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.

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)

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)

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.

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applicatio...(展开全部)

Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in scienc...(展开全部)

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。