This course introduces the theory and practice of data ethnography, demonstrating how qualitative data collection and analysis can be used to study of data settings and artifacts. Students will learn techniques in field-note writing, participant observation, in-depth interviewing, documentary analysis, and archival research and how they may be used to contextualize the cultural underpinnings of datasets. Students will learn how to visualize datasets in ways that foreground their sociopolitical provenance in R.

Data journalism is the practice of telling stories with data. This course will focus on journalistic practices, interviewing data as a source, and interpreting results in context. We will discuss the importance of audience in a journalistic context, and will focus on statistical ideas of variation and bias. The course will include hands-on work with data, using appropriate computational tools such as R, Python, and data APIs. In addition, we will explore the use of visualization and storytelling tools such as Tableau, plot.ly, and D3.

(Formerly MTH/SDS 220). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology.

(Formerly MTH/SDS 220). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology.

(Formerly MTH/SDS 220). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology.

(Formerly MTH/SDS 220). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology.

(Formerly MTH/PSY 201). An overview of the statistical methods needed for undergraduate research emphasizing methods for data collection, data description and statistical inference including an introduction to study design, confidence intervals, testing hypotheses, analysis of variance and regression analysis. Techniques for analyzing both quantitative and categorical data are discussed. Applications are emphasized, and students use R for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data.

An introduction to data science using Python, R and SQL. Students learn how to scrape, process and clean data from the web; manipulate data in a variety of formats; contextualize variation in data; construct point and interval estimates using resampling techniques; visualize multidimensional data; design accurate, clear and appropriate data graphics; create data maps and perform basic spatial analysis; and query large relational databases. SDS 100 is required for students who have not previously completed SDS 201, SDS 220, SDS 290 or SDS 291.

An introduction to data science using Python, R and SQL. Students learn how to scrape, process and clean data from the web; manipulate data in a variety of formats; contextualize variation in data; construct point and interval estimates using resampling techniques; visualize multidimensional data; design accurate, clear and appropriate data graphics; create data maps and perform basic spatial analysis; and query large relational databases. SDS 100 is required for students who have not previously completed SDS 201, SDS 220, SDS 290 or SDS 291.

The practice of data science rests upon computing environments that foster responsible uses of data and reproducible scientific inquiries. This course develops students’ ability to engage in data science work using modern workflows, open-source tools, and ethical practices. Students will learn how to author a scientific report written in a lightweight markup language (e.g., markdown) that includes code (e.g., R), data, graphics, text, and other media. Students will also learn to reason about ethical practices in data science.