DS203 - Programming for Data Science

DS203 - Programming for Data Science


Manjesh Hanawal, Amit Sethi, Sunita Sarawagi, S. Sudarshan


Autumn ‘20

Course Difficulty

Course covered the width of the subject rather than in-depth of each topic and so one had to put in efforts by themselves to cover most of the content. In all, I would say the course was moderate

Time Commitment Required

Apart from two 1.5 hours lecture in a week, weekly assignments were given that consumed a lot of time, approximately 3 hours or more. Many coding related assignments needed good Google search abilities to find out various functions and commands and understand more about the topic.

Grading Policy and Statistics

Grading policy included weightage for assignments and an end term project only. Grading statistics are:
AA 13
AB 35
AU 1
BB 18
BC 3
CC 3

Attendance Policy

There was no attendance policy in the online semester


No prerequisite as such but good command over the content of CS101 and knowledge of Python will give you an edge over others

Topics Covered in the Course

Probability and Statistics, Data Analysis, ML, DL, SQL

Teaching Style

Multiple professor took the course so we got varied insights and were taught by best in the field.


Theory wise they were easy but coding those parts required a lot of time commitment.

How strongly would I recommend this course?

This course covered the width of the subject and therefore enabled me to explore and know more about what lies ahead. One can take up the course and then figure out in which domain their interest lies exactly.

One can follow the Andrew NG course on Coursera for the later part of the course

DS 203 Review By: Priyam Vijayvargia