Explore a comprehensive Master of Science in Data Science syllabus: from data analysis and machine learning to ethics and advanced techniques.
The syllabus for a Master of Science in Data Science program typically covers a wide range of topics to provide students with a comprehensive understanding of data analysis, machine learning, and data-driven decision-making. Below is a sample syllabus that includes common courses found in such a program.
| S.No |
1st Year Syllabus of M.Sc in Data Science |
| 1 | Data Science Programming |
| 2 | Data Mining and Warehousing |
| 3 | Econometrics |
| 4 | Construction Economy and Finance |
| 5 | Data Analytics Mathematics |
| 6 | Computational Linear Algebra |
| 7 | Data Analytics and Graphs |
| 9 | Empirical Research |
| 10 | Advanced-Data Analytics |
| 11 | Inferential Statistics |
| 12 | Stochastic Processes |
| 13 | Optimization Techniques |
| 14 | Software Engineering |
| 15 | Software lab in Python |
2nd Year OR 3rd and 4th Semester Syllabus of M.Sc in Data Science
| S.No | Subjects |
| 1 | Machine Learning |
| 2 | Hadoop Programming |
| 3 | Big Data Analytics |
| 4 | Practicals |
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