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Modeling Data in the Tidyverse

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

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. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships.

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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. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships.

This course covers the types of questions you can ask of data and the various modeling approaches that you can apply. Topics covered include hypothesis testing, linear regression, nonlinear modeling, and machine learning. With this collection of tools at your disposal, as well as the techniques learned in the other courses in this specialization, you will be able to make key discoveries from your data for improving decision-making throughout your organization.

In this specialization we assume familiarity 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.

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What's inside

Syllabus

Modeling Data Basics
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. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships.
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Taught by PhDs who are recognized for their work in data analysis and related fields
Covers hypothesis testing, linear regression, nonlinear modeling, and machine learning
Assumes familiarity with the R programming language
Emphasizes the tidyverse, a collection of packages in R
Includes a project on modeling consumer complaints data
Multi-modal, with videos, readings, and interactive materials

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Reviews summary

Practical data modeling in r tidyverse

According to students, this course offers a strong foundation in data modeling using the R Tidyverse ecosystem. Learners particularly highlight the practical application of concepts like linear regression and machine learning. The hands-on project is often cited as a key strength, allowing students to classify real-world data and solidify their understanding. While providing clear explanations, some note that a solid background in R programming is essential to fully benefit. The course is seen as highly relevant for professionals looking to enhance their data analysis skills with modern R tools.
Provides a solid foundation but requires further exploration for mastery.
"This course gave me a great overview of modeling techniques, but for advanced topics, I'll need more specialized courses."
"It's an excellent introduction to various modeling types, preparing you for deeper dives into specific algorithms."
"While the course touches on machine learning, I feel like I'd need to supplement it with more advanced ML content."
Comprehensive introduction to modern R modeling tools.
"The tidymodels section was a game-changer for me; it really streamlined my approach to machine learning in R."
"I appreciated how the course introduced the full tidymodels suite, making complex modeling much more accessible."
"This course finally made sense of the numerous RStudio packages for predictive modeling."
Offers hands-on experience applying modeling techniques.
"The final project was incredibly helpful, I was able to apply what I learned to a real dataset and see tangible results."
"I found the case studies and project section very practical, it allowed me to actually build models and use the tidymodels framework."
"This course provided me with the tools to classify consumer complaints data, which is directly applicable to my work."
Requires solid prior knowledge of R programming.
"As someone new to R, I found myself constantly struggling; definitely complete 'R Programming' first."
"The course moves quickly, assuming you are very comfortable with R syntax and data manipulation."
"If you're not already proficient in R, you might find some parts challenging and need to pause to review."

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 Modeling Data in the Tidyverse with these activities:
Review Applied Statistical Modeling and Regression Analysis and Data Mining
Brings students up to speed on the basics of statistical modeling and regression necessary for successful participation in this data science course.
Browse courses on Data Mining
Show steps
  • Review statistical concepts such as probability, distributions, and hypothesis testing
  • Go over the fundamentals of linear regression
  • Brush up on techniques for building and evaluating regression models
Follow a tutorial on linear regression
Gain a better understanding of linear regression through a guided tutorial
Browse courses on Linear Regression
Show steps
  • Find a tutorial on linear regression
  • Follow the steps and complete the exercises in the tutorial
Practice Hypothesis Testing
Practice hypothesis testing to improve your ability to make inferences from data. This activity can help you develop your critical thinking skills and improve your understanding of different types of statistical tests.
Browse courses on Hypothesis Testing
Show steps
  • Review the different types of hypothesis tests.
  • Practice applying these tests on different datasets.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Explore the Different Linear Modeling Techniques
Explore the different types of linear modeling techniques to better understand how they are used in data analysis. This activity can help you to better select the right technique for your specific research or project.
Browse courses on Linear Regression
Show steps
  • Watch tutorials on different types of linear modeling techniques.
  • Apply these techniques to practice datasets.
Complete practice problems on hypothesis testing
Practice hypothesis testing to improve understanding of the concept
Browse courses on Hypothesis Testing
Show steps
  • Review lecture notes and textbook materials on hypothesis testing
  • Solve practice problems provided in the course
Create a presentation on modeling data in the tidyverse
Deepen understanding of data modeling by creating a presentation
Browse courses on Tidyverse
Show steps
  • Review course materials on modeling data in the tidyverse
  • Plan and outline the presentation
  • Create slides and write content for the presentation
  • Practice the presentation
Attend a workshop on advanced machine learning techniques
Expand knowledge and skills in machine learning by attending a specialized workshop
Browse courses on Machine Learning
Show steps
  • Find a relevant workshop on advanced machine learning techniques
  • Register and attend the workshop
  • Actively participate in the workshop and take notes
  • Apply the learned techniques to your own projects
Apply Multiple Linear Regression to a Dataset
To reinforce your understanding of multiple linear regression, apply it to a real-world dataset. This activity will allow you to practice and demonstrate your knowledge of multiple linear regression and its applications.
Browse courses on Multiple Linear Regression
Show steps
  • Choose an appropriate dataset.
  • Apply multiple linear regression to the dataset.
  • Interpret the results of the multiple linear regression analysis.
Enter a Kaggle competition on data modeling
Put skills to the test and gain practical experience through a data modeling competition
Browse courses on Data Modeling
Show steps
  • Find a relevant Kaggle competition
  • Explore the dataset and problem statement
  • Build a model and submit your predictions
  • Analyze the results and learn from the experience
Volunteer at a data science organization
Gain practical experience and build connections in the data science field
Show steps
  • Identify data science organizations that offer volunteer opportunities
  • Apply for a volunteer position and undergo any necessary training
  • Participate in data science projects and activities
  • Network with professionals and learn about different data science applications
Develop a predictive model for a real-world dataset
Apply course knowledge to a practical project and enhance problem-solving skills
Browse courses on Prediction Modeling
Show steps
  • Identify a dataset and define a problem statement
  • Explore the data and prepare it for modeling
  • Build and evaluate different models
  • Deploy the best model and interpret the results

