Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programming Key Features * Work your way through the entire data analysis pipeline with statistics concerns in mind and make reasonable decisions based on business requirements * Explore and understand how various data science algorithms function * Work with Python-based examples to gain a solid foundation in statistics for data science and machine learning Book Description Statistics remains the backbone of modern analysis tasks, helping us interpret the results produced by data science pipelines. This book is a comprehensive guide to understanding the math and various statistical methods required for undertaking data science tasks. You will start by learning how to preprocess data and inspect distributions and correlations from a statistical perspective. You'll then get to grips with the fundamentals of statistical analysis and apply the concepts to real-world datasets. As you advance, you'll discover how statistical concepts emerge from different stages of data science pipelines, understand the summary description of datasets in the language of statistics and use it to place robust data products such as explanatory models and predictive models. You'll also delve into the working mechanism of data science algorithms. Essential statistical concepts that can be applied to achieve efficient data collection, cleaning, mining, visualization, and analysis are also covered. Finally, you'll implement statistical methods in key machine learning tasks such as classification, regression, tree-based methods, and ensemble learning. By the end of this book, you will be able to confidently build and present a self-contained, statistics-backed data product to meet your business goals. What you will learn * Find out how to grab and load data into an analysis environment * Perform descriptive analysis and extract meaningful summary from data * Gain knowledge of probability, parameter estimation, hypothesis tests, and experiment design best practices * Get to grips with resampling and bootstrapping in Python * Delve into statistical tests with variance analysis, time series analysis, and A/B test examples * Understand the statistics behind popular machine learning algorithms * Answer statistics questions for data scientist interviews Who This Book Is For This book is an entry-level guide for data science enthusiasts, data analysts, and data scientists who want to enter the field of data science and learn the required statistics with the help of easy explanations and examples. This book will also appeal to developers and students with a non-mathematical background. Working knowledge of the Python programming language is expected.

Book Information
ISBN 9781838984847
Author Rongpeng Li
Format Paperback
Page Count 366
Imprint Packt Publishing Limited
Publisher Packt Publishing Limited
Weight(grams) 191g