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.

From Non-Tidy –> Tidy

The reason it’s important to discuss what tidy data are an what they look like is because out in the world, most data are untidy. If you are not the one entering the data but are instead handed the data from someone else to do a project, more often than not, those data will be untidy. Untidy data are often referred to simply as messy data. In order to work with these data easily, you’ll have to get them into a tidy data format. This means you’ll have to fully recognize untidy data and understand how to get data into a tidy format. The following common problems seen in messy datasets again come from Hadley Wickham’s paper on tidy data (http://vita.had.co.nz/papers/tidy-data.pdf). After briefly reviewing what each common problem is, we will then take a look at a few messy datasets. We’ll finally touch on the concepts of tidying untidy data, but we won’t actually do any practice yet. That’s coming soon!

The Data Science Life Cycle & Tidyverse Ecosystem

With a solid understanding of tidy data and how tidy data fit into the data science life cycle, we’ll take a bit of time to introduce you to the tidyverse and tidyverse-adjacent packages that we’ll be teaching and using throughout this specialization. Taken together, these packages make up what we’re referring to as the tidyverse ecosystem. The purpose for the rest of this course is not for you to understand how to use each of these packages (that’s coming soon!), but rather to help you familiarize yourself with which packages fit into which part of the data science life cycle. Note that the official tidyverse packages below are bold. All other packages are tidyverse-adjacent, meaning they follow the same conventions as the official tidyverse packages and work well within the tidy framework and structure of data analysis.

Data Science Project Organization & Workflows

Data science projects vary quite a lot so it can be difficult to give universal rules for how they should be organized. However, there are a few ways to organize projects that are commonly useful. In particular, almost all projects have to deal with files of various sorts—data files, code files, output files, etc. This section talks about how files work and how projects can be organized and customized.

Case Studies

Throughout this specialization, we’re going to make use of a number of case studies from Open Case Studies to demonstrate the concepts introduced in the course. We’ll generally make use of the same case studies throughout the specialization, providing continuity to allow you to focus on the concepts and skills being taught (rather than the context) while working with interesting data. These case studies aim to address a public-health question and all of them use real data.

Project: Organizing a New Data Science Project

This project will allow you to create a new project and organize the files that will be needed to engage in a future data analysis

Good to know

Know what's good

, what to watch for

, 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

Introduction to the tidyverse: well-received overview

According to students, this course offers a good overview of the Tidyverse, with important concepts and procedures for handling data science projects. Learners say that the course is easy to follow because it is closely aligned with the accompanying book. While some learners found the course to be informative, others found it to be impractical and barren, with little value. Overall, students found the course to be largely positive but noted that it does not discuss Hadley Wickham, nor why the Tidyverse and RStudio are developed by the same team.

Course is easy to follow because it aligns with the accompanying book.

"The course is a breeze to follow because it aligns seamlessly with the book."

"As such, rather than watching videos, you get to read the book; it's really a convenient approach."

Provides useful overview of Tidyverse concepts and procedures.

"Good overview of the Tidyverse and nice introduction."

"Covers really important concepts and procedures for managing data science projects. Very helpful."

Course lacks elaboration on R Markdown files.

"however, i was not very sure what an r markdown file was (it was requested in the final project)"

Course is impractical with little value.

"This course is completely impractical."

"It contains a list of packages with basically no useful information."

"Extremly high level course with very little value :("

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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Show all six activities

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 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

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.

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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.

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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.

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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.

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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.

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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 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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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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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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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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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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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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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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