Curriculum

The Behavioral and Social Data Science Major

The Behavioral and Social Data Science (BSDS) major brings together two pathways — Psychology and Humanities— for students who want to study people and culture with the tools of data science.

No matter which track you choose, you’ll gain a strong foundation in statistics, programming, and data visualization while also focusing deeply on the kinds of questions that drive your field. The Psychology Track looks at human thought, behavior, and emotion. The Humanities Track focuses on cultural expression in language, art, and media.

Across both tracks, students learn to design experiments, analyze data, and communicate results — while reflecting on the ethical, social, and cultural implications of data science in everyday life.

Psychology Track

The Behavioral and Social Data Science (BSDS) major combines psychology’s insights into human thought, behavior, and emotion with the tools of data science. Students gain a broad foundation in psychology while also developing skills in statistics, programming, data visualization, and ethical decision-making.

Unlike traditional programs where students had to connect the dots between psychology and data science on their own, BSDS integrates them directly. That means everything you learn — from experimental design to coding in Python or R — is grounded in the real problems and data of human behavior.

Below you can find details regarding the Course Requirements for the Psychology Track under the 2024-2026 Degree Plan. More complete information is available here.

Required Courses

🔹 PSY 301 – Introduction to Psychology
Explore the foundations of human thought, emotion, and behavior, with applications across everyday life. This class gives you the psychological perspective you’ll carry into all your data science training.

🔹 PSY 317L – Statistics for the Behavioral Sciences (or PSY 120R – R Programming, transfer version)
Learn R programming, descriptive and inferential statistics, and data visualization for psychology. You’ll cover t-tests, correlation, regressions, and nonparametric approaches, all with a focus on real-world behavioral data.

🔹 PSY 420M – Psychological Methods and Statistics
Move beyond the basics into experimental design and intermediate statistical approaches. You’ll learn how to evaluate new findings, design your own studies, and communicate research through writing and presentations.

🔹 PSY 371E – Psychological Data Science Foundations I
Build up your coding skills. This flipped, hands-on course introduces Python, Jupyter Notebooks, and GitHub. You’ll learn to visualize, simulate, and analyze behavioral data while building a personal coding portfolio.

🔹 PSY 371F – Psychological Data Science Foundations II
Take your skills further with regression, generalized linear models, and advanced analysis. You’ll code, visualize, and troubleshoot in class — culminating in a final project and GitHub repository that you can showcase to employers or graduate programs.

Upper-Division Electives (12 Hours)

After the core sequence, students choose four electives (12 hours) to tailor their BSDS training. Options include:

PSY 341K – Computer Simulations (Cormack)
Model complex psychological systems using computational simulations to understand behavior and psychological phenomena.
PSY 371M – Introduction to Machine Learning (Yu)
Learn the core algorithms that let computers detect patterns in behavioral data, from prediction to classification.
PSY 371T – Text Analysis for Behavioral Data Science (Ong)
Analyze language data from sources like social media, large language models, and news to uncover psychological insights.
PSY 371Q – Ethics in Behavioral Data Science (Ong)
Explore the ethical challenges of data use, including bias, privacy, consent, and fairness in applied contexts.
PSY 341K – Social Network Analysis (Curley)
Use R to visualize and analyze how social connections influence behavior, beliefs, and group dynamics.
PSY 341K – Applied Human Signal Analysis (de Barbaro)
Study human behavior through physiological and sensor signals, applying data science to the study of real-world interactions.
PSY 371P – Applied Data Science (Timmons)
Work on real-world data projects that apply statistical and computational techniques to psychology and health.
PSY 371S – Bayesian Data Analysis (Etz)
Learn Bayesian approaches to modeling uncertainty, updating beliefs with data, and applying them in psychology.
PSY 371N – Natural Behavior in the Real World (Yu)
Design studies outside the lab using mobile sensing and video data to analyze everyday human behavior.

Humanities Track

The Humanities Track of the BSDS major is for students who want to combine data science with the study of culture. Here, you’ll work with texts, images, sounds, and artifacts, asking how digital tools can help us better understand human creativity and communication.

Instead of learning data science in the abstract, you’ll practice it in relation to novels, films, podcasts, visual art, archives, and more. Alongside technical skills in R, Python, and data visualization, you’ll also reflect on how technology and AI shape society — and how culture shapes technology in return.

Below you can find details regarding the Course Requirements for the Humanities Track under the 2026-2028 Degree Plan.

Required Courses

🔹 BHD 301 - Introduction to Humanities Data Science

Introduction to core concepts, methods, and tools in data science from a liberal arts perspective with an emphasis on both technical skill-building and critical reflection. Discuss data ethics, machine learning, archival processing, and the cultural contexts of computational research

🔹 BHD 315 - Programming for Humanities Data Science

Introduction to computer programming for the humanities and to digital humanities as a research area. Examine basic programming concepts, data types, functions, and Application Programming Interfaces (API). Learn the first steps in building data pipelines and explore introductory natural language processing (NLP) techniques through humanities-centered applications.

🔹 BHD 319 – Gateway Course for Data Science in the Humanities

Introduction to digital humanities research. Examine how data, algorithms, Machine Learning/Artificial Intelligence, and interpretation shape cultural inquiry in the humanities through hands-on experimentation and critical tinkering using computer programming. Utilize exercises in artifact, text, image, and metadata analysis.

🔹 BHD 321 – Humanities Research Methods

Prerequisite: BHD 319
Explore approaches to research design in humanities data science with an emphasis on mixed methods, critical and theoretical connections between data science and humanities research programs, ethical frameworks, and the formulation of research questions within specific cultural, disciplinary, and cross-sectoral professional contexts.

🔹 BHD 340 – Advanced Programming for Humanities Data Science

Prerequisite: BHD 315
Focus on building and refining custom data pipelines and applications in a self-guided research project. Discuss research and data flow planning, critical evaluation, Natural Language Processing (NLP) and computational cultural artifact analysis, including archive processing. Explore modular programming, adapting tools for humanities-specific use cases, and interface design.

🔹 BHD 350 – Project Management for Humanities

Introduction to strategies for managing digital humanities projects from research design and planning to deployment. Discuss team collaboration, corpus and artifact curation, Institutional Review Board (IRB) protocols and legal questions, documentation practices, and navigating the institutional, ethical, and technical dimensions of humanities data science research.