Instruction

Students in classroom

Foundations of Data Science

INT 5, Fall 2018

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 2019

Instructors: Yekaterina (Kate) Kharitonova and Alex Franks 

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.

Introduction to Computing for Data Science

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

Instructor: Varies

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 2019

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.

Projects in Visualizing Information

MAT 259A, Winter 2019

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 @ 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 environment for data sciences classes at UCSB. For more information, please contact Ernesto Espinosa or Sang-Yun Oh.

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.