Implementing Statistics with Python

Statistics is an important skill set to have when working as a quality analyst, a mathematician, a data analyst, a software engineer, or any analytical job. This book, "Implementing Statistics with Python," will teach you the basics of statistics and how to use Python to analyze data. You will learn to find patterns, quantify uncertainty, and make data-driven predictions with confidence.

You will start with basic statistics and then use Python libraries like NumPy and Pandas for data manipulation. You will also learn data visualization with Matplotlib and Seaborn to create informative charts. The book covers probability theory and statistical inference to help you make data-driven decisions. You will be exploring regression and time series analysis with ARIMA for forecasting. Finally, the book introduces ML algorithms, preparing you for real-world data science projects.

The book focuses on applying statistics rather than theory, using popular libraries like NumPy, SciPy, Pandas, Matplotlib, and Scikit-Learn. Reading this book will give you a good foundation for working with ML, business analytics, and data-driven business challenges.

KEY FEATURES

* Learn the various aspects of statistics and its applications in real-world scenarios.
* Learn about the various libraries in Python for working with data.
* Adopt the learn-by-doing approach to solve real-world statistics problems.
* Learn how statistics is applied to Machine Learning.

WHAT YOU WILL LEARN

* Learn the fundamentals of Python and its libraries like Numpy, Pandas, Matplotlib and Seaborn.
* Grasp descriptive statistics and probability concepts.
* Perform statistical inference with Chi-square, ANOVA, and regression analysis.
* Skillfully navigate multivariate and time series analysis.
* Apply statistical techniques in practical ML.

WHO THIS BOOK IS FOR

This book is for readers with basic Python knowledge who want to apply statistics in real-life scenarios, and those pursuing careers in data analytics, data engineering, data science, ML, and AI. It is also ideal for students beginning a course in statistics.

1. Introduction to Statistics
2. Python Basics for Statistics
3. Introduction to NumPy and Pandas for Data Manipulation
4. Data Visualization with Matplotlib and Seaborn
5. Descriptive Statistics
6. Probability Theory
7. Statistical Inference
8. Regression Analysis
9. Multivariate Analysis
10. Time Series Analysis
11. Machine Learning for Statistics
12. Practical Statistical Analysis in Machine Learning

Wei-Meng Lee is a seasoned technologist, author, and educator known for his expertise in a wide range of topics, including software development, data science, and emerging technologies. With a strong background in computer science and a passion for innovation, Wei-Meng has made significant contributions to the tech industry through his writing, teaching, and hands-on experience.

As an author, Wei-Meng has written numerous books and articles that have helped professionals and enthusiasts alike deepen their understanding of programming languages, frameworks, and cutting-edge tools. His works often blend practical insights with theoretical concepts, making complex topics accessible and actionable for readers.

Wei-Meng’s teaching experience spans across various platforms, where he imparts his knowledge to students eager to learn about programming, data analysis, and technology trends. His engaging teaching style and ability to simplify complex ideas have earned him praise from learners worldwide. In addition to his writing and teaching, Wei-Meng is actively involved in the tech community, participating in conferences, workshops, and forums where he shares his insights and learns from fellow experts.