Statistical Learning for Data Science Specialization

Statistical Learning is a crucial specialization for those pursuing a career in data science or seeking to enhance their expertise in the field. This program builds upon your foundational knowledge of statistics and equips you with advanced techniques for model selection, including regression, classification, trees, SVM, unsupervised learning, splines, and resampling methods. Additionally, you will gain an in-depth understanding of coefficient estimation and interpretation, which will be valuable in explaining and justifying your models to clients and companies. Through this specialization, you will acquire conceptual knowledge and communication skills to effectively convey the rationale behind your model choices and coefficient interpretations.

Throughout the specialization, learners will complete many programming assignments designed to help learners master statistical learning concepts, including regression, classification, trees, SVM, unsupervised learning, splines, and resampling methods.

In this specialization, you will learn how to:

  • 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.

Courses

  • Regression and Classification
  • Resampling, Selection and Splines
  • Trees, SVM, and Unsupervised Learning

  This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science degree offered on the Coursera platform. Learn more: MS in Data Science.

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