EXECUTIVE EDUCATION

Data Science (Online)

Add Data Science to your career tool kit

Learn to Make Impactful Organizational Decisions

with Data

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Course Date

STARTS ON

May 19, 2021

Course Duration

DURATION

10 weeks, online
6-8 hours per week

Course Fee

Key Takeaways

Adopt a data-driven mindset

  • Learn to ask the right questions of the data
  • Common techniques for turning data into business insights
  • Knowing which method to use to answer specific business questions

Learn to communicate and interpret data

  • Effective methods for data presentation
  • The language used to communicate with data scientists
  • Interpret data more effectively by understanding the most common techniques

Create a data-driven culture

  • Use technology and process to drive a cultural shift where data is leveraged for strategy, decision making and execution
  • Learn the capabilities that make for successful data science teams

Who is This Program For?

This program is for mid-career managers wanting to upskill, C-suite professionals that make impactful organizational decisions and those at an executive level looking to develop their career in a fast-growing field.

  • 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.
  • Director, CEO, CTO, CIO, Vice –President, President, Founder, General Managers who are involved with making systematic data-driven decisions and would like to strengthen the application of data-science in their organizations.
  • Executives who are looking for an introduction to Data Science and who want to gain more experience in data analysis.

Representative roles include:

  • Director
  • CEO
  • CTO
  • CIO
  • Vice –President
  • President
  • Founder
  • General Manager
  • Product Manager
  • Project Manager
  • Marketing Manager
  • HR manager
  • Operations Manager
  • Sales Manager
  • Risk Manager
  • Executives

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 Topics

Over the course of ten weeks, you will be exposed to many of the most common techniques used to manipulate and analyze data. At the end of this program, you will be able to work effectively with data science and analytics teams to drive business decisions and successful outcomes for your organization.

Module 1:

Probabilistic Decision Making

This module provides a brief introduction to the foundations behind data science and analytics before exploring the fundamentals of data. In addition, you will review a tutorial on using Jupyter Notebook, an interactive computational environment that will allow you to combine code execution, rich text and data plots and analyses.

Module 2:

Creating Sample Data

Explore the science of surveys by way of understanding data samples and sampling variation and quality. This module will describe the methods by which sampling is used to analyze the pros and cons of business decisions through the exploration of sampling, type I and type II errors and control limits.

Module 3:

Testing Hypothesis

Learn about the importance of making business decisions based on conducting statistical tests, comparisons, confidence intervals and margins of error. You will explore these concepts through the lens of a case focused on direct mail advertising, and complete problem sets using the 4M model (Motivation, Method, Mechanics, Message).

Module 4:

Extrapolating Information from Sample Data

Explore how to maximize profits through the extrapolation of information from sample data. You will explore linear and curved patterns, demand, price setting and elasticities.

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 the ways in which these models are used, the assumptions that make their use valid and how to leverage these models to make better business decisions. The data set for this module focuses on using crime rates to predict housing pricing in Philadelphia.

Module 6:

Advanced Regression Models

Learn about two of the most ubiquitous uses of data science and analytics: forecasting and A/B testing. These will include the analysis of variance, time series regressions and the design and execution of simple and more complex A/B testing procedures. Application is based on the Capital Asset Pricing Model, a tool that describes the relationship between systematic risk and expected return for assets.

Module 7:

Forecasting Machine Learning

Explore some of the more fundamental machine learning methods and how they apply to business decisions. Concepts include supervised learning and ML applications such as spam detection.

Module 8:

Building Effective Data Science Teams

Wrap-up the program with a deep dive into the suite of competencies that define effective data science teams and how to build a data-driven culture in your organization. Common pitfalls will be stressed, and strategies to work effectively with data scientists will be laid out.

Module 1:

Probabilistic Decision Making

This module provides a brief introduction to the foundations behind data science and analytics before exploring the fundamentals of data. In addition, you will review a tutorial on using Jupyter Notebook, an interactive computational environment that will allow you to combine code execution, rich text and data plots and analyses.

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 the ways in which these models are used, the assumptions that make their use valid and how to leverage these models to make better business decisions. The data set for this module focuses on using crime rates to predict housing pricing in Philadelphia.

Module 2:

Creating Sample Data

Explore the science of surveys by way of understanding data samples and sampling variation and quality. This module will describe the methods by which sampling is used to analyze the pros and cons of business decisions through the exploration of sampling, type I and type II errors and control limits.

