Johns Hopkins University

Modeling Data in the Tidyverse

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

Taught in English

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Carrie Wright, PhD
Shannon Ellis, PhD
Stephanie Hicks, PhD

Instructors: Carrie Wright, PhD

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Course

Gain insight into a topic and learn the fundamentals

21 hours (approximately)
Flexible schedule
Learn at your own pace

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

Details to know

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Assessments

8 quizzes

Course

Gain insight into a topic and learn the fundamentals

21 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Tidyverse Skills for Data Science in R Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 11 modules in this course

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.

What's included

16 readings1 quiz

Inferential Analysis is what analysts carry out after they’ve described and explored their dataset. After understanding your dataset better, analysts often try to infer something from the data. This is done using statistical tests. We discussed a bit about how we can use models to perform inference and prediction analyses. What does this mean?

What's included

3 readings1 quiz

Linear models are the most commonly used models in data analysis because of their computational efficiency and their ease of interpretation. Having a solid understanding of linear models and how they work is critical for any work in data science. The tidyverse provides a set of tools for making linear modeling more efficient and streamlined.

What's included

12 readings1 quiz

Multiple linear regression is needed when you want to include confounding factors or other predictors in your model for the response. R provides a straightforward way to do this via the formula interface to the lm() function.

What's included

1 reading1 quiz

While we’ve focused on linear regression in this lesson on inference, linear regression isn’t the only analytical approach out there. However, it is arguably the most commonly used. And, beyond that, there are many statistical tests and approaches that are slight variations on linear regression, so having a solid foundation and understanding of linear regression makes understanding these other tests and approaches much simpler. For example, what if you didn’t want to measure the linear relationship between two variables, but instead wanted to know whether or not the average observed is different from expectation?

What's included

3 readings

Hypothesis testing describes a family of statistical techniques for determining whether the data you collect provides evidence for the value of an unknown parameter of interest. The goal of hypothesis tests is to make inferences while accounting for variability in the data that can lead to spurious results.

What's included

3 readings1 quiz1 plugin

Prediction modeling is an essential activity in data science and involves building systems for making predictions based on previously observed data. These models are typically very flexible and can capture a range of different relationships.

What's included

12 readings1 quiz

There are incredibly helpful packages available in R thanks to the work of RStudio. As mentioned above, there are hundreds of different machine learning algorithms. The tidymodels R packages have put many of them into a single framework, allowing you to use many different machine learning models easily.

What's included

5 readings1 quiz

This case study will demonstrate an approach to building a prediction model for predicting outdoor air pollution concentrations in the United States.

What's included

17 readings1 ungraded lab

The tidymodels collection of packages can be overwhelming at first glance. Here, we provide a quick summary chart to help navigate all of the packages and when they should be used.

What's included

1 reading

In this project, you will practice building models with the tidyverse for classifying consumer complaints data from the Consumer Financial Protection Bureau (CFPB). This project includes both a Peer Review step in which you'll upload R Markdown and knitted HTML files AND a Quiz step in which you'll answer questions about the predictions made by your classification algorithm.

What's included

1 reading1 quiz1 peer review

Instructors

Carrie Wright, PhD
Johns Hopkins University
7 Courses7,056 learners
Shannon Ellis, PhD
Johns Hopkins University
5 Courses5,523 learners
Stephanie Hicks, PhD
Johns Hopkins University
5 Courses5,523 learners

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