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 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 learn how to visualize datasets in ways that foreground their sociopolitical provenance in R.

As AI grows in popularity and ubiquity, considering its social, ethical, and interpersonal impacts is crucial. How can AI be understood as part of the sociocultural fabric? How does one balance the harms caused by AI systems with the benefits it (reportedly) provides? In this class, students explore the philosophical and ethical aspects of AI in conversation with the practical implementations of AI systems. Prerequisite: SDS 192, SDS 210, CSC 110, PHI 100, or PHI 102. Enrollment limited to 25.

(Formerly SDS 201). An application-oriented introduction to statistical modeling, covering topics of descriptive statistics, data visualization, point and interval estimates, bivariate and multiple regression modeling, and inferential hypothesis tests using both distributional and resampling methods. Lectures include “hands on” demonstrations of statistical phenomenon, with labs and assignments that emphasize analysis of real data. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.

(Formerly SDS 201). An application-oriented introduction to statistical modeling, covering topics of descriptive statistics, data visualization, point and interval estimates, bivariate and multiple regression modeling, and inferential hypothesis tests using both distributional and resampling methods. Lectures include “hands on” demonstrations of statistical phenomenon, with labs and assignments that emphasize analysis of real data. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.

(Formerly SDS 201). An application-oriented introduction to statistical modeling, covering topics of descriptive statistics, data visualization, point and interval estimates, bivariate and multiple regression modeling, and inferential hypothesis tests using both distributional and resampling methods. Lectures include “hands on” demonstrations of statistical phenomenon, with labs and assignments that emphasize analysis of real data. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.

(Formerly SDS 201). An application-oriented introduction to statistical modeling, covering topics of descriptive statistics, data visualization, point and interval estimates, bivariate and multiple regression modeling, and inferential hypothesis tests using both distributional and resampling methods. Lectures include “hands on” demonstrations of statistical phenomenon, with labs and assignments that emphasize analysis of real data. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.

(Formerly SDS 201). An application-oriented introduction to statistical modeling, covering topics of descriptive statistics, data visualization, point and interval estimates, bivariate and multiple regression modeling, and inferential hypothesis tests using both distributional and resampling methods. Lectures include “hands on” demonstrations of statistical phenomenon, with labs and assignments that emphasize analysis of real data. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.

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. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100. Enrollment limited to 40.

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. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100. Enrollment limited to 40.

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.