Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will provide an intuitive understanding of foundational integral calculus, including integration by parts, area under a curve, and integral computation. It will also cover root-finding methods, matrix decomposition, and partial derivatives. This course is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program. Logo courtesy of ThisisEngineering RAEng on Unsplash.com
Partial Derivatives examples, Part II
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Skills You'll Learn
Mathematics, Partial Derivative, matrix diagonalization, root-finding, Mathematical Theory & Analysis, Integrals, Calculus, Singular Value Decomposition (SVD)
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From the lesson
Partial Derivatives & Steepest Descent
We will learn a core calculus concept called partial derivatives, as well as delving into directional derivatives and their usefulness in higher level statistics.
Taught By
James Bird
Instructor
Jane Wall
Faculty Director of Data Science Programs