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.
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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.
Course 3 of 3 in the Expressway to Data Science: Essential Math Specialization.
What You Will Learn
- Practice integrating by parts for more complex problems.
- Identify how bisection works after its given an initial guess.
- Diagonalize a matrix by hand.
- Compute the partial derivatives of a function.
Syllabus
WEEK 1
Area Under The Curve
Explore the notion of area under a curve, how that relates to the integral and compute basic integrals.
WEEK 2
Numerical Analysis Intro
Introduction to Numerical Analysis using 2 root-finding methods.
WEEK 3
Diagonalization & SVD
Explore general matrix decomposition, as well as a specialized and useful version called Singular Value Decomposition.
WEEK 4
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.
