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.
Tidyverse Skills for Data Science in R Specialization
Tidyverse Skills for Data Science in R Specialization
Develop Insights from Data With Tidy Tools. Import, wrangle, visualize, and model data with the Tidyverse R packages
Instructors: Carrie Wright, PhD
Included with 
Get in-depth knowledge of a subject
(98 reviews)
Beginner level
Recommended experience
2 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
(98 reviews)
Beginner level
Recommended experience
2 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
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
Overview
Skills you'll gain
- Regression Analysis
- Data Import/Export
- Data Analysis Software
- Data Science
- Web Scraping
- Data Manipulation
- Data Modeling
- Data Analysis
- Data Presentation
- Data Visualization Software
- Tidyverse (R Package)
- Plot (Graphics)
- Exploratory Data Analysis
- Data Visualization
- Predictive Modeling
- Data Wrangling
- Statistical Hypothesis Testing
Tools you'll learn
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
31 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Johns Hopkins University
Specialization - 5 course series
What you'll learn
Distinguish between tidy and non-tidy data
Describe how non-tidy data can be transformed into tidy data
Describe the Tidyverse ecosystem of packages
Organize and initialize a data science project
Skills you'll gain
Tidyverse (R Package), Data Wrangling, File Management, Data Manipulation, Data Science, Exploratory Data Analysis, Data Transformation, Data Cleansing, R (Software), Data Import/Export, R Programming, Data Management, Data Analysis, and Data Visualization
What you'll learn
Describe different data formats
Apply Tidyverse functions to import data into R from external formats
Obtain data from a web API
Skills you'll gain
Tidyverse (R Package), Data Import/Export, R Programming, Web Scraping, Relational Databases, Google Sheets, Data Manipulation, SQL, Databases, Data Transformation, Spreadsheet Software, Unstructured Data, Data Integration, Application Programming Interface (API), and Extensible Markup Language (XML)
What you'll learn
Apply Tidyverse functions to transform non-tidy data to tidy data
Conduct basic exploratory data analysis
Conduct analyses of text data
Skills you'll gain
Data Wrangling, Data Manipulation, Tidyverse (R Package), R Programming, Data Transformation, Text Mining, Data Analysis, Data Cleansing, Exploratory Data Analysis, and Statistical Programming
What you'll learn
Distinguish between various types of plots and their uses
Use the ggplot2 R package to develop data visualizations
Build effective data summary tables
Build data animations for visual storytelling
Skills you'll gain
Ggplot2, Tidyverse (R Package), Scatter Plots, Histogram, R Programming, Data Visualization Software, Data Manipulation, Data Storytelling, Exploratory Data Analysis, Data Presentation, Animations, Plot (Graphics), and Box Plots
What you'll learn
Describe different types of data analytic questions
Conduct hypothesis tests of your data
Apply linear modeling techniques to answer multivariable questions
Apply machine learning workflows to detect complex patterns in your data
Skills you'll gain
Regression Analysis, Tidyverse (R Package), Statistical Inference, R Programming, Statistical Hypothesis Testing, Predictive Modeling, Data Science, Data Analysis, Rmarkdown, Descriptive Statistics, Classification And Regression Tree (CART), Data Modeling, Exploratory Data Analysis, Statistical Methods, Statistical Analysis, Machine Learning, and Probability & Statistics
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Instructors
14 Courses17,950 learners
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Frequently asked questions
This Specialization will take approximately 12-15 weeks.
Some familiarity with the R programming language is required.
The courses are cumulative, so it is recommended that students take the courses in order.
More questions
Financial aid available,