Introduction to the Tidyverse from Coursera

This course introduces a powerful set of data science tools known as the Tidyverse. The Tidyverse has revolutionized the way in which data scientists do almost every aspect of their job. We will cover the simple idea of "tidy data" and how this idea serves to organize data for analysis and modeling. We will also cover how non-tidy can be transformed to tidy data, the data science project life cycle, and the ecosystem of Tidyverse R packages that can be used to execute a data science project.

If you are new to data science, the Tidyverse ecosystem of R packages is an excellent way to learn the different aspects of the data science pipeline, from importing the data, tidying the data into a format that is easy to work with, exploring and visualizing the data, and fitting machine learning models. If you are already experienced in data science, the Tidyverse provides a power system for streamlining your workflow in a coherent manner that can easily connect with other data science tools.

In this course it is important that you be familiar with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.

What's inside

Syllabus

Tidy Data

Before we can discuss all the ways in which R makes it easy to work with tidy data, we have to first be sure we know what tidy data are. Tidy datasets, by design, are easier to manipulate, model, and visualize because the tidy data principles that we’ll discuss in this course impose a general framework and a consistent set of rules on data. In fact, a well-known quote from Hadley Wickham is that “tidy datasets are all alike but every messy dataset is messy in its own way.” Utilizing a consistent tidy data format allows for tools to be built that work well within this framework, ultimately simplifying the data wrangling, visualization, and analysis processes. By starting with data that are already in a tidy format or by spending the time at the beginning of a project to get data into a tidy format, the remaining steps of your data science project will be easier.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Introduces Tidyverse, which simplifies data analysis workflows from importing to visualization to modeling

Taught by reputable instructors with expertise in data science

Focuses on foundational concepts, making it suitable for beginners

Course materials include hands-on labs and interactive exercises

Covers tools and techniques highly relevant to industry practices

Reviews summary

Foundational tidyverse for r users

According to learners, "Introduction to the Tidyverse" is a largely positive experience, offering a strong foundation in tidy data principles and the Tidyverse ecosystem. Students particularly praise the clear explanations of complex topics and the practical application of key R packages like dplyr and ggplot2. Many found the real-world case studies to be highly beneficial for understanding the data science pipeline. While it's considered an excellent introduction for streamlining workflows, some students note a strong prerequisite for R programming knowledge, indicating the pacing might be fast for absolute beginners.

Serves as an excellent introduction, not an advanced deep dive.

"As an experienced R user, this course helped me transition my workflow to the Tidyverse way. It's a great overview of the ecosystem."

"Great for beginners to Tidyverse, but not enough for advanced users."

"It's a fantastic starting point for understanding the ecosystem, although I wish it went a bit deeper into some specific package functions."

Utilizes real-world data science case studies for practical learning.

"I loved how they integrated real-world case studies. The case studies made the concepts click."

"The case studies were interesting and appreciated using real data throughout the course."

"I liked seeing how these tools apply to public health questions, making the learning very concrete."

Offers hands-on experience with key Tidyverse R packages.

"I learned so much about dplyr and ggplot2 in practice, and I can now apply these packages in my projects."

"The focus on data wrangling and transformation from messy to tidy was exactly what I needed. The project was helpful for organizing my own data science work."

"This course is fantastic for anyone wanting to seriously improve their data manipulation skills in R."

Provides a solid grounding in tidy data principles and workflow.

"Absolutely brilliant! The concepts of tidy data were explained so clearly, and the hands-on exercises using dplyr and ggplot2 solidified my understanding."

"This course truly changed my workflow. It's an excellent introduction to the Tidyverse ecosystem."

"I finally understand what tidy data means and how to organize my data effectively."

Assumes prior R knowledge; pacing can be fast for some beginners.

"Make sure your R skills are fresh! While it's an 'introduction,' I felt like it jumped quickly into some concepts."

"I struggled a bit because my R was rusty... The pacing was too fast for me, and I felt lost quickly."

"This course is not for absolute beginners. It assumes too much prior knowledge, and the support resources were not sufficient."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to the Tidyverse with these activities:

Review Prerequisite R Programming

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Reinforce your understanding of foundational R programming concepts, solidifying them before starting the course.

