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Applied Computational Thinking with Python provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time. Developers working with Python will be able to put their knowledge to work with this practical guide using the computational thinking method for problem-solving.

• 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
• Other Books You May Enjoy
• Index