This site contains course websites for many of the courses in and adjacent to UCSD’s Data Science curriculum. The most recent offering of each course available is shown by default; click “Show past sites” to show sites from previous quarters.
With any questions with this site, feel free to email rampure@ucsd.edu.
⚠️ Note: This is not intended to be used as a list of all courses that HDSI offers, and the information here may be out of date. For official information about all of our course offerings, please refer to the Undergraduate and Graduate program pages on the official HDSI website.
A related list, compiled by Jason Fleischer, contains descriptions of all ML/AI-adjacent courses at UCSD as of 2020/2021.
| Course Code | Title | Websites |
|---|---|---|
| DSC 10 |
Principles of Data Science
Catalog DescriptionThis introductory course develops computational thinking and tools necessary to answer questions that arise from large-scale datasets. This course emphasizes an end-to-end approach to data science, introducing programming techniques in Python that cover data processing, modeling, and analysis. Prerequisites: none. |
Spring 2023 (Rampure) |
| DSC 20 |
Programming and Data Structures for Data Science
Catalog DescriptionProvides an understanding of the structures that underlie the programs, algorithms, and languages used in data science by expanding the repertoire of computational concepts introduced in DSC 10 and exposing students to techniques of abstraction. Course will be taught in Python and will cover topics including recursion, higher-order functions, function composition, object-oriented programming, interpreters, classes, and simple data structures such as arrays, lists, and linked lists. Prerequisites: DSC 10. Restricted to students within the DS25 major. All other students will be allowed as space permits. |
Winter 2023 (Langlois) |
| DSC 30 |
Data Structures and Algorithms for Data Science
Catalog DescriptionBuilds on topics covered in DSC 20 and provides practical experience in composing larger computational systems through several significant programming projects using Java. Students will study advanced programming techniques including encapsulation, abstract data types, interfaces, algorithms and complexity, and data structures such as stacks, queues, priority queues, heaps, linked lists, binary trees, binary search trees, and hash tables. Prerequisites: DSC 20. Restricted to students within the DS25 major. All other students will be allowed as space permits. |
Summer 2023 (Lin) |
| DSC 40A |
Theoretical Foundations of Data Science I
Catalog DescriptionThis course, the first of a two-course sequence (DSC 40A-B), will introduce the theoretical foundations of data science. Students will become familiar with mathematical language for expressing data analysis problems and solution strategies, and will receive training in probabilistic reasoning, mathematical modeling of data, and algorithmic problem solving. DSC 40A will introduce fundamental topics in machine learning, statistics, and linear algebra with applications to data analysis. DSC 40A-B connect to DSC 10, 20, and 30 by providing the theoretical foundation for the methods that underlie data science. Prerequisites: DSC 10, MATH 20C or MATH 31BH, and MATH 18 or MATH 20F or MATH 31AH. Restricted to students within the DS25 major. All other students will be allowed as space permits. |
Summer 2023 (Albuyeh) |
| DSC 40B |
Theoretical Foundations of Data Science II
Catalog DescriptionThe sequence DSC 40A-B introduces the theoretical foundations of data science and covers the following topics: mathematical language for expressing data analysis problems and solution strategies, probabilistic reasoning, mathematical modeling of data, and algorithmic problem solving. DSC 40B, the second course in the sequence, introduces fundamental topics in combinatorics, graph theory, probability, and continuous and discrete algorithms with applications to data analysis. Prerequisites: DSC 20 and 40A. Restricted to students within the DS25 major. All other students will be allowed as space permits. |
Winter 2023 (Eldridge and Wang) |
| DSC 80 |
Practice and Application of Data Science
Catalog DescriptionThe marriage of data, computation, and inferential thinking, or “data science,” is redefining how people and organizations solve challenging problems and understand the world. This course bridges lower- and upper-division data science courses as well as methods courses in other fields. Students master the data science life-cycle and learn many of the fundamental principles and techniques of data science spanning algorithms, statistics, machine learning, visualization, and data systems. Prerequisites: DSC 30 and DSC 40A. Restricted to students within the DS25 major. All other students will be allowed as space permits. |
Spring 2024 (Lau) |
| DSC 90* | History of Data Science |
Spring 2022 (Rampure) |
| DSC 95 | Tutor Apprenticeship in Data Science | Spring 2023 (Rampure) |
| DSC 100 |
Introduction to Data Management
Catalog DescriptionThis course is an introduction to storage and management of large-scale data using classical relational (SQL) systems, with an eye toward applications in data science. The course covers topics including the SQL data model and query language, relational data modeling and schema design, elements of cost-based query optimizations, relational data base architecture, and database-backed applications. Prerequisites: DSC 80 and DSC 40B. Restricted to students with upper-division standing. Restricted to students within the DS25 major. All other students will be allowed as space permits. |
Spring 2023 (Salimi) |
| DSC 102 |
Systems for Scalable Analytics
