Data Science

Gaby May Lagunes

Consultant
ESPM

Hello! I’m Gaby (she/her). I am PhD student at the ESPM department, I hold a masters in Data Science and Information from the Berkeley ISchool and I have 5+ years of industrial experience in different data roles. Before that I got a masters in Engineering for International Development and an undergraduate degree in Physics from University College London. And somewhere between all that I got married, survived the pandemic, and had two awesome boys. I’m very excited to help you use data to enhance your work and your experience here at Berkeley!

Thomas Lai

Consultant
School of Information

I am a Product Engineer passionate about applying engineering, data science, machine learning, and problem-solving principles to improve device performance and solve complex challenges. With experience in statistical analysis, lab bench automation, and Python scripting, I have developed a strong technical skill set that allows me to make meaningful contributions to any project. Beyond my work, I am also passionate about exploring new topics and ideas, from the latest technology trends to how to improve the overall well-being of humans. I enjoy applying the first principle to any...

Introduction to Propensity Score Matching with MatchIt

April 1, 2024
Alex Ramiller
by Alex Ramiller. When working with observational (i.e. non-experimental) data, it is often challenging to establish the existence of causal relationships between interventions and outcomes. Propensity Score Matching (PSM) provides a powerful tool for causal inference with observational data, enabling the creation of comparable groups that allow us to directly measure the impact of an intervention. This blog post introduces MatchIt – a software package that provides all of the necessary tools for conducting Propensity Score Matching in R – and provides step-by-step instructions on how to conduct and evaluate matches.

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.