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Python for Effect: Master Data Visualization and Analysis (2024) – PDF

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Institution
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Uploaded on
October 1, 2025
Number of pages
211
Written in
2025/2026
Type
Exam (elaborations)
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, Python For Effect: Master Data
Visualization and Analysis Copy


Learn Data Pipelines, Machine Learning,
Advanced Statistical Analysis and Visualization
with Jupyter Notebook




Tomasz Trebacz

,Copyright © 2024 by Tomasz Trebacz
All rights reserved.

No portion of this book may be reproduced in any form without written
permission from the publisher or author, except as permitted by U.S. copyright
law.

This publication is designed to provide accurate and authoritative information in
regard to the subject matter covered. It is sold with the understanding that
neither the author nor the publisher is engaged in rendering legal, investment,
accounting or other professional services. While the publisher and author have
used their best efforts in preparing this book, they make no representations or
warranties with respect to the accuracy or completeness of the contents of this
book and specifically disclaim any implied warranties of merchantability or
fitness for a particular purpose. No warranty may be created or extended by
sales representatives or written sales materials. The advice and strategies
contained herein may not be suitable for your situation. You should consult with
a professional when appropriate. Neither the publisher nor the author shall be
liable for any loss of profit or any other commercial damages, including but not
limited to special, incidental, consequential, personal, or other damages.

Book Cover by Tomasz Trebacz

Illustrations by Tomasz Trebacz
1st edition 2024

, Contents




Introduction
1. Setting Up Your Python Environment
2. Python Fundamentals for Data Analysis
3. Exploring Popular Python Libraries
4. Software Engineering Best Practices
5. Handling Big Data with Python
6. Data Cleaning and Preprocessing
7. Real-World Data Applications
8. Advanced Data Visualization Techniques
9. Introduction to Machine Learning
10. Statistical Analysis and Techniques
11. Integrating Python with Other Tools
12. Community and Continued Learning
Conclusion
Appendix
Refferences

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