This course introduces the theory and practice of data ethnography, demonstrating how qualitative data collection and analysis can be used to study 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 focuses on journalistic practices, interviewing data as a source, and interpreting results in context. The course discusses the importance of audience in a journalistic context and focuses on statistical ideas of variation and bias. The course includes hands-on work with data, using appropriate computational tools such as R, Python, and data APIs. In addition, the course explores the use of visualization and storytelling tools such as Tableau, plot.ly, and D3.

(Formerly MTH 220/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. This course satisfies the basic requirement for biological science, engineering, environmental science, neuroscience, and psychology.

(Formerly MTH 220/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. This course satisfies the basic requirement for biological science, engineering, environmental science, neuroscience, and psychology.

(Formerly MTH 201/ 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. This course satisfies the basic requirement for the psychology major.

(Formerly MTH 201/ 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. This course satisfies the basic requirement for the psychology major.

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. Prerequisite: concurrent registration in SDS 100 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. Prerequisite: concurrent registration in SDS 100 required for students who have not previously completed SDS 201, SDS 220, SDS 290 or SDS 291.

Offered as SDS 109 and CSC 109. The world is growing increasingly reliant on collecting and analyzing information to help people make decisions. Because of this, the ability to communicate effectively about data is an important component of future job prospects across nearly all disciplines. In this course, students learn the foundations of information visualization and sharpen their skills in communicating using data. This course explores concepts in decision-making, human perception, color theory and storytelling as they apply to data-driven communication.