Make data-driven, informed decisions and enhance your statistical expertise in Python by turning raw data into meaningful insights
Purchase of the print or Kindle book includes a free PDF eBook
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
- Sampling and Generalization
- Distributions of Data
- Hypothesis Testing
- Parametric Tests
- Non-Parametric Tests
- Linear Regression
- More Discussion on Model Selection & Regularization
- Logistic Regression
- Discriminant Analysis
- Introduction to Time Series
- ARIMA Models
- Multivariate Time Series Methods
- Time to Event variables - An introduction
- Models with Survival Responses
