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 practice challenges.

Key Benefits

  • Immerse in a 6 month cutting-edge program with the latest tools and techniques, to help you become an expert Data Scientist and lead a Data Science team
  • Gain hands-on practical experience, working on 12 Hackathons and Practice Challenges with real-world data sets
  • Work on 3 Individual Projects mentored by Practitioners, over 6 months; strengthen your GitHub profile and practical experience through the program
  • Learn from experienced Berkeley faculty and seasoned Data Scientists in the Silicon Valley Ecosystem
  • Leverage tailored career support and mentoring, to accelerate your career development
  • Graduate with a Certificate of Excellence in Data Analytics and Business Acumen and earn Berkeley-Haas Alumni Benefits

Register to Download eBrochure

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Participant Profiles

Professionals with Technology skills who aspire to transition to a Data Scientist role and lead a Data Science Team

  • Have 8+ years of experience in a technical, data-driven role
  • Possess technical skills in coding, modelling 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

  • Statistics for Data Science

    • Foundations in Data Science and Business Analytics
    • Statistical tests, Distributions and Likelihood estimators
    • Type I/II Errors, Power Calculations, and Sampling (Errors)
    • Sampling and Measurement
    • Analysis of Variance, Linear Regression, Diagnostics and Checks for Statistical Methods
    • Statistical Analysis in Managerial Decision Making
  • Applied Machine Learning
    and AI

    • Machine Learning Meets Causal Inference
    • Supervised Machine Learning: Regression, Classification, Random Forest, Time Series Analysis
    • Unsupervised Machine Learning: Clustering, K-means, Recommendation Systems, Association rules,
    • Deep Learning & Neural Networks
    • Modelling using Spark and TensorFlow
    • Algorithmic Game Theory and Computational Mechanism Design
  • Exploratory Data Analysis and Data Visualization

    • Data Mining Techniques: Cluster Analysis, Recommender Systems, Association Mining
    • Managing the Data pipeline
    • Analyzing Data and Creating Tools
    • Data Visualization
    • Assessing security risks and mitigations
    • Modelling Complex Systems
  • Leading a Data Science Team

    • Solving Business problems with Data Science
    • Building a high-performance Data Science Team
    • Communicating Data-Driven Insights to Stakeholders
    • Leading and delivering complex technical projects
    • Measuring RoI of Data Science Projects
  • Experimental and Observational Data Analytics

    • Experimental Design, Analysis & Testing
    • Causal Inference and Analysis
    • Observational Data Analysis
    • Data Generation Process and Regression Discontinuity
    • Instrumental Variables – Demand and Lotteries
  • 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
Deadline Application Fees