The Master of Science (M.Sc.) Data Science syllabus at Cochin University of Science and Technology (CUSAT), Ernakulam is designed to provide overall knowledge to the students with a strong foundation. Master of Science (M.Sc.) Data Science faculty at Cochin University of Science and Technology (CUSAT) specially focus on in-depth learning to relevant subjects. At first semester syllabus of Master of Science (M.Sc.) Data Science at Cochin University of Science and Technology (CUSAT), students learn the basics of programme. A strong foundation is very important for comprehensive learning. Master of Science (M.Sc.) Data Science syllabus at Cochin University of Science and Technology (CUSAT), Ernakulam maintains a balance between theoretical knowledge and practical knowledge.
Master of Science (M.Sc.) Data Science first year students at Cochin University of Science and Technology (CUSAT) are introduced with core subjects. Then they are encouraged to explore other area for a broader perspective. Cochin University of Science and Technology (CUSAT), Ernakulam also provides practical training sessions, workshops, projects, and case studies to enhance student skills. Master of Science (M.Sc.) Data Science syllabus at Cochin University of Science and Technology (CUSAT), Ernakulam is also frequently updated to give industry relevant training and knowledge to students. Cochin University of Science and Technology (CUSAT) strives to provide a nurturing environment where students can learn new skills. The hands-on training sessions at Cochin University of Science and Technology (CUSAT) enable Master of Science (M.Sc.) Data Science students to apply knowledge and skills in a controlled environment and get required experience.
According to syllabus of Master of Science (M.Sc.) Data Science progress, students learn advanced topics and complex concepts. The Master of Science (M.Sc.) Data Science curriculum at Cochin University of Science and Technology (CUSAT), Ernakulam mainly focuses on analytical and critical thinking. As the Master of Science (M.Sc.) Data Science course unfolds, students develop several important skills that increases their employability. As per syllabus of Master of Science (M.Sc.) Data Science at Cochin University of Science and Technology (CUSAT) also includes real-life projects and internship programs. It helps students critical thinking and gives them real-world experience.
Master of Science (M.Sc.) Data Science curriculum at Cochin University of Science and Technology (CUSAT) includes group discussions, guest lectures, case studies, and skill development workshops to enhance the learning experience. The Master of Science (M.Sc.) Data Science syllabus at Cochin University of Science and Technology (CUSAT) aims to create well-rounded professionals equipped with the necessary skills and knowledge to succeed in their chosen fields.
Additional curriculum at Cochin University of Science and Technology (CUSAT)
Note: Given below syllabus is based on the available web sources. Please verify with the Cochin University of Science and Technology (CUSAT), Ernakulam for latest Master of Science (M.Sc.) Data Science curriculum.
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 |