Book Image

Applied Computational Thinking with Python

By : Sofía De Jesús, Dayrene Martinez
Book Image

Applied Computational Thinking with Python

By: Sofía De Jesús, Dayrene Martinez

Overview of this book

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence. This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions. By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.

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Applied Computational Thinking with Python
Sofía De Jesús | Dayrene Martinez
Nov, 2020 | 420 pages
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Table of Contents (21 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
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Conventions used
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1
Section 1: Introduction to Computational Thinking
Section 1: Introduction to Computational Thinking
Free Chapter
2
Chapter 1: Fundamentals of Computer Science
Chapter 1: Fundamentals of Computer Science
Technical requirements
Introduction to computer science
Understanding theoretical computer science
Learning about a system's software
Understanding computing
Learning about data types and structures
Summary
3
Chapter 2: Elements of Computational Thinking
Chapter 2: Elements of Computational Thinking
Technical requirements
Understanding computational thinking
Decomposing problems
Recognizing patterns
Generalizing patterns
Designing algorithms
Additional problems
Summary
4
Chapter 3: Understanding Algorithms and Algorithmic Thinking
Chapter 3: Understanding Algorithms and Algorithmic Thinking
Technical requirements
Defining algorithms in depth
Designing algorithms
Analyzing algorithms
Summary
5
Chapter 4: Understanding Logical Reasoning
Chapter 4: Understanding Logical Reasoning
Technical requirements
Understanding the importance of logical reasoning
Using Boolean logic and operators
Identifying logic errors
Summary
6
Chapter 5: Exploring Problem Analysis
Chapter 5: Exploring Problem Analysis
Technical requirements
Understanding the problem definitions
Learning to decompose problems
Analyzing problems
Summary
7
Chapter 6: Designing Solutions and Solution Processes
Chapter 6: Designing Solutions and Solution Processes
Technical requirements
Designing solutions
Diagramming solutions
Creating solutions
Summary
8
Chapter 7: Identifying Challenges within Solutions
Chapter 7: Identifying Challenges within Solutions
Technical requirements
Identifying errors in algorithm design
Debugging algorithms
Comparing solutions
Refining and redefining solutions
Summary
9
Section 2:Applying Python and Computational Thinking
Section 2:Applying Python and Computational Thinking
10
Chapter 8: Introduction to Python
Chapter 8: Introduction to Python
Technical requirements
Introducing Python
Working with dictionaries and lists
Using variables and functions
Learning about files, data, and iteration
Using object-oriented programming
Problem 1 - Creating a book library
Problem 2 - Organizing information
Problem 3 - Loops and math
Summary
11
Chapter 9: Understanding Input and Output to Design a Solution Algorithm
Chapter 9: Understanding Input and Output to Design a Solution Algorithm
Technical requirements
Defining input and output
Understanding input and output in computational thinking
Summary
12
Chapter 10: Control Flow
Chapter 10: Control Flow
Technical requirements
Defining control flow and its tools
Using if, for, and range() and other control flow statements
Using other loops and conditionals
Revisiting functions
Summary
13
Chapter 11: Using Computational Thinking and Python in Simple Challenges
Chapter 11: Using Computational Thinking and Python in Simple Challenges
Technical requirements
Defining the problem and Python
Generalizing the problem and planning Python algorithms
Designing and testing the algorithm
Summary
14
Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
15
Chapter 12: Using Python in Experimental and Data Analysis Problems
Chapter 12: Using Python in Experimental and Data Analysis Problems
Technical requirements
Defining experimental data
Using data libraries in Python
Understanding data analysis with Python
Using additional libraries for plotting and analysis
Summary
16
Chapter 13: Using Classification and Clusters
Chapter 13: Using Classification and Clusters
Technical requirements
Data training and testing
Implementing data clustering
Summary
17
Chapter 14: Using Computational Thinking and Python in Statistical Analysis
Chapter 14: Using Computational Thinking and Python in Statistical Analysis
Technical requirements
Defining the problem and Python data selection
Preprocessing data
Processing, analyzing, and summarizing data using visualizations
Summary
18
Chapter 15: Applied Computational Thinking Problems
Chapter 15: Applied Computational Thinking Problems
Technical requirements
Problem 1 – Using Python to analyze historical speeches
Problem 2 – Using Python to write stories
Problem 3 – Using Python to calculate text readability
Problem 4 – Using Python to find most efficient route
Problem 5 – Using Python for cryptography
Problem 6 – Using Python in cybersecurity
Problem 7 – Using Python to create a chatbot
Summary
19
Chapter 16: Advanced Applied Computational Thinking Problems
Chapter 16: Advanced Applied Computational Thinking Problems
Technical requirements
Problem 1 – Using Python to create tessellations
Problem 2 – Using Python in biological data analysis
Problem 3 – Using Python to analyze data for specific populations
Problem 4 – Using Python to create models of housing data
Problem 5 – Using Python to create electric field lines
Problem 6 – Using Python to analyze genetic data
Problem 7 – Using Python to analyze stocks
Problem 8 – Using Python to create a convolutional neural network (CNN)
Summary
20
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Implementing data clustering

In this section, we're going to take a look at how to approach data clustering. First, let's define what we mean by data clustering. Data clustering refers to how we partition data into groups or clusters. Clusters can be meaningful if they provide an expanded understanding of domain knowledge. We use clustering for many applications, such as medicine, where clustering can help identify how a group of patients responds to treatment, or market research, where clustering is used to group consumers in order to appeal to that group based on that particular group's characteristics.

For the purpose of this discussion, we are going to look at synthetic clusters rather than applied clusters. In Chapter 16, Advanced Applied Computational Thinking Problems, you'll see some examples of clusters in context. A synthetic cluster is made from a synthetic dataset. That is, we generate the dataset using an algorithm. Let's take a look at the...

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