Data Science is growing rapidly, creating opportunities for careers across a variety of fields. This specialization is designed for learners embarking on careers in Data Science. Learners are provided with a concise overview of the foundational mathematics that are critical in Data Science. Topics include algebra, calculus, linear algebra, and some pertinent numerical analysis. Expressway to Data Science is also an excellent primer for students preparing to complete CU Boulder’s Master of Science in Data Science program.
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Data Science is growing rapidly, creating opportunities for careers across a variety of fields. This specialization is designed for learners embarking on careers in Data Science. Learners are provided with a concise overview of the foundational mathematics that are critical in Data Science. Topics include algebra, calculus, linear algebra, and some pertinent numerical analysis. Expressway to Data Science is also an excellent primer for students preparing to complete CU Boulder’s Master of Science in Data Science program.
Data Science is growing rapidly, creating opportunities for careers across a variety of fields. This specialization is designed for learners embarking on careers in Data Science. Learners are provided with a concise overview of the foundational mathematics that are critical in Data Science. Topics include algebra, calculus, linear algebra, and some pertinent numerical analysis. Expressway to Data Science is also an excellent primer for students preparing to complete CU Boulder’s Master of Science in Data Science program.
This specialization is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program.
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What's inside
Three courses
Algebra and Differential Calculus for Data Science
(6 hours)
Algebra and Differential Calculus for Data Science
(6 hours)
Essential Linear Algebra for Data Science
(5 hours)
Essential Linear Algebra for Data Science
(5 hours)
Integral Calculus and Numerical Analysis for Data Science
(4 hours)
Integral Calculus and Numerical Analysis for Data Science
(4 hours)Learning objectives
- Compute simple derivatives.
- Convert between linear systems and matrix notation and use matrix algebra to solve linear systems.
- Factor a simple matrix using singular value decomposition (svd).
- Compute simple derivatives.
- Convert between linear systems and matrix notation and use matrix algebra to solve linear systems.
- Factor a simple matrix using singular value decomposition (svd).
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