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Introduction to the Tidyverse

This course is part of Tidyverse Skills for Data Science in R.

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Tidyverse Skills for Data Science in R Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

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Introduction to the Tidyverse

This course includes

7 Hours

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

Free course

What you'll learn

  • Distinguish between tidy and non-tidy data structures

  • Transform messy data into tidy formats

  • Organize and initialize data science projects

  • Understand the Tidyverse ecosystem

  • Implement effective data science workflows

Skills you'll gain

Data Management
Data Visualization
R Programming
Tidying Data
Project Organization
Data Analysis
Tidyverse Tools
Data Science Workflow
Data Structures

This course includes:

1.2 Hours PreRecorded video

5 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

Get a Completion Certificate

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Certificate

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There are 6 modules in this course

This comprehensive course introduces students to the Tidyverse ecosystem of R packages for data science. The curriculum covers fundamental concepts of tidy data, data science project organization, and the Tidyverse package ecosystem. Students learn to distinguish between tidy and non-tidy data, transform messy data into tidy formats, and organize data science projects effectively using modern tools and workflows.

Tidy Data

Module 1 · 1 Hours to complete

From Non-Tidy –> Tidy

Module 2 · 1 Hours to complete

The Data Science Life Cycle & Tidyverse Ecosystem

Module 3 · 0 Hours to complete

Data Science Project Organization & Workflows

Module 4 · 2 Hours to complete

Case Studies

Module 5 · 0 Hours to complete

Project: Organizing a New Data Science Project

Module 6 · 1 Hours to complete

Fee Structure

Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Tidyverse Skills for Data Science in R

Instructors

Carrie Wright, PhD
Carrie Wright, PhD

3.9 rating

16 Reviews

3,977 Students

6 Courses

Pioneering Data Science Education and Computational Biology Innovation

Carrie Wright, PhD, serves as a Senior Staff Scientist at Fred Hutchinson Cancer Center and holds an affiliated faculty position at Johns Hopkins Bloomberg School of Public Health, where she focuses on making data science and computational biology more accessible to diverse audiences. Her expertise spans multiple domains, teaching courses including "AI for Decision Makers," "AI for Efficient Programming," "Avoiding AI Harm," "Best Practices for Ethical Data Handling," "Data Management and Sharing for NIH Proposals," and "Write Smarter with Overleaf and LaTeX." Her distinguished career includes significant contributions as a former Assistant Scientist in Biostatistics at Johns Hopkins and postdoctoral research at the Lieber Institute for Brain Development, where she studied genetic mechanisms in psychiatric disease. As a member of the Open Case Studies team, the Genomic Data Science Community Network, and chair of the ITCR OPEN Group, she demonstrates her commitment to advancing science, medicine, and social justice through accessible data science education. Her innovative work includes co-founding the LIBD rstats club and teaching at various institutions, including the Baltimore Underground Science Space and Johns Hopkins Center for Talented Youth.

Roger D. Peng, PhD
Roger D. Peng, PhD

4.7 rating

236 Reviews

16,11,468 Students

37 Courses

Professor of Biostatistics at Johns Hopkins University

Dr. Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and serves as a Co-Editor of the Simply Statistics blog. He earned his PhD in Statistics from the University of California, Los Angeles, and is recognized for his research in air pollution, health risk assessment, and statistical methods for environmental data. In 2016, he received the Mortimer Spiegelman Award from the American Public Health Association, honoring his significant contributions to health statistics. Dr. Peng developed the Statistical Programming course at Johns Hopkins to equip students with essential computational tools for data analysis. Additionally, he is a national leader in promoting reproducible research practices and serves as the Reproducible Research editor for the journal Biostatistics. His interdisciplinary research has been published in prestigious journals, including the Journal of the American Medical Association and the Journal of the Royal Statistical Society. He has authored over a dozen software packages that implement statistical methods for environmental studies and reproducible research, and he regularly conducts workshops and tutorials on statistical computing and data analysis.

Introduction to the Tidyverse

This course includes

7 Hours

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

Free course

Testimonials

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Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.