Ratings and Reviews for Introduction to Data Science in Python

I certainly learnt a lot through this course. The hands on oriented approach of this course works wonders for those who intend to dig deeper and improve their Python programming. The lectures are crisp and clear, but the main learning happens while doing the weekly assignments. Each assignment forces us to go search for content beyond what has been taught in the lectures and in this process we find new things. Also, the lecturer informed us about some good reference books for individual learning. I look forward to taking part two of this course. The mentors in the discussions forum are quite active and helpful. The forum was of big help to me for clearing the assignments. The only flaw I found was that the autograder does not clearly define our mistakes and that's where the mentors come in and explain in the discussion forum. This part could be better automated. Finally, I believe that a prior introduction to Python programming is an essential prerequisite to this course.

The assignments were challenging and cool. Lot of self-study needed to crack them. The lecture videos could have been a bit more interesting.

I have struggled a lot during this course to complete the assignments. It is hard, it is well taught and you learn a lot!

The lecturer is excellent, the forums are active and useful and the exercises are a good mixture of challenging and enjoyable. A very good course overall.

The course material is very short and precise. Previous knowledge of Python is helpful. This course serves its purpose as an introduction to Python libraries and how they are used in the field of Data Science.

Too much focus on databases and database operations, rather than data science. The assignments are too difficult for a 4 week course.

This course was over all okay. My primary complaints are that I felt that the class moved too quickly and relied too heavily on students to teach themselves through the Pandas documentation. The Pandas documentation is really only so-so and it would have been nice to have more guidance through the course materials.

Very nice Course, You will Learn about how to effectively use Pandas Library for Python and how to treat DataFrames in that ambient, there are nice functions and methods for parsing. The Course is very fast pace, I only have time on the weekends (some of those), so I had to switch dates two times. Also, some materials are very fast, so If you are new in Python, got to be sure if you have mastered prior concepts of the course (Week 2 depends on week 1 and so on ...). A large part of the course involves your own research in Python Docs and StackOverFlow page. As I am an R user, some things are intuitive (and maybe more easier for me to do in R), several of the things in comparison I thought: "Wow, but this is so much easier on R", but at other times I saw the power of Python for parsing tasks or webdata that in the R require too much memory or are more complicated to obtain. R have the problem to treat everything like an object and guided by vectors, but at some parts that makes understand coding details more deeply, at least for me. I still think that the documentation of R the best there is for my purposes, so I will stick with that, but it's great to know how to develop some things in Python, mainly because of my goal of getting some applications to end users. Finally, not much related to the course, but maybe with the change in the platform of the Coursera, the forums seemed a little more confusing and a little more slower than former courses that I took, I think it might have to do with the same course running in parallel on different dates.

The course content is great - Prof Christopher seriously made justice to the content. He is great instructor. He has presented course in right manner, with right speed. Looking for more courses from Him.

I learnt a lot, and enjoyed the process! It was more engaging than I expected of an online course. Excited to go forward with the specialization.