Ratings and Reviews for Introduction to Data Science in Python

It was hard and I could not have passed nor learned much except frustration if it had not bee for Sophie Green (TA) superb support. I could not start the course on time but the first week was easy. So I was surprised by the work excepctation from the 2nd week. Also it did not match what was forcasted by instructors. 2h --> 10h, 4h--> 20h... and I'm not count the night thinking of ways to solve the problem. I think that difficult comes from the expectation that documentation is understandable by newbies. Also question were often tested on type but the expected type output was not mentioned in the questions. Finaly, I think personally would need to learn how to debug properly a python program (going step by step in it) Hard and challenging. Thank you Sophie

Among more than 20 MOOC followed, I guess this is the worst one. I won't repeat here the arguments of other learners.

Perfect course. I have learned more about python in data science. Exercises can be really helpful!

I really tried to get into this course. I wanted a more advanced and fast paced course so a lot of the reviews about it being difficult didn't turn me off. However, the course just is not good. The assignments are not conducive to experimenting and learning and I got almost nothing from the lectures which were basically dry lists of facts.

Very good content. One downside for me was that being new to Python, Pandas, Numpy, Scipy etc, I found the amount of new information being thrown at me to be a bit overwhelming. Each of these languages/packages could be a separate course even before you start talking about Data Analysis concepts. I was able to complete all the assignments, but I feel like I know "just enough to be dangerous". Speaking of the assignments, if you're a newbie like me, give yourself plenty of time to complete to work on them. My rule of thumb was to multiply the "estimated time" for each assignment by a factor of 4. The assignment that was supposed to take 2 hours ended up taking my whole Saturday and the 4 hour project at the end of the course pretty much consumed an entire weekend. This might not apply if you have previous experience in this development environment or are just smarter than me ;-) Not everything that you need to know to do the homework is provided in the lecture, so expect to spend a lot of time in StackOverflow. The discussion forums are also very useful. Sometimes a teaching assistant will offer some hints that make all the difference. One gripe I have is with the automated grader. It's a great idea, but sometimes you can submit a fairly complicated bit of code and the only feedback you get from the grader is: "Wrong!". My suggestion: have two data sets, one for testing and another for grading. Then students could openly discuss and debug their test results in the discussion forums without violating the Honor Code. They would still have to submit a valid algorithm to pass against the test data.

Videos are a bit short without detailed explanation.

Extremely good, however requires a much more time and patience than specified (estimations).

Excellent course on introduction to data science in Python

Excellent course!

Awesome curse to start with data science.