Data Science, machine learning, and AI have exploded in popularity, and the big data revolution has affected how science is done in all fields, from the physical sciences to the behavioral and social sciences. This course explores the goals, values, skills, and practices of how data science is conducted within a variety of fields within the social sciences. It will enable students to identify questions and challenges that data science can address and help them find the data science discipline that matches their own values and goals.

An introduction to modern methods of data analysis for social scientists. This course emphasizes teaching students who have no previous coding experience how to analyze data and extract meaning in a social science context. Students will gain critical programming skills and learn inferential thinking through examples and projects with real-world relevance.

Python has become an extremely powerful programming language in advanced statistics and data analytics. It includes expansive packages for data handling and processing, including the latest developments in machine learning, and offers Integrated Development Environments (IDE) for code development, testing, debugging, and graphical representation. This course offers a thorough introduction to Python and those packages that are fundamental to data processing and analysis.

Introduces the student to a set of principles of survey and data science that are the basis of standard practices in these fields. The course exposes the student to key terminology and concepts of collecting and analyzing data from surveys and other data sources to gain insights and to test hypotheses about the nature of human and social behavior and interaction. It will also present a framework that will allow the student to evaluate the influence of different error sources on the quality of data.