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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.
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Table of Contents
- 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
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