Tough course for a "Introduction to..." course title. Good support resources saved the day. Learnt from doing the assignments and following the tips from the teaching staff. Very practical assignments. Gave a flavor of what data science work involves. Challenging and engaging. The course videos are very well produced. Prof. Brooks is motivating and energizing. The course also gave a human dimension to the work of data science. Thank you.

I enjoyed the course, overall. The assignments were challenging and required some help via stack overflow, but they were do-able and not impossible. The notebooks are great for experimenting and self-learning, but the lectures didn't go into enough depth, for my liking. In addition, the speed of the lectures was too fast on many occasions. I'd recommend having some programming background and/or familiarity with Python before taking this.

The course has indeed taken place over much of what can be considered introductory in data science. However, there is nothing introductory about python, which may bother a more novice student, and the second week of the curos is much more challenging than the others. I believe it is possible to build this same course in 5 weeks to teach more about python and reduce the difficulty of the second week without compromising depth and quality.

This course is difficult. It stresses a lot of core skills in pandas and python. I wish there was more instructor support for the times that code just doesn't seem to match up with the grader's expectations. There is still a question in the course that I am relatively positive I answered correctly yet did not receive credit. Overall, the format is incredibly well done and the use of Jupyter notebooks makes the tasks very approachable.

I learned in this course that pandas is a way deeper rabbit hole than it appears on the surface. However rather than teaching me pandas this course mostly just helped me verify that I was learning pandas. The questions in this class need more scaffolding. I ended up skipping most of the in-video questions because I felt that the work I was investing in getting them correct was not teaching me much. More scaffolding could fix this.

As compared to some other machine learning/data science courses offered "Introduction to Data Science in Python" was very pragmatic. Starting out with simple data cleaning and data structuring may not be the most exciting thing ever, but it was extremely useful to learn the basic tools needed to be a competent data scientist. One point of warning - the homework and projects took me about twice a long as suggested by the course notes.

Introduction to Data Science in Python is a challenging and rewarding Course, the instructor explanations are excellent, and the recommendations in relation to best practices utilizing Python, Pandas and Numpy are super valuable, the assignments are super challenging in particular because of the auto-grader and the substantial amount of pre-processing of data required for the assignments so book extra time to complete this Course.

Great course material! Learned a lot of useful information. My only suggestion to the instructors, would be to refine some small technicalities in the Auto Grader (such as Q8 for assignment #3, or Q5 in assignment #4), which sometimes prevent a correct answer from being awarded points.

If you are considering taking this class, please be prepared to spend at least 50% more time than what Coursera estimates for each week's material!

Lectures clear and well-organized. Homework needlessly complicated and with large gap from lecture material. Grader did not give enough info when rejecting submitted work. Weighting last problem at 50% of final week was unpleasant. Hosted platform allowed importing re and io.StringIO, but grader rejected them. Hosted platform had consistent kernel failure on my last solution, but it worked on another system and grader accepted it.

The course was challenging and the assignments well thought of. While I appreciate that a lot of learning was left to be done on stackoverflow with the intent of making us self reliant, a lot of us are already used to those forums and gathered around this course so information would be available in a centralized manner and time spent searching online could be minimized. I think the course designers totally did not get that point.

I learned a lot about data handling and manipulation in python, pandas and NumPy. But I feel the course instruction was too fast to follow up, even to a python coder like me. The course expected one to learn a great deal of part from your own rather than relying on the video lectures. The assignments and quizzes were very challenging, pushing you towards your best. To conclude I think the instruction couldhave been much better.

Before Machine Learning comes a lot of Human Action. This Data Science course provides a solid basis for understanding and learning the inner works of manipulating very large datasets in Python. Besides the technical aspects I was pleasantly surprised to read and think about the ethical sides as well. I would rate this course 5-star if some exercices were better phrased or if more examples to make some exercises more manageable.

