Modeling Data in the Tidyverse from Coursera

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.

What's inside

Syllabus

Modeling Data Basics

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?

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.

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.

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?

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.

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.

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.

Case Studies

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

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.

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.

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Tidyverse data modeling explored

Learners say that Modeling Data in the Tidyverse offers a solid foundation in using the tidyverse for data modeling. The course is well presented and easy to understand, making it a good choice for those new to the tidyverse or those looking to transition from caret to tidymodels for machine learning. The course content is based on the book, but students note that the course would be improved with the addition of videos.

Students appreciated the clear presentation and found the course easy to understand.

"Well presented and clearly understandable course."

"Pretty decent course that goes through the crucial aspects of the tidymodels workflow."

The entire course is based upon the book.

"The entire course is built upon the book."

Students recommend adding videos to the course to enhance the learning experience.

"The course Content is good, but there should be some videos."

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

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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

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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

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Gain a better understanding of linear regression through a guided tutorial

Browse courses on Linear Regression

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Find a tutorial on linear regression
Follow the steps and complete the exercises in the tutorial

Practice Hypothesis Testing

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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

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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

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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

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Watch tutorials on different types of linear modeling techniques.
Apply these techniques to practice datasets.

Complete practice problems on hypothesis testing

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Practice hypothesis testing to improve understanding of the concept

Browse courses on Hypothesis Testing

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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

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Deepen understanding of data modeling by creating a presentation

Browse courses on Tidyverse

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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

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Expand knowledge and skills in machine learning by attending a specialized workshop

Browse courses on Machine Learning

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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

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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

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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

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Put skills to the test and gain practical experience through a data modeling competition

Browse courses on Data Modeling

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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

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Gain practical experience and build connections in the data science field

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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

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Apply course knowledge to a practical project and enhance problem-solving skills

Browse courses on Prediction Modeling

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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 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

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

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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