Mathematical Problems in Data Science

by Li M. Chen (Author), Zhixun Su (Author), Bo Jiang (Author)
Explains the most current methods for solving cutting edge problems in data science and big data
Provides problem solving techniques and case studies
Covers a wide range of mathematical problems in data science in detail

From Foundations to Advanced Applications: Master Mathematical Statistics, Probability, and Core Mathematics

An introduction to mathematical statistics for data science, covering method of moments, maximum likelihood, and more

This textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used? Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success.

A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers