Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining.You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models.By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional.This Learning Path includes content from the following Packt products:Statistics for Machine Learning by Pratap Dangeti.Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim.Pandas Cookbook by Theodore Petrou.

This is a lively introductory textbook in numerical methods, MATLAB, and technical computing, with an emphasis on the informed use of mathematical software. Readers will learn how to use the mathematical functions in MATLAB correctly, appreciate their limitations, and modify them appropriately. Computer graphics are made extensive use of.

This is a lively textbook for an introductory course in numerical methods, MATLAB and technical computing, which emphasises the informed use of mathematical software. Numerical Computing with MATLAB helps readers learn about the mathematical functions in MATLAB, how to appreciate their limitations, and how to use and modify them appropriately. The book makes extensive use of co...(展开全部)

Mastering Numerical Computing with NumPy is for you if you are a Python programmer, data analyst, data engineer, or a data science enthusiast, who wants to master the intricacies of NumPy and build solutions for your numeric and scientific computational problems. You are expected to have familiarity with mathematics to get the most out of this book.

This book is for Python developers who have developed Python programs for data processing and now want to learn how to write fast, efficient programs that perform CPU-intensive data processing tasks.

David Kincaid 得克萨斯大学奥斯汀分校计算机科学系及数学系的高级讲师，他还是得克萨斯计算及应用数学学会数值分析中心的代主任。 Ward Cheney 得克萨斯大学奥斯汀分校数学系教授，他的研究方向主要是逼近论、数值分析和极大化问题。

David Kincaid 得克萨斯大学奥斯汀分校计算机科学系及数学系的高级讲师，他还是得克萨斯计算及应用数学学会数值分析中心的代主任。 Ward Cheney 得克萨斯大学奥斯汀分校数学系教授，他的研究方向主要是逼近论、数值分析和极大化问题。

This highly successful and scholarly book introduces readers with diverse backgrounds to the various types of mathematical analysis that are commonly needed in scientific computing. The subject of numerical analysis is treated from a mathematical point of view, offering a complete analysis of methods for scientific computing with careful proofs and scientific background. An in-...(展开全部)

This new edition of Numerical Ecology with R guides readers through an applied exploration of the major methods of multivariate data analysis, as seen through the eyes of three ecologists. It provides a bridge between a textbook of numerical ecology and the implementation of this discipline in the R language. The book begins by examining some exploratory approaches. It proceeds...(展开全部)