This might interest some people here:
I’m doing this class! You can take it for free, although the Professional Certificate costs $$$. For what it’s worth, I like it so far, although it’s geared towards beginners and since I’ve been studying for quite awhile I’ve found week 1 very easy.
So one thing that I’ve learned, is that the course does not teach pandas. They use a custom library called datascience, apparently developed at UC Berkeley. I think they are pretty similar, but nonetheless it is a bit annoying that the course doesn’t just use pandas.
It must be difficult to search Google for help for a project called “datascience”.
I have been taking the class too.
It looks like they built their package on top of numpy and pandas.
This seems pretty common for data science books and tutorials - the authors making their own packages. Perhaps along with the idea they are making things simpler is that they hope it will take off.
But for an intro course, I think a lot of times it just causes more confusion to beginners. Particularly, when you don’t explain the difference or distinguish between just pure Python, the common packages, then then these one off specialized packages.
Example of confusion in the message boards was that in one video, they said that they provided the students with a function (so you assume a method in the datascience package) to make a list (here you assume a Python list) but it was numpy’s arange to make a numpy array.
I took a look at the code, and some if it does seem a bit unnecessary.
import numpy as np def make_array(*elements): return np.array(elements)
Yeah, I don’t know for how long I’m going to stick with this, it seems like it’s mostly watching videos then doing multiple choice tests, working on a project may be better… not like I don’t have a ton of those though, haha
So far through week four I’ve been working on the labs and referring to the textbook. Haven’t found the lecture videos necessary. Not sure if I am going to stick with it but leaning towards just pushing through.
Edit: I realized that there were two separate threads about this course, so I move this other post into this longer thread.
This was mentioned at a meetup:
Data 8: The Foundations of Data Science
“Each offering site includes links to assignments, slides, and readings. You are welcome to use any of the materials you find.”
The UC Berkeley Foundations of Data Science course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.