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.


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

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.


Take the first step to a Global Education

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  • Starts on


  • Duration

    3 Months, Online

    (4-6 hours per week)
  • Course Fees

    US$ 1,400*

Curriculum & Faculty


  • 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
  • What is R and R Studio
  • R packages
  • Objects and data classes
  • Data structures
  • Working with data
  • Visualizing data in R
  • Introduction to {ggplot2}
  • Introduction to data wrangling and the “tidyverse”
  • Introduction to {dplyr}
  • Importing and exploring data
  • Reshaping data with {dplyr}
  • Cleaning data with {dplyr}
  • Exporting data
  • Exporting
  • Fundamental statistical concepts and their application to data science
  • Distributions
  • Sampling
  • Simpson’s paradox
  • Scoping tests with stakeholders
  • Determining statistical significance
  • Confidence Intervals
  • A/B Test Design
  • Interpreting results
  • Making recommendations
  • When can we make causal inferences?
  • Exploratory Data Analysis (EDA)
  • Intro to models
  • Types of models
  • Linear regression
  • Limitations of linear models
  • Naive model
  • Univariate models
  • Multivariate models
  • Model diagnostics
  • Predictions
  • Model Comparisons
  • Classification problems
  • Logistic function
  • Interpreting coefficients
  • Making predictions
  • Calculating loss functions
  • Model performance
  • Using RShiny
  • Creating a Shiny application
  • Git and Github
  • Database connection + writing back to a database
  • SQL
  • SparkR
  • 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

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

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 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

A/B Testing: Web Page Variations

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


Kristen Kehrer
Kristen Kehrer

Course Instructor, Emeritus

Learning Experience

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

  • Orientation Week

    Orientation WeekThe 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

    Weekly GoalsOn 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.

  • Learning Videos

    Recorded Video LecturesThe recorded video lectures are by faculty from the collaborating university.

  • Live Webinars

    Live WebinarsEvery 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

    Clarifying DoubtsIn 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.

  • Follow-Up

    Follow-UpThe 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

    Continued Course AccessYou will continue to have access to the course videos and learning material for up to 12 months from the course start date.


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.


Program Highlights

12 Live Online Teaching Sessions
12 Live Online Teaching Sessions
Real-world Datasets
Real-world Datasets



  • You can pay for the course either with an international debit or credit card (unfortunately we are unable to accept Diners credit cards), or through a bank wire transfer. On clicking the apply now button below, you will be directed to the application form and the payment page.
  • We provide deferrals and refunds in specific cases. The deferrals and refund policy is available here.
  • You will be provided a course login within 48 hours of making a payment.


  • Please provide your work experience and your current employer via the application.
  • You can apply by clicking the Apply Now button


For any questions regarding Emeritus, the learning experience, admission & fees, grading & evaluation please visit COMMON FAQs