September 7, 2023


10 weeks, online
6-8 hours per week

Program FEE

US$2,850 US$2,423

and get US$285 off with a referral

September 7, 2023


10 weeks, online
6-8 hours per week


US$2,850 US$2,423

and get US$285 off with a referral

Application Details

Enrolling in this program is the first step in your journey to alumni benefits.

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Every Company Is a Data Company

Don’t let a data-driven organization overtake you. Become a data organization too. No matter the industry, every organization must be in the data business.

According to NewVantage Partners’ 2021 survey of senior executives, 96% said AI and big data had produced successful business outcomes, up more than 25% over the previous year. Still, only 41% say they are competing on analytics, and fewer than a quarter say their organizations have built a data-driven culture. The challenge to realizing the potential of big data lies not in the technology itself, but rather in the transformation of teams, culture, and processes.

In this program, prepare to dive right into that transformation by getting dirty in the data. Data Science: Bridging Principles and Practice provides a foundational understanding of what data science and analytics is all about. By the end of this program, you will be able to work effectively with data teams to drive successful outcomes for your organization.

Key Takeaways

In this program, you will be introduced to the basics of statistics and analytics in order to build a foundation in data science. You will acquaint yourself with the tools of analytics, explore the business applications of data concepts and tools, and develop the language and skills to work effectively with your data team. By the end of the program, you will be prepared to do the following:

  • Adopt a data-driven mindset, ask the right questions, turn data into business insights, and identify the best methods to answer questions.
  • Learn to communicate and interpret data, master data presentation methods, communicate with data scientists, and interpret data effectively.
  • Create a data-driven culture, use technology and processes to drive a cultural shift where data is leveraged for strategy, decision making, and execution.

Who is This Program For?

This program is for mid-career managers who want to upskill, C-suite professionals that make impactful organizational decisions, and executives who want to develop their career in a fast-growing field.

  • Product Managers, Product managers, project managers, marketing managers, and others in managerial positions who are integral to the decision-making process and want to get deeper actionable insights for their work.
  • Directors, CEOs, CTOs, CIOs, vice presidents, presidents, founders, and general managers who are involved in making systematic data-driven decisions and would like to strengthen the application of data science in their organizations.
  • Executives who want an introduction to Data Science and want to gain more experience in data analysis.

Preparing for Data Science Literacy

While there are no formal prerequisites such as coding knowledge, having an aptitude for quantitative concepts is important.
As pre-term work and in week 1, there will be a review of basic mathematical and statistical concepts such as mean, standard deviation, graphs, histograms, and linear and logarithmic functions. In addition, there will be a weekly 'prep session' to introduce key concepts from the next module that participants may want a refresher on. To gain true literacy in data science, be prepared to get dirty in the data and embrace some math and stats. We'll fully support you along the way.

Program Modules

As you work through the hands-on modules, you will gain meaningful business insights from the data and example cases derived from a broad sampling of industries.

  • Module 1: Probabilistic Decision Making
    We’ll introduce the foundational concepts behind data science and analytics before exploring the fundamentals of data.
    • Compare categorical vs. numerical data.
    • Explore the basic ways that data reveal information.
    • Learn from a healthcare example: HMO membership and doctor visits using aggregated data.
    • Become acquainted with Jupyter Notebook, Python, and Panda.
  • Module 2: Creating Sample Data
    Learn the definitions of key survey terms as well as methods that use sampling to analyze the pros and cons of business decisions through the exploration of sampling, type I and type II errors, and control limits.
    • Learn to define types of data samples, sampling variation, and quality.
    • Identify and define foundational sampling concepts.
    • Identify and mitigate bias when sampling data.
    • Evaluate examples that illustrate joint, marginal, and conditional probability: Comcast, Google, and Nextag.
  • Module 3: Testing Hypotheses
    Making data-driven business decisions relies on well-articulated hypotheses that lend themselves to statistical tests. We’ll cover the foundations of this approach, including statistical comparisons, confidence intervals, and margins of error.
    • Identify the basic tenets of experimentation.
    • Identify and discriminate between one-sided and two-sided statistical tests.
    • Complete problem sets using the 4M model (Motivation, Method, Mechanics, and Message).
    • Analyze an industry example: 24 Hour Fitness tests a new proprietary diet—testing between control and treatment groups.
  • Module 4: Extrapolating Information from Sample Data
    We’ll explore the most common linear and curved patterns and understand different ways to fit data to linear models. A central application will be understanding market demand, price setting, and elasticities.
    • Identify conditions for using and interpreting linear and curved patterns.
    • Examine curved (non-linear) patterns as applied to vehicle weight and fuel efficiency.
    • Complete problem sets using the 4M model for credit cards, crime, and housing prices in Philadelphia.
  • Module 5: Basic Regression Models
    Simple regression analyses are at the heart of more elaborate data-driven business decision making. We’ll focus on understanding how these models are used, the assumptions that make their use valid, and how to leverage these models to make better business decisions.
    • Define and apply the simple regression model and identify conditions for its use.
    • Apply and interpret prediction intervals.
    • Identify three major problems that affect regression models: changing variation in data, outliers, and dependence among observations.
    • Practice with a retail example: use regression modeling to determine the location of a franchise outlet.
  • Module 6: Advanced Regression Models
    Build on the basics to define the multiple regression model and explore different use cases.
    • Discriminate between marginal and partial slopes.
    • Articulate inference in the multiple regression model.
    • Summarize the process of fitting and building a multiple regression model.
    • Learn from a financial example: build a multiple regression model to explain the returns on Sony’s stock.
    • Practice with a human resources example: analyze salary data using MRM to identify gender imbalances.
  • Module 7: Forecasting and Machine Learning
    We’ll demystify machine learning by mastering the fundamentals and studying different applications.
    • Discriminate between supervised, semi-supervised, and unsupervised learning.
    • Examine machine learning approaches, including the “bag-of-words” method for supervised learning.
    • Practice forecasting by using time series regressions.
    • Explore a cybersecurity example: machine learning for spam detection.
  • Module 8: A/B Testing & Building Effective Data Science Teams
    With the fundamentals and some of the most common tools under our belts, we’ll dive deep into the competencies that define effective data science teams and show you how to build a data-driven culture in your organization. We will stress common pitfalls and strategies to work effectively with data scientists.
    • Review the requirements for building effective data science teams.
    • Continue the exploration of building a data-driven culture.
    • Evaluate an advertising example: Rocket Fuel’s conversion rate, benefit, ROI, opportunity cost, and A/B testing.