Career center

Learners who complete Modeling Data in the Tidyverse will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists analyze data to extract insights, provide actionable recommendations, and build predictive models. With proficiency in the Tidyverse, you'll be well-equipped to perform exploratory data analysis, machine learning, and create interactive data visualizations, essential skills for the role.
See salaries and explore the career path for Data Scientist
Data Analyst
Data Analysts play a crucial role in analyzing and interpreting data to uncover hidden patterns and derive meaningful conclusions. The Tidyverse's suite of packages empowers you to clean, transform, model, and visualize data efficiently, making you a highly sought-after candidate for this role.
See salaries and explore the career path for Data Analyst
Quantitative Analyst
Quantitative Analysts apply mathematical and statistical techniques to solve complex business problems. Developing a strong foundation in data modeling through the Tidyverse will prepare you to build sophisticated models for risk assessment, pricing, and forecasting, making you a valuable asset to any financial institution.
See salaries and explore the career path for Quantitative Analyst
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models to automate decision-making and improve efficiency. By mastering the Tidyverse and its integration with tidymodels, you'll strengthen your ability to build and deploy robust machine learning solutions, enhancing your competitiveness in this in-demand field.
See salaries and explore the career path for Machine Learning Engineer
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. Familiarity with the Tidyverse can enhance your ability to analyze large datasets, perform complex calculations, and interpret results. Its visualization capabilities can also help you effectively communicate your findings to stakeholders, making you a valuable asset in the insurance and risk management industries.
See salaries and explore the career path for Actuary
Business Analyst
Business Analysts bridge the gap between business and technology, analyzing data to identify opportunities and solve problems. Leveraging the Tidyverse, you'll gain proficiency in data exploration, modeling, and visualization, empowering you to derive insights from complex datasets and make informed recommendations for your organization.
See salaries and explore the career path for Business Analyst
Statistician
Statisticians apply statistical methods to collect, analyze, and interpret data, drawing meaningful conclusions from uncertainty. By mastering the Tidyverse, you'll enhance your ability to explore and visualize data, build statistical models, and communicate your findings effectively. This course can provide a valuable foundation for your career as a Statistician.
See salaries and explore the career path for Statistician
Data Engineer
Data Engineers design and maintain data pipelines and infrastructure to ensure data quality and accessibility. The Tidyverse's tools for data cleaning, transformation, and visualization can help you streamline your data management processes, enhance data quality, and ultimately support data-driven decision-making.
See salaries and explore the career path for Data Engineer
Marketing Analyst
Marketing Analysts use data to understand customer behavior, optimize marketing campaigns, and measure ROI. The Tidyverse's capabilities in data exploration, visualization, and modeling can empower you to uncover hidden insights, identify trends, and make data-informed marketing decisions.
See salaries and explore the career path for Marketing Analyst
Product Manager
Product Managers are responsible for developing and managing products that meet customer needs. By mastering the Tidyverse, you'll gain proficiency in analyzing user data, understanding customer behavior, and identifying areas for product improvement. This course can help you build a solid foundation for a successful career as a Product Manager.
See salaries and explore the career path for Product Manager
Financial Analyst
Financial Analysts evaluate investment opportunities, analyze financial data, and develop financial models. The Tidyverse's tools for data cleaning, manipulation, and visualization can help you efficiently analyze financial data, build models, and make informed investment recommendations.
See salaries and explore the career path for Financial Analyst
Consultant
Consultants provide expertise and guidance to organizations on various business issues. By becoming proficient in the Tidyverse, you'll enhance your ability to analyze data, identify patterns, and develop data-driven recommendations. This course can provide you with a valuable skillset for a successful career as a Consultant.
See salaries and explore the career path for Consultant
Educator
Educators teach and inspire students in various academic settings. Incorporating the Tidyverse into your teaching can help you effectively introduce data science concepts, demonstrate data analysis techniques, and instill data literacy in your students. This course can provide you with the necessary knowledge and skills to enhance your teaching of data-related subjects.
See salaries and explore the career path for Educator
Researcher
Researchers conduct original research to expand knowledge in various fields. The Tidyverse's tools for data cleaning, manipulation, and visualization can streamline your research process, helping you analyze data, identify patterns, and draw meaningful conclusions. This course can provide a valuable foundation for your research endeavors.
See salaries and explore the career path for Researcher
Healthcare Analyst
Healthcare Analysts use data to improve healthcare delivery, identify trends, and optimize patient outcomes. The Tidyverse's tools for data exploration, visualization, and modeling can empower you to analyze healthcare data, uncover hidden insights, and contribute to evidence-based decision-making in the healthcare industry.
See salaries and explore the career path for Healthcare Analyst

