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.
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.
- 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
- Peter Gerstoft San Diego Supercomputer Center, Structural Bioinformatics Laboratory
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.
- 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
- Peter Gerstoft San Diego Supercomputer Center, Structural Bioinformatics Laboratory