Expressway to Data Science: Essential Math

University of Colorado Boulder

Data Science is a rapidly growing field with diverse career opportunities. The "Expressway to Data Science: Essential Math" course from University of Colorado Boulder is tailored for aspiring Data Science professionals. This specialization offers a concise yet comprehensive overview of essential mathematics crucial for Data Science. Learners will delve into algebra, calculus, linear algebra, and numerical analysis, gaining the necessary skills to excel in the field. From computing derivatives to using matrix algebra to solve linear systems, this course equips learners with the foundational knowledge required for success in Data Science.

The course modules delve deeply into key mathematical concepts, providing hands-on practice and real-world problem-solving scenarios. Learners will gain proficiency in logarithm properties, derivatives, matrix operations, solving real-world problems using matrices, understanding n-dimensional space, integral calculus, numerical analysis, and more. With a focus on practical applications, this course prepares learners to excel in Statistical Modeling for Data Science Application, a crucial component of CU Boulder's Master of Science in Data Science program.

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Expressway to Data Science: Essential Math
Course Modules

This course covers algebra, calculus, linear algebra, and numerical analysis, providing essential foundational knowledge for Data Science applications.

Algebra and Differential Calculus for Data Science

The "Algebra and Differential Calculus for Data Science" module delves into logarithm properties, derivative calculations, and graphing functions. Learners will gain a solid understanding of derivatives and their graphical representations, essential for Data Science applications.

Essential Linear Algebra for Data Science

The "Essential Linear Algebra for Data Science" module explores the foundational concept of matrices and their real-world applications. Learners will understand matrix representations in n-dimensional space, key properties of systems of equations, and practical skills for solving real-world problems using matrices and transformations.

Integral Calculus and Numerical Analysis for Data Science

The "Integral Calculus and Numerical Analysis for Data Science" module provides hands-on practice in integrating by parts, bisection, diagonalizing matrices, and computing partial derivatives. Learners will gain essential skills for applying integral calculus and numerical analysis to real-world Data Science problems.

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