دانلود کتاب مقدمه‌ای ریاضی بر علم داده با پایتون

This textbook serves as a companion to our book, A Mathematical Introduction to Data Science, published by Springer. This companion book contains sample Python code that will implement and illustrate most of the mathematics found in A Mathematical Introduction to Data Science. This is not a book intended to teach program-ming in Python, or indeed programming in general. Our primary goal is to help readers deepen their understanding of mathematical concepts through programming, in particular programming in Python. By learning the sample code and understanding the mathematical principles behind it, we hope the reader will be able to grasp more complex programs involving mathematical algorithms and construct a full program that implements their own particular application whenever needed. We have chosen Python as the programming language for this purpose for two reasons. Firstly, it is widely used in the field of data science, and secondly, there are many accessible resources available to supplement any gaps in our code, as we do not claim to be Python experts. We also do not claim that the code presented is the only, or best, way of implementing the chosen mathematical concepts, but is one way of doing so. We have tried to give code examples and exercises that cover a wide range of Python constructs and so give readers experience with many different facets of Python. In fact, we encourage readers to explore better programming solutions once they grasp the underlying mathematical principles. There are lots of libraries, containing an amazing number of modules of functions accessible in Python that can be explored, and we have only scratched the surface by giving the basic versions—so go ahead and experiment. This book provides a comprehensive foundation in mathematics through Python programming for anyone with a basic mathematical background, including students and self-learners interested in data science. Readers are expected to be proficient in handling numbers in various formats, including fractions, decimals, percentages and surds. They should also have a knowledge of introductory algebra, such as manipulating simple algebraic expressions, solving simple equations and graphing elementary functions, along with a basic understanding of geometry, including angles, trigonometry and Pythagoras’ theorem. Some examples from A Mathematical Introduction to Data Science are used to illustrate key concepts such as sets, functions, linear algebra, calculus and probability and statistics essential for understanding the algorithms that power today’s data science applications through Python programming. Further, this textbook shows how those mathematical concepts can be applied in widely used computational algorithms, such as principal component analysis, linear regression and neural networks. Ideally, readers should have both this book and its companion, A Mathematical Introduction to Data Science. However, those with a strong mathematical back-ground and an interest in programming implementations can benefit from reading this textbook on its own.