Practical Data Science

UC Berkeley Extension

UC Berkeley Extension


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Who should attend

Typical students who enroll in this course include:

  • Data science enthusiasts at the beginner level
  • People with science and technical capability who want an intro to data science
  • Technical project managers
  • Professionals with experience with marketing and business with an interest in deepening their capabilities with data
  • Marketing and business professionals who want to better understand data
  • Business analysts without R coding experience

About the course

Data science is expanding rapidly, transforming jobs and entire industries as it grows. Getting into this fast-paced and continuously evolving field starts by learning the core concepts of data science through the R programming language.

The Practical Data Science course from UC Berkeley Extension is designed to give new and aspiring practitioners a broad, practical introduction to the data science process and its fundamental concepts, with lessons and examples illustrated through R programming. As a participant, you’ll gain a high-level understanding of data science and build a solid foundation you can use as a stepping stone to programming and modeling courses.

Emeritus and UC Berkeley Extension

UC Berkeley Extension is collaborating with online education provider Emeritus to offer a portfolio of high-impact online courses. These courses leverage UC Berkeley’s thought leadership in technical practice developed over years of research, teaching, and practice. By collaborating with Emeritus, we are able to broaden access beyond our on-campus offerings in a collaborative and engaging format that stays true to the quality of UC Berkeley. Emeritus’ approach to learning is formulated on a cohort-based design to maximize peer-to-peer sharing and includes live teaching with world-class faculty and hands-on project-based learning. In the last year, more than 30,000 students from over 120 countries have benefited professionally from Emeritus.


Module 1: Data Science: Exploration and Processes

  • Why Data Science?
  • What is Data Science, and why do we need it?
  • The Data Scientist’s Toolbox
  • The data science process and project lifecycle
  • Emphasis on collaboration, reproducibility, ethics and integrity in data science
  • Ethics in Data Science

Module 2 - Introduction to R

  • What is R and R Studio
  • R packages
  • Objects and data classes
  • Data structures
  • Working with data

Module 3 - Data Visualization

  • Visualizing data in R
  • Introduction to {ggplot2}
  • Introduction to data wrangling and the “tidyverse”
  • Introduction to {dplyr}

Module 4 - Tidying and Reshaping Data

  • Importing and exploring data
  • Reshaping data with {dplyr}
  • Cleaning data with {dplyr}
  • Exporting data
  • Exporting

Module 5 - Introduction to Statistics and Probabilities

  • Fundamental statistical concepts and their application to data science
  • Distributions
  • Sampling
  • Simpson’s paradox

Module 6 - A/B Testing

  • Scoping tests with stakeholders
  • Determining statistical significance
  • Confidence Intervals
  • A/B Test Design
  • Interpreting results
  • Making recommendations
  • When can we make causal inferences?

Module 7 - Exploratory Data Analysis (EDA) and Introduction to Models

  • Exploratory Data Analysis (EDA)
  • Intro to models
  • Types of models

Module 8 - Introduction to Linear Regression

  • Linear regression
  • Limitations of linear models
  • Naive model
  • Univariate models
  • Multivariate models
  • Model diagnostics
  • Predictions
  • Model Comparisons

Module 9 - Introduction to Logistic Regression

  • Classification problems
  • Logistic function
  • Interpreting coefficients
  • Making predictions
  • Calculating loss functions
  • Model performance

Module 10 - Interactive User Interfaces

  • Using RShiny
  • Creating a Shiny application

Module 11 - Accessing and Versioning Your Data

  • Git and Github
  • Database connection + writing back to a database
  • SQL
  • SparkR

Module 12 - Preparing for a Data Science Career

  • Building a data science portfolio
  • Data Science résumés
  • Connecting with and learning from the data science community
  • Self-learning approaches
  • Fields requiring data science
  • What do I learn next?


Navigating and Using RStudio

  • Installing R packages
  • Clean and visualize data using {dplyr} and {ggplot2}
  • Apply simple statistics (confidence intervals and sampling populations)

Data Visualization Using R

  • Create a GitHub account and upload your Shiny application code. You will also discuss action steps for continuing to build your data science portfolio.

Create an R Shiny Application

  • Create an interactive {shiny} application using income data
  • Visualize patterns between demographics and income characteristics using a reactive figure

A/B Testing: Web Page Variations

  • Use output from Google Analytics
  • Clean web data
  • Analyze the influence of website design on user engagement


Emeritus follows a unique online model. This model has ensured that nearly 90 percent of our learners complete their course.

Orientation Week

The first week is orientation week. During this week you will be introduced to the other participants in the class from across the world. You will also learn how to use the learning platform and other learning tools provided.

Weekly Goals

On other weeks, you have learning goals set for the week. The goals would include watching the video lectures and completing the assignments. All assignments have weekly deadlines.
 Recorded Video Lectures

The recorded video lectures are by faculty from the collaborating university.

Live Webinars

Every few weeks, there are live webinars conducted by Emeritus course leaders. Course leaders are highly-experienced industry practitioners who contextualize the video lectures and assist with questions you may have regarding your assignments. Live webinars are usually conducted between 1 pm and 3 pm UTC on Tuesdays and Wednesdays.

Clarifying Doubts

In addition to the live webinars, for some courses, the course leaders conduct Office Hours, which are webinar sessions that are open to all learners. During Office Hours, learners ask questions and course leaders respond. These are usually conducted every alternate week to help participants clarify their doubts pertaining to the content.


The Emeritus Program Support team members will follow up and assist over email and via phone calls with learners who are unable to submit their assignments on time.

Continued Course Access

You will continue to have access to the course videos and learning material for up to 12 months from the course start date.   Assignments/Application Projects

Assignments are given out weekly and they are based on the lectures or tutorials provided. They need to be completed and submitted as per the deadline for grading purposes. Extensions may be provided based on a request sent to the support team.

Discussion Boards

It is an open forum where participants pin their opinions or thoughts regarding the topic under discussion.

  Emeritus Program Support Team

  • If at any point in the course you need tech, content or academic support, you can email program support and you will typically receive a response within 24 working hours or less.   Device Support

  • You can access Emeritus courses on tablets, phones and laptops. You will require a high-speed internet connection.   Emeritus Network

  • On completing the course you join a global community of 5000+ learners on the Emeritus Network. The Network allows you to connect with Emeritus past participants across the world.


Kristen Kehrer

№8 Global LinkedIn Top Voice in Data Science & Analytics. Kristen has 10 years of experience in data science delivering innovative and actionable machine learning solutions across the eCommerce, healthcare, & utility industries. Kristen is currently an instructor at UC Berkeley Ext. teach...

Videos and materials

Practical Data Science at UC Berkeley Extension

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Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with any business school or university.

Full disclaimer.

Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.

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