Adam

“Getting a data science job would have been much harder without Dataquest. It’s a great product. I recommend it to anyone who asks me about how to get started.”

Adam Zabrodski

Data Science Manager @ Later

Course overview

In this course, you’ll learn how and when to use linear regression models to make predictions. You’ll learn how to build linear regression models, how to interpret their output, and how to assess model accuracy. You’ll also explore the limitations of linear regression models when data isn’t linear. Finally, you’ll learn to use programming tools to fit and visualize many linear regression models at once.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll complete a project to practice your skills with a subset of condominium sales data from all five boroughs of New York City.

Key skills

  • Processing numerical and text data
  • Interpreting linear regression model outputs
  • Assessing model fit and accuracy

Course outline

Linear Regression Modeling in R [6 lessons]

Introduction to Modeling 1h

Lesson Objectives
  • Define predictive modeling
  • Define prediction and inference
  • Define errors and residuals

Bivariate Relationships - Correlation and Scatterplots 1h

Lesson Objectives
  • Define correlation
  • Identify relationships using scatterplots
  • Select variables for linear regression

Estimating the Coefficients and Fitting Linear Models 1h

Lesson Objectives
  • Define coefficients
  • Fit a linear regression model
  • Interpret linear regression model outputs

Assessing the Accuracy of the Model 1h

Lesson Objectives
  • Assess the quality of coefficient estimates
  • Compute linear regression model quality of fit
  • Interpret linear regression model quality of fit

Fitting Many Linear Models 1h

Lesson Objectives
  • Generate tidy linear models with the broom package
  • Fit linear regression models
  • Visualize linear regression models.

Guided Project - Predicting Condominium Sale Prices 1h

Lesson Objectives
  • Expand your portfolio with a guided bivariate linear regression model project
  • Overcome errors in the dataset that can influence modeling results

Projects in this course

Guided Project - Predicting Condominium Sale Prices

Combine the skills you learned in this course to perform linear regression modeling of condominium sale price on the basis of size.

The Dataquest guarantee

Guarantee

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

Money

We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

Master skills faster with Dataquest

Go from zero to job-ready

Go from zero to job-ready

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

Build your project portfolio

Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

Challenge yourself with exercises

Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

Showcase your path certification

Showcase your path certification

Impress employers by completing a capstone project and certifying it with an expert review.

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Aaron

Aaron Melton

Business Analyst at Aditi Consulting

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Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

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Victoria E. Guzik

Associate Data Scientist at Callisto Media

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