Applied Data Science

University of California, Berkeley

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, data mining, 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.


Visit Program Website


Any UC Berkeley graduate student in good standing may apply. To apply, students should submit the following materials on the School of Information website:

  • a letter of intent,
  • a proposed study plan,
  • a description of their Python programming and statistics competencies,
  • their curriculum vitae or resume,
  • their Berkeley course transcript.

Applications are accepted twice a year, in the middle of the fall and spring semesters.

Students may apply at any time during their UC Berkeley graduate career, either before or after taking courses that would count toward the certificate.

Certificate Requirements


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:

  1. An introductory data science class

  2. A course in analytical methods and techniques of data science

  3. 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 an INFO course offered by the School of Information.

1. INtroductory data science course
One of the following:
INFO 201Research Design and Applications for Data and Analysis 13
DATASCI 201Research 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:

BIO ENG 245Introduction to Machine Learning for Computational Biology4
COMPSCI C200APrinciples and Techniques of Data Science4
COMPSCI C281AStatistical Learning Theory3
COMPSCI 289AIntroduction to Machine Learning4
CYBER 207Applied Machine Learning for Cybersecurity (MIDS and MICS students only)3
DATA C200Principles and Techniques of Data Science4
DATASCI 207Applied Machine Learning (MIDS and MICS students only)3
EDUC 244Data Mining and Analytics3
INFO 251Applied Machine Learning4
INFO 258Data Engineering4
INFO 271BQuantitative Research Methods for Information Systems and Management3
STAT C200CPrinciples and Techniques of Data Science4
PB HLTH 241Intermediate Biostatistics for Public Health4
PB HLTH W241Intermediate Biostatistics for Public Health4
PSYCH 208Methods in Computational Modeling for Cognitive Science3
SOCIOL 273LComputational Social Science3
STAT C200CPrinciples and Techniques of Data Science4
STAT C241AStatistical Learning Theory3
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:

CIV ENG 263NScalable Spatial Analytics3
COMPSCI C267Applications of Parallel Computers3-4
COMPSCI 286AIntroduction to Database Systems4
COMPSCI C281BAdvanced Topics in Learning and Decision Making3
COMPSCI 288Natural Language Processing4
CY PLAN 204CAnalytic and Research Methods for Planners: Introduction to GIS and City Planning4
CY PLAN 255Urban Informatics and Visualization3
CY PLAN 257Data Science for Human Mobility and Socio-technical Systems4
DATASCI 209Data Visualization (MIDS and MICS students only)3
DATASCI 241Experiments and Causal Inference (MIDS and MICS students only)3
DATASCI 266Natural Language Processing with Deep Learning (MIDS and MICS students only)3
EDUC 275BData Analysis in Educational Research II4
EDUC 275GHierarchical and Longitudinal Modeling5
EDUC 276EResearch Design and Methods for Program and Policy Evaluation3
EECS 227ATOptimization Models in Engineering4
EL ENG 227BTConvex Optimization4
EL ENG C227CConvex Optimization and Approximation3
EL ENG C227TIntroduction to Convex Optimization4
ENGIN C233Applications of Parallel Computers3-4
ESPM 215Hierarchical Statistical Modeling in Environmental Science2
ESPM 288Reproducible and Collaborative Data Science3
EWMBA 263Marketing Analytics3
GEOG 249Spatiotemporal Data Analysis in the Climate Sciences3
GEOG 279Statistics and Multivariate Data Analysis for Research3
GEOG 282Geographic Information Systems: Applications in Geographical Research4
GEOG 285Topics in Earth System Remote Sensing3
IND ENG C227AIntroduction to Convex Optimization4
IND ENG C227BConvex Optimization and Approximation3
IND ENG 242AMachine Learning and Data Analytics4
IND ENG 262AMathematical Programming I4
IND ENG 262BMathematical Programming II3
IND ENG 264Computational Optimization3
IND ENG 265Learning and Optimization3
IND ENG 266Network Flows and Graphs3
IND ENG 269Integer Programming and Combinatorial Optimization3
INFO 241Experiments and Causal Inference3
INFO 247Information Visualization and Presentation4
INFO 256Applied Natural Language Processing3
INFO 259Natural Language Processing4
INFO 288Big Data and Development3
JOURN 221Introduction to Data Visualization3
LD ARCH 289Applied Remote Sensing3
MAT SCI 215Computational Materials Science3
MBA 263Marketing Analytics3
MEC ENG 249Machine Learning Tools for Modeling Energy Transport and Conversion Processes3
MFE 230PFinancial Data Science2
PB HLTH 231AAnalytic Methods for Health Policy and Management3
PB HLTH C240AIntroduction to Modern Biostatistical Theory and Practice4
PB HLTH C240BBiostatistical Methods: Survival Analysis and Causality4
PB HLTH C240CBiostatistical Methods: Computational Statistics with Applications in Biology and Medicine4
PB HLTH C240DBiostatistical Methods: Computational Statistics with Applications in Biology and Medicine II4
PB HLTH C242CLongitudinal Data Analysis4
PB HLTH 244Big Data: A Public Health Perspective3
PB HLTH W251BData Visualization for Public Health2
PB HLTH 251CCausal Inference and Meta-Analysis in Epidemiology2
PB HLTH 252Epidemiological Analysis4
PB HLTH W252Epidemiologic Analysis4
PHYSICS 288Bayesian Data Analysis and Machine Learning for Physical Sciences4
POL SCI C236AThe Statistics of Causal Inference in the Social Science4
POL SCI C236BQuantitative Methodology in the Social Sciences Seminar4
POL SCI 239TAn Introduction to Computational Tools and Techniques for Social Science Research4
PSYCH 206Structural Equation Modeling3
PSYCH 207Person-Specific Data Analysis3
PUB POL 249Statistics for Program Evaluation4
PUB POL 275Spatial Data and Analysis4
PUB POL 279Research Design and Data Collection for Public Policy Analysis3
PUB POL 288Risk and Optimization Models for Policy4
SOCIOL C271DQuantitative/Statistical Research Methods in Social Sciences3
SOCIOL 273LComputational Social Science3
SOCIOL 273MComputational Social Science3
STAT 215AStatistical Models: Theory and Application4
STAT 215BStatistical Models: Theory and Application4
STAT 238Bayesian Statistics3
STAT C239AThe Statistics of Causal Inference in the Social Science4
STAT C239BQuantitative Methodology in the Social Sciences Seminar4
STAT C241BAdvanced Topics in Learning and Decision Making3
STAT 243Introduction to Statistical Computing4
STAT 244Statistical Computing4
STAT C245AIntroduction to Modern Biostatistical Theory and Practice4
STAT C245BBiostatistical Methods: Survival Analysis and Causality4
STAT C245CBiostatistical Methods: Computational Statistics with Applications in Biology and Medicine4
STAT C245DBiostatistical Methods: Computational Statistics with Applications in Biology and Medicine II4
STAT C247CLongitudinal Data Analysis4
STAT 248Analysis of Time Series4
STAT 256Causal Inference4
STAT 259Reproducible and Collaborative Statistical Data Science4
STAT C261Quantitative/Statistical Research Methods in Social Sciences3
VIS SCI 265Neural Computation3

Contact Information

School of Information

Visit Program Website

Applied Data Science Certificate

Back to Top