Book Image

Building Statistical Models in Python

By : Huy Hoang Nguyen, Paul N Adams, Stuart J Miller
Book Image

Building Statistical Models in Python

By: Huy Hoang Nguyen, Paul N Adams, Stuart J Miller

Overview of this book

The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.
Table of Contents (22 chapters)
1
Part 1:Introduction to Statistics
7
Part 2:Regression Models
10
Part 3:Classification Models
13
Part 4:Time Series Models
17
Part 5:Survival Analysis

Who this book is for

Professionals in most industries can benefit from the tools in this book. The tools provided are useful primarily at a higher level of inferential analysis, but can be applied to deeper levels depending on the industry in which the practitioner wishes to apply them. The target audiences of this book are:

  • Industry professionals with limited statistical or programming knowledge who would like to learn to use data for testing hypotheses they have in their business domain
  • Data analysts and scientists who wish to broaden their statistical knowledge and find a set of tools and their implementations for performing various data-oriented tasks

The ground-up approach of this book seeks to provide entry into the knowledge base for a wide audience and therefore should neither discourage novice-level practitioners nor exclude advanced-level practitioners from the benefits of the materials presented.