# B.A. Degree Requirements

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Gateway Course: Solving Social Problems with Data (4 units)

Solving Social Problems with Data (DATASCI 154) introduces students to the interdisciplinary intersection of data science and the social sciences through an in-depth examination of contemporary social problems. The course provides a foundational skill set for solving social problems with data including quantitative analysis, modeling approaches from the social sciences and engineering, and coding skills for working directly with big data. Students will also consider the ethical dimensions of working with data and learn strategies for translating quantitative results into actionable policies and recommendations.

Lectures will introduce students to the methods of data science and social science and apply these frameworks to critical 21st century challenges, including climate change, educational equity, health policy, and political polarization. In-class exercises and problem sets will provide students with the opportunity to use real-world datasets to discover meaningful insights for policymakers and communities.

This course is the required gateway course for the new major in Data Science & Social Systems. Course material and presentation will be at an introductory level. CS106A is the only prerequisite.

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Data Science Core

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Math (9-10 units)

- One of the following:
- Linear Algebra, Multivariable Calculus, and Modern Applications (Math 51, 5 units)
- Modern Mathematics: Continuous Methods (Math 61CM, 5 units)

- One of the following:
- Introduction to Matrix Methods (ENGR 108, 5 units)
- Applied Matrix Theory (Math 104, 4 units)

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Computer Science (16-19 units)

- Programming Methodology (CS 106A, 5 units)
- Programming Abstractions (CS 106B or X, 5 units)
- One of the following:
- Computer Organization and Systems (CS 107, 5 units)
- Introduction to Scientific Computing (CME 108, 3 units)
- Mathematical Foundations of Computing (CS 103, 5 units)
- Introduction to the Theory of Computation (CS 154, 4 units)
- Design and Analysis of Algorithms (CS 161, 5 units)

- One of the following:
- Applied Machine Learning (CS 129, 4 units)
- From Languages to Machine Learning (CS 124, 4 units)
- Data Mining and Analysis (STATS 202, 3 units)
- Introduction to Statistical Learning (STATS 216, 3 units)
- Introduction to Computational Social Science (MS&E 231, 4 units)
- Artificial Intelligence: Principles and Techniques (CS 221, 4 units)

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Statistical Inference (10 - 14 units)

- Theory of Probability (STATS 116, 5 units) or Introduction to Probability (MS&E 120, 4 units)
- One of the following:
- Introduction to Applied Statistics (STATS 191, 3 units)
- Introduction to Statistical Inference (STATS 200, 3 units)
- Introduction to Applied Statistics (MS&E 125, 4 units)
- Fundamentals of Data Science: Prediction, Inference, Causality (MS&E 226, 3 units)

- One of the following:
- Causal Inference for Social Science (POLISCI 150C/POLISCI355C, 5 units)
- Quasi-Experimental Research Design & Analysis (SOC258B, 5 units)
- Advanced Topics in Econometrics (ECON 102C, 5 units)
- Applications of Causal Inference Methods (EDUC 260A, 3 units)

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Optimization (3 units)

- Introduction to Optimization (MS&E 111/211/ENGR 62, 3 units)

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Ethics (3-5 units)

- One of the following:
- Justice (POLISCI 103 - 5 units)
- Ethics, Public Policy, and Technology Change (CS 182 - 4 units)
- Data Privacy and Ethics (MS&E 234 - 3 units)
- Introduction to Moral Philosophy (ETHICSOC 20 – 4-5 units)
- The Politics of Algorithms (COMM 154 / COMM 254 / CSRE 154T / SOC 154 - 5 units)

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Social Systems Core

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Behavioral Science (17-20 units)

**Complete four courses that cover at least two of the following four areas:**

- Psychology (“Individuals”)
- Introduction to Psychology (PSYCH 1 - 5 units)
- Minds and Machines (PSYCH 35 - 4 units)
- Introduction to Personality and Affective Science (PSYCH 80 - 4 units)
- Additional courses that match a student’s substantive interests, e.g. social psychology, clinical psychology, cultural psychology

- Sociology (“Groups”)
- Economic Sociology (SOC 114 - 5 units)
- Race and Ethnic Relations in the USA (SOC 145 - 4 units)
- Formal Organizations (SOC 160 – 4 units)
- Introduction to Social Networks (SOC 126 - 4 units)
- Introduction to Computational Social Science (SOC 10 - 4 units)
- America: Unequal (SOC 3 - 4 units)

- Political Science (“Institutions”)
- The Science of Politics (POLSCI 1 - 5 units)
- Additional courses that match a student’s substantive interests, e.g. American politics, comparative politics, international relations

- Economics (“Markets”)
- Economic Analysis I (ECON 50 - 5 units)
- Economic Analysis II (ECON 51 – 5 units)
- Economic Analysis III (ECON 52 – 5 units)

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Pathway (16 units)

The pathway is an opportunity for students to develop specific expertise on a topic they are passionate about and where data science methods and approaches have something compelling to offer.

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Capstone (5 units)

To integrate their computational skills and social science domain knowledge, Data Science & Social Systems majors will complete a 5-unit practicum. This practicum will be offered annually. During the practicum, students will work in teams to provide actionable recommendations and practical tools to partners in government agencies, community organizations, or various social science labs that are striving to address an important societal challenge. Through this partnership, students will integrate material from their coursework, gain experience applying data science techniques to complex, real-world problems, and develop their ability to work in teams.

In exceptional cases, students may also submit a proposal to the Associate Director for other research opportunities or an independent research project to fulfill their capstone requirement.

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Additional Degree Requirements

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Writing in the Major (WIM) (3-5 units)

- One of the following:
- Computers, Ethics and Public Policy (CS 181W)
- Ethics, Public Policy, and Technology Change (CS 182W) *may not double count towards the ethics requirement
- A WIM course associated with students’ selected pathway