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Updates to Master Statistics with Python: Linear regression, probability, and more

Great news! We've updated our Master Statistics with Python Skill Path with new tracks, modules, and content.

In April, we made several changes to our Master Statistics with Python course. First, we added new tracks about linear regression and probability — two essential components of machine learning, testing, statistics, and data science.

Probability is an essential building block for many data analysis techniques and machine learning algorithms. The Master Statistics Path has always assumed some understanding of probability, but it's now officially part of the syllabus!

Linear regression is a machine learning technique used to model quantitative outcomes. For example, we can use linear regression to understand the relationship between a person's salary and other attributes, such as years of experience, location, gender, and ethnicity. We could also use linear regression to predict a person's adult height based on data collected during early childhood.

We've also updated the path to include another new track from the Data Analyst Career Path (Visualizing Categorical Data), and integrated eight videos from our recent livestream series throughout the path.

What are the specific changes?

Each of the new tracks includes several new modules. Probability is broken down into modules about the rules of probability, probability distributions, and sampling distributions. The Linear Regression track consists of modules about simple linear regression, multiple linear regression, and choosing a linear regression model. Visualizing Categorical Data includes a module in which you'll learn how to create bar charts and pie charts.

The eight videos added to the course were originally featured as livestreams on our YouTube channel, in which three of our Curriculum Developers — Sophie, Alex, and Jamie — walk us through:

  • Inspecting a dataset
  • Summary statistics and data viz
  • Associations between variables
  • The central limit theorem
  • Simulating a binomial test
  • Significance thresholds and multiple hypothesis tests
  • Hypothesis testing for an association
  • A/B testing

Watching the videos isn't required to complete the course — simply opening them will mark them as complete.  Still, we highly recommend checking them out! They include live demos and additional sample code and data you can download from GitHub. They're also a great way to engage with our community.

How does this affect you?

If you're already enrolled in Master Statistics with Python, you'll notice a decrease in your progress — but don't worry. The lessons you've finished will remain completed.

If you've already completed the course, you'll keep your certification PDF, but you'll need to complete the new material for the updated certification to appear on your profile.


We've updated our Master Statistics with Python course with new lessons and modules around data visualization, linear regression, and probability. You'll retain your certificate if you've already completed the course, but you may notice a decrease in your progress because of the added content.

Get more practice, more projects, and more guidance.

Jacob Johnson

Jacob Johnson

Jacob Johnson is a Content Marketing Associate at Codecademy with a background in writing about technology.

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Updates to Master Statistics with Python: Linear regression, probability, and more
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