Information Technology
Hands on Training icon
Hands On Training
Hands on Training icon

Exploratory Data Analysis in R

Course Cover
compare button icon
Course Report - Exploratory Data Analysis in R

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

Course Features

icon

Duration

4 hours

icon

Delivery Method

Online

icon

Available on

Limited Access

icon

Accessibility

Mobile, Desktop, Laptop

icon

Language

English

icon

Subtitles

English

icon

Level

Intermediate

icon

Teaching Type

Self Paced

icon

Video Content

4 hours

Course Description

If your data is presented in a table or database, it's difficult to see beyond the types and size of the variables. This course will show you how to use both numerical and graphic techniques to discover the structure of your data. What variables are indicative of interesting relationships? What are the most surprising observations? These questions will be answered at the end of the course. Beautiful graphics will also be possible.

Course Overview

projects-img

Virtual Labs

projects-img

International Faculty

projects-img

Case Based Learning

projects-img

Post Course Interactions

projects-img

Case Studies,Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Introduction to Data in R

What You Will Learn

In this course, you'll learn how to use graphical and numerical techniques to begin uncovering the structure of your data

You will learn how to create graphical and numerical summaries of two categorical variables

You will learn how to graphically summarize numerical data

Course Instructors

Author Image

Andrew Bray

Assistant Professor of Statistics at Reed College

Andrew Bray is an assistant professor of statistics at Reed College. His interests are in computing, differential privacy, environmental statistics, and statistics education. He is a co-author of the infer package for tidy statistical inference.
Course Cover