Data Science

R Fundamentals: Parts 1-4

April 29, 2024, 9:00am
This workshop is a four-part introductory series that will teach you R from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the open-sourced R Studio software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.

Python Fundamentals: Parts 1-3

April 29, 2024, 2:00pm
This three-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Computational Social Science in a Social World: Challenges and Opportunities

March 26, 2024
José Aveldanes
by José Aveldanes. The rise of AI, Machine Learning, and Data Science are harbingers of the need for a significant shift in social science research. Computational Social Science enables us to go beyond traditional methods such as Ordinary Least Squares, which face challenges in addressing complexities of social phenomena, particularly in modeling nonlinear relationships and managing high-dimensionality data. This paradigmatic shift requires that we embrace these new tools to understand social life and necessitates understanding methodological and ethical challenges, including bias and representation. The integration of these technologies into social science research calls for a collaborative approach among social scientists, technologists, and policymakers to navigate the associated risk and possibilities of these new tools.

GPT Fundamentals

April 17, 2024, 3:00pm
This workshop offers a general introduction to the GPT (Generative Pretrained Transformers) model. We will explore how they reflect and shape our cultural narratives and social interactions, and which drawbacks and constraints they have.

Using Big Data for Development Economics

March 18, 2024
Leïla Njee Bugha
by Leïla Njee Bugha. The proliferation of new sources of data emerging from 20th and 21st century technologies such as social media, internet, and mobile phones offers new opportunities for development economics research. Where such research was limited or impeded by existing data gaps or limited statistical capacity, big data can be used as a stopgap and help accurately quantify economic activity and inform policymaking in many different fields of research. Reduced cost and improved reliability are some key benefits of using big data for development economics, but as with all research designs, it requires thoughtful consideration of potential risks and harms.

Python Text Analysis Fundamentals: Parts 1-2

March 21, 2024, 10:00am
This two-part workshop series will prepare participants to move forward with research that uses text analysis, with a special focus on humanities and social science applications.

Data Science for Social Justice 2024 (Apply by April 15)

March 15, 2024, 12:00pm
This 8-week workshop will give you the opportunity to learn the essential tools and methods for data science analysis and be introduced to critical frameworks that will enable you to create a project of your own design and to tell stories that can counter the market-first mentality of data science. This workshop has a heavy emphasis on collaboration and peer-to-peer learning, with a significant group work component.
See event details for participation information.

Design Your Observational Study with the Joint Variable Importance Plot

March 12, 2024
Lauren Liao
by Lauren Liao. When evaluating causal inference in observational studies, there often is a natural imbalance in the data. Luckily, variables are often measured alongside that can be helpful for adjustment. However, deciding which variables should be prioritized for adjustment is not trivial – since not all variables are equally important to the intervention or the outcome. I recommend using the joint variable importance plot during the observational study design phase to visualize which variables should be prioritized. This post provides a gentle guide on how to do so and why it is important.

Python Text Analysis: Word Embeddings

April 11, 2024, 10:00am
How can we use neural networks to create meaningful representations of words? The bag-of-words is limited in its ability to characterize text, because it does not utilize word context.

Python Machine Learning Fundamentals: Parts 1-2

April 16, 2024, 2:00pm
This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.