Numerical Analysis for Engineering Students
Root Finding, Curve Fitting, Numerical Integration, Initial Value Problems and More!
What is Numerical Analysis?
Numerical Analysis is one of the most useful math courses there is. It is also required in many Engineering degree programs. Why?
Well, numerical analysis techniques give you a way to approximate solutions to problems when analytical solutions aren't possible. These methods are widely used in many engineering disciplines such as aerospace, mechanical, and electrical.
What comes with the course?
 13.5+ hours of ondemand lecture videos
 32 fullyworked examples  includes both handwritten and MATLAB problems
 Downloadable outline of notes with all example problem statements
 50 downloadable MATLAB scripts needed to run all the examples
 Certificate of Completion once you finish the course
 Email access to the instructor in case you get stuck on a topic
 30 day money back guarantee so there's no risk to try it out. Please see the Terms of Use here for more details.
What will you learn?
This course will focus on the numerical analysis techniques most frequently covered at the undergraduate level. Here's what you'll learn:
 Root finding: Bisection, Newton's, secant methods
 Solving systems of equations: Gaussian elimination and Gauss Jordan techniques
 Curve fitting: Least squares regression, polynomial regression, Lagrange interpolating polynomials, cubic splines & more
 Numerical Differentiation: Finite difference & Lagrange polynomial techniques
 Numerical Integration: Rectangle, midpoint, trapezoidal and Simpson's methods
 Ordinary differential equations: Euler's, modified Euler's, and 2nd, 3rd, & 4th order Runge Kutta techniques
You will learn the theory behind the techniques as well as the coding aspects. We will work examples by hand and then follow those with MATLAB examples.
Why MATLAB?
MATLAB is widely used in undergraduate engineering programs as well as in industry. Because of this, MATLAB is used in this course to demonstrate how to successfully code each of the methods presented. In addition, it should be noted that this course can be used to enhance your coding skills.
Click here to see the full curriculum
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Course Curriculum

StartDownload the Outline of Notes Here

Start1.1 Error Calculations (14:43)

Start1.2 Bisection Method (10:56)

Start1.3 Bisection Method Algorithm (5:38)

Start1.4 Example 1 (12:21)

Start1.5 MATLAB Bisection Method (9:05)

Start1.6 Newtons Method (11:17)

Start1.7 Newtons Algorithm and Example 2 (12:53)

Start1.8 MATLAB Example 2 (5:26)

Start1.9 MATLAB Example 3 (7:33)

Start1.10 Secant Method (8:11)

Start1.11 Secant Method Algorithm and Example 4 (12:24)

Start1.12 MATLAB Example 4 Secant Method (4:01)

Start1.13 MATLAB Example 5 Secant Method (5:51)

Start2.1 Solving Systems of Linear Equations (13:23)

Start2.2 Gaussian Elimination (16:05)

Start2.3 Gaussian Elimination Algorithm and MATLAB example (8:35)

Start2.4 MATLAB Example 6 Gaussian Elimination (9:39)

Start2.5 Gaussian Elimination with Pivoting (8:58)

Start2.6 Gauss Jordan Elimination and Example 7 (16:00)

Start2.7 Gauss Jordan and MATLAB example (5:01)

Start3.1 Least Squares Regression (24:41)

Start3.2 Example 8 with MATLAB (10:50)

Start3.3 Polynomial Regression (18:02)

Start3.4 Example 9 (21:36)

Start3.5 Example 9 with MATLAB functions (12:39)

Start3.6 Lagrange Interpolating Polynomials (6:08)

Start3.7 Example 10 (21:28)

Start3.8 Example 11 (10:49)

Start3.9 Lagrange Interpolating Polynoimal Alorithm with MATLAB (8:45)

Start3.10 Newtons Divided Differences (21:17)

Start3.11 Example 12 Divided Differences (17:45)

Start3.12 Divided Differences Algorithm and MATLAB Example 12 (8:03)

Start3.13 Linear Splines (10:15)

Start3.14 Example 13 MATLAB Linear Spline (7:39)

Start3.15 Quadratic Splines (15:28)

Start3.16 Example 14 Quadratic Spline with MATLAB (28:48)

Start3.17 Cubic Splines (16:31)

Start3.18 Example 15 Cubic Spline with MATLAB (15:24)

Start3.19 Cubic Spline with Lagrange Polynomials (16:45)

Start3.20 Example 16 Cubic Spline (15:22)

Start3.21 Cubic Spline Algorithm with MATLAB (10:01)

Start3.22 Cubic Spline built in MATLB function (6:42)
What are the prerequisites?
This course uses a lot of derivatives and integrals, as well as differential equations. Therefore, a solid understanding of Calculus and Differential Equations is needed to fully understand the material. Basic knowledge of matrices is also recommended.
MATLAB will be used to practice techniques so the ability to code in MATLAB is also needed. If you don't have access to MATLAB, go to the MathWorks website and check out the student options.
Is there a recommended textbook?
Yes! The textbook I use for this course is Numerical Methods for Engineers and Scientists, 3rd Edition by Gilat and Subramaniam. This text is not required for the course but it provides many examples and is a great resource.
Note: the link is an affiliate link. This means that if you make a purchase through the link I may, at no cost to you, earn a small commission. This helps me keep the site affordable for students. Thanks for your support!
Your Instructor
Teaching is my passion. As a University professor I have taught 1000's of students and watched them transform from freshmen into successful engineers. Unlike many STEM professors, I believe in teaching complex material in simple, easytounderstand terms. I teach my courses in a way I wish I had been taught: straightforward lectures with plenty of examples on how to apply the theory being learned.
In addition to University experience, I also worked as an engineer for 8 years in industry at a wellknown defense company. This experience enables me to focus in on topics that are actually applicable in the real world, not just textbook problems.
Come learn with me!