Data Science

Tom van Nuenen, Ph.D.

Data/Research Scientist, Senior Consultant, and Senior Instructor
D-Lab
Social Sciences
Digital Humanities

I work as a Lecturer, Data Scientist, and Senior Consultant at UC Berkeley's D-Lab. I lead the curriculum design for D-Lab’s data science workshop portfolio, as well as the Digital Humanities Summer Program at Berkeley.

Former research projects include a Research Associate position in the ‘Discovering and Attesting Digital Discrimination’ project at King’s College London (2019-2022) and a researcher-in-residence role for the UK’s National Research Centre on Privacy, Harm Reduction, and Adversarial Influence Online (2022). My research uses Natural Language Processing methods to...

Institutional Review Board (IRB) Fundamentals

February 18, 2025, 9:00am
Are you starting a research project at UC Berkeley that involves human subjects? If so, one of the first steps you will need to take is getting IRB approval.

R Fundamentals: Parts 1-4

May 5, 2025, 10:00am
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 Web Scraping

March 5, 2025, 10:00am
In this workshop, we cover how to scrape data from the web using Python. Web scraping involves downloading a webpage's source code and sifting through the material to extract desired data.

Python Web APIs

March 3, 2025, 10:00am
In this workshop, we cover how to extract data from the web with APIs using Python. APIs are often official services offered by companies and other entities, which allow you to directly query their servers in order to retrieve their data. Platforms like The New York Times, Twitter and Reddit offer APIs to retrieve data.

Python Machine Learning Fundamentals: Parts 1-2

April 8, 2025, 12:00pm
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.

Stata Fundamentals: Parts 1-3

February 24, 2025, 1:00pm
This workshop is a three-part introductory series that will teach you Stata from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the Stata 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 Text Analysis: Parts 1-3

March 17, 2025, 2:00pm
This three-part workshop will prepare participants to move forward with research that uses text analysis, with a special focus on social science applications. We explore fundamental approaches to applying computational methods to text in Python. We cover some of the major packages used in natural language processing, including scikit-learn, NLTK, spaCy, and Gensim.

Qualtrics Fundamentals

March 5, 2025, 3:00pm
Qualtrics is a powerful online tool available to Berkeley community members that can be used for a range of data collection activities. Primarily, Qualtrics is designed to make web surveys easy to write, test, and implement, but the software can be used for data entry, training, quality control, evaluation, market research, pre/post-event feedback, and other uses with some creativity.

Python GPT Fundamentals

February 20, 2025, 10:00am
This workshop offers a general introduction to the GPT (Generative Pretrained Transformers) model. No technical background is required. We will explore the transformer architecture upon which GPT models are built, how transformer models encode natural language into embeddings, and how GPT predicts text.