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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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Toc

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 discussed the basics of Python programming. We looked at some of the built-in functions, worked with dictionaries and lists, used variables and functions, learned about files, data, and iteration, and learned about classes and OOP.

As we mentioned in this chapter and when solving previous problems, Python provides multiple ways for us to solve the same problems. One example of that is provided in the Problem 2 - Organizing information section of this chapter, where we used the eval() and sum() functions in two different algorithms to produce the same result. As we continue to learn about Python and how to write our algorithms, choosing which functions, variables, arguments, and more to use will start to become second nature. Some of the more challenging concepts and content in this chapter have to do with data analysis, such as the survey we used when introducing data in Python in the Data in Python section of this chapter, and classes. It's important...

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