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Modeling Data in the Tidyverse.
Cover image
R Packages
Save
Provides a comprehensive introduction to advanced R programming techniques. It covers a wide range of topics, from data manipulation to visualization and modeling.
R Packages
Paperback
Check
R Packages
Kindle Edition
Check
Cover image
An Introduction to Statistical Learning
Save
Provides a comprehensive introduction to statistical learning methods, including linear regression, logistic regression, and tree-based methods. It valuable resource for both students and practitioners who want to learn more about data modeling.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
Probabilistic Machine Learning
Save
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It covers a wide range of topics, from basic concepts to more advanced techniques such as Bayesian inference.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
R for Data Science
Save
Provides a practical introduction to data science with R. It covers a wide range of topics, from data wrangling to modeling and visualization.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
Tidy Modeling with R
Save
Provides a collection of recipes for using the tidyverse, a collection of R packages for data science. It valuable resource for both students and practitioners who want to learn more about the tidyverse.
Tidy Modeling with R: A Framework for Modeling in...
Paperback
$$
Tidy Modeling with R
Kindle Edition
$$
Cover image
R Markdown
Save
Provides a comprehensive introduction to R Markdown, a powerful tool for creating dynamic reports and presentations. It valuable resource for both students and practitioners who want to learn more about R Markdown.
R Markdown: The Definitive Guide (Chapman &...
Paperback
$$
R Markdown: The Definitive Guide (Chapman &...
Kindle Edition
$$
Cover image
Machine Learning with R
Save
Provides a comprehensive introduction to machine learning with R. It covers a wide range of topics, from basic concepts to more advanced techniques such as deep learning.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
$$
R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
The Book of R
Save
Provides a comprehensive introduction to the R programming language. It valuable resource for both students and practitioners who want to learn more about R.
The Book of R: A First Course in Programming and...
Paperback
$$
The Book of R: A First Course in Programming and...
Kindle Edition
$$
Cover image
An R Companion to Applied Regression
Save
Provides a comprehensive treatment of regression analysis and generalized linear models, with a focus on practical applications. It valuable resource for both students and practitioners who want to learn more about these topics.
An R Companion to Applied Regression
Paperback
$$$
An R Companion to Applied Regression
Paperback
$$
An R Companion to Applied Regression by John Fox...
Paperback Bunko
$$$
An R Companion to Applied Regression
Kindle Edition
$$$
Cover image
The Elements of Statistical Learning
Save
Provides a broad overview of statistical learning methods, including linear regression, logistic regression, tree-based methods, and support vector machines. It valuable resource for both students and practitioners who want to learn more about these topics.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Hands-On Machine Learning with R
Save
Provides a practical introduction to machine learning with R. It covers a wide range of topics, from basic concepts to more advanced techniques such as deep learning.
Hands-On Machine Learning with R (Chapman &...
Hardcover
$$$
Hands-On Machine Learning with R (Chapman &...
Kindle Edition
$$$
Cover image
Machine Learning for Hackers
Save
Provides a practical introduction to machine learning for non-experts. It covers a wide range of topics, from basic concepts to more advanced techniques such as neural networks.
Machine Learning for Hackers: Case Studies and...
Paperback
$$
Machine Learning for Hackers: Case Studies and...
Kindle Edition
$$

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Course image
Effort
21 hours to complete
Via
Coursera
Institution
Johns Hopkins University
Instructors
Carrie Wright, PhD
Shannon Ellis, PhD
Stephanie Hicks, PhD
Roger D. Peng, PhD
Language
English
This course belongs to a collection called Tidyverse Skills for Data Science in R. It's comprised of five courses:
  1. Modeling Data in the Tidyverse (viewing)
  2. Wrangling Data in the Tidyverse
  3. Importing Data in the Tidyverse
  4. Visualizing Data in the Tidyverse
  5. Introduction to the Tidyverse
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Taught by PhDs who are recognized for their work in data analysis and related fields
Covers hypothesis testing, linear regression, nonlinear modeling, and machine learning
Assumes familiarity with the R programming language
Emphasizes the tidyverse, a collection of packages in R
Includes a project on modeling consumer complaints data
Multi-modal, with videos, readings, and interactive materials
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