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This online data science course will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and determining whether other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more!

In this course, you will learn how to:

• 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

This course 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. .