Ratings and Reviews for Introduction to Data Science in Python

A bit short, but good info. I wish you got access to an optimal code for each of the assignments once you submit yours and pass, so you could see how you can improve.

The course was a very efficient intro to using Python to get data from various simple sources and the different structures that can be used to hold and manipulate the data. The course starts with the intuitive methods to solve problems and then introduces more complex and process friendly alternatives to demonstrate how creativity and knowledge of Python can be used to generate elegant and efficient code. The auto-grading of assignments can be a little maddening as the correct answer needs to be formatted precisely as is expected or else it will be counted as incorrect. The good thing here is that it forces you to fully understand the data types and data, the bad is it can be frustrating not knowing if the answer is wrong or just the format of the answer. Admittedly, I feel this effort forced me to become a better Python programmer.

Great course, requires much self-initiative because not all useful methods for the assignments are covered in the videos. Sometimes Assignment Questions are not described very clearly. All in all I enjoyed the course very much :)

Not for beginners. Level of difficulty increase very fast. You need to dig external reference sites and use discussion forums in order to figure out some assignments requirements. Overall quality is very good. Should have some better tips and validation scripts in order to help debugging assignments.

Very good introduction to Pandas Series and DataFrames for Data Science. Fast paced course with good supplementary materials. The homework is progressively challenging. Sophie the Teaching Assistant is particularly helpful in the forums. I don't recommend this course for those without programming or python scripting experience. Also, the homework exercises took me significantly longer than the estimates projected, but I budgeted about double the time and was able to complete the course on time.

Only 37 minutes of video, average per week. Really nice, pleasant video, but don't expect to learn how to solve the problems, because there is little connection from the problems to the videos. Teaching assistants are hard -working and knowledgable and each has a different way to do things. Very little in the way of effective educational design. But they do create useful questions to answer, if you are stubborn enough not to need actual instruction. The estimate of time required is woefully inadequate. The best thing you can do is lookup Brandon Rhodes on Youtube. He will actually explain Pandas. Expect to invest about four hours in his videos and still have questions. Use the course forum extensively — only there will you get a hint of how to do what they ask. Google and Stack Overflow: that's their extensive list of references.

This course was really worth paying!!! Thank you for providing this course!

I found that the videos weren't entirely helpful when tackling the assignments. I had to outsource most, if not all, of my inquiries to external sources (read: stack overflow). Even simple things such as advanced indexing, such as multi-indexing, was unclear. I understand that not everything can be covered, but I feel that basic things should be. Other than that, I enjoyed using the Jupyter notebook, and thought this was integrated very nicely into the coursework.

The course was helpful in presenting typical problems in data science, but the lectures showed you examples in one area of pandas and the assignments asked questions in different areas of pandas. I found the assignments frustrating and spent much of my time trying to understand what they were asking rather than actually solving the problems.

Excellent course. It really motivated me to work very hard to make all assignments on time