Berkeley Data Scientist Program

The Berkeley Data Scientist program delivers industry professionals the hands-on analytic experience necessary to make critical business decisions based on data and serve as the technical leader on a data analytics team. Designed for the experienced and technically-proficient individual who wants to bring their career to that “next level,” this in-person and online course features a unique mixture of theory and practical knowledge delivered through hands-on and immersive lectures, lab work, and group projects.

Key Benefits

  • Develop a working knowledge of data sourcing and data enrichment aimed at adding value to business data
  • Improve business analytics through the application of appropriate methods
  • Practice with hands-on statistical tests, distributions and likelihood estimators grounded in business needs
  • Apply experimental design and machine learning techniques to real-world industry scenarios
  • Gain expertise working with dashboard and visualization software and communicating data effectively across the business organization
  • Graduate with a Certificate of Excellence in Data Science and earn Berkeley-Haas alumni benefits

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Participant Profiles

Professionals with technology skills who wants to move into a Data Scientist or Business Improvement Engineer role which requires decision-making with the business-side of the organization

  • Have 8+ years of experience in a technical, data-driven role
  • Possess technical skills in coding, modeling techniques, data visualization skills and experience applying statistical methods
  • Experience with Programming Tools such as R, Python, C, Tableau, SQL, advanced Excel, etc

Program Curriculum

  • Experiments

    • Key Concepts in Data Science and Business Analytics
    • Experimental Design, Analysis & Testing
    • Type I/II Errors, Power Calculations, and Sampling (Errors)
    • Compliance, Attrition, and Spill over
    • Experimental Design Issues
    • Leadership Development: Problem Solving for Data Science & Experimental Analysis in Managerial Decision Making
  • Causal Inference and Observational Data

    • Causal Inference & Observational Data
    • Challenge/OVB and Data Generation
    • Matching/IPW (Inverse Probability Rating)
    • IV (Instrumental Variables) – Demand
    • IV Lotteries
    • Process/DGP (Data Generation Process) and RD (Regression Discontinuity)
    • Difference in Differences/Synthesize Control
    • Event Studies
    • Observation “Experiments”
  • Prediction and Forecasting

    • Prediction and Forecasting
    • Leadership Action Plan
    • Machine Learning
    • Machine Learning Meets Causal Inference
    • Communicating Data-Driven Insights
    • Regression – Model Building
    • Business Simulation: Communicating Data-Driven Insights to Different Stakeholders
    • Workshop: Executing Your Business Improvements
    • Engineering Leadership Action Plan
    • Leadership Development
  • Practicals

    • Intermodular Webinars
    • Cases/Exercises
    • Individual & group project work
    • Workshops
    • Hackathons & practice challenges
    • Guest Speaker/ Panel Discussions
Note: The list of topics is not exhaustive and is subject to change.


  • Fred Finan

    Fred Finan

    Professor of Economics & Business Administration

  • Paul Gertler

    Paul Gertler

    Professor, Haas School of Business, UC Berkeley

  • Reed Walker

    Reed Walker

    Associate Professor of Business & Public Policy & Economics


Dates & Fees

Dates Length Location Tuition Fees
Jun 26, 2019 Online $27,000
Aug 12 – 16, 2019 5 Days Berkeley Campus
Aug 18 – Oct 18, 2019 6 Weeks Online
Oct 21 – 25, 2019 5 Days Berkeley Campus
Oct 28 – Dec 6, 2019 6 Weeks Online
Dec 9 – 13, 2019 5 Days Berkeley Campus
Deadline Application Fees
Round 1 April 22, 2019 $500
Round 2 May 20, 2019 $600
Round 3 June 17, 2019 $700