It is a very nice course, providing you are able to pour a LOT of individual efforts into it, especially into the assignments. The knowledge required to finish the course is far beyond what they taught. I spent 70% of my time in the course on Stackoverflow or Google, or asking my friends. However, I admitted I gained huge amount of knowledge and experiences, as well as "individual research skills" and confidence, from this course. I would recommend going through this course in a much shorter period of time than recommended (like in a week), during your vacation or week off.

Solid introduction to using Python for basic data science applications. By the end of the course I feel confident that I know how to go about cleaning data and doing some basic analysis on it. The practical programming assignments are great, you can really dig your teeth into them without them being so challenging that it scares you off. Expect it to take you longer to complete the assignments than the official estimated time.

What's also great is that all the code from the lectures is posted so you can easily play with it to be able to follow along with the lectures.

I didn't utilize the discussion pages until the third week, but they are extremely helpful. The pseudo code that was sometimes given in those discussions was also helpful for learning the material because it revealed programming solutions that I was unaware of given my inexperience with the python language. My only real issue is that I wasn't expecting the self-learning for the end of week assignments, but it wasn't that bad. The in-video problems were nice, I would like to see more small, non-graded assignments to help practice the language and get a feeling for it.

This is a really great course. It's super informative, the video lectures are more engaging than those of any other online class I've taken, and I have learned so so much. However, I started this course as more or less a complete beginner. If that's you, then be prepared to spend a ton of time and energy on the homework (around 6 hours per assignment). I fortunately had a friend who helped me out a lot, but if you're a complete beginner, short on time, and don't have access to someone who can help you, I'd recommend finding another course or working up to this one.

CONs: I did find some of the ungraded peer evaluations could use a little refinement. I've shared this feedback with the team though and I doubt it will get in the way of anyone else's learning.

PROs: Found the forum useful and between that and stackoverflow, getting the hang of Python for Data Science shouldn't be any trouble with those coming from an analyst or programming background. The ethics discussions and articles presented were good foundations for folks starting out in a field where communication of risks and biases is critical before acting on results.

This course is accurately advertised as requiring intermediate programming experience. It focusses on some of the basic python tools that are used in data science like pandas, specifically dataframes. For a python newbie like me, some of the assignments were quite challenging as they required significant additional reading and search. I would have appreciated if all of the concepts required to solve the assignments were introduced in the same week, so my additional reading and search would have been more targeted. Very little stats if any is required or taught.

Was a great overview of the Pandas and Numpy frameworks.I loved the course as it gives a quick overview to every aspect of pandas framework.But, what I absolutely hated was that the instructors just introduce you to every aspect and just do it themselves without much of an explanation, specially after week 2 ends.So, we need a lot of self studying which is super overwhelming to those of us who are just being introduced to data science in Python.Self-studying is a key aspect, but still a bit of more explanation in each video would have made the course 5-star.

I really like the lectures. They are fast paced, full of useful information. I also like the design and intentions of the assignments. They really help enhance the learning. I love the required reading too. It really expands your vision from a course to a much bigger world.

My gripe is mostly on the lack of attention to details for the assignment questions. There is a lot of ambiguity there. Forums and tips helped, but I would rather they spec out the questions in the first place. I hate to guess what the auto-grader want.

All in all, it's a very good course!

Loved the lectures and structure. Great course. The assignments were very educational, but I was a bit frustrated with the autograding at times. It felt like, what was preventing me from advancing had nothing to do with the actual course material, but the technical set-up instead. I don't know if it's possible, but having the auto-grader tell you what went wrong (which criteria your submission didn't live up to) would be a huge help and help students like me avoid too much frustration. Just a thought. Still a great course - keep up the good work :)

I found this course very useful and interesting. Assignments are very helpful to test your skills and how one can apply the content learned during lectures in actual problems. There is a lot of functionality and details in the Pandas library but, this course is designed very well to enable the students to get a good understanding of Pandas. Although, I faced some problems regarding the assignment grader. Moreover, the course videos end abruptly and it gets difficult to understand what the Professor was explaining towards the end of the lecture.

Com certeza o curso melhorou o meu conhecimento em python. Antes mesmo de terminá-lo já aplicava os conhecimentos no meu trabalho. Contudo, terminei trabalhando muito mais tempo nas provas do que o tempo estipulado pela equipe de instrutores. Cheguei a pensar que estava fazendo tudo errado, mas vi nos fóruns que outros também tiveram a mesma dificuldade. Estou muito satisfeito, não quero provas mais fáceis, apenas uma noção mais adequada do tempo que será gasto (levando em conta que estrangeiros podem demorar mais para entender o enunciado).

The videos were little fast paced. The explanation were good. It would be better if more detailed explanation on pandas has been given. Some more functions of pandas must be given because in assignment part the questions were little tricky and the question of that type has not discussed. You have to learn more from other resources. However overall course was good, it gives a good explanation on how to use python to manipulate data in table and many more. Before opting this course the individual should have a good grasp on basics of python.

