Pre-arrival

The Summer Institute will bring together people from many fields and backgrounds. In order to use our time together efficiently, there are a number of things that you should do before participating in SICSS-Stanford 2025.

• Complete the readings
• Prepare your computing environment
• Complete coding bootcamp (if needed)
• Watch lecture videos (if needed)

Organizers will be available via Slack to support you as you work through these pre-arrival materials. Please feel free to reach out to us!

Reading

In order to prepare for SICSS-Stanford 2025, we recommend you read Matt Salganik’s book, Bit by Bit: Social Research in the Digital Age (Read online or purchase from Amazon, Barnes & Noble, IndieBound, or Princeton University Press). Parts of this book, which is a broad introduction to computational social science, will be review for most of you, but if we all read this book ahead of time, then we can use our time together for more advanced topics.

For SICSS-Stanford 2025, we also recommend:

• Reading chapter 1 of the book Network Science for a broad overview of this field, which we will draw both tools and ideas from.
• Reading through Inspect Element, an open source resource for building your own datasets that introduces techniques, tools, and considerations that we will explore.

Coding

The majority of the code presented at SICSS-Stanford 2025 will use Python. We recommend the participants have basic working knowledge and experience with using Python prior to arriving (e.g., able to run a Python script or Jupyter notebook on your computer). You are welcome to employ a language of your choice, such as R, Julia, or other languages that are commonly used for data science, however, we cannot support all these languages.

Independent of any language, we recommend adopting an Integrated Development Environment (IDE) for writing and running code, and recommend getting familiar with your IDE and the terminal/shell/console before arriving. For SICSS-Stanford 2025, we will primarily use Visual Studio Code. If you’ve never heard of an IDE or a terminal, we recommend reading:

• The first three chapters of Kieran Healy’s Modern Plain Text Computing, which provide a gentle introduction to the terminal, text editors, and other useful tools.
• The Missing Semester of Your CS Education, a free course from MIT on a “critical subject that’s rarely covered, and is instead left to students to figure out on their own: proficiency with their tools.”
• Data Science for Economists, Lecture 3: Learning to love the shell by Grant McDermott
• “Just” - a brief, cathartic rant about computing environments and assumed knowledge

If you are using a Windows machine, we highly recommend installing Windows Subsystem for Linux (WSL), which will give you a comparable base computing environment to those found on Mac and Linux operating systems. Many of the previously listed resources—and many public programming resources/configuration guides more broadly—are only compatible with Mac, Linux, and Windows machines that have WSL installed. You can find instructions for installing the latest version of WSL here.

Python

Before SICSS you should install a modern, stable-release version of Python that you can use in Visual Studio Code to run scripts or Jupyter notebooks.

For those new to python, there are many free courses and guides to getting started available online, including:

• Practical Python Programming by David Beazley, a free course that covers the basics of Python programming.
• Python Basics - Real Python, a collection of tutorials on the basics of Python programming.
• Introduction to Computational Social Science with Python by Patrick Gildersleve and Milena Tsvetkova, GESIS
• Introduction to Cultural Analytics & Python by Melanie Walsh, University of Washington

R

If you prefer to pursue learning R, the SICSS Boot Camp is an online training program created by Chris Bail that can provide you with beginner level skills in coding so that you can follow the more advanced curriculum we teach at SICSS. The videos and materials are designed for complete beginners and are best viewed as a sequence since each video builds upon content introduced in previous tutorials. If you are already familiar with the topics in these videos, you do not need to complete them.

If you would like more practice after completing the Boot Camp videos, some other materials that we can recommend are:

• RStudio Primers, which can be supplemented by the open access book R for Data Science by Garrett Grolemund and Hadley Wickham
• R for Social Science, Data Carpentry.
• Introduction to R for Social Scientists, Taught by Charles Lanfear at University of Washington. This course includes videos of lectures, slides, and assignments.
• Learn R, Code Academy (requires registration)

Slack

Before participating at SICSS-Stanford 2025, you should have an account in the SICSS Slack workspace. If you have not used Slack before, you should review these getting started materials. Slack can be hard to use at first, but we’ve found that it is the best way to enable everyone to collaborate.

GitHub

Many participants at SICSS use GitHub to collaborate. If you do not yet have one, you should create a GitHub account. If you are a student, we recommend that you apply for a GitHub Student Developer Pack.