Module 6:

Advanced Regression Models

Learn about two of the most ubiquitous uses of data science and analytics: forecasting and A/B testing. These will include the analysis of variance, time series regressions and the design and execution of simple and more complex A/B testing procedures. Application is based on the Capital Asset Pricing Model, a tool that describes the relationship between systematic risk and expected return for assets.

Module 3:

Testing Hypothesis

Learn about the importance of making business decisions based on conducting statistical tests, comparisons, confidence intervals and margins of error. You will explore these concepts through the lens of a case focused on direct mail advertising, and complete problem sets using the 4M model (Motivation, Method, Mechanics, Message).

Module 7:

Forecasting Machine Learning

Explore some of the more fundamental machine learning methods and how they apply to business decisions. Concepts include supervised learning and ML applications such as spam detection.

Module 4:

Extrapolating Information from Sample Data

Explore how to maximize profits through the extrapolation of information from sample data. You will explore linear and curved patterns, demand, price setting and elasticities.

Module 8:

Building Effective Data Science Teams

Wrap-up the program with a deep dive into the suite of competencies that define effective data science teams and how to build a data-driven culture in your organization. Common pitfalls will be stressed, and strategies to work effectively with data scientists will be laid out.

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 week-long learning labs as an opportunity to dig deeper into the data. This makes for a 10-week long program in total.

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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 have strong relationships with industry, including many of the top tech firms in and around Silicon Valley. Content from the program is either inspired by or directly derived from research and applications from companies that include:

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Amazon

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Uber

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Ebay

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Gallup

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StubHub

Note: All product and company names are trademarks or registered trademarks of their respective holders. The study of these products and/or companies does not imply any affiliation with or endorsement by them.

Industry Examples

We exist in the analytics economy, where every organization can benefit from improving its data literacy skills. Examples come from a broad range of industries, including:

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Fintech/Financial Svs.

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Healthcare

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Information Technology

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Manufacturing

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Retail

Participant Testimonials

Bita Luliano

"Glad to share this amazing experience with anyone eager to lean more about Data Science and Machine Learning. This Executive Program from UC Berkeley is an agile learning experience, seeking a truly rigorous, hands-on experience with modern data analysis methods. The program also provides a good introduction to Python, in order to support data analysis using Jupyter Notebook, an interactive open-source platform used for computational analysis and a review of basic mathematical and statistical concepts. I'd like to thank the Professors Steve Tadelis and Shachar Kariv for this impressive learning experience."

— Daniel Franco, Chief Technology, Innovation and Business Development Officer

Dmitry Karablinov

"Working with Jupyter Notebook, having access to a good online platform with a focus on real life cases and learning facilitators added a lot of value in my learning journey throughout this program."

— Dmitry Karablinov , Business Development Manager

Michael Wolff

"I enjoyed the combination of videos, hands-on exercises and Jupyter Notebook exercises in the program."

— Michael Wolff , Executive Director, Digital Platforms & Products

Saloni Sonawala

“This program gave me the required insight into the world of data science, the different languages, models, algorithms as well as the value and pros/cons of using these.”

— Saloni Sonawala, Application Scientist

"The Jupyter Notebook assignments and the weekly office hours were the best part of the program for me."

— Bita Luliano, Talent Management Analyst

Program Faculty

STEVE TADELIS

James J. and Marianne B. Lowrey Chair in Business, Haas School of Business

Prior to starting his position at Berkeley Haas, Steve was an assistant professor at Stanford University for eight years. Steve also held positions as a senior director and distinguished economist at eBay Research Labs (2011–13) and vice president of economics and market design at Amazon (2016–17) where he applied economic research tools to a variety of product and business applications... More info

SHACHAR KARIV

Benjamin N. Ward Professor of Economics, Haas School of Business

Shachar is the former department chair and faculty director of the Experimental Social Science Laboratory (Xlab). His research in behavioral and experimental economics provides novel tools for understanding individual preferences and attitudes towards risk and time, which inform nearly all aspects of decision making. The research has uncovered valuable new insights about individuals' financial... More info

Certificate

Certificate

Upon successful completion of the program, UC Berkeley Executive Education grants a verified digital certificate of completion to participants. This program is graded as a pass or fail; participants must receive 80 percent to pass and obtain the certificate of completion.

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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.

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.

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