Statistical Learning for Data Science Specialization

Statistical Learning for Data Science Specialization | Coursera [Update 10/2023]
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Genre: eLearning

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

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.

Specialization – 3 course series
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.

Applied Learning Project

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.

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