 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 gain real time processing of data by doing Prediction analysis, Pattern Recognition, Anomaly Detection, Clustering, Actionable Insights, Automated Processes and decision making, Segmentation, Optimization and Forecasting. 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.
Harvard was right about data scientists. It’s an extremely important and high-demand role that can have significant impact on a business’ ability to achieve its goals, whether they are financial, operational, strategic, and so on.
Salient Features of the Program
| Date | Shift 1 (9.00 AM to 11.00 AM) |
Shift 2 (12.00 PM to 2.00 PM) |
Shift 3 (3.00 PM to 5.00 PM) |
|---|---|---|---|
| May 12, 2023 | AEEB | AEEL | AEEP |
| May 13, 2023 | AEEL | AEEP | AEEB |
| May 14, 2023 | AEEP | AEEB | AEEL |
| Semester 1 | |||
| Course Code | Course Title | L T P | Cr |
| 18ENG101 | Communicative English | 2 0 2 | 3 |
| Language Paper I | 1 0 2 | 2 | |
| 18MAT101 | Calculus | 3 1 0 | 4 |
| 18MAT104 | Linear Algebra | 3 1 0 | 4 |
| 18PHY101 | Physics | 3 0 0 | 3 |
| 18CSE100 | Problem Solving and Computer Programming | 3 0 0 | 3 |
| 18CSE180 | Problem solving computer Programming Lab | 0 0 2 | 1 |
| 18PHY181 | Physics Lab | 0 0 2 | 1 |
| 18CUL101 | Cultural Education I | 2 0 0 | 2 |
| Total | 23 | ||
| Semester 2 | |||
| Course Code | Course Title | L T P | Cr |
| 18ENG121 | Professional Communication | 1 0 2 | 2 |
| Language Paper II | 1 0 2 | 2 | |
| 18MAT115 | Vector Calculus | 3 1 0 | 4 |
| 18MAT112 | Discrete Mathematics | 3 0 2 | 4 |
| 18CSE116 | Advanced Computer Programming | 3 0 0 | 3 |
| 18CSE117 | Digital Electronics | 3 0 0 | 3 |
| 18CSE181 | Advanced Computer Programming Lab | 0 0 2 | 1 |
| 18CSE185 | Digital Electronics Lab | 0 0 2 | 1 |
| 18CUL111 | Cultural Education II | 2 0 0 | 2 |
| Total | 22 | ||
| Semester 3 | |||
| Course Code | Course Title | L T P | Cr |
| 18CSC201 | Data Structures | 3 1 0 | 4 |
| 18MAT231 | Optimization Techniques | 3 1 0 | 4 |
| 18MAT232 | Probability Theory | 3 1 0 | 4 |
| 18MAT 233 | Numerical Methods | 3 1 0 | 4 |
| 18CSC202 | Foundations Of Data science | 2 0 2 | 3 |
| 18ENV300 | Environmental Science and Sustainability | 3 0 0 | 3 |
| 18MAT288 | Data science Lab 1: Statistics and Numerical Methods Lab |
0 0 2 | 1 |
| 18AVP201 | Amrita Values Programme I | 1 0 0 | 1 |
| 18CSC281 | Data Structures Lab | 0 0 2 | 1 |
| 18SSK201 | Life Skills I | 1 0 2 | 2 |
| Total | 27 | ||
| Semester 4 | |||
| Course Code | Course Title | L T P | Cr |
| 18MAT241 | Statistical Inference Theory | 3 0 0 | 3 |
| 18MAT242 | Introduction to Modern Algebra | 3 0 0 | 3 |
| 18CSC211 | Convex Optimization | 3 0 0 | 3 |
| 18CSC212 | Design and Analysis of Algorithms | 3 1 0 | 4 |
| 18CSC213 | Database Management Systems | 3 1 0 | 4 |
| Open Elective A* | 3 0 0 | 3 | |
| 18MAT289 | Data Science Lab-II: Inference Theory | 0 0 2 | 1 |
| 18SSK211 | Life Skills II | 1 0 2 | 2 |
| 18CSC282 | Design and Analysis of Algorithms Lab | 0 0 2 | 1 |
| 18AVP211 | Amrita Values Programme II | 1 0 0 | 1 |
| Total | 25 | ||
| Semester 5 | |||
| Course Code | Course Title | L T P | Cr |
| 18CSC301 | Operating Systems | 3 0 0 | 3 |
| 18MAT331 | Transfrom Techniques | 3 1 0 | 4 |
| 18CSC302 | Number Theory and Information Security |
3 1 0 | 4 |
| 18MAT332 | Random Process | 3 0 0 | 3 |
| 18CSC303 | Database Design | 3 1 0 | 4 |
| 18MAT390 | Live-in-Lab.@ / Open Elective B* | 3 0 0 | 3 |
