Applied Computational Thinking with Python

Cover
Title Page
Copyright and Credits
Dedicated
About Packt
Contributors
Table of Contents
Preface
Section 1: Introduction to Computational Thinking
Chapter 1: Fundamentals of Computer Science
	Technical requirements
	Introduction to computer science
		Learning about computers and the binary system
	Understanding theoretical computer science
		Algorithms
		Coding theory
		Computational biology
		Data structures
		Information theory
		Automata theory
		Formal language theory
		Symbolic computation
		Computational geometry
		Computational number theory
	Learning about a system's software
		Operating systems
		Application software
	Understanding computing
		Architecture
		Programming languages
	Learning about data types and structures
		Data types
		Data structures
	Summary
Chapter 2: Elements of Computational Thinking
	Technical requirements
	Understanding computational thinking
		Problem 1 - Conditions
	Decomposing problems
	Recognizing patterns
		Problem 2 - Mathematical algorithms and generalization
	Generalizing patterns
	Designing algorithms
	Additional problems
		Problem 2 - Children's soccer party
		Problem 3 - Savings and interest
	Summary
Chapter 3: Understanding Algorithms and Algorithmic Thinking
	Technical requirements
	Defining algorithms in depth
		Algorithms should be clear and unambiguous
		Algorithms should have inputs and outputs that are well defined
		Algorithms should have finiteness
		Algorithms have to be feasible
		Algorithms are language-independent
	Designing algorithms
		Problem 1 – An office lunch
		Problem 2 – A catering company
	Analyzing algorithms
		Algorithm analysis 1 – States and capitals
		Algorithm analysis 2 – Terminating or not terminating?
	Summary
Chapter 4: Understanding Logical Reasoning
	Technical requirements
	Understanding the importance of logical reasoning
		Applying inductive reasoning
		Applying deductive reasoning
	Using Boolean logic and operators
		The and operator
		The or operator
		The not operator
	Identifying logic errors
	Summary
Chapter 5: Exploring Problem Analysis
	Technical requirements
	Understanding the problem definitions
		Problem 5A – Building an online store
	Learning to decompose problems
		Converting the flowchart into an algorithm
	Analyzing problems
		Problem 5B – Analyzing a simple game problem
	Summary
Chapter 6: Designing Solutions and Solution Processes
	Technical requirements
	Designing solutions
		Problem 1 - A marketing survey
	Diagramming solutions
	Creating solutions
		Problem 2 - Pizza order
		Problem 3 - Delays and Python
	Summary
Chapter 7: Identifying Challenges within Solutions
	Technical requirements
	Identifying errors in algorithm design
		Syntax errors
		Errors in logic
	Debugging algorithms
	Comparing solutions
		Problem 1 - Printing even numbers
	Refining and redefining solutions
	Summary
Section 2:Applying Python and Computational Thinking
Chapter 8: Introduction to Python
	Technical requirements
	Introducing Python
		Mathematical built-in functions
	Working with dictionaries and lists
		Defining and using dictionaries
		Defining and using lists
	Using variables and functions
		Variables in Python
		Working with functions
	Learning about files, data, and iteration
		Handling files in Python
		Data in Python
		Using iteration in algorithms
	Using object-oriented programming
	Problem 1 - Creating a book library
	Problem 2 - Organizing information
	Problem 3 - Loops and math
		Using inheritance
	Summary
Chapter 9: Understanding Input and Output to Design a Solution Algorithm
	Technical requirements
	Defining input and output
	Understanding input and output in computational thinking
		Problem 1 – Building a Caesar cipher
		Problem 2 – Finding maximums
		Problem 3 – Building a guessing game
	Summary
Chapter 10: Control Flow
	Technical requirements
	Defining control flow and its tools
	Using if, for, and range() and other control flow statements
		Using nested if statements
		Using for loops and range
	Using other loops and conditionals
	Revisiting functions
	Summary
Chapter 11: Using Computational Thinking and Python in Simple Challenges
	Technical requirements
	Defining the problem and Python
		Decomposing the problem and using Python functionalities
	Generalizing the problem and planning Python algorithms
	Designing and testing the algorithm
	Summary
Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
Chapter 12: Using Python in Experimental and Data Analysis Problems
	Technical requirements
	Defining experimental data
	Using data libraries in Python
		Installing libraries
		Using NumPy and pandas
		Using Matplotlib
	Understanding data analysis with Python
	Using additional libraries for plotting and analysis
		Using the Seaborn library
		Using the SciPy library
		Using the Scikit-Learn library
	Summary
Chapter 13: Using Classification and Clusters
	Technical requirements
	Data training and testing
		Classifying data example
		Using the Scikit-Learn library
		Defining optimization models
	Implementing data clustering
		Using the BIRCH algorithm
		Using the K-means clustering algorithm
	Summary
Chapter 14: Using Computational Thinking and Python in Statistical Analysis
	Technical requirements
	Defining the problem and Python data selection
		Defining pandas
		Determining when to use pandas
	Preprocessing data
		Data cleaning
		Transforming data
		Reducing data
	Processing, analyzing, and summarizing data using visualizations
		Processing data
		Analyzing and summarizing data
		Using data visualization
	Summary
Chapter 15: Applied Computational Thinking Problems
	Technical requirements
	Problem 1 – Using Python to analyze historical speeches
	Problem 2 – Using Python to write stories
		Defining, decomposing, and planning a story
	Problem 3 – Using Python to calculate text readability
	Problem 4 – Using Python to find most efficient route
		Defining the problem (TSP)
		Recognizing the pattern (TSP)
		Generalizing (TSP)
		Designing the algorithm (TSP)
	Problem 5 – Using Python for cryptography
		Defining the problem (cryptography)
		Recognizing the pattern (cryptography)
		Generalizing (cryptography)
		Designing the algorithm (cryptography)
	Problem 6 – Using Python in cybersecurity
	Problem 7 – Using Python to create a chatbot
	Summary
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
		Defining the specific problem to analyze and identify the population
	Problem 4 – Using Python to create models of housing data
		Defining the problem
		Algorithm and visual representations of 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
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