resources.md

Resources

General

• Office hours
• Stack Overflow
• RealPython
• FreeCodeCamp
• Crash Course Computer Science
• Data Analysis with Python for Excel Users
• 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
• Full Stack Python
• GitHub Student Developer Pack
  • Includes learning resources and various tools

{% if id == "columbia" -%}

Columbia

• Python at Columbia Business School self-paced course with videos, open to anyone at Columbia
• Foundations for Research Computing
• Data Science Institute
• Library
  • Data Club
  • Research Data Services
  • Studio

{% else -%}

NYU

• Wagner Quantitative Support
  • Tutoring
  • Math Review
• NYU Library Data Services
  • Consultation
  • Classes
• The Coding Lab

{% endif -%}

Pandas

• Documentation
• Cheat sheets
  • Official
  • DataCamp
  • DataQuest
• Python for Data Analysis book
• NYU's Quantitative Analysis Guide: Python

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 with Python after this class? See the sections below for specifics, and Developer Roadmaps for directions you can go at a high level.

Python fundamentals

Recommended focusing on fundamentals of Python 3. 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, such as:

• Automate the Boring Stuff with Python
• 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

• Kaggle's Intro to Machine Learning
• DataCamp's Natural Language Processing in Python track

{% if id == "columbia" -%}

Columbia

• Other Data Analytics & Quantitative Analysis (DAQA) courses
• Library Workshops

{% else -%}

NYU

• Wagner
  • Data Science and Data Management
  • Pre-approved courses outside of Wagner (for Wagner students)
• Center for Data Science
• Center for Urban Science + Progress (CUSP)
  • Applied Data Science
  • Machine Learning for Cities

{% endif -%}

Jupyter outside this course

We use a cloud-based Jupyter environment ({{coding_env_name}}) for this course to avoid installation issues across student computers. This is the only environment that's supported for course work.

{% if id == "nyu" -%} After this class, however, you'll no longer have access. To download the files:

1. Open a notebook
2. In a code cell, run !tar -czvf ~/python_files.tar.gz ~
3. From the file browser, check the box next to python_files.tar.gz, then click Download
4. On your computer, unzip the file.
   • On Windows, you may need to install 7-Zip to do so. {%- endif %}

A non-exhaustive list of alternatives:

Local

• Anaconda Distribution
• Visual Studio Code

Cloud-based

• Anaconda Notebooks
• Google Cloud Vertex AI Notebooks {% if id != "columbia" -%}
• Google Colab {%- endif %}
• Kaggle Notebooks
• Mode Notebooks

Matching the class environment

Advanced

Note these instructions won't work in Colab.

1. Install Mamba.

2. Clone the repository.

3. Check out the {{school_slug}} branch.

4. Set strict channel priority.

   conda config --set channel_priority strict

5. Create the environment. From this directory, run:

   mamba env create --file extras/environment.lock.yml

6. Activate the environment:

   conda activate python-public-policy

7. Start the Jupyter server:

   make notebook

See also

• 14 Principles of Open Government Data
• Blog post about GitHub for non-developers
• DataJournalism.com