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Following the campus guidelines for Coronavirus all UC Davis classes, lectures, seminars, labs and discussion sections will move to virtual instruction and remain virtual through the end of spring quarter 2020.  The best way to reach staff and faculty is by email.  See our Humans page for our directory.

Current remote office hours - this document will be updated as needed.

Data Studies

Data Studies courses prepare students to take their critical thinking and analysis skills and apply them to data-oriented environments, including industry, government, and non-profit organizations.

Science and Technology Studies and the Data Sciences Initiative have developed a new program in Data Studies with courses open to students from all majors. These courses are designed to offer critical understanding of the social life of data, as well as important aspects of data analysis. They prepare students to apply their critical thinking and analytical skills to data-oriented environments in industry, government, and non-profit organizations.

Learn more about the program by visiting these websites:

Division of Social Sciences page on Data Studies

College of Letters and Science page on Data Studies

Data Studies Courses

STS 101: Data & Society 

Data & Society

Basic concepts in data science from a socio-cultural perspective. Identifying data stakeholders and their biases, reading and evaluating data documentation, exploring data through analysis and visualization, identifying knowledge gaps, and assessing data ethics.

Key topics:
  • Cultural and Political Contexts of Data Production and Availability (freedom of information laws and open data directives, surveillance capitalism, history of data activism and governance, informed consent and privacy laws)
  • Data Stakeholders (conducting stakeholder analysis, identifying stakeholder constraints and benefits, along with interests and influences)
  • Understanding Data (reading metadata, recognizing data formats, identifying observations and variables, recognizing variable types, assessing data integrity and quality)
  • Reading a Dataset (assessing the politics behind how people and things are defined and counted in data, evaluating classifications in data and their politics, reading and evaluating data dictionaries and documentation, recognizing uncertainties introduced during data cleaning, discerning which questions can and cannot be answered with data)
  • Data Exploration, Manipulation, and Visualization (indexing data, tidying data, subsetting data, grouping data, summarizing data, graphing and charting data)
  • Interpreting Data (comparing data values to benchmarks, identifying biases and knowledge gaps, acknowledging opportunities for proxy discrimination, recognizing data manipulation techniques such as cherrypicking variables and p-hacking)


STS 112: Visualizing Society with Data

Visualizing Society

Analysis and visualization of historical and contemporary data about populations and societies using R. Critical exploration of visual communication of information about people over time and critical assessment of role of data collection and analysis in societies. DD, QL, SS.


STS 115: Data Sense and Exploration

Data Sense and Exploration: Critical Storytelling and Analysis
This course introduces students to data science analysis through case studies of working with data to develop and tell meaningful stories about interesting questions. The course has the students work with real questions and real-world (messy) data, learn to think critically about how to quantify and measure concepts, learn to visualize data for exploratory data analysis (EDA) and for communicating final results to different types of audiences. The students develop data literacy, intuition about sampling variability, skepticism about quantitative claims, best practices in data visualization, and an introduction to programming.

We use case studies to explore problems with real data that require students to think about the context of the problem and the data. The case studies also encourage the students to bring in other sources of data that may offer improved insights into the problem. They also raise issues with “found” data that are not randomly sampled and how naive inferences can be highly misleading.
Students will learn to clean data, considering how the data were collected and how errors may have been introduced.

The course also introduces elementary aspects of the R computing environment for data analysis. The primary purpose for this is so that students can use this in subsequent courses, especially the capstone course (the fifth course in the forthcoming “Data Studies" minor).

The course involves: 1) using data to reason about and answer questions, 2) skeptically framing and evaluating questions and answers, 3) presenting results to different audiences, 4) manipulating data with a high-level programming language.

Learning Objectives: Reason about how the data were obtained; understand limitations of the sampling mechanism; identify the population from which the data were sampled and what relevant inferences we might be able to perform.

Visualization covers many types of plots; single variable plots; two variable plots; multivariable plots; choice of glyphs, colors; conditional plots - panels; geospatial maps; visualizing “big data”.

This courses uses a high-level programming language (R) rather than Excel and so requires explaining fundamental concepts in that language.


STS 195: Research in Data Studies

Capstone Individual Research Project in Data Studies

New Class -- Coming Soon!