Name: Mathematics for Data Analysis and Modeling
Rating: 5 (7 reviews)

Mathematics for Data Analysis and Modeling

To study many applied disciplines, it is necessary to have basic knowledge of higher mathematics and linear algebra. Mathematics is often used in data analysis, system modeling, building machine learning models, etc. Let's look at some of the most important topics and learn how to use the mathematical apparatus to solve real problems.Show more

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Intermediate

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21 chapters

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Outcomes

Learning outcomes

Modules

Testimonials

Recommendations

Codefinity advantages

FAQs

What you will learn

Grasp core mathematical concepts and definitions crucial for data analysis and system modeling.
Acquire proficiency in linear algebra, mastering vectors, matrices and their numerical operations.
Tackle matrix multiplication, inverses, transposes, and learn to calculate determinants and system of linear equations.
Understand the fundamentals of mathematical analysis, including functions, series, derivatives, and optimization problems.

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There are 3 modules in this course

Let's start with some basic definitions and concepts we'll use later. Consider the idea of a function, a numerical sequence, and its sum, and also understand what a coordinate system's basis is.

The simplest and most commonly used type of relationship is the linear relationship. Linear algebra is a branch of higher mathematics entirely devoted to linear functions and linear spaces. Let's look at some of the most important topics in linear algebra: vectors, matrices, solving linear equations, and solving the spectral problem for matrices.

Mathematical analysis is a discipline that allows you to analyze functions according to various criteria. Consider how to check numerical sequences for convergence, find the maximum/minimum values of functions, solve nonlinear equations, and use integrals to solve applied problems.

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Saul Vione Winik

Reviewed on Apr 2, 2025

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