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.
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Integral Calculus and Numerical Analysis for Data Science
University of Colorado Boulder via Coursera
Overview
Syllabus
- Area Under The Curve
- Explore the notion of area under a curve, how that relates to the integral and compute basic integrals.
- Numerical Analysis Intro
- Introduction to Numerical Analysis using 2 root-finding methods.
- Diagonalization & SVD
- Explore general matrix decomposition, as well as a specialized and useful version called Singular Value Decomposition.
- 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 and Jane Wall
