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Self-Paced Course

Data Science in Python: Regression

Master the foundations for regression analysis in Python, including linear & regularized regression, forecasting, validation & testing, and more

Course Hours14.5 hours
Skills Learned
Machine Learning
Data Analysis
Course Level

Course Description

This is a hands-on, project-based course designed to help you master the foundations for regression analysis in Python.

We’ll start by reviewing the data science workflow, discussing the primary goals & types of regression analysis, and do a deep dive into the regression modeling steps we’ll be using throughout the course.

You’ll learn to perform exploratory data analysis, fit simple & multiple linear regression models, and build an intuition for interpreting models and evaluating their performance using tools like hypothesis tests, residual plots, and error metrics. We’ll also review the assumptions of linear regression, and learn how to diagnose and fix each one.

From there, we’ll cover the model testing & validation steps that help ensure our models perform well on new, unseen data, including the concepts of data splitting, tuning, and model selection. You’ll also learn how to improve model performance by leveraging feature engineering techniques and regularized regression algorithms.

Throughout the course you'll play the role of Associate Data Scientist for Maven Consulting Group on a team that focuses on pricing strategy for their clients. Using the skills you learn throughout the course, you'll use Python to explore their data and build regression models to help firms accurately predict prices and understand the variables that impact them.

Last but not least, you'll get an introduction to time series analysis & forecasting techniques. You’ll learn to analyze trends & seasonality, perform decomposition, and forecast future values.

If you're an aspiring data scientist looking for an introduction to the world of regression modeling with Python, this is the course for you.


  • 8.5 hours on-demand video

  • 14 homework assignments

  • 10 quizzes

  • 3 projects

  • 2 skills assessments (1 benchmark, 1 final)



  • Data analysts or BI experts looking to transition into a data science role

  • Python users who want to build the core skills for applying regression models in Python

  • Anyone interested in learning one of the most popular open source programming languages in the world


  • We strongly recommend taking our Data Prep & EDA course first
  • Jupyter Notebooks (free download, we'll walk through the install)
  • Familiarity with base Python and Pandas is recommended, but not required

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Every subscription includes access to the following course materials

  • Interactive Project files
  • Downloadable e-books
  • Graded quizzes and assessments
  • 1-on-1 Expert support
  • 100% satisfaction guarantee
  • Verified credentials & accredited badges
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