Ratings and Reviews for Introduction to Data Science in Python

A challenging and fast moving course. I recommend studying up on basic python before taking the course and to pause the videos often to understand each piece.

As others said, this course is fast paced, has only brief information in the videos, and has challenging programming tasks that requires students to get the required information elsewhere that was not given in the intros. Whether you like it or not depends on whether you are able to learn by yourself (with guidance on what to look for) or do you want to be fully nursed. For me, I LOVE IT! The material has enough information that I need, and I don't mind searching for references myself. The programming tasks are also challenging as it requires you to be really careful in reading the specs, and that is good. If you're not able to enjoy this course, maybe you need to take other introductory courses first.

The course was great and I learned a lot. Minus one star, because course required a lot more work than initially expected. Note, though, that this was probably mainly due to me being new to Python (coming from another language).

The course gives a good introduction to the python pandas library, with assignments that challenge enough to give a solid learning experience.

Amazing Course. Set my foundation in data science.

A lot of work. Not easy to pass exams. Good investment of time.

The course was a unnecessarily hard because of the lack of feedback from the grader, unclear requirements / function definition in the final project and difference in the file downloaded for the project and the one used in the Coursera python notebook.

Overall I would certainly recommend this course, I've found it immediately relevant in my field of science/engineering. It is fast-paced and difficult yes, especially for those of us with limited python experience, but that kick it gives leaves you with some solid, immediately-applicable skills. Where it goes well: Strong content and excellent delivery. Fosters independence. The course starts from the basics but accelerates at a fast pace, introducing you to roots of concepts but expecting you to expand on them yourself with outside resources rather than rote-learning. In this manner it leaves you VERY prepared to tackle unscripted challenges. Concise content. The lectures videos themselves contain almost zero fluff. The lecturer conveys relevant information in a very smooth and efficient manner. Replaying specific parts to revise or solidify understanding becomes a pleasure due to this. Where it could be improved: Could be a more polished. Time estimates for the assignments were WAY off. I do not mind a challenging assignment, however if it advertises that it will take 3 hours, I would hope not to expect to spend closer to 20 hours, however this certainly was the case. Multiply the estimated time by 4-5 to get a more realistic time. The assignment wording can sometimes be a little ambiguous. It's almost mandatory to go through the forum posts for clarification. I realise some things were noticed after the publication of the course, and contained by pinned posts in the forum, but perhaps if the next installment of the course could be updated, ironing out some of these wrinkles.

I found some assignment questions quite unclear. This, together with the grader sometimes marking answers as correct even though they were wrong, forced me to spend many hours trying to find the underlying problem to incorrectly answered questions down the line.

I appreciate why the data cleaning and debugging steps are included - I imagine this is a key component of working with real world data, but I think the time I spent debugging and cleaning could be better spent purely manipulating the data to get the answers to the questions in the assignments. I don't think the introductory videos on python are necessary - they would not be enough for someone to do the rest of the course. I would replace that with explanations on how to use jupyter notebook and getting more from the course in that way. In all i enjoyed this course, I particularly enjoyed Week 4's lectures on hypothesis testing.