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Review R basics and syntax
Practice using basic data structures and functions
Complete practice problems or exercises

Complete Tidyverse Practice Exercises

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Reinforce your knowledge of Tidyverse concepts and techniques through practical exercises, improving your proficiency.

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Solve coding exercises or challenges related to Tidyverse
Participate in online coding competitions or platforms

Explore Tidyverse Packages

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Enhance your understanding of the Tidyverse ecosystem and its packages, complementing the course material.

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Follow video tutorials or online resources on Tidyverse packages
Experiment with different Tidyverse packages
Create small projects using Tidyverse packages

Three other activities

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Join a Tidyverse Study Group

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Connect with peers, share knowledge, and engage in discussions, enhancing your understanding of Tidyverse and its applications.

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Find or create a study group with fellow learners
Meet regularly to discuss course concepts, work on projects, and provide support
Collaborate on projects or assignments

Develop a Tidyverse Visualization

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Showcase your understanding of Tidyverse's visualization capabilities by creating a compelling visualization for a given dataset.

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Choose a dataset and explore its structure
Use Tidyverse tools to transform and visualize the data
Create an interactive or static visualization

Develop Tidyverse Project Proposal

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Apply your knowledge of Tidyverse to a real-world problem, fostering deeper understanding and practical application.

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Identify a topic or problem that can be addressed using Tidyverse
Research and gather relevant data
Develop a proposal outlining the project's aims, methods, and expected outcomes

Career center

Learners who complete Introduction to the Tidyverse will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data scientists use a variety of techniques to extract insights from data and create models that can help businesses make better decisions. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills are essential for data scientists, as they allow them to work with data efficiently and effectively.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians use data to make informed decisions in a variety of fields, such as healthcare, finance, and marketing. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills are essential for statisticians, as they allow them to work with data efficiently and effectively.

See salaries and explore the career path for Statistician

Data Analyst

Data analysts use data to identify trends, patterns, and relationships. This information can be used to make better decisions, such as how to improve customer service or increase sales. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills are essential for data analysts, as they allow them to work with data efficiently and effectively.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine learning engineers build and maintain machine learning models. These models can be used to automate tasks, make predictions, and improve decision-making. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills are essential for machine learning engineers, as they allow them to work with data efficiently and effectively.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business analysts use data to identify and solve business problems. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills are essential for business analysts, as they allow them to work with data efficiently and effectively.

See salaries and explore the career path for Business Analyst

Software Engineer

Software engineers design, develop, and maintain software applications. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills can be useful for software engineers who work with data, as they allow them to work with data efficiently and effectively.

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Quantitative Analyst

Quantitative analysts use mathematical and statistical techniques to analyze data and make investment decisions. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills can be useful for quantitative analysts, as they allow them to work with data efficiently and effectively.

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Data Engineer

Data engineers build and maintain data pipelines. These pipelines are used to collect, clean, and transform data so that it can be used for analysis and modeling. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills can be useful for data engineers, as they allow them to work with data efficiently and effectively.

See salaries and explore the career path for Data Engineer

Research Analyst

Research analysts use data to conduct research and make recommendations. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills can be useful for research analysts, as they allow them to work with data efficiently and effectively.

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Market Researcher

Market researchers use data to understand consumer behavior and make marketing decisions. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills can be useful for market researchers, as they allow them to work with data efficiently and effectively.

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Financial Analyst

Financial analysts use data to make investment decisions. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills can be useful for financial analysts, as they allow them to work with data efficiently and effectively.

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Epidemiologist

Epidemiologists use data to study the causes and spread of disease. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills can be useful for epidemiologists, as they allow them to work with data efficiently and effectively.

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Actuary

Actuaries use data to assess risk and make financial decisions. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills can be useful for actuaries, as they allow them to work with data efficiently and effectively.

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Data Journalist

Data journalists use data to tell stories and inform the public. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills can be useful for data journalists, as they allow them to work with data efficiently and effectively.

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UX Researcher

UX researchers use data to understand user experience and make design decisions. This course provides a strong foundation in the Tidyverse, a powerful set of tools that can be used to import, tidy, explore, and visualize data. These skills can be useful for UX researchers, as they allow them to work with data efficiently and effectively.

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