Ratings and Reviews for Introduction to Data Science in Python

Very good course, forces to do a lot of self study.

Pretty good

Sometimes need more words in tasks, so it can help understand it better.

More programming details should be included.

Dreadful course. Instructors saw no value in presenting elements of course that would help learners complete the assignments; rather you are sent off to teach yourself about uncovered techniques needed to complete the assignments. From some of the posts from previous students on GitHub, they resorted to deriving the answer from another means (Excel?) and simply providing the answer as a constant value, in order to receive credit for particular questions. Not exactly sterling knowledge transfer, from instructor to student! This course should be presented as a challenge course to people that have already learned Python Pandas from some other venue. (BTW, Pandas documentation is also dreadful, as of this writing.) This is definitely not the way to learn Python for Data Science if you are a busy professional software engineer. (Wish I had a good recommendation as an alternative.) The only positive aspect of this course is the challenge to work with defined datasets, to complete specific tasks, during week 3. (This was as much time as I could afford to allocate to this course.) From a 40+ year software engineer, with doctorate in CS, a part-time instructor at a private university, with a very challenging technology job in a multi-national corporation.

The course is a great course in terms of the knowledge and experience of the instructor and the helpfulness of the staff. I gave it 2 stars for two reasons. 1) The videos are deceptively short. In MOOC instructional design, you normally design short videos because the attention span of an online learner is tends to be much shorter than an in-person university student. However, in this course, even though the video is short, it is really 5-10 times longer because they speed over the equations and teaching so fast that you have to pause and replay and rewind and replay several times while trying it yourself. So the timings are not truly accurate. If you were actually teaching it in a classroom with actual students you would go much more slowly. In this case, I wish they were more honest with the times by actually typing the code in real time while teaching. I would have preferred a lecture to make it more digestible. 2) The hardest thing for me about the course was the fact that instead of practicing computational thinking within data science (decomposition, algorithmic thinking, etc.), I was really just searching on stack overflow for how to put it into python. It's poor instructional design to only teach somethings and expect students to complete assignments without giving them all the tools they will use in the assignment. It would be ok if it were an accidental mistake, but this seems to be purposeful. This happened not just in course assignments but sometimes even in mid-roll video-overlaid quizzes where the answer was something not explained or taught or shown. This was really strange to me and caused a huge amount of time to be spent searching online or trying mid-lecture problems to no avail. It cause all the timings of the course to be off (#1 caused the video timings to be severely off and #2 meant that the course assignment estimates were HUGELY miscalculated). Good instructional design would mean that the professor should show all the tools one could use to faithfully complete and achieve the assignment. For some reason that was avoided again and again in this course. Great material though. I loved learning. I just wish it were better structured and supported and that I learned more about the work rather than just searching online for how to write something.

This is a good course if you have had some experience with the Pandas module in Python prior to taking the course. Pandas is a very powerful module but it has a fairly significant learning curve. There are all sorts of free Pandas tutorials available on the web. I highly recommend familiarizing yourself with the basics of Pandas prior to taking this course or you will probably struggle.

This class was super useful. I had a minimal amount of base Python under my belt and some data analysis in R, but this course really got me off the ground using the pandas data analysis library for Python and Jupyter notebooks.

Solid explanations of the basics of python and pandas. Strong emphasis on self-learning in assignments, which may be difficult for those without background in programming.

Thanks for the course. A few things that can be improved: 1- The video material was very short. I expected same amount of teaching like homework, but it's more like 30 minutes versus 7 hours every week. As a result it's mostly googling and copy-pasting code, which I'm very sceptical if solving issues this way will enter my long-term memory. Probably next time I will need to look it up again. I'd prefer if the videos were the source of knowledge instead of stack overflow and the forum. 2 - As an experienced programmer but being new to Python I found it difficult to load the data. I was not aware that the files are available on the server and can just be loaded by read_csv(filename.csv). Instead I tried to load the files from my local drive, which worked, but not for the grader. Then I tried to submit it offline which also failed. I wasted half a day on this. I suggest to mention quickly how the online and offline assignments work, in particular in how to load data. 3 - The feedback from the grader is usually not telling much. It was often unclear to me what was the expected outcome. I think a screenshot of the expected answer for the more advanced questions (first few rows+columns) would help a lot and save us a lot of suffering.