Note: In order to help you explore some of the hands-on techniques that lead directly to making better data-driven decisions, there will be two weeklong learning labs that provide an opportunity to dig deeper into the data. This extends the program to a total of 10 weeks.


Your Learning Journey

During this ten-week online journey, you’ll connect directly with UC Berkeley Executive Education's faculty, industry leaders and peers from every corner of the globe. Taking a rigorous, hands-on approach, you’ll analyze data sets using Jupyter Notebook, an interactive open-source platform we will use for computational analysis. While the curriculum is pre-determined, this is an agile learning experience and there may be dynamic opportunities that present themselves based on real-world happenings.

  • Interviews with industry experts who are driven by data, from leading companies including Google, the Oakland A's, Uber and more.
  • Live weekly 'prep sessions' to introduce any technical concepts for next module, weekly office hours and live assignment reviews
  • Live webinars with UC Berkeley Executive Education faculty including Q&A
  • Two week-long learning labs to focus on hands-on assignments and dig deeper into the data
  • Application exercises using Python in Jupyter Notebook to visualize and analyze data (graded as complete or incomplete)
  • Moderated discussion boards

Company Examples

UC Berkeley Executive Education’s faculty members have built strong relationships with industry, including many of the top organizations in and around Silicon Valley. The program's content is either inspired by or directly derived from research and applications from companies that include:






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Note: All product and company names are trademarks™ or registered® trademarks of their respective holders. Their use does not imply any affiliation with or endorsement by them.

Learning Across Industries

Since every company is a data company and every organization can benefit from improving its data literacy, we will explore examples from a range of industries, including:

Fintech/Financial Svs.


Information Technology



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What Participants Say

Program Faculty

Path to Alumni Benefits

Enrolling in the Data Science (Online) program can become your first step toward pursuing the UC Berkeley Executive Education Certificate of Business Excellence (COBE). The Certificate of Business Excellence gives individuals the opportunity to acquire and hone new skills and do it on a timeline that works with your busy schedule. Participants will earn a mark of distinction with certification from a world-class university, and enjoy the flexibility of completing the program in up to three years.
Learn more about the program and associated alumni benefits here.

Networking and events

  • Join local alumni chapters or clubs in your region
  • Participate in the annual Berkeley Haas Alumni Conference
  • Attend select Berkeley Haas and Berkeley Executive Education Networking events open to the COBE community

Exclusive Berkeley resources

  • Activate an email forwarding address
  • 15% discount on the list price of future eligible Berkeley Executive Education programs
  • 30% discount on the list price of future eligible programs after completion of your Certificate of Business Excellence.
  • Public visitor access to select campus libraries and university database services

News and communication

  • A one year complimentary digital subscription to California Management Review
  • Berkeley Haas Alumni newsletter
  • Berkeley Haas Alumni Jobs e-Newsletter featuring job postings from distinguished employers
  • Haas Insights offering the latest research and thought leadership from industry speakers and faculty

Note: All benefits subject to change.




Get recognized! Upon successful completion of the program, UC Berkeley Executive Education grants a verified digital certificate of completion to participants. Participants must complete 80 percent of the required activities including a capstone project (if any) to obtain the certificate of completion. This program also counts toward a Certificate of Business Excellence.


Note: This program results in a digital certificate of completion and is not eligible for degree credit/CEUs. After successful completion of the program, your verified digital certificate will be emailed to you in the name you used when registering for the program. All certificate images are for illustrative purposes only and may be subject to change at the discretion of UC Berkeley Executive Education.

Certificate of Business Excellence logo

This program counts toward a Certificate of Business Excellence

  • Curriculum Days: Two days
  • Pillar(s): Strategy & Management

A UC Berkeley Certificate of Business Excellence gives individuals the opportunity to create a personal plan of study structured by our four academic pillars. Participants will earn a mark of distinction with certification from a world-class university, and enjoy the flexibility of completing the program in up to three years.

Learn more

Early registrations are encouraged. Seats fill up quickly!

Flexible payment options available. Learn more

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