Modeling Data in the Tidyverse

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

Carrie Wright, PhD +3 more instructors

Offered By

Tidyverse Skills for Data Science in R SpecializationJohns Hopkins University

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 5 of 5 in the

Tidyverse Skills for Data Science in R Specialization

Approx. 21 hours to complete

English

Subtitles: English

What you will 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

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 5 of 5 in the

Tidyverse Skills for Data Science in R Specialization

Approx. 21 hours to complete

English

Subtitles: English

Instructors

Offered by

Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Syllabus - What you will learn from this course

Week

Week 1

4 hours to complete

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.

4 hours to complete

16 readings

16 readings

Course Textbook10m

The Purpose of Data Science5m

Types of Data Science Questions10m

Data Needs5m

Number of observations is too small5m

Dataset does not contain the exact variables you are looking for10m

Variables in the dataset are not collected in the same year5m

Dataset is not representative of the population that you are interested in10m

Some variables in the dataset are measured with error5m

Variables are confounded10m

Descriptive and Exploratory Data Analysis15m

Missing Values10m

Shape25m

Identifying Outliers20m

Evaluating Variables20m

Evaluating Relationships30m

1 practice exercise

Modeling Data Basics Quiz30m

Week

Week 2

1 hour to complete

Inference

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?

1 hour to complete

3 readings

Week

Week 3

2 hours to complete

Linear Modeling

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.

2 hours to complete

12 readings

Week

Week 4

1 hour to complete

Multiple Linear Regression

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.

1 hour to complete

1 reading

Week

Week 5

23 minutes to complete

Beyond Linear Regression

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?

23 minutes to complete

3 readings

Week

Week 6

1 hour to complete

Hypothesis Testing

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.

1 hour to complete

3 readings

Week

Week 7

3 hours to complete

Prediction Modeling

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.

3 hours to complete

12 readings

Week

Week 8

2 hours to complete

The tidymodels Ecosystem

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.

2 hours to complete

5 readings

Week

Week 9

5 hours to complete

Case Studies

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

5 hours to complete

17 readings

17 readings

Case Study #1: Predicting Annual Air Pollution5m

The Data5m

Data Import5m

Data Exploration and Wrangling20m

Evaluate Correlation15m

Splitting the Data10m

Making a Recipe30m

Running Preprocessing30m

Specifying the Model20m

Assessing the Model Fit15m

Model Performance: Getting Predicted Values15m

Visualizing Model Performance5m

Quantifying Model Performance10m

Assessing Model Performance on v -folds Using tune30m

Random Forest30m

Model Tuning30m

Final model performance evaluation30m

Week

Week 10

5 minutes to complete

Summary of tidymodels

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.

5 minutes to complete

1 reading

Week

Week 11

2 hours to complete

Project: Modeling Data in the Tidyverse

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.

2 hours to complete

1 reading

About the Tidyverse Skills for Data Science in R Specialization

This Specialization is intended for data scientists with some familiarity with the R programming language who are seeking to do data science using the Tidyverse family of packages. Through 5 courses, you will cover importing, wrangling, visualizing, and modeling data using the powerful Tidyverse framework. The Tidyverse packages provide a simple but powerful approach to data science which scales from the most basic analyses to massive data deployments. This course covers the entire life cycle of a data science project and presents specific tidy tools for each stage.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.

Modeling Data in the Tidyverse

About this Course

What you will learn

Instructors

Carrie Wright, PhD

Shannon Ellis, PhD

Stephanie Hicks, PhD

Roger D. Peng, PhD

Offered by

Johns Hopkins University

Syllabus - What you will learn from this course

Week 1

Modeling Data Basics

Week 2

Inference

Week 3

Linear Modeling

Week 4

Multiple Linear Regression

Week 5

Beyond Linear Regression

Week 6

Hypothesis Testing

Week 7

Prediction Modeling

Week 8

The tidymodels Ecosystem

Week 9

Case Studies

Week 10

Summary of tidymodels

Week 11

Project: Modeling Data in the Tidyverse

About the Tidyverse Skills for Data Science in R Specialization

Frequently Asked Questions

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