• Specialization: Intro to Statistical Learning 
  • Instructor: James Bird, Instructor
  • Prior knowledge needed: Intro Statistics and Foundational Math

Learning Outcomes 

  • Express why Statistical Learning is important and how it can be used.
  • Identify the strengths, weaknesses and caveats of different models and choose the most appropriate model for a given statistical problem.
  • Determine what type of data and problems require supervised vs. unsupervised techniques.

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Course Content

Duration: 1h

Introduction to overarching and foundational concepts in Statistical Learning like Supervised vs Unsupervised, Prediction, Inference, Interpretability vs Flexibility, Parametric Methods, Quantitative vs. Qualitative, etc.

Duration: 7h

Exploration into assessing models in different situations. How do we define a "best" model for given data?

Duration: 1h

Introduction to Simple Linear Regression, such as when and how to use it.

Duration: 10h

 A deep dive into multiple linear regression, a strong and extremely popular technique for a continuous target.

Duration: 52min

Exploration of Linear Regression pitfalls and the strengths of Logistic Regression in certain situations. Foundational generative models will also be covered.

Duration: 16h

Investigation of popular classification techniques, such as LDA and QDA. We will also explore another Regression, Poisson Regression, as well as link functions, which connect all Regressions together.

Duration: 15h

You will complete a programming assignment worth 31% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.

Note: This page is periodically updated. Course information on the Coursera platform supersedes the information on this page. Click View on Coursera button above for the most up-to-date information.