This course helps students translate mathematical/statistical/scientific

concepts into code. This is a foundational course for writing code to

solve Data Science ML & AI problems. It introduces basic programming

concepts (like control structures, recursion, classes and objects) from

scratch, assuming no prerequisites, to make this course accessible to

students from non-computational scientific fields like Biology, Physics,

Medicine, Chemistry, Civil & Mechanical Engineering etc. After building a

strong foundation, the course advances to dive deep into core

Mathematical libraries like NumPy, Scipy and Pandas. We also learn

when and how to use inbuilt-data structures like Lists, Dicts, Sets and

Tuples. We introduce the concepts of computational complexity to help

students write optimized code using appropriate data structures and

algorithmic design methods. We would not dive deep into the data

structures and algorithm design methods in this course. That would be

available in a separate graduate level Data structures and Algorithms

courses. This course would be mandatory for all students specializing in

mathematical sub-areas of CS like ML, Data Science, Scientific

Computing etc.

- Accreditation: ECTS Accredited (EQF7)
- Total workload: 125 hours
- Requires extra purchases (outside texts, etc.): No, all materials included
- ID verification: Required
- Admission requirements: Application required
- Minimum education requirement for students: Undergraduate