Название: Math for Data Science
Автор: Omar Hijab
Издательство: Springer
Год: 2025
Страниц: 588
Язык: английский
Формат: pdf (true)
Размер: 20.8 MB

Math for Data Science presents the mathematical foundations necessary for studying and working in Data Science. The book is suitable for courses in applied mathematics, business analytics, Computer Science, Data Science, and engineering. The text covers the portions of linear algebra, calculus, probability, and statistics prerequisite to Data Science. The highlight of the book is the Machine Learning chapter, where the results of the previous chapters are applied to neural network training and stochastic gradient descent. Also included in this last chapter are advanced topics such as accelerated gradient descent and logistic regression trainability.

Clear examples are supported with detailed figures and Python code; Jupyter notebooks and supporting files are available on the author's website. More than 380 exercises and nine detailed appendices covering background elementary material are provided to aid understanding. The standard Python data science packages are used, and a Python index lists the functions used in the text. The book begins at a gentle pace, by focusing on two-dimensional datasets. As the text progresses, foundational topics are expanded upon, leading to deeper results at a more advanced level. Because Python is used to highlight concepts, the supporting code snippets are, as pedagogical tools, sometimes longer than necessary.

Because SQL is usually part of a data scientist’s toolkit, an introduction to using SQL, from within Python, is included in an appendix. Also, in case the instructor wishes to de-emphasize it, integration is presented separately in an appendix. Other appendices cover combinations and permutations, the binomial theorem, the exponential function, complex numbers, asymptotics, and minimizers, to be used according to the instructor’s emphasis and preferences.