Dr Casey Kearney

This course is compulsory on the MPA in Data Science for Public Policy. This course is not available as an outside option.

The bootcamp introduces students to coding and Data Science in Python and reviews key Math and Statistics topics for policy. It first introduces the building blocks of programming (variables, objects, conditions, loops, functions, etc) and some foundational libraries in Python (NumPy, Pandas, Matplotlib, plotnine). It then covers basic data sourcing, data manipulation and (very) basic regression analysis in the contexts of simple applied policy exercises. It then refreshes key concepts in probability, maths and statistics. The final week focuses on moving from coding notebooks to packaged code and works on debugging.

Approximately 30 hours of lectures.

Exact hours of teaching will be confirmed when the programme Welcome schedules are prepared.

Students will attend lectures in the morning and then complete coding challenges and problem sets throughout the day.

Students will be expected to produce coursework in August.

Students will complete daily coding challenges to apply new tools and concepts taught in the bootcamp. Most of these challenges will be on Jupyter notebooks.  

Prior to the start of the programming and mathematics bootcamp, students will receive a list of online resources to start learning basic computer programming on Python. These materials will emphasize basic syntax and programming logic. 

Some particularly useful texts include:

1. McKinney, Wes. Python for data analysis. “ O’Reilly Media, Inc.”, 2022.
2. Downey, Allen. Think python. " O'Reilly Media, Inc.", 2012.
3. Sweigart, Al. Automate the boring stuff with Python: practical programming for total beginners. no starch press, 2019.
4. Müller, Andreas C., and Sarah Guido. Introduction to machine learning with Python: a guide for data scientists. “ O’Reilly Media, Inc.”, 2016.