A nice course to kick start Data Science. Doing the assignments will improve the learning and will boost the confidence about the topic. Tutor, TAs and discussion forums are very helpful, so, consult them if you get stuck somewhere. Coursera platform was flawless, course structure was good. But I expected more content would be covered in the course. So, overall it is good course to get an insight into the world of Data Science.

After having taken several Data Science-related courses, this course seems like a good introduction to Python for Data Science applications. Not much 'actual Data Science' is covered in the course, however. It's more practically-oriented in the sense that it deals with data preparation (loading, cleaning and merging data). You don't get the luxury of the common perfectly-prepared csv-files in this course, which is a good thing!

Overall this was a great class. The programming assignments were the most valuable part of the course for me and were good practice for wrangling data with pandas. I did find some of the assignments asked questions in a way that were confusing and it was difficult to debug the answer based on the automatic grader. However, I'm looking forward to when the rest of the specialization is available, as this looks like a good track!

Even though I am already a heavy user of Pandas in my daily work, this course forced me to learn several useful features that I had never knew about or bothered to learn. The exercises were challenging enough that it took a decent amount of time and effort to complete them. There were many technical challenges with the autograder and the coursera hosted notebooks that made this more of a challenge than it should have been.

A great starting point for venturing into Data Science, for students/engineers who have some programming background. In my case I had the basics of Python covered , so it wasn't too hard to catch up.However, for enthusiasts with very limited programming experience.... Beware !!! It will appear to be too fast. My final conclusion .... spend 3-4 weeks to learn Python fundamentals and then enroll .... its very enlightening.

The assignments were the best part of the course. The autograder needs work and the problem statements could use some review to make sure all the stylistic requirements of the autograder are spelled out. In some cases, it is well done, e.g. 'your answer should be a number'. In other cases, you have to guess at the order of your answers, since they are expected to come in a particular order, which is not spelled out.

The lectures seem to be giving the bare minimum description of functions and stuff that makes it hard to understand the intuition behind the syntax and working of, say, a line of code that a given lecture tries to teach explaining how it helps serve a purpose. This, in turn, makes it hard to remember the syntax of functions. The assignments are very useful but take a long time since I barely learn from these lectures.

I really liked this course. It gives a good overview of the pandas library and some associated topics. For me, it aligned very nicely with my personal interests. I would have liked some more advanced topics as well, but I understand that this is an introductory course, so it is not in its scope. The integrated Jupyter notebook feature of Coursera is very neat - both for reviewing code from lectures and for assignments.

Pretty good course. I have definitely learned a lot and would like to thank you the lecturer and all the people who were working to create this course. The only comment I have is following. Please, try to formulate the questions more clear in the homework assignments. The assignment # 4 is especially bad in this sense. You can look at the comments of people in the forum to understand that it is not just my own problem.

Good introduction to pandas/numpy. Requires some programming knowledge. Overall I would have liked more guidance during the videos or through course materials, assignments require a lot of self learning (mostly searching through pandas documentation and stack overflow). However the discussion forums are helpful and the assignments are very well designed to guide the student through learning the basics of data science.

The biggest reason for taking the course is it pulls together a few interesting datasets and has a data manipulation project based on the dataset.

The course also pulls together some interesting papers on ethical issues that could confront data scientists, traps data scientists fall into (p-hacking).

However, the material on dataframes covered is too sparse. User should learn dataframes from a pandas book / web sources.

The value in this course comes primarily from the assignments but the instructions tied to these assignments fell a bit short. It would be immensely helpful to have a short FAQ explaining how to set up your environment (i.e. which packages and versions to use) along with test files to verify assignment outputs. Digging through the discussion forum is sufficient but the ambiguity does lead to unnecessary frustration.

IMHO I had to do too much self learning besides this course. I didn't come here to listen to instructions that I should browse stackoverflow and documentation papers -- I am doing that without this course as well. On the other hand it was very clearly undersandable and well said whichever was said during videos. Moreover examples were from real world, which made me work out practices that will come handy later on.