- Level Professional
- المدة 4 ساعات hours
- الطبع بواسطة University of Colorado Boulder
-
Offered by
عن
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.comالوحدات
Integral Calculus
4
Assignment
- Integral and Area under the Curve Quiz
- Fundamental Theorem of Calculus and Computing Simple Integrals Quiz
- Computing Area bounded by Lines and Curves Quiz
- Integration by Parts Quiz
5
Videos
- Integrals and Area under the Curve
- Fundamental Theorem of Calculus and Computing Simple Integrals
- Computing Area bounded by Lines and Curves
- Indefinite Integrals
- Integration by Parts
1
Readings
- Where to Find Help
Introduction to Numerical Analysis using 2 root-finding methods
2
Assignment
- Simple bisection method for root-finding Quiz
- Newton's method for root-finding Quiz
2
Videos
- Root-finding using Bisection Method
- Root-finding using Newton's Method
Diagonalization
1
Assignment
- Diagonalization of a 3x3 Matrix Quiz
5
Videos
- Diagonalization Introduction
- Diagonalization
- Diagonalization Example 3x3
- Symmetric Matrix
- Diagonalization of Symmetric Matrix 3x3
Singular Value Decomposition
2
Videos
- Singular Value Decomposition Overview
- Singular Value Decomposition Example
Partial Derivatives
1
Assignment
- Partial Derivatives Quiz
3
Videos
- Partial Derivatives Introduction
- Partial Derivatives examples, Part I
- Partial Derivatives examples, Part II
Directional Derivatives & Gradient Vectors
2
Videos
- Directional Derivatives with example
- Gradient Vectors & Steepest Descent

Instructors
James Bird

Instructors
Jane Wall