About the Program
The Graduate Certificate in Applied Data Science, offered by the UC Berkeley School of Information, introduces the tools, methods, and conceptual approaches used to support modern data analysis and decision-making in professional and applied research settings. It exposes students to the challenges of working with data (e.g., asking a good question, inference and causality, decision-making) as well as to the new tools and techniques for data analytics (machine learning, natural language processing, and more).
The certificate is particularly designed to meet the needs of the graduate students in Berkeley’s professional schools — both professional master’s students and doctoral students — as well as graduate students in the social sciences and the arts & humanities.
The need for expertise in data analytics continues to grow in all organizations and disciplines. Graduate students in every field are now working with data from new sources: websites, electronic medical records, transaction records, sensor networks, smart phones, and digitized records and documents. The analytical tools and methods traditionally used to derive insights from structured and well-curated data sets (census, surveys, and administrative data) are not sufficient for this new, unstructured and often user-generated data.
The Graduate Certificate in Applied Data Science provides hands-on practice working with unstructured and user-generated data to identify new ways to inform decision-making. The curriculum educates professionals and scholars to be intelligent consumers of data science techniques in a variety of domains, with a foundation of skills for applying these techniques in their own domains.
Admissions
Any UC Berkeley graduate student who meets the following prerequisites is eligible to pursue the certificate.
- be registered and enrolled in a graduate degree at UC Berkeley,
- be in good academic standing,
- meet course and subject matter prerequisites for courses taken in the certificate program, including Python programming and basic statistics knowledge.
The certificate completion application should be submitted on the School of Information website during the second half of the semester in which the student completes the certificate requirement. All three courses must be either completed or currently in progress. Applications are accepted three times a year, in the second half of the fall, spring or summer semesters.
Certificate Requirements
Prerequisites
Applicants must:
- Be registered and enrolled in a graduate degree program at UC Berkeley
- Be in good academic standing
- Meet course and subject matter prerequisites for courses taken in the certificate program, typically including Python programming and basic statistics knowledge.
Certificate Requirements
The certificate requires three 3-unit courses, taken from the following approved lists:
-
An introductory data science class
-
A course in analytical methods and techniques of data science
-
An additional elective: either a domain-specific data science course or a second methods course.
Courses should be taken for a letter grade and must be completed with a grade of B or higher. At least one of these courses must be a course offered outside the student's graduate program.
1. INtroductory data science course
| Code | Title | Units |
|---|---|---|
| One of the following: | ||
| INFO 201 | Research Design and Applications for Data and Analysis 1 | 3 |
| DATASCI 201 | Research Design and Applications for Data and Analysis (MIDS and MICS students only) | 3 |
2. Analytical Methods and Techniques of Data Science
Students must take at least one course from this list:
| Code | Title | Units |
|---|---|---|
| BIO ENG 245 | Introduction to Machine Learning for Computational Biology | 4 |
| COMPSCI C200A | Principles and Techniques of Data Science | 4 |
