University of Colorado Boulder
Statistical Learning for Data Science Specialization
University of Colorado Boulder

Statistical Learning for Data Science Specialization

Advanced Stats for Data Science Mastery. Master knowledge and skills to communicate model choices and interpretations effectively

Taught in English

Some content may not be translated

Osita Onyejekwe
James Bird

Instructors: Osita Onyejekwe

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Specialization - 3 course series

Get in-depth knowledge of a subject

Intermediate level

Recommended experience

4 months at 9 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Express why Statistical Learning is important and how it can be used.

  • Explain the pros and cons of certain models in certain situations.

  • Apply many regression and classification techniques.

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Specialization - 3 course series

Get in-depth knowledge of a subject

Intermediate level

Recommended experience

4 months at 9 hours a week
Flexible schedule
Learn at your own pace

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  • Develop a deep understanding of key concepts
  • Earn a career certificate from University of Colorado Boulder
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Specialization - 3 course series

What you'll learn

  • 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.

Skills you'll gain

Category: Modeling
Category: Data Science
Category: Machine Learning
Category: Statistical Analysis
Category: R Programming

What you'll learn

  • Apply resampling methods in order to obtain additional information about fitted models.

  • Optimize fitting procedures to improve prediction accuracy and interpretability.

  • Identify the benefits and approach of non-linear models.

Skills you'll gain

Category: Statistics
Category: Data Science
Category: Selection
Category: Resampling
Category: Splines

What you'll learn

  • Describe the advantages and disadvantages of trees, and how and when to use them.

  • Apply SVMs for binary classification or K > 2 classes.

  • Analyze the strengths and weaknesses of neural networks compared to other machine learning algorithms, such as SVMs.

Skills you'll gain

Category: Statistics
Category: Unsupervised Learning
Category: regression
Category: Trees
Category: Support Vector Machine (SVM)

Instructors

Osita Onyejekwe
University of Colorado Boulder
2 Courses672 learners

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