Education, Training and Curricula Design

The goal of Education cluster is to enable training of students in methods and tools of Data Science regardless of their majors or degree program. We seek to enhance skills of our graduates in experimental design, hypothesis testing and data analysis by offering courses -- online and in class -- that provide opportunities for significant hands-on learning experience.

View the complete list of Education, Training and Curricula Design cluster founding faculty.

Designing Data Science Curriculum: Structure and Organization

The Data Science major servers as a platform for creating new courses especially in the upper-division that can be part of elective courses in Data Science major as well as in the Data Science minors.

Designing Data Science Curriculum: Structure and Organization Members

Henry Abarbanel Physics and SIO

John Ahlquist Global Policy & Strategy

Ludmil Alexandrov Bioengineering

Christine Alvarado Computer Science & Engineering

Terrence August Rady School of Management

Natasha Balac Qualcomm Institute

Chaitan Baru San Diego Supercomputer Center

Eli Berman Economics

Cinnamon Bloss Family Medicine & Public Health

Jelena Bradic Mathematics

Benjamin Bratton Visual Arts

Alex Cloninger Mathematics

Jade d'AlpoimGuedes Anthropology and Scripps Institution of Oceanography

Sanjoy Dasgupta Computer Science & Engineering

Virginia deSa Cognitive Science

Steven Dow Cognitive Science

Peter Ebenfelt Mathematics

Jeff Elman Cognitive Science

Ron Graham Computer Science & Engineering and Mathematics

Barry Grant Molecular Biology

Philip Guo Cognitive Science

Jim Hollan Cognitive Science

Michael Holst Mathematics and Physics

Todd Kemp Mathematics

Scott Klemmer Cognitive Science and Computer Science & Engineering

Grace Kuo Clinical Pharmacy and Family Medicine & Public Health

Lilly LillyIrani Communication & Science Studies

Mai Nguyen San Diego Supercomputer Center

JuanPablo PardoGuerra Sociology

Leo Porter Computer Science & Engineering

Molly Roberts Political Science

Akos Rona-Tas Sociology

Armin Schwartzman Biostatistics

Brett Stalbaum Visual Arts

Ed Vul Psychology

Kirk Christian Bansak Political Science

Data-Enabled Learning Methods: Online Learning, Innovations in Learning Methods

Learning skills requires deliberate practice with constructive feedback, but the design, evaluation, and feedback for such training problems imposes a bottleneck on the scalability of online learning. Fortunately, modern data science tools and methods may offer a window for training machines to procedurally generate problems, to provide useful feedback, and to learn the dependency structure between concepts to automatically construct learner-specific scaffolded instruction sequences.

Data-Enabled Learning Methods: Online Learning, Innovations in Learning Methods Members

John Ahlquist Global Policy & Strategy

Christine Alvarado Computer Science & Engineering

Terrence August Rady School of Management

Natasha Balac Qualcomm Institute

Nuno Bandeira Computer Science & Engineering and Skaggs School of Pharmacy & Pharmaceutical Sciences

Jelena Bradic Mathematics

Jade d'AlpoimGuedes Anthropology and Scripps Institution of Oceanography

Sanjoy Dasgupta Computer Science & Engineering

Steven Dow Cognitive Science

Jeff Elman Cognitive Science

Yoav Freund Computer Science & Engineering

Barry Grant Molecular Biology

William Griswold Computer Science & Engineering and Design Lab

Philip Guo Cognitive Science

Jim Hollan Cognitive Science

Scott Klemmer Cognitive Science and Computer Science & Engineering

Grace Kuo Clinical Pharmacy and Family Medicine & Public Health

Mai Nguyen San Diego Supercomputer Center

JuanPablo PardoGuerra Sociology

Pavel Pevzner Computer Science & Engineering

Leo Porter Computer Science & Engineering

Molly Roberts Political Science

Beth Simon Education Studies

Brett Stalbaum Visual Arts

Ed Vul Psychology

Kirk Christian Bansak Political Science