Data Analysis Foundations with Python
eBook - ePub

Data Analysis Foundations with Python

Master Data Analysis with Python: From Basics to Advanced Techniques

  1. 551 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Data Analysis Foundations with Python

Master Data Analysis with Python: From Basics to Advanced Techniques

Book details
Table of contents
Citations

About This Book

Dive into data analysis with Python, starting from the basics to advanced techniques. This course covers Python programming, data manipulation with Pandas, data visualization, exploratory data analysis, and machine learning.

Key Features

  • From Python basics to advanced data analysis techniques.
  • Apply your skills to practical scenarios through real-world case studies.
  • Detailed projects and quizzes to help gain the necessary skills.

Book Description

Embark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently.Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling.To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects.

What you will learn

  • Develop a strong foundation in Python for data analysis.
  • Manipulate and analyze data using NumPy and Pandas.
  • Create insightful data visualizations with Matplotlib and Seaborn.
  • Understand and apply probability theory and hypothesis testing.
  • Implement supervised and unsupervised machine learning algorithms.
  • Execute real-world data analysis projects with confidence.

Who this book is for

This course adopts a hands-on approach, seamlessly blending theoretical lessons with practical exercises and real-world case studies. Practical exercises are designed to apply theoretical knowledge, providing learners with the opportunity to experiment and learn through doing. Real-world applications and examples are integrated throughout the course to contextualize concepts, making the learning process engaging, relevant, and effective. By the end of the course, students will have a thorough understanding of the subject matter and the ability to apply their knowledge in practical scenarios.

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Yes, you can access Data Analysis Foundations with Python by Cuantum Technologies LLC in PDF and/or ePUB format, as well as other popular books in Informatique & Sciences générales de l'informatique. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Packt Publishing
Year
2024
ISBN
9781836209065
Topic
Informatique
Subtopic
Sciences générales de l'informatique

Table of contents

  1. Code Blocks Resource
  2. Who we are
  3. Introduction
  4. Who is This Book For?
  5. How to Use This Book
  6. Acknowledgments
  7. Chapter 1: Introduction to Data Analysis and Python
  8. Quiz for Part I: Introduction to Data Analysis and Python
  9. Chapter 2: Getting Started with Python
  10. Chapter 3: Basic Python Programming
  11. Chapter 4: Setting Up Your Data Analysis Environment
  12. Quiz for Part II: Python Basics for Data Analysis
  13. Chapter 5: NumPy Fundamentals
  14. Chapter 6: Data Manipulation with Pandas
  15. Chapter 7: Data Visualization with Matplotlib and Seaborn
  16. Quiz for Part III: Core Libraries for Data Analysis
  17. Chapter 8: Understanding EDA
  18. Chapter 9: Data Preprocessing
  19. Chapter 10: Visual Exploratory Data Analysis
  20. Quiz for Part IV: Exploratory Data Analysis (EDA)
  21. Project 1: Analyzing Customer Reviews
  22. Chapter 11: Probability Theory
  23. Chapter 12: Hypothesis Testing
  24. Quiz for Part V: Statistical Foundations
  25. Chapter 13: Introduction to Machine Learning
  26. Chapter 14: Supervised Learning
  27. Chapter 15: Unsupervised Learning
  28. Quiz Part VI: Machine Learning Basics
  29. Project 2: Predicting House Prices
  30. Chapter 16: Case Study 1: Sales Data Analysis
  31. Chapter 17: Case Study 2: Social Media Sentiment Analysis
  32. Quiz Part VII: Case Studies
  33. Project 3: Capstone Project: Building a Recommender System
  34. Chapter 18: Best Practices and Tips
  35. Conclusion
  36. Know more about us