resources.md

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

• Office hours
• Stack Overflow {% if id == "columbia" -%}
• Research Data Services {% else -%}
• NYU Library Data Services
• The Coding Lab
• Wagner Quantitative Support {%- endif %}

pandas cheat sheets

• Official
• DataCamp
• DataQuest

Tutorials

• Crash Course Computer Science
• FreeCodeCamp
• NYU's Quantitative Analysis Guide: Python
• RealPython

Books

• Automate the Boring Stuff with Python
• Python for Data Analysis
• Python for MBAs "and those in business roles that include coding or working with coding teams"

Courses

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

• Other Data Analytics & Quantitative Analysis (DAQA) courses {% else -%}
• Pre-approved courses outside of Wagner (for Wagner students) {%- endif %}
• Free trials for online courses through the GitHub Student Developer Pack

Python fundamentals

• DataCamp's Python Fundamentals or Python Programmer tracks
• Full Stack Python {% if id == "columbia" -%}
• Python at Columbia Business School self-paced course with videos, open to anyone at Columbia {%- endif %}

Data analysis/science

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

• Wagner Data Science and Data Management
• Center for Data Science
• Center for Urban Science + Progress (CUSP)
  • Applied Data Science
  • Machine Learning for Cities {%- endif %}
• Data Analysis with Python for Excel Users
• freeCodeCamp's Scientific Computing with Python class
• IBM Data Analyst Course - can jump to specific parts

Machine learning

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

Workshops

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

• Butler Studio
• Data Club
• Foundations for Research Computing
• Library Workshops
• Research Data Services {% else -%}
• NYU Library Data Services {%- endif %}

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 ({{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. Create the environment. From this directory, run:

   mamba env create --file extras/environment.yml

5. Activate the environment:

   conda activate python-public-policy

6. Start the Jupyter server:

   make notebook

See also

• 14 Principles of Open Government Data
• Blog post about GitHub for non-developers {% if id == "columbia" -%}
• Data Science Institute {%- endif %}
• DataJournalism.com
• GitHub Student Developer Pack
• Generative AI offerings from the University