Ratings and Reviews for Introduction to Data Science in Python

it does really teach you the basics of data manipulation but without the "teaching", meaning you have to do a lot! of in depth learning and research by your self

Basic information about numpy and pandas

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)

amazing course great help with the introduction and great explanations. sometimes jupiter grader gave me bad grades for no apparent reason, after i reloaded it was fine, it held me back a while because you are trying to understand if it is the you who got the question wrong or the grader.

Great course

Great course!

excellent courser, i will recommend to everyone who would like to study python data analytics

I am a PhD scientist and heavy user of matlab, R, Stata, bash scripting, and some more esoteric computer languages. I took this course with the idea of covering some background in python skills in a structured manner, the goal being to move many of my data science and some of my data processing code to python. I found the exercises useful. The lectures are not bad, I just felt they were an overview that either didn't connect much with some of the minutiae of the assignments or they were not always key to me given my background. Eg I found the week 2 videos more interesting; week 4 videos far less so especially the video about running a t-test in python (my statistical skillset is far more advanced). The real point of frustration is the grader which is extremely sensitive to slight variations. I feel there should be a feedback system where users/students document such cases that could then become a FAQ. Examples: Grader chokes on type but won't tell me: Submitting string 'True' instead of Boolean True. Grader chokes on useless (non)significant digits: using round(*,2) at one point crashes the submitted work. These "errors" are so slight that are almost beyond the human ability to catch them. The result is that, in part, the course turns from 'learning python skills' to 'getting to understand minutiae of what the grader does' which can be really frustrating. In sum, I believe there is value in this course but the grader is fairly broken and needs a FAQ or similar to warn re choke points generated from trivial differences. I am subtracting stars in the review for that particular reason.

This course is a great opportunity to understand and deepen into de topics related to Data Science.

A good course. The timescale for completion is realistic and the assessments are not trivial.