Building Statistical Models in Python

Key Features

• Gain expertise in identifying and modeling patterns that generate success
• Explore the concepts with Python using important libraries such as stats models
• Learn how to build models on real-world data sets and find solutions to practical challenges

Book Description

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.

What you will learn

• Explore the use of statistics to make decisions under uncertainty
• Answer questions about data using hypothesis tests
• Understand the difference between regression and classification models
• Build models with stats models in Python
• Analyze time series data and provide forecasts
• Discover Survival Analysis and the problems it can solve

Who this book is for

If you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you’ll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.

Table of Contents

1. Sampling and Generalization
2. Distributions of Data
3. Hypothesis Testing
4. Parametric Tests
5. Non-Parametric Tests
6. Linear Regression
7. More Discussion on Model Selection & Regularization
8. Logistic Regression
9. Discriminant Analysis
10. Introduction to Time Series
11. ARIMA Models
12. Multivariate Time Series Methods
13. Time to Event variables - An introduction
14. Models with Survival Responses

Huy Hoang Nguyen is a Mathematician and a Data Scientist with far-ranging experience, championing advanced mathematics and strategic leadership, and applied machine learning research. He holds a Master’s in Data Science and a PhD in Mathematics. His previous work was related to Partial Differential Equations, Functional Analysis and their applications in Fluid Mechanics. He transitioned from academia to the healthcare industry and has performed different Data Science projects from traditional Machine Learning to Deep Learning.

Paul Adams is a Data Scientist with a background primarily in the healthcare industry. Paul applies statistics and machine learning in multiple areas of industry, focusing on projects in process engineering, process improvement, metrics and business rules development, anomaly detection, forecasting, clustering and classification. Paul holds a Master of Science in Data Science from Southern Methodist University.

Stuart Miller is a Machine Learning Engineer with degrees in Data Science, Electrical Engineering, and Engineering Physics. Stuart has worked at several Fortune 500 companies, including Texas Instruments and StateFarm, where he built software that utilized statistical and machine learning techniques. Stuart is currently an engineer at Toyota Connected helping to build a more modern cockpit experience for drivers using machine learning.