Undergraduate Certificate in Numerical Analysis for Data Science
-- ViewingNowThe Undergraduate Certificate in Numerical Analysis for Data Science is a crucial course for learners seeking to excel in the data science industry. This program focuses on teaching students how to apply mathematical and statistical methods to analyze and interpret large datasets, providing a solid foundation in numerical analysis.
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⢠Introduction to Numerical Analysis: Basic concepts, numerical methods, and their applications in data science.
⢠Linear Algebra and Matrix Computations: Vector and matrix operations, linear systems, eigenvalues and eigenvectors, and their use in numerical analysis.
⢠Numerical Methods for Root Finding: Bisection method, Newton-Raphson method, and Secant method for finding roots of nonlinear equations.
⢠Interpolation and Approximation: Polynomial and spline interpolation, curve fitting, and numerical differentiation and integration.
⢠Numerical Linear Algebra: Singular value decomposition, QR factorization, eigenvalue decomposition, and their applications.
⢠Optimization Methods: Linear and nonlinear optimization, gradient descent, and convex optimization.
⢠Differential Equations: Numerical solution of ordinary and partial differential equations, stability analysis, and finite difference methods.
⢠Data Fitting and Regression Analysis: Linear and nonlinear regression, least squares method, and regularization techniques.
⢠Numerical Methods for Data Science: Numerical methods for large-scale data analysis, randomized algorithms, and parallel computing.
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