| 18SSK301 | Life Skills III | 1 0 2 | 2 |
| 18CSC381 | DBMS Lab | 0 0 2 | 1 |
| 18CSC382/CSE | Open Lab-I (JAVA/C/C++/R programming/ Azure…) |
0 0 2 | 1 |
| Total | 25 | ||
| Semester 6 | |||
| Course Code | Course Title | L T P | Cr |
| 18MAT333 | Graph Analytics and Algorithms | 3 0 2 | 4 |
| 18MAT334 | Regression Analysis | 3 1 0 | 4 |
| 18CSC311 | Machine Learning | 3 1 0 | 4 |
| 18CSC312 | Data Visualization | 3 0 2 | 4 |
| 18CSC313 | Theory of Computation | 3 1 0 | 4 |
| 18CSC383 | Machine Learning-Lab | 0 0 2 | 1 |
| 18CSC314 | Ethics for Data Scientist | 1 0 0 | 1 |
| 18CSC384 | Open Lab II (Algorithms.io /Hadoop / Cascading / web design) |
0 0 2 | 1 |
| Total | 23 | ||
| Semester 7 | |||
| Course Code | Course Title | L T P | Cr |
| 18CSC401 | Parallel and Distributed Systems | 3 1 0 | 4 |
| 18CSC402 | Deep Learning | 3 0 2 | 4 |
| 18CSC403 | Practical Techniques for Big Data Processing | 3 0 2 | 4 |
| 18CSC404 | Reinforcement Learning | 3 0 0 | 3 |
| 18CSC405 | Data Security | 3 0 0 | 3 |
| Elective I | 3 0 0 | 3 | |
| Elective II | 3 0 0 | 3 | |
| 18CSC481 | Open Lab III | 0 0 2 | 1 |
| Total | 25 | ||
| Semester 8 | |||
| Course Code | Course Title | L T P | Cr |
| 18CSC411 | Software Engineering | 3 1 0 | 4 |
| 18CSC412 | Deep Learning for Natural Language Processing | 3 0 2 | 4 |
| Elective III | 3 0 0 | 3 | |
| Elective IV | 3 0 0 | 3 | |
| Elective V | 3 0 0 | 3 | |
| 18CSC491 | Mini Project | 5 | |
| Total | 22 | ||
| Semester 9 | |||
| Course Code | Course Title | L T P | Cr |
| 18CSC591 | Project–I (Internship in Industries / Universities) | 8 | |
| Elective VI | 3 0 0 | 3 | |
| Elective VII | 3 0 0 | 3 | |
| Total | 14 | ||
| Semester 10 | |||
| Course Code | Course Title | L T P | Cr |
| 18CSC599 | Project–II (Research based) Dissertation | 12 | |
| Total | 12 | ||
| Mathematics Stream | |||
| 18MAT441 | Advanced Algebra | 3 0 0 | 3 |
| 18MAT442 | Advanced Big Data Analytics | 3 0 0 | 3 |
| 18MAT443 | Differential Equations | 3 0 0 | 3 |
| 18MAT444 | Multivariate Statistics and Time Series Analysis | 3 0 0 | 3 |
| 18MAT445 | Wavelets | 3 0 0 | 3 |
| 18MAT446 | Computational Geometry | 3 0 0 | 3 |
| 18MAT447 | Queuing Theory and Inventory Control Theory | 3 0 0 | 3 |
| 18MAT448 | Theory of Sampling and Design of Experiments for Data Analysis |
3 0 0 | 3 |
| 18MAT449 | Computational Financial Mathematics | 3 0 0 | 3 |
| 18MAT450 | Data Analytics in Computational Biology | 3 0 0 | 3 |
| 18MAT451 | Computer Aided Drug Designing | 3 0 0 | 3 |
| 18MAT452 | Statistical Quality Control | 3 0 0 | 3 |
| 18MAT453 | Six Sigma Analysis | 3 0 0 | 3 |
| 18MAT454 | Statistical Pattern Recognition | 3 0 0 | 3 |
| Computer Science Stream | |||
| 18CSC441 | Soft Computing | 3 0 0 | 3 |
| 18CSC442 | Cryptography | 3 0 0 | 3 |
| 18CSC443 | Business Analytics | 3 0 0 | 3 |
| 18CSC444 | Deep Learning for Image Processing | 3 0 0 | 3 |
| 18CSC445 | Predictive Analytics | 3 0 0 | 3 |
| 18CSC446 | Mining of Massive Datasets | 3 0 0 | 3 |
| 18CSC447 | Data Compression | 3 0 0 | 3 |
| 18CSC448 | Computer Networks | 3 0 0 | 3 |
| 18CSC449 | IoT (workshop based course) | 3 0 0 | 3 |
| 18CSC450 | Introduction to Embedded Systems | 3 0 0 | 3 |
| 18CSC451 | Information retrieval (NL) | 3 0 0 | 3 |
| 18CSC452 | Social Network Analytics | 3 0 0 | 3 |
| 18CSC453 | Big Data Storage and Analysis | 3 0 0 | 3 |
| 18CSC454 | Probabilistic Graphical Models | 3 0 0 | 3 |
To be Updated
