DS203 - Programming for Data Science
Manjesh Hanawal, Amit Sethi, Sunita Sarawagi, S. Sudarshan
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:
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
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.
Interesting relevant links
One can follow the Andrew NG course on Coursera for the later part of the course
DS 203 Review By: Priyam Vijayvargia