Artificial Intelligence & Data Science
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Categorical Data in the Tidyverse

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Course Report - Categorical Data in the Tidyverse

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

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Data scientists work often with non-numerical data, such as job titles and survey responses. Factors are a way R can represent them. This course will show you how to use tidyverse, forcats to aid you. You will also use tidyr, ggplot2, and dplyrstringr. Real-world datasets will be used, such as Kaggle State Data Science, fivethirtyeight flight dataset and ML Survey. You'll learn how to efficiently identify and manipulate factor variables, and visualize your data. You're ready to categorize!

Course Overview

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Virtual Labs

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International Faculty

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Case Based Learning

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Post Course Interactions

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

Skills You Will Gain

Prerequisites/Requirements

Working with Data in the Tidyverse

What You Will Learn

Following this course, you’ll be able to identify and manipulate factor variables, quickly and efficiently visualize your data, and effectively communicate your results

In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape

Course Instructors

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Emily Robinson

Data Scientist at DataCamp

Emily is a senior data scientist at Warby Parker. Follow her at @robinson_es on Twitter and on her blog, Hooked on Data
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