Berkeley Program on Data Science & Analytics
The Berkeley Program on Data Science and Analytics (BPDSA) is a six-month learning journey into the world of applied data science and analytics. The program leverages 5 modules, three on-campus and two online. The program provides you with tools to build and lead data science teams through data-driven decision-making. You will learn how to promote a data-driven culture, how to translate business problems, and how to lead a team with a diverse set of skill sets towards solving these problems. Participants graduate with Certificate of Excellence in Data Science and Analytics and earn Alumni Benefits.
- Develop skills to be a data-driven leader: Acquire critical skills to leverage data and analytics in order to drive strategy and innovation across the organization; Gain in-depth understanding in statistical methods for decision-making, Experiments, A/B Testing, Predictive Analytics, AI/ML tools for business insights, assembling a data science team, to name a few.
- Immersive modules at Berkeley: Learn in the Silicon Valley innovation ecosystem, an unparalleled learning lab for innovation unlike anywhere in the world.
- World-class Faculty and Practitioners: The program is taught by world leading experts who are Berkeley Faculty, many of who have engaged with some of the largest data-driven companies like eBay, Amazon and more.
- Project work: Benefit from experiential sessions which combine theory, real life case studies and project work to help you gain practical perspectives. Work on projects to fortify the tools, frameworks and key aspects learned in the program.
- Alumni Benefits: Participants graduate with Certificate of Excellence in Data Science and Analytics and earn Alumni Benefits and be a part of network of 41,000+ alumni across the globe.
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This program is ideal for managers and leaders who aim to leverage data analytics in their decision making processes and to build data science teams in their organizations. Pre-requisites:
- A minimum of 10 years of work experience
- Fluency in written and spoken English
- No coding experience required
Data and Decisions
- Decision Analysis – Decision trees, Backward induction
- Decision making under uncertainty
- Statistical methods to solve business problems
- Effective data visualization
- Game Theory
Economic Analysis for Decision Making
- Economic Costs
- Demand Estimation and Pricing Strategies
- Market Segmentation
- Tools for competitive advantage
- Designing effective incentives
Inference and measurement
- Sampling, Surveys and Biases
- Estimating parameters
- Statistical Tests
- Micro-econometric data analysis
- A/B Testing
- Experimentation and Evaluation
Forecasting and trends
- Predictive analysis
- Time Series forecasting and analysis
- Linear, Multiple and ANOVA Regression Models
- Regression Diagnostics
Machine Learning and Artificial Intelligence
- Trees, Random Forest and Boosting
- Comparing Machine Learning Approaches: Neural Networks, Support Vector Machines, Trees, Multivariate adaptive regression splines, k-NN
- Lasso and Linear Regularization
- Leverage ML and AI for business insights from big data
- Tools for Supervised Learning Methods
- Tools for Unsupervised Learning Methods
Building a Data Science Team
- Mapping of resources from expert backgrounds to solve problems
- Build a data science team
- Organizational Structure: Centralized, Distributed or Hybrid
- Design a tech eco-system which complements the data science team
- Develop a data-driven culture
Sachar Kariv is the Benjamin N. Ward Professor of Economics and former Department Chairperson, and Faculty Director of Berkeley’s Experimental Social Science Laboratory (XLab). Sachar is also the Co-Founder and Chief Scientist of Capital Preferences, where he applies his research on individual saving, investment and insurance choices to help clients make better decisions about how to design and market products and services, and improve customer acquisition, relationship, and retention.
Co - Faculty Director
Steve Tadelis is the James J. and Marianne B. Lowrey Chair in Business and Professor of Economics, Business and Public Policy at Haas. Steve was a Senior Director and Distinguished Economist at eBay Research Labs (2011-2013) and Vice President of Economics and Market Design at Amazon (2016-2017) where he applied economics research tools to a variety of product and business applications, working with technologists, machine learning scientists, and business leaders. He continues to advice Amazon part-time as an Amazon Economist Fellow.
Tenured Associate Professor | Distinguished Teaching Fellow
Oxfam Professor in Environmental and Resource Economics
Dates & Fees
|Mar 02–06, 2020||5 Days||Berkeley Campus||USD $29,000|
|Mar 23, 2019–Apr 30, 2020||Online|
|May 11–15, 2020||5 Days||Berkeley Campus|
|Jun 15–Aug 7, 2020||Online|
|Aug 31–Sep 4, 2020||5 Days||Berkeley Campus|
|Round 1||October 21, 2019||USD $300|
|Round 2||November 25, 2019||USD $400|
|Round 3||December 23, 2019||USD $500|
|Round 4||January 27, 2020||USD $600|
- 1) Inclusions: Program Materials, Coffee Breaks, Lunches and Select Dinners
- 2) Exclusions: Travel Costs and Accommodation