Implementing Statistics with Python
eBook - ePub

Implementing Statistics with Python

Optimize decision-making with statistical inference and Python (English Edition)

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Implementing Statistics with Python

Optimize decision-making with statistical inference and Python (English Edition)

About this book

Description
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.

Table of Contents
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

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Yes, you can access Implementing Statistics with Python by Wei-Meng Lee in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Science General. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Dedication Page
  5. About the Author
  6. About the Reviewer
  7. Acknowledgement
  8. Preface
  9. Table of Contents
  10. 1. Introduction to Statistics
  11. 2. Python Basics for Statistics
  12. 3. Introduction to NumPy and Pandas for Data Manipulation
  13. 4. Data Visualization with Matplotlib and Seaborn
  14. 5. Descriptive Statistics
  15. 6. Probability Theory
  16. 7. Statisical Inference
  17. 8. Regression Analysis
  18. 9. Multivariate Analysis
  19. 10. Time Series Analysis
  20. 11. Machine Learning for Statistics
  21. 12. Practical Statistical Analysis in Machine Learning
  22. Index