Hands On Training

Exploratory Data Analysis in R

Name: Exploratory Data Analysis in R
Brand: Careervira
Price: 150 USD
Availability: InStock
Rating: 1 (1 reviews)

via

DataCamp

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.

Visit Course Report

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

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

Virtual Labs

International Faculty

Case Based Learning

Post Course Interactions

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 Content

Expand all sections

Module 1: Exploring Categorical Data

Module 2: Exploring Numerical Data

Module 3: Numerical Summaries

Module 4: Case Study

Course Instructors

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.