Resources#
General#
Python for MBAs “and those in business roles that include coding or working with coding teams”
IBM Data Analyst Course - can jump to specific parts
-
Includes learning resources and various tools
Columbia#
Python at Columbia Business School self-paced course with videos, open to anyone at Columbia
Library
Pandas#
Cheat sheets
There are countless other 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.
Learning more#
Want to keep going after this class?
Python fundamentals#
Recommended focusing on fundamentals of Python 3. Many “learn Python” resources will be web development-oriented (they will probably mention Django/Flask), so you might want to look for ones that focus on data science or Python 3 on its own. Some that are data-oriented:
freeCodeCamp’s Scientific Computing with Python class
DataCamp’s Python Fundamentals or Python Programmer tracks
Countless other “learn Python” resources/courses/videos/books out there; there isn’t one right choice for everyone.
Machine learning#
Columbia#
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.-
conda config --set channel_priority strict
Create the environment. From this directory, run:
mamba env create --file extras/environment.lock.yml
Activate the environment:
conda activate python-public-policy
Start the Jupyter server:
make notebook