This repository contains content for NEU Python for Policy course.

This is a hands-on course that trains students in public policy, public administration, and other related fields who have little or no programming experience to analyze data, create useful visualization, and conduct statistical analysis using the Python programming language. This course will provide students with a basic understanding of Python, and introduces open source toolkits for use in public and nonprofit sector settings. We will use Python and Jupyter notebook to introduce coding and practical application using Python that students can reproduce and experiment with.

• Fundamentals of programming with Python and Jupyter notebook
• The basic Python programming language and its uses
• Cleaning, manipulating, and analyzing urban data with Python’s pandas library
• Descriptive statistics and data visualization in Python
• Querying APIs and scraping for public open data
• Spatial analysis and mapping
• Inferential statistics and regression

This course is asynchronous online. Students should check out the Canvas Modules site for each week's tasks and resources. Coding assignments will be assigned each week to reinforce the skills and topics being presented. The final project requires students to ask a critical research question in public and policy settings and use relevant data and practical tools learned from this course to help answer the question.

Coursework will be based on free open-source software.

• Downey AB. Think Python: How to Think Like a Computer Scientist, Second Edition, Version 2e. (Green Tea Press and O’Reilly, 2018). This is an open textbook, available for free at com/wp/think-python-2e/ (Links to an external site.).

• McKinney, W., & Safari, Python for Data Analysis (O'Reilly Media Company, 2012). Available as e-book from NEU library

Other copyrighted course reading materials are available via the course Canvas site for enrolled students to download.

Students are expected to complete readings, class modules, participate in discussion, and submit weekly assignments and final project. Important information will be distributed through the course site and via email. It is important that you check your email regularly.

Technology Requirements

• Students will need access to a laptop, Windows or Mac (Linux is OK, but note that since I do not use it, I may not be able to help you with issues related to the operating system), with internet connectivity.
• This course will be taught in the open-source programming language Python (version 3) and the programming environment Jupyter.
• Software installation will be instructed prior to the first week module. Try your very best to have this done before you start the course lecture.
  • Anaconda
  • Atom

The Python basics and spatial analysis learning modules are largely based on the Advanced Spatial Analysis of Urban Systems and Advanced Urban Analytics taught by Professor Geoff Boeing (Great thanks!)