Workshop Description

The class is designed for people with some experience programming, but no experience in Python. We will introduce each topic enough so that you can quickly start using Python for your own problems knowing what tools are most appropriate. The workshop will be interactive with many examples (that the participants can play with during the session).

Topics covered

Introduction to Python (variables, strings, control-flow, containers, file-IO)
Pandas (i.e. dataframes)

Note that the course notes in this repository are intended to be read through in sequence according to the numerical prefixes in each filename. I.e. we anticipate that learners know all of the information in 1_Introduction_to_Python before moving on to 2_Numerical_Python, etc.

Note: if upon opening the appendix notebook, it stats Notebook Not Found try clicking Authorize with GitHub, then log in with GitHub: a new pop-up window will appear where you should select asantucci/Python-Workshop as the Repository and main as the Branch, and then (scroll down to) click on file 4_Appendix_Scikit_learn.ipynb. The notebook should then open in Colab!

About the Instructor

Github Profile Page

I am a computational mathematician, currently practicing Data Science and Engineering at Google and Lecturing at Stanford University. At Google, I work on YouTube recommendation algorithms and core statistical inference, and at Stanford I teach graduate students in STEM fields how to develop software in Python and C++. My research interests formerly lay at the intersection of causal inference and machine learning, but more recently I have been developing a passion for (deep) learning algorithms. In general, I strongly believe in leveraging AI for social good. I serve as a board member for the Society for Industrial and Applied Mathematics where I specialize in the education committee. I’ve been privileged to work closely under the guidance of Nobel Laureates on two separate occasions (Daniel McFadden and Guido Imbens).

My undergraduate curricula was spread across communications and economics. In graduate school, I pursued a skillset in computational and mathematical engineering at ICME, with an emphasis on Data Science. Whilst there, I passed Ph.D. qualifying exams in both Discrete Mathematics and Algorithms and also Stochastic Processes. I was advised on my M.S. thesis by Guido Imbens, now a Nobel Laureate.

Workshop Materials

Pre-workshop Checklist

Please go through module 0_Testing_Your_Python_Workspace.ipynb before the course starts.

Schedule

Session 1 Aug 3 (8:00 - 11 A.M. PST)

Introduction to Python (variables, strings, control-flow, containers, file-IO)
15 minute Q&A

Session 2 Aug 4 (8:00 - 11 A.M. PST)

Pandas (i.e. dataframes)

Additional Resources

If you’re curious for more course notes, please see lectures 0-8 from CME 211: https://github.com/CME211/notes#cme-211-notes.