Data Science Minor

This course introduces students to data science and research design. This course is divided into two parts. During the first half of the semester, students will be introduced to theories of science and how systematic and falsifiable analysis applies to a wide variety of fields of study. During the second half of the semester, students will be introduced to data management, statistical and computer programming software, and econometrics.

Critical survey of emerging digital tools, techniques, and theories for analyzing, interpreting, and visualizing texts and networks as data. This course explores a broad toolkit of computational and quantitative methods—word frequency and usage, sentiment analysis, topic modeling, and social network analysis—and considers how cultural and theoretical constructs like authorship, gender, race, sociality, and influence can be applied at very large scale. Labs introduce use of open-source software for statistical analysis and data visualization. Prerequisite:
Any 100-level course in SOC or CIMS; MUS_CLAS-213; OR any 200-level course in ENG or foreign languages and literatures; OR any gateway course for area, ethnic, group, or global studies majors (e.g. LAS 145).

Examination of the use of visual dimensions and materials we produce as data for analysis through a sociological lens. The goal of the course is to allow students to understand how visual materials carry powerful messages that explicitly or implicitly reflect societal attitudes, norms and values. The course teaches students how to collect visual materials, thematically code them, quantify these codes, and conduct statistical analysis. The course discusses research ethics in Visual Studies. Prerequisite: SOC 101, SOC 102, or SOC 103.

Data scientists apply methods from statistics, data analysis, computer science, and machine learning in order to gain insight from data. In Data Science Programming, we focus on developing the programming and machine learning skills necessary to gain such insight. Through experiential learning, we equip students with the fundamental computer problem-solving skills and tools to clean raw data, engineer data features, build statistical and machine learning models, predict unknown values and/or discern patterns, and present data insights.

Massive data collection and sophisticated “Big Data” methods are used in marketing and recruiting, teacher evaluation, criminal justice, national security, and many other fields. These methods have potential to improve social outcomes, but can also perpetuate historical socioeconomic injustices, reinforce biases, and paint a veneer of rational objectivity over erroneous assumptions. This course introduces relevant statistical and computational concepts through lab activities, while also reflecting critically on these methods’ limitations and ethical implications through discussion of specific case studies. Prerequisite: Previous credit or concurrent registration in one of: CS 107 or 111; OR Math 107 or equivalent; OR DS 256.

Increasingly, data influence our world; data inform business decisions, enable scientific discovery, shape policy, track our health, and sway public opinion. Effective visualizations summarize data as readily digestible charts and graphs, revealing patterns and trends. In this course, students think critically about how data are presented and learn to craft impactful data visualizations. Through a blend of theory and hands-on practice, students explore topics including graphic design, accessibility, interactivity, data storytelling, and the use of leading data visualization tools. Prerequisite: DS 256

Advanced treatment of data science concepts. Through a series of case studies, students explore datasets from a variety of domains and extract meaningful information and insights using mathematical, computational, and other scientific methods and algorithms. Topics include the fundamental algorithms of data science: regression, decision trees, support vector machines, clustering, and neural networks. Through a semester-long project, students demonstrate knowledge of fundamental data science concepts and ability to interpret and communicate effectively the results of the analysis.