Resources#
There are countless blog posts, videos, books, etc. out there. There is no “best” resource, as individuals prefer different formats, come in with different experience, and learn at different speeds. Anything that comes up near the top of a Google search will likely be fine.
Questions#
Cheat sheets#
pandas cheat sheets
Tutorials#
Books#
Python for MBAs “and those in business roles that include coding or working with coding teams”
Courses#
Python fundamentals#
DataCamp’s Python Fundamentals or Python Programmer tracks
Python at Columbia Business School self-paced course with videos, open to anyone at Columbia
Data analysis/science#
freeCodeCamp’s Scientific Computing with Python class
IBM Data Analyst Course - can jump to specific parts
Machine learning#
Workshops#
Learning more#
Want to keep going with Python after this class? See Developer Roadmaps for directions you can go. This course doesn’t spend a lot of time on Python fundamentals, so it’s recommended that you do that first.
Many “learn Python” resources will be web development-oriented — they will probably mention Django/Flask. If you want to stay focused on data, you might want to look for ones that focus on data science or Python 3 generally.
Jupyter outside this course#
We use a cloud-based Jupyter environment (Google Colab) for this course to avoid installation issues across student computers. This is the only environment that’s supported for course work.
A non-exhaustive list of alternatives:
Local#
Cloud-based#
Matching the class environment#
Advanced
Note these instructions won’t work in Colab.
Install Mamba.
Check out the
columbiabranch.Create the environment. From this directory, run:
mamba env create --file extras/environment.yml
Activate the environment:
conda activate python-public-policy
Start the Jupyter server:
make notebook