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Quantitative Methods for Data Science: I - Introductory Applied Mathematics By James Godfrey
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This reference book is intended for data scientists and presents introductory applied mathematics appropriate for understanding methodologies used in a variety of disciplines: statistics, engineering... More > and the natural sciences, advanced computing, economics and finance, social media, advertising, climate analysis, healthcare and genetics, and other data-intensive areas. The presentation includes vector and matrix algebra, differential and integral calculus, complex variables, vector calculus, transforms, and ordinary and partial differential equations. The primary objective is to develop tools and techniques for understanding more advanced quantitative methods. The reader is provided an approach that is implementation-driven rather than theoretically-driven. The material is appropriate for college-level applied mathematics courses with an emphasis on data science.< Less
Quantitative Methods for Data Science: II - Problems By James Godfrey
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This reference contains problems and solutions in introductory applied mathematics for the following areas: vector and matrix algebra, differential and integral calculus, complex variables, series,... More > vector calculus, transforms, and ordinary and partial differential equations. The focus is on understanding material for solving problems related to the following disciplines: statistics, engineering and the natural sciences, advanced software development, economics and finance, climate analysis, and other data-driven areas.< Less