Numerical Analysis With Matlab

Course Overview

What does this course tell?
(Please note that the following overview content is from the original platform)


Iterative Methods for Linear Systems, Jacobi, Gauss Seidel, and Relaxation (Matlab Implementation).
Newton and Lagrange Interpolating Polynomial :: Full Lecture.
Trapezoid and Simpson's 1/3 Rule :: Newton Coates :: Matlab Implementation.


We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
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Numerical Analysis with Matlab is an online course that teaches learners how to use Matlab to solve numerical problems. Learners can learn about iterative methods for linear systems, such as Jacobi, Gauss Seidel, and Relaxation, and how to implement them in Matlab. They can also learn about Newton and Lagrange interpolating polynomials, and how to use the Trapezoid and Simpson's 1/3 Rule to solve numerical problems. Finally, learners can learn about Newton Coates and how to implement it in Matlab. By the end of the course, learners will have a comprehensive understanding of numerical analysis and how to use Matlab to solve numerical problems.

[Applications]
Students who have completed the course Numerical Analysis With Matlab can apply their knowledge to a variety of applications. They can use the iterative methods for linear systems, Jacobi, Gauss Seidel, and relaxation (Matlab implementation) to solve linear equations. They can also use Newton and Lagrange interpolating polynomials to approximate functions. Additionally, they can use the Trapezoid and Simpson's 1/3 Rule, as well as Newton Coates (Matlab implementation) to approximate integrals.

[Career Paths]
1. Data Scientist: Data Scientists use mathematical and statistical techniques to analyze large datasets and uncover patterns and trends. They use their findings to develop predictive models and algorithms that can be used to make decisions and improve processes. Data Scientists are in high demand as businesses increasingly rely on data-driven decision making.

2. Machine Learning Engineer: Machine Learning Engineers use algorithms and techniques from Machine Learning to develop and deploy predictive models and applications. They are responsible for designing, building, and maintaining Machine Learning systems, as well as optimizing them for performance and accuracy. The demand for Machine Learning Engineers is growing rapidly as businesses look to leverage the power of Machine Learning to gain a competitive edge.

3. Quantitative Analyst: Quantitative Analysts use mathematical and statistical techniques to analyze financial data and develop models and algorithms to make predictions and inform decisions. They are responsible for developing and maintaining quantitative models, as well as analyzing and interpreting data. The demand for Quantitative Analysts is increasing as businesses look to leverage the power of data to make better decisions.

4. Matlab Developer: Matlab Developers use the Matlab programming language to develop applications and algorithms. They are responsible for designing, coding, testing, and debugging Matlab programs. The demand for Matlab Developers is increasing as businesses look to leverage the power of Matlab to develop powerful applications and algorithms.