Applied Computational Thinking with Python

Name: Applied Computational Thinking with Python | Algorithm design for complex real-world problems
Brand: Packt Publishing
Price: 29.99 EUR
Availability: OnlineOnly

Algorithm design for complex real-world problems

Sofía De Jesús Dayrene Martinez(Autor*in)

Packt Publishing

1. Auflage

Erschienen am 29. Dezember 2023

438 Seiten

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978-1-83763-108-7 (ISBN)

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Inhalt

Cover
Copyright
Contributors
Table of Contents
Preface
Part 1: An 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 3 - children's soccer party
Problem 4 - 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 should be feasible
Algorithms should be 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
Summary
Chapter 5: Errors
Technical requirements
Understanding errors
Syntax errors
Learning to identify logical errors
Errors and debugging
Summary
Chapter 6: Exploring Problem Analysis
Technical requirements
Understanding the problem definitions
Problem 6A - building an online store
Learning how to decompose problems
Converting the flowchart into an algorithm
Analyzing problems
Problem 6B - analyzing a simple game problem
Summary
Chapter 7: Designing Solutions and Solution Processes
Designing solutions
Technical requirements
Problem 1 - a marketing survey
Diagramming solutions
Creating solutions
Problem 2 - pizza order
Problem 3 - Delays and Python
Summary
Chapter 8: 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
Part 2: Applying Python and Computational Thinking
Chapter 9: 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 OOP
Problem 1 - creating a book library
Problem 2 - Organizing information
Problem 3 - Loops and math
Using inheritance
Summary
Chapter 10: 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 11: 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 12: Using Computational Thinking and Python in Simple Challenges
Technical requirements
Problem definition and Python
Decomposing the problem and using Python functionalities
Generalizing the problem and planning Python algorithms
Designing and testing the algorithm
Summary
Chapter 13: Debugging
Technical requirements
Error messages and identifying bugs
Errors in punctuation
Errors with indentation
Bugs that don't generate error messages
Global variables
Local variables
Errors when using global and local variables
Summary
Part 3: Data Processing, Analysis, and Applications Using Computational Thinking and Python
Chapter 14: 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 15: Introduction to Machine Learning
Technical requirements
Defining ML
Navigating the ML life cycle - a practical approach
Phase 1 - preparation and problem definition
Phase 2 - data preprocessing and model development
Phase 3 - optimization and deployment
Chocolate cake analogy to ML life cycle
Types of ML algorithms
Introduction to DL
Classifying data
Using the scikit-learn library
Defining optimization models
Implementing data clustering
Using the BIRCH algorithm
Using the K-means clustering algorithm
Summary
Chapter 16: 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
Summary
Chapter 17: 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 the 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
Problem 8 - web scraping in Python
Step 1 - import the required libraries
Step 2 - define the URL to scrape
Step 3 - make an HTTP request
Step 4 - parse the HTML content
Step 5 - locate the quote containers
Step 6 - loop through containers and extract data
Problem 9 - using Python to create a QR code
Summary
Chapter 18: 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 for language detection
The fundamentals of the Multinomial Event Model
Problem 6 - using Python to analyze genetic data
Problem 7 - using Python to analyze stocks
Problem 8 - using Python to create a CNN
Summary
Chapter 19: Integrating Python with Amazon Web Services (AWS)
Technical requirements
AWS and Python in cloud computing - a brief overview
Setting up for AWS
Creating a new AWS account
Understanding IAM in AWS
Understanding AWS pricing and the Free Tier
AWS computer services overview
Boto3 in Python and AWS
Setting up Boto3
Basic Python examples using Boto3
Summary
Further reading
Index
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