Catalog DescriptionThis course introduces the principles of computing systems and infrastructure for scaling analytics to large datasets. Topics include memory hierarchy, distributed systems, model selection, heterogeneous datasets, and deployment at scale. The course will also discuss the design of systems such as MapReduce/Hadoop and Spark, in conjunction with their implementation. Students will also learn how dataflow operations can be used to perform data preparation, cleaning, and feature engineering. Prerequisites: DSC 100. Restricted to students with upper-division standing. Restricted to students within the DS25 major. All other students will be allowed as space permits. |
Spring 2023 (Albuyeh) |
| DSC 106 |
Introduction to Data Visualization
Catalog DescriptionData visualization helps explore and interpret data through interaction. This course introduces the principles, techniques, and algorithms for creating effective visualizations. The course draws on the knowledge from several disciplines including computer graphics, human-computer interaction, cognitive psychology, design, and statistical graphics and synthesizes relevant ideas. Students will design visualization systems using D3 or other web-based software and evaluate their effectiveness. Prerequisites: DSC 80. Restricted to students with upper-division standing. Restricted to students within the DS25 major. All other students will be allowed as space permits. |
Spring 2024 (Wilber) |
| DSC 140A | Probabilistic Modeling and Machine Learning |
Winter 2023 (Aoi and Eldridge) |
| DSC 140B | Representation Learning |
Spring 2023 (Eldridge and Hu) |
| DSC 148 | Introduction to Data Mining |
Winter 2023 (Shang) |
| DSC 180AB |
Capstone Sequence
Catalog DescriptionIn this two-course sequence students will investigate a topic and design a system to produce statistically informed output. The investigation will span the entire lifecycle, including assessing the problem, learning domain knowledge, collecting/cleaning data, creating a model, addressing ethical issues, designing the system, analyzing the output, and presenting the results. 180A deals with research, methodology, and system design. Students will produce a research summary and a project proposal. Prerequisites: DSC 102 and MATH 189 and CSE 151A or COGS 188 or CSE 158, DSC 106. Restricted to students with upper-division standing. Restricted to students within the DS25 major. |
Fall 2022 + Winter 2023 (Rampure) |
| DSC 190* | Advanced Algorithms |
Winter 2022 (Eldridge) |
| DSC 190* | Algorithms for Data Science |
Winter 2021 (Mazumdar) |
“Special topics” courses are marked with an asterisk*.
| Course Code | Title | Websites |
|---|---|---|
| COGS 9 | Introduction to Data Science
Catalog DescriptionConcepts of data and its role in science will be introduced, as well as the ideas behind data-mining, text-mining, machine learning, and graph theory, and how scientists and companies are leveraging those methods to uncover new insights into human cognition. |
Spring 2023 (Ellis)
Show past sitesWinter 2023 (Shannon) |
| COGS 18 | Introduction to Python
Catalog DescriptionThis class will teach fundamental Python programming skills and practices, including the “Zen of Python.” Students will focus on scientific computing and learn to write functions and tests, as well as how to debug code using the Jupyter Notebook programming environment. Students may receive credit for one of the following: COGS 18, CSE 8A, or CSE 6R. Recommended preparation: students with limited computing experience may take COGS 3. |
Winter 2023 (Fleischer) |
| COGS 108 | Data Science in Practice
Catalog DescriptionData science is multidisciplinary, covering computer science, statistics, cognitive science and psychology, data visualization, artificial intelligence and machine learning, among others. This course teaches critical skills needed to pursue a data science career using hands-on programming and experimental challenges. Prerequisites: CSE 11 or CSE 8A or COGS 18 or DSC 10 or MAE 8. |
All offerings |
| COGS 137 | Practical Data Science in R
Catalog DescriptionLearn coding for data analysis using the R programming language. Course focus will be on practical and applied skills in asking data-informed questions, data wrangling, data visualization, building statistical learning models, and communicating your findings to advance your career. Prerequisites: COGS 14B or MATH 11 or PSYC 60. |
Winter 2023 (Ellis) |
| Course Code | Title | Websites |
|---|---|---|
| COGS 260 | Signal processing and machine learning for EEG-based brain-computer interfaces (BCIs)
Catalog DescriptionThis course is designed to give students background in EEG-based brain-computer interfaces followed by in depth exploration of signal processing and classification algorithms used. The first few lectures will be given by the Professor on introduction to EEG and BCIs. Future classes will involve students presenting classification or signal processing algorithms of their choice from recent or classic papers. Students will meet with the Professor before their class presentation for any help needed in understanding the paper. Final projects may be done in groups and will involve either detailed or comparative analysis of different algorithms or application of algorithms to well curated data. Evaluation will be dependent on paper presentation, participation during other presentations, and the final project. Background or interest in EEG and Neuroscience are required. A prior upper division undergraduate or graduate course in machine learning, signal processing, or other mathematical course is also required. Students interested in BCIs without a mathematical background are encouraged to consider taking COGS 189 offered M/W/F 9AM WI 2023. |
Winter 2023 (de Sa) |
| DSC 204 | Scalable Data Systems |
Spring 2023 (Jin) |
| DSC 253 | Advanced Data-Driven Text Mining |
Spring 2023 (Shang) |
| DSC 291 | Trustworthy Machine Learning |
Spring 2023 (Weng) |
| DSC 291 | Privacy-Preserving Data Systems |
Winter 2023 (Jin) |
| DSC 291 | Mobile and Ubiquitous Computing |
Winter 2023 (Rahman) |
| DSC 291 | Coding Theory |
Fall 2022 (Mazumdar) |