Data Science

Why We Need Digital Hermeneutics

July 13, 2023
Tom van Nuenen
by Tom van Nuenen. Tom van Nuenen discusses the sixth iteration of his course named Digital Hermeneutics at Berkeley. The class teaches the practices of data science and text analysis in the context of hermeneutics, the study of interpretation. In the course, students analyze texts from Reddit communities, focusing on how these communities make sense of the world. This task combines both close and distant readings of texts, as students employ computational tools to find broader patterns and themes. The article reflects on the rise of AI language models like ChatGPT, and how these machines interpret human interpretations. The popularity and profitability of language models presents an issue for the future of open research, due to the monetization of social media data.

Introduction to Field Experiments and Randomized Controlled Trials

July 24, 2023
Leena Bhai
by Leena Bhai. This blog post provides an introduction to field experimentation and its significance in understanding cause and effect. It explains how randomized experiments represent an unbiased method for determining what works. It delves into essential features of experiments such as intervention, excludability, and non-interference. It then works through a fictional example of a randomized controlled trial of the efficacy of an experimental drug Covi-Mapp.

Mapping Time-Series Satellite Images with Google Earth Engine API

July 17, 2023
Meiqing Li
by Meiqing Li. Remote sensing imagery has the potential to reveal land use patterns and human activities at a planetary scale. For example, nighttime light intensity extracted from can shed light on spatial patterns of human activities and settlements, especially in places where traditional data are scarce. This blog post introduces Google Earth Engine (GEE) as a general purpose tool to extract time-series remote sensing data from GEE data catalog. I walk through using GEE to obtain data, filter by time and geographic region, and visualize it on static and interactive maps.

D-Lab & Graduate Division create inclusive data science summer program

August 9, 2023
Vanessa Navarro Rodriguez
by Vanessa Navarro Rodriguez. UC Berkeley's Social Sciences D-Lab and Graduate Division created the Data Science for Social Justice Program to address underrepresentation in data science. The program teaches diverse students critical data analysis and its applications in addressing societal injustices. The 8-week free summer course for admitted University of California students focuses on Python programming, Natural Language Processing, and value-informed data practices. It aims to empower students from underrepresented backgrounds and to bridge STEM with social justice. This blog post elaborates on the program's creation and features one of the DSSJ students, Robin López, and his reasons for participating.

The Geography of Cannabis: Does California’s dual licensing program (de)criminalize cannabis and drive unnecessary anthropogenic activity in remote rural environments?

August 29, 2023
Chevon Holmes
by Chevon Holmes. When California voters (de)criminalized cannabis production, the state’s dual licensure requirement forced local jurisdictions to create permitting programs or uphold prohibition. Many Counties developed ersatz zoning ordinances to regulate cannabis activities and hired staff to administer local permits. As an inspector, administrator, and project planner for Mendocino County from 2017-2021, I visited hundreds of cultivation sites and production facilities where I learned first-hand how two legal pathways impacted the ways in which operators could transition their businesses. This post details a dataset created to track, aggregate, and analyze the relationship between cannabis infrastructure and licensing.

Unlock the Joy and Power of Reading in Language Learning

August 21, 2023
Bowen Wang-Kildegaard
by Bowen Wang-Kildegaard. I share my story of how reading for pleasure transformed my English speaking and writing skills. This experience inspired my passion to promote the joy and power of reading to all language learners. Using natural language processing techniques, I dive into the Language Learning subreddit, revealing a trend: Learners are often highly anxious about output practices, but are generally positive about input methods like reading and listening. I then distill complex language learning theories into actionable language learning tips, emphasizing the value of extensive reading for pleasure, pointing to potential methods like using ChatGPT for customization of reading materials, and advocating for joy in the learning journey.

My Summer Exploring Data Science for Social Justice: Learnings, Tensions & Recommendations

September 5, 2023
Genevieve Smith
by Genevieve Smith. This summer I joined the D-Lab hosted Data Science for Social Justice workshop at UC Berkeley diving into Python – including TF-IDF, sentiment analysis, word embeddings, and more – with a lens towards leveraging data science for social justice. My team explored a Reddit channel on abortion and used computational analysis to answer key questions related to abortion access from before versus after Roe vs. Wade was overturned. Computational social science is incredibly powerful, but I continue to grapple with tensions particularly as it relates to employing machine learning and large language in international research, and end with key recommendations for CSS practitioners.

Artificial Intelligence (AI) Systems, the Poor, and Consent: A Feminist Anti-Colonial Lens to Digitalized Surveillance

September 18, 2023
Alejandro Nuñez
By Alejandro Nuñez. Today’s digital age has created a sea of endless datafication where our everyday interactions, actions, and conversations are turned into data. The advancements of automated artificial intelligence (AI) systems, and their infrastructure in which they are created and trained on, have catapulted us into an era of consistent monitoring and surveillance.

James Hall

Consultant
Department of Statistics

James Hall is a graduate student in the Statistics MA program at University of California, Berkeley. He is a husband and father to three awesome kids. Originally from Baltimore, MD, James earned his bachelors in Mathematics at the United States Military Academy at West Point, NY in 2011, and served as a U.S. Army officer. He’s served as a leader at multiple levels within large organizations with a professional focus on visualizing and communicating complex analysis to decision makers. James’ experience and coursework give him expertise in navigating different statistical methods,...

Python Machine Learning Fundamentals: Parts 1-2

October 2, 2023, 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.