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Tidyverse Skills for Data Science in R

Roger D. Peng, PhD, Carrie Wright, PhD, Shannon Ellis, PhD, and Stephanie Hicks, PhD

This Specialization is intended for data scientists with some familiarity with the R programming language who are seeking to do data science using the Tidyverse family of packages. Through 5 courses, you will cover importing, wrangling, visualizing, and modeling data using the powerful Tidyverse framework. The Tidyverse packages provide a simple but powerful approach to data science which scales from the most basic analyses to massive data deployments. This course covers the entire life cycle of a data science project and presents specific tidy tools for each stage.

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What's inside

Five courses

Introduction to the Tidyverse

(0 hours)

This course introduces the Tidyverse, a powerful set of data science tools that has revolutionized the field. We'll cover the concept of "tidy data" and how to transform non-tidy data into a format suitable for analysis and modeling. We'll also explore the data science project life cycle and the ecosystem of Tidyverse R packages that can be used to execute a data science project.

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Importing Data in the Tidyverse

(0 hours)

Getting data into your statistical analysis system can be one of the most challenging parts of any data science project. Data must be imported and harmonized into a coherent format before any insights can be obtained. This course introduces the Tidyverse tools for importing data into R so that it can be prepared for analysis, visualization, and modeling.

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Wrangling Data in the Tidyverse

(0 hours)

Data never arrive in a usable format for analysis. They need to be reshaped, rearranged, and reformatted. This course addresses the problem of wrangling data to bring it under control. The key goal is transforming non-tidy data into tidy data.

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Visualizing Data in the Tidyverse

(0 hours)

Data visualization is a crucial step in any data science project. It helps you understand your data and communicate your findings effectively. This course introduces you to the ggplot2 R package, the industry standard for creating stunning data graphics.

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Modeling Data in the Tidyverse

(0 hours)

Developing insights about your organization, business, or research project depends on effective modeling and analysis of the data you collect. Building effective models requires understanding the different types of questions you can ask and how to map those questions to your data.

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Learning objectives

Organize a data science project
Import data from common spreadsheet, database, and web-based formats
Wrangle and manipulate messy data and build tidy datasets

Build presentation quality data graphics
Build predictive machine learning models

Organize a data science project
Import data from common spreadsheet, database, and web-based formats
Wrangle and manipulate messy data and build tidy datasets
Build presentation quality data graphics
Build predictive machine learning models

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Time

1 hours

Level

Introductory

Via

Coursera

Institution

Johns Hopkins University

Instructors

Roger D. Peng, PhD

Carrie Wright, PhD

Shannon Ellis, PhD

Stephanie Hicks, PhD

Level

Introductory

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