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  • Berkeley Global

Introduction to R: Data Exploration and Visualization

EL ENG X480.1

40986
Delivery Options Classroom
Get an introduction to the R programming language in order to perform data manipulation, statistical analysis and graphics applications. Thousands of libraries or packages are available for everything from time-series analysis and machine learning to data visualization. As a result, R is rapidly becoming the language of choice for engineers, scientists, researchers and business analysts who engage in data preparation and analysis to generate high-quality typographical and graphical results.

Course Outline

Expand or collapse section

Course Objectives

  • Install and configure R and essential R development tools, write R programs, and run them to generate tabular and graphical results.
  • Use R to create, or read-in external datasets, storing data in all of the commonly used R data structures (vectors, matrices, arrays, data frames, factors and lists).
  • Manage and manipulate data; perform data type conversions; merge data sets; deal with missing values; and extract, delete, or transform subsets of data based on logical criteria.
  • Use basic R language constructs such as variables, branching and looping statements, write and call programmer defined, built-in and externally installed (package) functions.
  • Employ R to perform basic data analysis using data exploration, statistical analysis and machine-learning techniques.

What You Learn

  • Writing and running R programs
  • Creating datasets
  • R language elements
  • R functions
  • Basic graphs; bar plots, pie charts, histograms, density, box and dot plots
  • Descriptive statistics
  • Correlation and hypothesis testing; regression, variance
  • Advanced graphing
  • Statistical modeling: linear models, regression, classification trees

How You Learn

  • Lectures
  • In-class exercises
  • Online discussion boards
  • In-class exams

Is This Course Right for You?

If you want to learn the fundamentals of the R programming language and gain a solid foundation for future study, then this course is geared to your needs. Also, those who want to learn R to perform data exploration and analysis using statistical and machine learning techniques should enroll. Scientists, engineers, business analysts and social science researchers who explore and analyze data, and wish to present their results in well-formatted textual or graphical forms will find this course a fit.

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Sections

Section 015

Jan 04, 2021 to Mar 17, 2021 Live Online

Course Fee(s)

Course Fee credit (2 units)

$845.00


Section Schedule

Jan 04, 2021 to Mar 17, 2021

Type Live Online

Access classroom-style interactive learning from anywhere in the world! Attend scheduled online sessions with your instructor and classmates in addition to completing your coursework.

Live Online format allows you to take classes from anywhere with an internet connection. Classroom sections will be taught in this format through Spring 2021. Learn more about this format.

Beginning August 15, 2020, you must have a Zoom account to participate.

Many schools are now accepting transfer credit for online coursework, including health and sciences programs. Check with your institution before enrolling.

Days

M, W

Time

6:30PM to 8:00PM Pacific Time

Dates

Jan 04, 2021 to Jan 13, 2021

Type Live Online

Days

W, M

Time

6:30PM to 8:00PM Pacific Time

Dates

Jan 20, 2021 to Feb 10, 2021

Type Live Online

Days

W, M

Time

6:30PM to 8:00PM Pacific Time

Dates

Feb 17, 2021 to Mar 17, 2021

Schedule and Location

View Details

Instructional Hours

30.0

Delivery Options

Classroom

Available for Credit

2 semester units

Instructors

  • Allan Miller

Section Notes

No meetings on 1/18 and 2/15 for holidays.

Section Materials

  • Textbook (Optional) R in Action: Data Analysis and Graphics with R by Robert Kabacoff © 2015 Manning Publications 2nd edition ISBN 978-1617291388

This course applies to the following programs:

Advanced Program in Software Development

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Programming Courses

  • Java: Discovering Its Power
  • Mastering Python
  • C++ Programming
  • Data Structures and Algorithms
  • Software Design Patterns

Data and Databases Courses

  • Machine Learning and Deep Learning With Spark
  • Practical Machine Learning (With R)
  • Introduction to R: Data Exploration and Visualization
  • Python for Data Analysis and Scientific Computing

Learn More About this Program

Certificate Program in Data Science

Expand or collapse section

Programming

  • Introduction to R: Data Exploration and Visualization
  • Python for Data Analysis and Scientific Computing
  • Introduction to Data Science
  • Introduction to Data Science Using R

Machine Learning

  • Introduction to Machine Learning Using Python
  • Machine Learning and Deep Learning With Spark
  • Practical Machine Learning (With R)
  • Machine Learning With TensorFlow

Core Courses

  • Practical Statistics for Data Scientists Using R
  • Introduction to Big Data
  • Data Science Principles and Practice Using Python
  • Data Visualization

Electives

  • Introduction to Databases
  • Introduction to SQL

Learn More About this Program

Professional Program in Data Analysis

Expand or collapse section

Data Analysis Courses

  • Introduction to Data Analytics
  • Introduction to R: Data Exploration and Visualization
  • Python for Data Analysis and Scientific Computing
  • Data Analytics and Visualization
  • Practical Statistics for Data Scientists Using R

SAS Analytics Courses

Data Management Courses

  • Introduction to Databases
  • Introduction to SQL
  • Business Intelligence With SQL Server
  • Data Mining Using SQL

Learn More About this Program

Notes

Departmental contact: extension-techeng@berkeley.edu | (510) 642-4151

Prerequisites

To be successful in this course, it is strongly recommended to have the following knowledge, skills and abilities before you enroll:

  • Knowledge of statistics as covered in a first semester undergraduate course. Need to fulfill this prereq? Take a course in:
    • Introduction to Statistics STAT X10
       
  • Ability to program in at least one high-level programming language such as Python, Java, Ruby, JavaScript, C or C++. Need to fulfill this prereq? Take a course in:
    • Introduction to Computers and Programming COMPSCI X444.4
    • Programming Python COMPSCI X434
    • First Course in Java EL ENG X429.9
    • JavaScript and jQuery: An Introduction COMPSCI X452.1
    • Introduction to C Language Programming EL ENG X24
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Course Fee

EL ENG X480.1 - 015 - Programming With R

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Classroom

Allan Miller

Allan Miller is a veteran data scientist and educator. He is an active member of the San Francisco Bay Area R-language data science community, founding and current faculty in the UC Berkeley Extension Data Science program, and a member of the Extension Program's data science advisory board.

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