Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their...
Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications.
Key Features
Get up and running with the Jupyter ecosystem and some example datasets
Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.
Key Features
Get up and running with the Jupyter ecosystem and some example datasets
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...
This book bridges the latest software applications with the benefits of modern resampling techniques
Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques...
This book serves as a comprehensive guide to understanding the theories and applications in managing the Asian fashion supply chain, presenting both quantitative and exploratory studies. Providing academicians and practitioners insights into the latest developments and models, it also offers diverse perspectives on areas like strategic...
