Ratings and Reviews for Introduction to Data Science in Python

Really enjoyed the pace and realistic assignments. The skills I have learned would surely help me apply these techniques in the real world. The various discussions and articles though optional were excellent to give a thorough understanding. I would only point out that althogh the assignments were challenging and pose realistic problems, the automatic grader seemed to require the correct answers in too narrowly defined format which was often never clear from the instructions. Either the instructions should be made clearer or else the auto-grader should be 'smarter' explaining the actual reasons why something could be wrong.

After studying the course of Andrew on Machine Learning, I want to study a course , which focus on python. I once chose the Machine Learning Specification , but the course use the non open source python packages. And I tried this course. I used about 4 days to finish the 4 weeks, and get myself familiar with pandas though I have no experience on python and pandas programming.

It was a good course, and I found I learned a good amount from it. I feel the following changes would give future participants even more value: First, assignment grading is too rigid. There are questions I failed that looked perfectly OK to me. If a question fails, hints on how to get it to pass need to be provided. One great example had something like "incorrect output. Be sure that DataFrame.loc['Texas']['2012q3'] returns a float64" or something like that. I tried that, found out I didn't strip spaces (I had 'Texas '), sorted that out, and got the right answer. I feel spending too much time on that is "hacking the grader" vs. learning the material. Second, the course emphasized following along in the notebook, playing with it, and doing the assignments via checking Google / Stack Overflow. That works, but it doesn't lead to as "Pandorable" code as it could otherwise. Some of my solutions were slow, and I started to get into "one-trick pony" mode using .apply() everywhere. That oftentimes isn't fast enough. Pandas, by nature of optimizations under the hood, is a very "meta" language. Teaching the primitives and building to those pieces could use more emphasis.

Amazing class to get a first effective insight of python

I loved the course. The assignments were challenging, but rewarding to complete!

On balance, I'd recommend the course, but use it as only one of many sources you'll use to learn data science. My main complaints: 1. the course concentrates on mechanics of Python rather than on understanding of Data Science as a discipline, 2. It's rushed. Pros Good information on how to use the pandas library to manipulate data. Information on "advanced" Python functionality. Challenging assignments. Give yourself time (a weekend or several evenings for each assignment) and you'll learn a lot. Cons Very little information is given in the way of conceptual understanding of data science as a discipline. Focuses on the "how" but not the "why." Not much context given for the examples, so they seem a bit contrived. (For the opposite extreme, see the John Hopkins Data Science specialization.) Lectures are rushed. Looks like the prof is reading cue cars straight down without pausing for the students to digest anything. (The one or two lectures given by the TA just fly by without nary a pause for breath.) If you're trying to follow along, keep your finger on the pause button. The Jupyter notebooks are useful, but contain minimal commenting (barely more than a few headers), so if you plan to refer to the notebooks later (for reference), you'll need to put comments in yourself. Given code is sometimes sloppy (e.g., standard Python naming conventions are not always followed.) The automated grader is very picky. Read through the discussion board!

The course is very well structured from notes to assignments. Lecture content is to the point and lecture length is just right. A lot of work has been put into the Assignments and Juniper notebooks. This element makes the course invaluable as you learn through practical experience. through well thought out and planned questions. Thank you for making this quality of education available to all.

The course assignments were very helpful in understanding the concepts better while encouraging the students to self-learn

This was a good course. The professor (Chris Brooks) was EXCELLENT, although much of the material was presented very quickly and was difficult to follow at times. The real learning came during the exercises, which I generally found to be very difficult but also very good at ensuring the material presented in lecture was actually reinforced through hands-on programming. I would recommend making more exercises, perhaps two per week, rather than one very detailed exercise at the end. The last week's exercise, in particular, was very difficult for me and I would recommend breaking part 5 (of 6) of that exercise into several smaller chunks that could be completed and graded individually. Rather than 10-10-10-10-10-50 as the points awarded in that exercise, I would also recommend changing the scoring to 10-10-10-10-35-25 as the next-to-last part of the exercise was by far the most difficult and time consuming.

Horrible course. The instructor is reading the course from a prompter. '95% of the time, you see the instructor reading from the prompter, and two people working in the background. When they actually show the code the instructor is talking about, it goes so fast that it's impossible to read the screen. The instructor provides no motivation or background to what he says. He just rattles off the words in a uni