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

October 27, 2021

Course Duration

DURATION

10 weeks, online
6-8 hours per week

Course Fee

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.

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 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 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 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 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 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 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 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.

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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 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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Amazon

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Uber

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Ebay

Gallup

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StubHub

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:

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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

What Participants Say

“It was the right level of difficulty and fun to learn some basic Python.”

— Moritz Marti, Senior Consultant Digital & Business Transformation, Campana & Schott, New York City

“The notebooks were great because they were practical.”

— Melanie Somiah, Senior Manager, Deloitte, Canada

“The examples and exercises helped me put theory and practice together in a fun way.”

— Angel Camacho, Director of Product Marketing, Aerospike, California

“The math is the core for data science and gives the proper understanding of the mechanics.”

— Marco Evangelista, Key Account Director, Oracle, Brazil

“It offered a simple transition into coding for a novice.”

— Chris Campbell , Medical Science Liaison at Eisai US, Chicago

“I really enjoyed the mix of media, from lectures to videos to notebooks – a nice way to blend the learning.”

— Robert Fox, CTO, HG Insights, California

“The Jupyter Notebook assignments and problem sets were great – nothing like getting your hands dirty, translating lectures into action.”

— Jerry Yen, CEO & Cofounder, Advice Analytics, California

“Easy to follow for a statistics/Python novice.”

— Natalie Duffy, Strategic Workforce Planning Manager, Nissan Motor Corp., UAE

“I was looking for a data science class designed for business people, focused more on the real world. And this course delivered exactly what I was looking for.”

— Sarah Wang, Senior Product Manager, iRobot, Boston

Program Faculty

Faculty Member STEVE TADELIS

STEVE TADELIS

Professor of Economics and Sarin Chair in Leadership and Strategy, Berkeley Haas

An expert in e-commerce and the economics of the internet, Prof. Tadelis has extensive experience in the field, including a position as senior director and distinguished economist at eBay (2011–13) and vice president of economics and market design at Amazon (2016–17)... More info

Faculty Member SHACHAR KARIV

SHACHAR KARIV

Benjamin N. Ward Professor of Economics, Berkeley Haas

Former chair of the department of economics and faculty director of the Experimental Social Science Lab (Xlab), Prof. Kariv is an expert in behavioral and experimental economics, focused on individuals’ financial and non-financial decisions... More info

Path to Alumni Benefits

Enrolling in the Data Science program is your first step toward the Certificate of Business Excellence. You will have access to a private global network of more than 41,000 UC Berkeley alumni in more than 80 countries, along with exclusive benefits available only to UC Berkeley alumni:

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

Berkeley resources

  • Activate an @haas.executivealumni.berkeley.edu email forwarding address
  • Enjoy a 30% discount on eligible programs after completion of your COBE program
  • Gain online access to the Long Business Library and other university databases

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.

Certificate

Example image of certificate that will be awarded after successful completion of this program

Certificate

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.

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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.

Example image of UC Berkeley Certificate of Business Excellence
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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