A lot of the harder questions were unclear or not covered by the course. Many were passed onto "look this up yourself", which is fine, but sometimes a different approach resulted in a slightly different answer marked as wrong. The group_by stuff was especially tricky, as it was unlike its SQL counterpart and could have used a better example, eg with a function to split into quarters or group columns. Other than that, very interesting overall and very neat datasets. I would encourage having students check out kaggle.com for extra credit.

Small criticism - either requirements.txt should be published (version dependencies) or the course should be updated for to reflect modern versions of pandas, or perhaps both. Autograder has a tendency not to be able to process subtle differences in dataframe shapes from time to time and this is discussed at length in the forums but nothing much is done to rectify. Possibly related to obsolete versions and dependencies. Questions could be slightly better formed. All in all, a great course, which has kept me busy for a couple of weeks!

Introduction to Data Science in Python is a nice introductory course to jumpstart your career in the field of data-science while working with python. The course assumes you have basic knowledge of python programming including its syntax and semantics. Later, the course quickly dives into topics like list comprehensions, lambda expressions, numpy arrays, pandas series/dataframes. Most, of the common tasks involved in the field of data-science such as data cleansing, data analysis and manipulation are thoroughly covered in this course.

More about Python skills related to Data Science than Data Science itself.

Good quality of lectures, although they are very dry and brief. Assignments are challenging and require some googling/extra study. Supplementary materials are very inspiring - I enjoyed them the most.

If you want to be well prepared to use Python in data science field and have some time to spend working on the assignments - this is the course for you.

If you want the gentle introduction to the whole discipline - this is not the starting point I would recommend.

This was way above my level knowledge. One will definitely a strong level of knowledge in stats and data analysis to begin with. The Python part is but only a tool in this module / course.

For that reason I found the tutorials way too fast paced. I had to go back on everything and test, so a 5 mins tutorial video would sometimes take me 30 mins or longer to complete.

The 3 hour / 4 hour assessments took me days!

Yet - a great way to jump into something new! The course itself is great. The presentation and content is just as great.

Excellent course challenging at every stage. Especially the emphasis on assignments without spoon feeding is really brilliant.

One suggestion is during the assignments, especially week4 there are situations where Pandas was giving lot of trouble. For beginners it is becoming trial and error in between. Will be good if you can collate such challenging aspects and include optional tutorial for students to specialize in those topics. YOu need not cover everything but some comprehensive on such topics. Otherwise course is excellent

Overall I have enjoyed the course and got useful skills from it. I really appreciate the effort the course development team has put to create it. It would be better if the programming assignments include more 'textbook problems' where we get to practice the basic uses of python (eg. rename, groupby, apply) before delving into these long and hard real-world problems. Sometimes I just got so frustrated when I found out I did not actually know how to carry out the basic operations while doing the assignments.

Thank you!

Good information but the auto-grader could be re-tooled to provide more feed back, especially for common issues. I would like more reference material for basic python/panda operations with examples. I should not need to dig into a forum to get a link to commonly used syntax. Having a basic reference guide with clear examples would have saved me hours of time and my retention and the overall experience would have been much improved (please use your class to share what you know, it does not have to be this painful)

The theory and examples are very good and cover a lot of functionalities. I like most the questions that pop up in the middle of the videos, it is a great way to check whether you understood the concepts.

Some parts require refinements, the questions in the middle of the videos must be synchronized with the content (there is a little time gap) and the questions in the weekly assignments must be reviewed in depth, I dislike wasting a lot of time trying to understanding what is required to answer in each question.

Good introductory course overall. I would have spent more time and assignments on some Python basic, such as list comprehension and lambda functions, as opposed to focusing the first part on regex. However, it does say that this is an 'intermediate' course for someone with prior programming experience. After I completed it, I felt quite positive about what I learnt and it was definitely a more thorough and better organised course than any other Python bootcamp/course that you would find on other websites

Very technical course, heavily focused on working with data, oriented on self-study. It is more about Python how to deal with datasets in it, in the very end, you got introduced to t-test co make basic statistical test, without any underlying math concepts.

Get ready to visit official Python websites, libraries documentation, forums like GeeksforGeeks, Stack, etc. Assignments are hard but very useful, however, I would prefer more lectures on Python functionality, since course material does not cover much.

The material covered is solid, however the one missing star in my rating is attributed to poor design of the assignments. To be specific, there's a lack of clarity in the instructions, also there is a HUGE leap between the learning examples and the last assignment. Understood that it takes some self-learning and searching over the internet, but for the fees I paid I would expect the course to provide the materials readily. Otherwise, what is the point of me coming here, I could have get a book and learn!

The content of this course is excellent and the assignements deliver a good learning experience which encourages independant problem solving. The autograder can be frustrating at times due to version errors which occasionally arise when working offline. This can be demoralising at the outset, but all of the information required to pass the assignments can be found in the lecture materials and discussion posts. 5-stars for an updated autograder or some more obvious information regarding its limitations.