Ratings and Reviews for Introduction to Data Science in Python

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.

The course content is very good. The videos are very good. Unfortunately this course is severely hurt by a very high ratio of non-learning work to learning work. This is due to some issues that could be easily addressed. The questions are poorly worded or ambiguous about critical details. Some of these details are hidden in the forums, but that's a waste of time. Some of the assignments do not directly bear on the course content and involves much "self-learning". Unfortunately this means I do not know if my self-taught methods are optimal - there is no feed back or checking. So you can do very poor coding but still pass in scoring and never get any feedback to improve your coding skills. All along, some very simple hints about what libraries and methods to use for each question would prevent lots of blind searching on the web. There are some helpful instructors and helpers haunting the forums, but they are not always around, and they are not always implementing permanent fixes to the problems that are frustrating students. One shouldn't have to hunt around forums to find out about broken pieces of the application or other errors in the course. Finally, the grading system is unstable and the Jupyter Notebook system is also not very stable, leading to many submissions and resubmissions just to make sure it got through for grading. For these reasons it took much more time than three weeks for me personally. I would not have signed up had I known.

The lectures are much too concise, practice is scarce which renders an overall frustrating experience.

Nice course to get started with Data science, hopefully i can combine this with kaggle based challenges. looking forward to the rest of the courses in the specialization