Ratings and Reviews for Introduction to Data Science in Python

Great for someone with intermediate experience in Python. A quick way to learn the pandas package, and apply it to interesting examples. A big thank you to the instructors!

Very informative and helpful!

Very nice course. Professor explains clearly. Materials are complete and not difficult to understand.

I felt like the test-candidate for a new course. While the course-work and the presentations itsself were excellent, the "homework" was a real pain as very often the Auto-grader didn't work and it took me hours to get a project which was correct, to be accepted as the right solution. Cannot recommend it to be honest unless they improve their systems. Also it was said that the other course specialization start soon but it seems they don't and I paid for the full package....

The instructor should lower the speed

The course is challenging, and designed to encourage learners to be independent and make full use of the discussion forum. As long as learners do not lose sight of the fact that help is available, they will enjoy this course. But if they lose faith, this would be a disaster.

I really like Prof. Brooks's way of teaching. He developed a very good introductory level course. Apart from some talks about data science in a whole, he concentrated on the preparatory work in this field -- data cleaning. Instead of delving into theories, he paid most of his attention to how to make things work by using python. I actually have a background in C, and I was a bit reluctant to learn python at first since C is already strong enough to attack most tasks. However, I have fallen in love with python now, and I think it is a much more suitable language for daily use especially when your projects aren't very large. Among its many merits, the best thing about python is of course its numerous libraries like numpy and pandas which free us from tedious low-level programming. I am quite convinced that I will move to python from now on. In addition to lectures, I truly recommend you go over extra reading materials. Those articles are very thought provoking. For example, the first one "50 Years of Data Science" totally changed my previous view towards this field. It made me realize that data science is not a simple combination of statistics and machine learning, that it is a distinct way of obtaining new knowledge, and that its advancement shall benefit the whole science society. About the assignments, those taught in the lecture are not enough and you should refer to python documents and stack overflow. I think knowing how to solve problems and where to find help is more important than solving problems itself, and that's why I consider those assignments well designed. Finally, thanks to all the efforts made by the teaching staff.

Videos are fast-paced, material is limited (no real slides or extensive doc). Exercises are sometimes not clear in their statement. Activity on the forum compensates that. Exercises are however close to challenges you face in real life. Finally, the start date was delayed and there is no clear visibility on when the other modules are started. So, the course could be better given, provide more material and be better coordinated. I followed one Python course at Rice University which was by far better given.

Very nice course with good demonstration and practical assignments.

Better course for Beginners.