![]() ![]() Environmental Health and Development (4 units) Theory and Method in the Digital Humanities (3 units) - offered in Summer only Human Contexts and Ethics of Data (4 units) Introduction to Urban Data Analytics (4 units) Ethics in Science and Engineering (3 units) Information Technology and Society (4 units) The purpose of this requirement is to equip the student with an understanding of the human and social structures, formations, and practices that shape data science activity (such as data collection and analysis, data stewardship and governance, work to ensure privacy and security, deployment of data in societal or organizational settings, decision-making with data, engagements of data with justice, practices of data ethics) and to allow them to gain experience and practice with modes of critical thinking, reflection, and engagement with these experiences and the choices involved. Students will be required to take one course from a curated list of courses that establish a human, social, and ethical context in which data analytics and computational inference play a central role. Data Engineering (4 units) - approved only with this topic in Spring 2021 ![]() UGBA 142 - Advanced Business Analytics (3 units) offered as UGBA 147 prior to Summer 2023.Reproducible and Collaborative Statistical Data Science (4 units) The Design and Analysis of Experiments (4 units) Linear Modelling: Theory and Applications (4 units) PHYSICS 188. Bayesian Data Analysis and Machine Learning for Physical Sciences (4 units).Introduction to Data Visualization (4 units) - only when offered with this topic Introduction to Stochastic Processes (3 units) Engineering Statistics, Quality Control and Forecasting (4 units) Linear Programming and Network Flows (3 units) Nonlinear and Discrete Optimization (3 units) Applied Data Science with Venture Applications: Data-X (3 units) Industrial and Commercial Data Systems (3 units) Design and Analysis of Ecological Research (4 units) Introductory Applied Econometrics (4 units) Neural and Nonlinear Information Processing (3 units) Optimization Models in Engineering (4 units) Economic Statistics and Econometrics (4 units) Data Mining and Analytics (3 units) formerly offered as Info 154 Introduction to Artificial Intelligence (4 units) Introduction to Database Systems (4 units) Efficient Algorithms and Intractable Problems (4 units) Software Engineering Team Project (2 units) may be combined with COMPSCI 169A or W169A, may not be combined with COMPSCI 169 Introduction to the Internet: Architecture and Protocols (4 units) Programming Languages and Compilers (4 units) Operating Systems and Systems Programming (4 units) Astronomy Data Science Laboratory (4 units) *Not all courses on the approved list may be available for Data Science majors to enroll in every semester. However, many options are available that do not place such demands. It is recognized that, currently, some of these courses have prerequisites that are not formally within the major, so for some combinations students may need to use electives to complete those. **Students may only count ONE of these three courses towards the major: IND ENG 173 or STAT 150 (from C&ID), or EECS 126 (from Probability).Ī student will be required to take two courses comprising 7 or more units from a list of advanced courses providing computational and inferential depth (C&ID) beyond that provided in Data 100 and the lower division (see below). Probability is a prerequisite to most, if not all, of the approved courses to meet the Modeling, Learning, & Decision-Making requirement, so all students should plan to complete Probability at least 1 full semester before their expected graduation term, not including summers. Mathematical Probability Theory (4 units) Probability and Risk Analysis for Engineers (4 units) Probability and Random Processes (4 units) A student will be required to take one course in probability. An understanding of probability is essential for dealing with uncertainty and randomness, the algebraic properties of estimation, the ability to formulate and comprehend stochastic simulations, and many other aspects of data science theory and practice.
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