Foundations of Data Science
INT 5, Fall 2019
Instructor: Yekaterina (Kate) Kharitonova
This course introduces students to inferential thinking and computational thinking in the context of real-world problems. How does one analyze data resulting from a real-world process in order to understand the process? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.
Principles & Techniques of Data Science
INT 15, Spring 2020
This course explores the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. It focuses on quantitative critical thinking and the key principles and techniques that are needed. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.
Principles of Data Science with R
PSTAT 10, Offered every quarter
Instructor: Dawn Holmes
An overview of data analytic thinking through examples. Introduction to descriptive statistics and linear regression. Fundamentals of programming using R. Basic graphics in R. Relational database management systems and simple data manipulation using SQL.
Introduction to Statistical Machine Learning
PSTAT 131, Offered every quarter
This course explores Statistical Machine Learning to discover patterns and relationships in large data sets. Topics will include: data exploration, classification and regression tress, random forests, clustering and association rules. Building predictive models focusing on model selection, model comparison and performance evaluation. Emphasis will be on concepts, methods and data analysis using R. Students complete a significant class project, individual or team based, using real-world data.
Statistical Data Science
PSTAT 134/234, Spring 2020
Instructor: Sang-Yun Oh
Overview and use of data science tools in R and Python for data retrieval, analysis, visualization, reproducible research and automated report generation. Case studies will illustrate practical use of these tools.
Big Data Analytics
PSTAT 135, Winter 2019
Instructor: Adam Tashman
This course introduces concepts of distributed data storage, retrieval, processing and cloud computing. Overview of methods for analyzing big data from both high dimensional statistics and machine learning - topics chosen from penalized regression, classification/clustering, dimension reduction, random projections, kernel methods, network clustering, graph analytics, supervised and unsupervised learning among others.
The Humanities and Data Science
Instructor: Alan Liu
This course explores today’s quickening mutation of the “liberal arts” into “data science,” a new universal mode of knowledge touching all fields. The course focuses on the join, but also split, between how the humanities and data science find meaning (scientific, epistemological, sociopolitical, and cultural) in patterns. Topics to be probed include: the history and present state of the humanities, the concept of “data science” (including the shape of today’s new programs and majors in the field), the idea and structures of “data,” the idea and infrastructures of “big data,” humanities corpora and datasets (including the social and ethical problem of “representative” datasets), narrativizing data, visualizing data, and interpreting data. The course includes but is not limited to approaches related to the digital humanities.
Projects in Visualizing Information
MAT 259A, Winter 2020
Instructor: George Legrady
This is a ten-week comprehensive overview of visualization for Data Science, from data queries/knowledge discovery, and algorithms, resulting in projects in 2D and interactive 3D. Enrollment is limited to 15, and the course usually includes participants from Bren, COE, Geography, Statistics, Physics, Art, Political Science, etc. Students who have taken or have been teaching assistants for the course have since organized, curated paper and exhibition sessions at VISAP, Siggraph, etc. Past project results.
Data Science Use in Communication Research
COMM 160DS, Spring 2020
Instructor: Ziad Matni
This special topics course in the Department of Communication begins by asking, “What is Data Science and why should we care?” The course provides an introduction to Computational Social Sciences and an overview of the societal and ethical impacts of Big Data research. Core subject matter includes Quantitative Research Methods in Communication, Data Visualization and Interpretation, and Computational Techniques such as basic programming, network analysis, and APIs. Students will discuss case studies from actual social science research. Professor Matni will reserve 20 add codes for undergraduate data science students, available on a first-come, first-served basis. Please contact him directly for a code.
Principles of Environmental Data Analysis (Special “Shelter-in-place Edition”)
GEOG 136, Spring 2020
Instructors: Kelly Caylor and Bryn Morgan
This course will provide an introduction to the principles of environmental physics and their application to ecological sciences, with a focus on programming and data analysis in Python. Course activities will use data analysis to quantify environmental patterns and processes. Emphasis will be placed developing coding skills in Python and applying these skills to environmental and biophysical problems. Course goals:
- To develop expertise in the Python programming language and the use of Python’s data science stack to effectively store, manipulate, and gain insight into environmental data.
- To be able to apply this understanding to characterize data on environmental patterns and processes at varying spatial and temporal scales.
- To use data to model environmental processes of energy and mass transfer.
Data Science @ UCSB Student Organization
Data Science at UCSB is a popular student-run club centered on developing career paths in the field of data science. The club offers resources, project experience and community to students from any major who are interested in data science.
Data Science Instructional Computing Working Group
Data Science Instructional Computing Working Group seeks to simplify using browser-based Jupyter notebooks or R studio environments on the cloud for data sciences classes at UCSB. Additional information is available here (UCSB NetID is needed to view): Jupyterhub in the Classroom. Please contact Patrick Windmiller for more information and to start setting up a cluster for your class!
Members: Andreas Boschke, Ernesto Espinosa, Alexander Franks, Matthew Hall, Shea Lovan, B. S. Manjunath, George Michaels, Jeff Oakes, Sang-Yun Oh, Hector Villicana, Patrick Windmiller, and Yekaterina (Kate) Kharitonova.