| COMPSCI C281A | Statistical Learning Theory | 3 |
| COMPSCI 289A | Introduction to Machine Learning | 4 |
| CYBER 207 | Applied Machine Learning for Cybersecurity (MIDS and MICS students only) | 3 |
| DATA C200 | Principles and Techniques of Data Science | 4 |
| DATASCI 207 | Applied Machine Learning (MIDS and MICS students only) | 3 |
| EDUC 244 | Data Mining and Analytics | 3 |
| INFO 251 | Applied Machine Learning | 4 |
| INFO 258 | Data Engineering | 4 |
| INFO 271B | Quantitative Research Methods for Information Systems and Management | 3 |
| PB HLTH 241 | Intermediate Biostatistics for Public Health | 4 |
| PB HLTH W241 | Course Not Available | 4 |
| PSYCH 208 | Methods in Computational Modeling for Cognitive Science | 3 |
| SOCIOL 273L | Computational Social Science | 3 |
| STAT C200C | Principles and Techniques of Data Science | 4 |
| STAT C241A | Statistical Learning Theory | 3 |
3. Electives
Students must take one domain-specific data science course from the following list or a second methods course from the list in Section 2 above:
| Code | Title | Units |
|---|---|---|
| A,RESEC 213 | Applied Econometrics | 4 |
| CIV ENG 263N | Scalable Spatial Analytics | 3 |
| CIV ENG C263H | Human Mobility and Network Science | 3 |
| COMPSCI C267 | Applications of Parallel Computers | 3-4 |
| COMPSCI 286A | Introduction to Database Systems | 4 |
| COMPSCI C281B | Advanced Topics in Learning and Decision Making | 3 |
| COMPSCI 288 | Natural Language Processing | 4 |
| CY PLAN 204C | Analytic and Research Methods for Planners: Introduction to GIS and City Planning | 4 |
| CY PLAN 255 | Urban Informatics and Visualization | 3 |
| CY PLAN 257 | Data Science for Human Mobility and Socio-technical Systems | 4 |
| CY PLAN C257H | Human Mobility and Network Science | 3 |
| DEVP 229 | Quantitative Methods and Impact Evaluation | 3 |
| DATASCI 209 | Data Visualization (MIDS and MICS students only) | 3 |
| DATASCI 241 | Experiments and Causal Inference (MIDS and MICS students only) | 3 |
| DATASCI 266 | Natural Language Processing with Deep Learning (MIDS and MICS students only) | 3 |
| EDUC 275B | Data Analysis in Educational Research II | 4 |
| EDUC 275G | Hierarchical and Longitudinal Modeling | 5 |
| EDUC 276E | Research Design and Methods for Program and Policy Evaluation | 3 |
| EECS 227AT | Optimization Models in Engineering | 4 |
| EL ENG 227BT | Convex Optimization | 4 |
| EL ENG C227C | Convex Optimization and Approximation | 3 |
| EL ENG C227T | Introduction to Convex Optimization | 4 |
| ENGIN C233 | Applications of Parallel Computers | 3-4 |
| ESPM 215 | Hierarchical Statistical Modeling in Environmental Science | 2 |
| ESPM 288 | Reproducible and Collaborative Data Science | 3 |
| EWMBA 247 | Topics in Operations and Information Technology Management (Fall 2022 & Fall 2023, topic “Descriptive and Predictive Data Mining” only) | 2 |
| EWMBA 263 | Marketing Analytics | 3 |
| GEOG 249 | Spatiotemporal Data Analysis in the Climate Sciences | 3 |
| GEOG 279 | Statistics and Multivariate Data Analysis for Research | 3 |
| GEOG 282 | Geographic Information Systems: Applications in Geographical Research | 4 |
| GEOG 285 | Topics in Earth System Remote Sensing | 3 |
| IND ENG C227A | Introduction to Convex Optimization | 4 |
| IND ENG C227B | Convex Optimization and Approximation | 3 |
| IND ENG 242A | Machine Learning and Data Analytics | 4 |
| IND ENG 262A | Mathematical Programming I | 4 |
| IND ENG 262B | Mathematical Programming II | 3 |
| IND ENG 264 | Computational Optimization | 3 |
| IND ENG 265 | Learning and Optimization | 3 |
| IND ENG 266 | Network Flows and Graphs | 3 |
| IND ENG 269 | Integer Programming and Combinatorial Optimization | 3 |
| INFO 213 | Introduction to User Experience Design | 4 |
| INFO 241 | Experiments and Causal Inference | 3 |
| INFO 247 | Information Visualization and Presentation | 4 |
