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James Bird and Osita Onyejekwe

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.

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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.

This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

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What's inside

Three courses

Regression and Classification

(0 hours)
Introduction to Statistical Learning explores concepts in statistical modeling, including when to use certain models, how to tune them, and if other options provide trade-offs. Topics include Regression, Classification, Trees, Resampling, and Unsupervised techniques.

Resampling, Selection and Splines

(0 hours)
Statistical Learning for Data Science is an advanced course for working professionals. Students will learn how to apply resampling methods, optimize fitting procedures, and identify the benefits of non-linear models.

Trees, SVM and Unsupervised Learning

(0 hours)
Trees, SVM and Unsupervised Learning provides a foundation in support vector machines, neural networks, decision trees, and XG boost. Through instruction and hands-on experience, you will learn to build predictive models using these techniques and understand their advantages and disadvantages.

Learning objectives

  • 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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