Quite a fast-paced course, with very quick and packed lectures. Would have preferred the course to be slightly longer e.g. 6 weeks and covering more content. I actually found the style of the coursework to be a plus - though it involved a lot of Googling, this is more reflective of real life, where answers aren't just handed to you on a plate. However, the instructions for the assignments aren't very clear in places, which can easily cause you to become stuck.

This course is worth due to its challenging assignments. If you are taking this with no previous knowledge of data science, you will be expected to make a lot of complementary research.

There is definitely room for improvement in the video lectures - the contents are somewhat limited when compared to the difficulty and what is asked on the assignments. The lecture could and should present more examples/problems and how to solve them in the most efficient way.

Nice course to learn data science. I learn a lot of new thing about data science, different python packages used for DS. I felt like week 3 and week 4 assignment were a bit difficult and video lectures should have been in more details and assignments should be more related to the video lectures.

One should have coding background in python in order to complete the course assignments without too much difficulty.

Anyway I'm glad I took and completed this course.

Feels great, I am introduces to the Data Science in Python. But this was too much of crash course. It needed a lot of homework outside this course - Youtube, Python docs, stackoverflow and some blogs. That work should have been added as part of the course. And those statistics almost went over my head. As I have no foundation in statistics, I may not choose for the next level of course - seeing the difficulty of the statistics it has introduces so far.

This course was illuminating and rich in information. However, I noticed a huge gap between the content covered in lecture videos and the knowledge level needed to complete the assignments, which made the assignments way harder than expected. This might be intentional, in order to prompt students to explore the full functionality of python and take advantage of online documentation. Overall, this was a good course to really get my feet wet with python.

I liked this course, it gives a strong introduction to useful data analysis packages like re, Numpy and Pandas. I think it should be used as an integration to other, more structured courses at a university. I loved especially the regex part, because it is really well-structured and helped me reviewing the regex syntax; while the Pandas introduction is less useful because the Pandas package has updated a lot from the date this course was recorded.

This is a great course for those with some Python background, as the professor clearly states at the first lecture. This was not my case but since we were a team of friends we could solve all the homeworks. If you have not taken an introductory course in Python, neither you have a group of friends to work with, then I suggest you not to take this course. Otherwise, It is a great opportunity to explore advanced Python data analysis techniques.

Overall I thought this course was good quality. The videos and lecture material were informative. My only constructive suggestion is that that code is shown too briefly and moves on to new functions too quickly to absorb. The instructor participation in the forum was essential for being able to complete the assignments. But overall I thought this was effective in getting me to learn some Python independently and I would recommend it.

This is a pretty good course to start immersion in data science. Lectures are very compact and without extra water. Links to books and additional resources are provided. You can get support from teachers in the comments on lectures and assignments.

At the same time, the course requires updating in accordance with the latest version of pandas. During the course, I was faced with the need to rewrite my code to satisfy the old version of pandas.

J'ai appris énormément avec ce cours et je remercie les professeurs et tout le staff ainsi que tous ceux qui ont participé dans le forum et qui étaient d'une aide précieuse.

Cependant je trouve que plusieurs questions sont mal posés et une grand partie de mon temps je l'ai passé à essayer de comprendre ce qui est demandé.

J'espère que les questions seront mieux posés et que l'effort sera consacré à l'apprentissage de nouvelles techniques.

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!