Data Science (Online)
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    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 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.


    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.

    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:




    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:

    Fintech/Financial Svs.


    Information Technology


    Upon successful completion of the program, participants will be awarded a verified digital diploma by Emeritus Institute of Management, in collaboration with MIT Sloan, Columbia Business School Executive Education & Tuck School of Business at Dartmouth.