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


Take the first step to a Global Education


  • 12 Live Online Teaching Sessions
  • Assignments
  • Discussions
    • Real-world Datasets

      Live sessions are usually conducted at 11 am EDT (8 am PST) on Tuesdays.

      If you are unable to attend the live sessions, a recording of the session would be made available on the EMERITUS Learning Management System.


      • 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

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

      A/B Testing: Web Page Variations

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

      Create an R Shiny Application

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

      Build Your Own Data Science Portfolio

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


      • Kristen Kehrer
        Kristen Kehrer
        Instructor at UC Berkeley Extension
        UC Berkeley Extension

      Kristen is #8 LinkedIn Global Top Voice 2018 – Data Science & Analytics. Since 2010, Kristen has been a data scientist across multiple industries, including the utilities, healthcare and eCommerce. She finished a BS in Mathematics in 2004 and a Master’s Degree in Applied Statistics. Prior to attaining her Master’s Degree, she was a high school math teacher and has always enjoyed tutoring, coaching and mentoring.

      • Danielle Quinn
        Danielle Quinn
        UC Berkeley Extension

      Danielle Quinn is a PhD Candidate at Memorial University in Newfoundland, Canada, where she is developing computational tools for tackling common challenges in marine conservation. She has been using R since 2009 for a wide range of research tasks, including data wrangling and visualization, statistical analyses, app development, and machine learning. She has an MSc (’13) and BScH (’10) in Biology from Acadia University in Nova Scotia, Canada, with a primary focus in fisheries and ecology, and is the President and co-founder of Terranaut Club, a non-profit organization dedicated to providing hands-on science and nature exploration opportunities for girls.


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

      • OrientationOrientation 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 and you will learn how to use the learning management system and other learning tools provided.
      • Goal SettingWeekly Goals
        On other weeks, you have learning goals set for the week, including watching the video lectures and completing the exercises. All exercises have weekly deadlines.
      • Live WebinarsLive 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 sessions are usually conducted at 11 am EDT (8 am PST) on Wednesdays.
      • Live Q&A SessionsLive Q&A Sessions
        In addition to the live webinars, the course leaders also conduct Q&A sessions every week or every alternate week to help participants clarify any questions they may have pertaining to the content.
      • Follow-UpFollow-Up
        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.
      • Continuous Course AccessContinued 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.

      Assignment/Application Project

      An assignment/application project is given out toward the end of the course that is based on the lectures or tutorials provided. It needs 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.


      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.



      • Starts TBD
      • 3 Months
      • 4-6 hours per week
      • Course Fees $1,400


      • The course requires an understanding of introductory statistics.
      • This course is designed for working professionals and requires proficiency in English. All videos are recorded in English. All assignments are written in English and are required to be responded to in English.


      • 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 ONLINE CERTIFICATE COURSE FAQs


      Take the first step to a Global Education

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