| INFO 256 | Applied Natural Language Processing | 3 |
| INFO 259 | Natural Language Processing | 4 |
| INFO 288 | Big Data and Development | 3 |
| INFO 290T | Special Topics in Technology (“Biosensory Computing” topic only) | 2-4 |
| JOURN 221 | Introduction to Data Visualization | 3 |
| LD ARCH 289 | Applied Remote Sensing | 3 |
| LINGUIS 252 | COMPUTATIONAL LINGUISTICS | 3 |
| MAT SCI 215 | Computational Materials Science | 3 |
| MBA 247 | Topics in Operations and Information Technology Management (Fall 2022 & Fall 2023, topic “Descriptive and Predictive Data Mining” only) | 2 |
| MBA 263 | Marketing Analytics | 3 |
| MBA 296 | Special Topics in Business Administration (Fall 2019 & 2020, section 7B, & Spring 2023, Section 8: “Data Science Applications in Finance and Accounting” only) | 2 |
| MEC ENG 249 | Machine Learning Tools for Modeling Energy Transport and Conversion Processes | 3 |
| MFE 230P | Financial Data Science | 2 |
| PB HLTH 231A | Analytic Methods for Health Policy and Management | 3 |
| PB HLTH C240A | Introduction to Modern Biostatistical Theory and Practice | 4 |
| PB HLTH C240B | Biostatistical Methods: Survival Analysis and Causality | 4 |
| PB HLTH C240C | Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine | 4 |
| PB HLTH C240D | Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II | 4 |
| PB HLTH C242C | Longitudinal Data Analysis | 4 |
| PB HLTH 244 | Big Data: A Public Health Perspective | 3 |
| PB HLTH W251B | Course Not Available | 2 |
| PB HLTH 251C | Course Not Available | 2 |
| PB HLTH 252 | Epidemiological Analysis | 4 |
| PB HLTH W252 | Course Not Available | 4 |
| PHYSICS 288 | Bayesian Data Analysis and Machine Learning for Physical Sciences | 4 |
| POL SCI C236A | The Statistics of Causal Inference in the Social Science | 4 |
| POL SCI C236B | Quantitative Methodology in the Social Sciences Seminar | 4 |
| POL SCI 231B | Quantitative Analysis in Political Research | 4 |
| POL SCI 239T | An Introduction to Computational Tools and Techniques for Social Science Research | 4 |
| PSYCH 206 | Structural Equation Modeling | 3 |
| PSYCH 207 | Person-Specific Data Analysis | 3 |
| PUB POL 249 | Statistics for Program Evaluation | 4 |
| PUB POL 275 | Spatial Data and Analysis | 4 |
| PUB POL 279 | Research Design and Data Collection for Public Policy Analysis | 3 |
| PUB POL 288 | Risk and Optimization Models for Policy | 4 |
| PUB POL 290 | Special Topics in Public Policy (“Data Science for Public Policy” or “Quantitative Methods and Evaluation” topics only) | 1-4 |
| SOCIOL C271D | Quantitative/Statistical Research Methods in Social Sciences | 3 |
| SOCIOL 273L | Computational Social Science | 3 |
| SOCIOL 273M | Computational Social Science | 3 |
| STAT 215A | Applied Statistics and Machine Learning | 4 |
| STAT 215B | Statistical Models: Theory and Application | 4 |
| STAT 238 | Bayesian Statistics | 3 |
| STAT C239A | The Statistics of Causal Inference in the Social Science | 4 |
| STAT C239B | Quantitative Methodology in the Social Sciences Seminar | 4 |
| STAT C241B | Advanced Topics in Learning and Decision Making | 3 |
| STAT 243 | Introduction to Statistical Computing | 4 |
| STAT 244 | Computing for Statistics and Data Science with Julia,Statistical Computing | 4 |
| STAT C245A | Introduction to Modern Biostatistical Theory and Practice | 4 |
| STAT C245B | Biostatistical Methods: Survival Analysis and Causality | 4 |
| STAT C245C | Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine | 4 |
| STAT C245D | Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II | 4 |
| STAT C247C | Longitudinal Data Analysis | 4 |
| STAT 248 | Analysis of Time Series | 4 |
| STAT 256 | Causal Inference | 4 |
| STAT 259 | Reproducible and Collaborative Statistical Data Science | 4 |
| STAT C261 | Quantitative/Statistical Research Methods in Social Sciences | 3 |
| VIS SCI 265 | Neural Computation | 3 |