PRACTICAL DATA SCIENCE

ONLINE CERTIFICATE COURSE

OBJECTIVE

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.

 

WHO IS THIS COURSE FOR?

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

GET PROGRAM INFO

Take the first step to a Global Education

Your details will not be shared with third parties. Privacy Policy

Duration and Course Fee

  • Starts 26 September 2019
  • 3 Months
  • 4-6 hours per week
  • Course Fees $1,600

 

 

Faculty

Kristen Kehrer Kristen Kehrer
Instructor at UC Berkeley Extension

COURSE HIGHLIGHTS

  • null
    12 Live Online Teaching Sessions
  • null
    Assignments
  • null
    Discussions
    • null
      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.

      SYLLABUS

      • 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?

      Assignments

      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.

      FACULTY

      Duration and Course Fee

      • Starts 26 September 2019
      • 3 Months
      • 4-6 hours per week
      • Course Fees $1,600

       

       

      Faculty

      Kristen Kehrer Kristen Kehrer
      Instructor at UC Berkeley Extension

      COURSE FAQs

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

      GET PROGRAM INFO

      Take the first step to a Global Education

      Your details will not be shared with third parties. Privacy Policy

      Duration and Course Fee

      • Starts 26 September 2019
      • 3 Months
      • 4-6 hours per week
      • Course Fees $1,600

       

       

      Faculty

      Kristen Kehrer Kristen Kehrer
      Instructor at UC Berkeley Extension

      OTHER PROGRAMS

      Global Ivy Emeritus Institute of Management