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The Data Analysis with Python course is a decent intro into data handling, cleaning and some basic model building. I would say however that without prior Python or data analysis experience, it is probably quite a steep learning curve. I am from a science background with a bit of advanced maths knowledge, and the Model evaluation part got a little bit crazy even for me, I'll probably have to revisit it a few times. But you also can't expect to do a course like this and have a complete grasp of everything guaranteed, still have to be willing to do your own further reading/research outside of it, just like you would for uni. Some of the notebooks contained errors or the answers were spelled out which was a bit disappointing. My last project was graded immediately which was great, and I made sure to grade a couple extra to help other learners. Over all a great intro into some trickier topics, I enjoyed it.

I had to remove 1 star just because of the fact that a project is not included in this one. Yes, you do have labs but there you are forced to write code in way so that you don't encounter problems later in the notebook.

In a project or an assignment work, you have to play with variables and confusions and errors out of wonderland show up which lead to greater clarification.

The course in itself is great and undoubtedly good in functioning as a prerequisite for Machine Learning and surely I'd recommend it to anyone who asks for an opinion. The explanations are good and much easier to understand along with the visual demonstrations.

I'd advise that after learning anything during this course, look for some database online and play with it yourself(I didn't but had to regret cuz I'd forget the code again and again).

OVERALL : GO AHEAD, it's worth it

The final assignment for this course is frustrating because it uses Watson Studio instead of the learning environment we've used up to that point in the course. There is registration, learning curve, additional complexity. One of the final questions doesn't accept a file upload. One of the questions asks you to calculate a regplot which takes about 15-20 minutes to complete. Nothing tells you to expect that until I finally found a student comment in the forum. I was so frustrated at one point I was ready to abandon the course because I didn't know what was wrong and couldn't find any help. The issues with the final assignment need to be addressed, but I'm glad I decided to stay with it. The course presented a lot of good material. I recommend the course, but I hope they will address the rough edges.

Overall a good introductory course. It features several interesting aspects of pandas, numpy and matplotlib. The focus lies on using statistical methods in Python, not on explaining statistics itself, bu a qualititative (short) explanation is given for all the items shown in the course.

A few things could definetly be improved:

Some parts of the videos or their accompanying notebooks have some errors and should be checked.

The course kinda suffers from only testing he students via quiz options. While the structure ad high frequency of the quizzes is to be commended and helps students stay on topic, there should be some final assignment hich consists of actual coding tasks. Not too difficult, but with some of the methods explained each week.