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Applied Computational Thinking with Python

You're reading from   Applied Computational Thinking with Python Design algorithmic solutions for complex and challenging real-world problems

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781839219436
Length 420 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Dayrene Martinez Dayrene Martinez
Author Profile Icon Dayrene Martinez
Dayrene Martinez
Sofía De Jesús Sofía De Jesús
Author Profile Icon Sofía De Jesús
Sofía De Jesús
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Introduction to Computational Thinking
2. Chapter 1: Fundamentals of Computer Science FREE CHAPTER 3. Chapter 2: Elements of Computational Thinking 4. Chapter 3: Understanding Algorithms and Algorithmic Thinking 5. Chapter 4: Understanding Logical Reasoning 6. Chapter 5: Exploring Problem Analysis 7. Chapter 6: Designing Solutions and Solution Processes 8. Chapter 7: Identifying Challenges within Solutions 9. Section 2:Applying Python and Computational Thinking
10. Chapter 8: Introduction to Python 11. Chapter 9: Understanding Input and Output to Design a Solution Algorithm 12. Chapter 10: Control Flow 13. Chapter 11: Using Computational Thinking and Python in Simple Challenges 14. Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
15. Chapter 12: Using Python in Experimental and Data Analysis Problems 16. Chapter 13: Using Classification and Clusters 17. Chapter 14: Using Computational Thinking and Python in Statistical Analysis 18. Chapter 15: Applied Computational Thinking Problems 19. Chapter 16: Advanced Applied Computational Thinking Problems 20. Other Books You May Enjoy

Summary

In this chapter, we were able to explore more topics in computational thinking, especially in dealing with data and deep learning, using the Python programming language. We learned how to create pairplots in order to determine the relationship between variables in a dataset. We also learned how to produce various types of plots to visually represent our datasets. We also learned how to create electric field lines using Python. In short, we applied what we'd learned throughout the previous chapters and extended our knowledge while working in applied problems.

And that's really what this book sought to do: Show a wide variety of Python applications while looking at real problems in context. Did we cover everything Python can do? That's fairly impossible, as Python capabilities continue to grow because of its ease of use, how easy it is to learn, and how many applications continue to be added because of its open source nature. Hopefully, you got to work with...

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