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Graduate Certificate in Applied Data Science: Approved Courses

The Graduate Certificate requires three 3-unit (or greater) courses.

At least one of these courses must be an INFO course in the School of Information.

1. Introductory Data Science Course

One required synthetic introduction to data science class.

2. Course in Analytical Methods and Techniques of Data Science

Students must take at least one course from this list.

  • INFO 251 Applied Machine Learning (3 units)
  • INFO 254 Data Mining and Analytics (3 units)
  • BIO ENG 245 Introduction to Machine Learning in Computational Biology (4 units)
  • COMPSCI C200A Principles and Techniques of Data Science (4 units)
  • COMPSCI C281A Statistical Learning Theory (3 units)
  • COMPSCI 289A Introduction to Machine Learning (4 units)
  • STAT C200C Principles and Techniques of Data Science (4 units)
  • STAT C241A Statistical Learning Theory (3 units)

3. Elective

A domain-specific data science course or a second methods course.

Students must take at least 3 units from this list or a second course from the list under (2), above.

All classes are 3 units except where noted otherwise.

  • CIV ENG 263N Scalable Spatial Analytics
  • COMPSCI C267 Applications of Parallel Computers
  • COMPSCI 286A Introduction to Database Systems (4 units)
  • COMPSCI 281B Advanced Topics in Machine Learning and Decision Making
  • COMPSCI 288 Natural Language Processing (4 units)
  • CY PLAN 204C Introduction to GIS and City Planning (4 units)
  • CY PLAN 255 Urban Informatics and Visualization
  • CY PLAN 257 Data Science for Human Mobility and Socio-technical Systems (4 units)
  • DATASCI W209 Data Visualization (MIDS & MICS students only)
  • DATASCI W241 Experiments and Causal Inference (MIDS & MICS students only)
  • DATASCI W266 Natural Language Processing with Deep Learning (MIDS & MICS students only)
  • EDUC C260F Machine Learning in Education
  • EDUC 275B Data Analysis in Educational Research II (4 units)
  • EDUC 275G Hierarchical and Longitudinal Modeling
  • EDUC 276E Research Design and Methods for Program and Policy Eval
  • EECS 227AT Optimization Models in Engineering (4 units)
  • EL ENG 227BT Convex Optimization (4 units)
  • EL ENG C227C Convex Optimization and Approximation
  • EL ENG C227T Convex Optimization
  • ENGIN C233 Applications of Parallel Computers
  • ESPM 215 Hierarchical Statistical Modeling in Envir Science (2 units)
  • ESPM 288 Reproducible and Collaborative Data Science
  • GEOG 249 Spatiotemporal Data Analysis in the Climate Sciences
  • GEOG 279 Statistics and Multivariate Data Analysis for Research
  • GEOG 282 GIS: Applications in Geographical Research (4 units)
  • GEOG 285 Topics in Earth System Remote Sensing
  • INFO 247 Information Visualization and Presentation (4 units)
  • INFO 259 Applied Natural Language Processing
  • INFO 288 Big Data and Development
  • * INFO 241 Experiments and Causal Inference
  • INFO C260F Machine Learning in Education
  • IND ENG 242 Applications in Data Analysis
  • IND ENG C227A Convex Optimization (4 units)
  • IND ENG C227B Convex Optimization and Approximation
  • JOURN 221 Introduction to Data Visualization
  • LD ARCH 289 Applied Remote Sensing
  • EWMBA 263 Marketing Analytics
  • MFE 230P Financial Data Science (2 units)
  • PB HLTH 231A Analytic Methods for Health Policy and Management
  • PB HLTH C240A Intro to Modern Biostatistical Theory and Practice (4 units)
  • PB HLTH C240B Survival Analysis and Causality (4 units)
  • PB HLTH C240C Comp Statistics with Apps in Biology and Medicine (4 units)
  • PB HLTH C240D Comp Statistics with Apps in Biology and Medicine II (4 units)
  • PB HLTH 241 Statistical Analysis of Categorical Data (4 units)
  • PB HLTH C242C Longitudinal Data Analysis (4 units)
  • PB HLTH 244 Big Data: A Public Health Perspective (3-4 unit)
  • PB HLTH 251C Causal Inference and Meta-Analysis in Epidemiology (2 units)
  • POL SCI C236A The Statistics of Causal Inference in the Social Science (4 units)
  • POL SCI C236B Quantitative Methodology in the Social Sciences (4 units)
  • POL SCI 239T Intro to Computational Tools & Techniques for Soc Sci Research
  • PUB POL 249 Statistics for Program Evaluation (4 units)
  • PUB POL 275 Spatial Data and Analysis (4 units)
  • PUB POL 279 Research Design and Data Collection for Public Policy Analysis
  • PUB POL 288 Risk and Optimization Models for Policy (4 units)
  • PUB POL 290 Special Topics in Public Policy (Spring 2020, Section 5: Data Science for Public Policy only)
  • SOCIOL C271D Quantitative/Statistical Research Methods in Social Sciences
  • STAT 215A Statistical Models: Theory and Application (4 units)
  • STAT 215B Statistical Models: Theory and Application (4 units)
  • STAT 238 Bayesian Statistics
  • STAT C239A The Statistics of Causal Inference in the Social Science (4 units)
  • STAT C239B Quantitative Methodology in the Social Sciences (4 units)
  • STAT 241B Advanced Topics in Machine Learning and Decision Making
  • STAT 243 Introduction to Statistical Computing (4 units)
  • STAT 244 Statistical Computing (4 units)
  • STAT C245A Intro to Modern Biostatistical Theory and Practice (4 units)
  • STAT C245B Survival Analysis and Causality (4 units)
  • STAT C245C Comp Statistics with Apps in Biology and Medicine (4 units)
  • STAT C245D Comp Statistics with Apps in Biology and Medicine II (4 units)
  • STAT C247C Longitudinal Data Analysis (4 units)
  • STAT 248 Analysis of Time Series (4 units)
  • STAT 259 Reproducible and Collaborative Statistical Data Science (4 units)
  • STAT C261 Quantitative/Statistical Research Methods in Social Sciences
  • VIS SCI 265 Neural Computation

* Course pending COCI approval

Last updated:

November 12, 2019