DATSC-BA - Data Science (BA)

Mission of the Undergraduate Program in Data Science 

The mission of the undergraduate program in Data Science is to provide students with an analytical and quantitative foundation for tackling data-driven problems in science, industry, and society. Data science is an interdisciplinary field that combines computational and inferential reasoning to extract knowledge or insights from data for use in a broad range of applications. It synthesizes the most relevant parts of foundational disciplines in order to solve particular classes of problems or applications.  As more data and new ways of analyzing data become available, our economy, society, and daily life will become even more dependent on our ability to systematically learn from data.  

Students pursuing the B.A. in Data Science and Social Systems will develop a rigorous understanding of methods and approaches from computer science, statistics, and the social sciences, and will learn to apply technical tools to address substantive problems in that domain. They will develop multiple fluencies: in computational methods, in the domain knowledge and applications of social science broadly, and in the moral and political frameworks that shape social systems. The B.A. in Data Science and Social Systems is an ideal major to prepare students for work in the technology sector and a range of industries that require integrating quantitative thinking with an understanding of human behavior or public policy, or for careers in the public and social sectors where data-intensive approaches are prized.

B.A. Degree Requirements

Data Science and Social Systems majors will complete an introductory course, courses in a set of core areas, a practicum in which they apply data science skills to a well-defined problem, and a pathway in a particular area of concentration. To declare this major, students should select the B.A. in Data Science and the Social Systems subplan. There are three parts to the core: quantitative (math, computation, statistics, and optimization), behavioral science (psychology, sociology, political science, and economics), and ethics. In addition, the introductory course to the major and the capstone will provide students with real-world examples and opportunities to apply modern computational and statistical techniques to address key social challenges. Interdisciplinary thinking is essential for data-driven analysis of complex social problems, and the program is designed to prepare students with superb technical training and in-depth knowledge of the social sciences.

External Credit Policies

Course transfer credit is subject to department evaluation and to the Office of the Registrar's external credit evaluation. These courses may result in a replacement course for Data Science required course or may establish placement in a higher-level course. Transfer requests must first be submitted to Student Services Center prior to being evaluated by your advisor.

Transfer Credit for Data Science Declared Students

Transfer credit must be approved first by the University Registrar's Office (and listed on your Stanford transcript), and then by the department before it can be used towards your Data Science major requirements. It is the student's responsibility to compile the documents required for course transfer evaluation form.

1. Complete a Data Science Course Equivalency Petition for each course and attach all necessary documentation (course description, syllabus, and unofficial transcript).

2. Submit the above documents to the student services officer in Sequoia Hall, Room 124.

3. Ask the Student Services Center to send a copy of the specific course approval paperwork (Request for Transfer Credit Evaluation form) to the department program administrator: Sequoia Hall, room 124 - Data Science program.

Unit Information

To be recommended for the B.A. degree in Data Science, the student must complete at least 89 units in the program

Students in the Data Science Program are expected to achieve the following learning outcomes. These learning outcomes are used both in evaluating students and the undergraduate program. By the time they graduate, majors are expected to:

1. Frame questions of interest from a variety of disciplines in quantitative terms and identify what data types might be useful in addressing them.

2. Demonstrate familiarity with the key statistical, mathematical, and computational concepts and use them appropriately to solve quantitative problems.

3. Appraise the major ethical questions that arise in the collection, analysis, and use of data for decision-making based on quantitative reasoning.

4. Convey quantitative analysis and technical results to a wide audience, effectively communicating the uncertainty associated with their conclusions, and taking care to assure the reproducibility of results.

Additionally, students in the Bachelors of Arts in Data Science - Social Systems major are expected to:

5. Integrate traditional theoretical approaches from the social sciences and engineering with modern computational tools to frame, understand and analyze social-scientific problems.  Specifically, students will be able to:

   5.a) Develop analytical approaches to address unstructured and multifaceted problems.

   5.b) Design and employ experimental and quasi-experimental approaches to understand causal effects in the social sciences.

   5.c) Implement and apply statistical analyses and machine learning algorithms using modern software engineering principles.

   5.d) Draw correct inferences from data with an appreciation for the assumptions and limits of quantitative methods.

6. Understand frontier research and practice at the intersection of data and social science as it relates to key social, political, and economic issues.