Cleaning Data in R

via

DataCamp

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

Data scientists spend around 80% of their time cleaning and manipulating data and 20% on analysing it. Spending time cleaning data is crucial as it can lead to incorrect conclusions.

This course will show you how to clean up data. This course will teach you how to use R to identify errors in data and correct them with fuzzy string matching and data transformation. Learn how to work with real data such as restaurant reviews and bike-share trips. This will allow you to sharpen your skills and gain amazing insights from raw data quickly.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Joining Data with dplyr

What You Will Learn

As you learn, you’ll brush up on your skills by working with real-world datasets, including bike-share trips, customer asset portfolios, and restaurant reviews—developing the skills you need to go from raw data to awesome insights as quickly and accuratel

Develop the skills you need to go from raw data to awesome insights as quickly and accurately as possible

Using R, you'll learn how to identify values that don't look right and fix dirty data by converting data types, filling in missing values, and using fuzzy string matching

Course Content

Expand all sections

Module 1: Common Data Problems

Module 2: Categorical and Text Data

Module 3: Advanced Data Problems

Module 4: Record Linkage

Course Instructors

Maggie Matsui

Curriculum Manager at DataCamp

Maggie is a Curriculum Manager at DataCamp. She holds a Bachelor's degree in Statistics and Computer Science from Brown University, where she spent lots of time teaching math, programming, and statis...