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## Introduction to Computational Thinking

Problem Solving, Algorithms, Data Structures, and More

## Aarhus N, Denmark

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An in-depth broad introduction to computational thinking

Uses the popular Python programming language

The author is an expert computational programmer and teacher

## About this book

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## Table of contents (18 chapters)

## Introducing Python Programming

- Think in a computational way
- Acquire general techniques for problem solving
- See general and concrete algorithmic techniques
- Program solutions that are both computationally efficient and maintainable

Book Title : Introduction to Computational Thinking

Book Subtitle : Problem Solving, Algorithms, Data Structures, and More

DOI : https://doi.org/10.1007/978-1-4842-7077-6

Publisher : Apress Berkeley, CA

Copyright Information : Thomas Mailund 2021

Softcover ISBN : 978-1-4842-7076-9 Published: 17 July 2021

eBook ISBN : 978-1-4842-7077-6 Published: 16 July 2021

Number of Illustrations : 78 b/w illustrations

Topics : Computer Science , Algorithms , Python , Programming Techniques

## Introduction to Computational Thinking: Problem Solving, Algorithms, Data Structures, and More by Thomas Mailund

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## 2.Â Introducing Python Programming

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## Unit: Algorithms

- What is an algorithm and why should you care? (Opens a modal)
- A guessing game (Opens a modal)
- Route-finding (Opens a modal)
- Discuss: Algorithms in your life (Opens a modal)

## Binary search

- Binary search (Opens a modal)
- Implementing binary search of an array (Opens a modal)
- Challenge: Binary search (Opens a modal)
- Running time of binary search (Opens a modal)
- Running time of binary search 5 questions Practice

## Asymptotic notation

- Asymptotic notation (Opens a modal)
- Big-θ (Big-Theta) notation (Opens a modal)
- Functions in asymptotic notation (Opens a modal)
- Big-O notation (Opens a modal)
- Big-Ω (Big-Omega) notation (Opens a modal)
- Comparing function growth 4 questions Practice
- Asymptotic notation 5 questions Practice

## Selection sort

- Sorting (Opens a modal)
- Challenge: implement swap (Opens a modal)
- Selection sort pseudocode (Opens a modal)
- Challenge: Find minimum in subarray (Opens a modal)
- Challenge: implement selection sort (Opens a modal)
- Analysis of selection sort (Opens a modal)
- Project: Selection sort visualizer (Opens a modal)

## Insertion sort

- Insertion sort (Opens a modal)
- Challenge: implement insert (Opens a modal)
- Insertion sort pseudocode (Opens a modal)
- Challenge: Implement insertion sort (Opens a modal)
- Analysis of insertion sort (Opens a modal)

## Recursive algorithms

- Recursion (Opens a modal)
- The factorial function (Opens a modal)
- Challenge: Iterative factorial (Opens a modal)
- Recursive factorial (Opens a modal)
- Challenge: Recursive factorial (Opens a modal)
- Properties of recursive algorithms (Opens a modal)
- Using recursion to determine whether a word is a palindrome (Opens a modal)
- Challenge: is a string a palindrome? (Opens a modal)
- Computing powers of a number (Opens a modal)
- Challenge: Recursive powers (Opens a modal)
- Multiple recursion with the Sierpinski gasket (Opens a modal)
- Improving efficiency of recursive functions (Opens a modal)
- Project: Recursive art (Opens a modal)

## Towers of Hanoi

- Towers of Hanoi (Opens a modal)
- Towers of Hanoi, continued (Opens a modal)
- Challenge: Solve Hanoi recursively (Opens a modal)
- Move three disks in Towers of Hanoi 3 questions Practice
- Divide and conquer algorithms (Opens a modal)
- Overview of merge sort (Opens a modal)
- Challenge: Implement merge sort (Opens a modal)
- Linear-time merging (Opens a modal)
- Challenge: Implement merge (Opens a modal)
- Analysis of merge sort (Opens a modal)
- Overview of quicksort (Opens a modal)
- Challenge: Implement quicksort (Opens a modal)
- Linear-time partitioning (Opens a modal)
- Challenge: Implement partition (Opens a modal)
- Analysis of quicksort (Opens a modal)

## Graph representation

- Describing graphs (Opens a modal)
- Representing graphs (Opens a modal)
- Challenge: Store a graph (Opens a modal)
- Describing graphs 6 questions Practice
- Representing graphs 5 questions Practice

## Breadth-first search

- Breadth-first search and its uses (Opens a modal)
- The breadth-first search algorithm (Opens a modal)
- Challenge: Implement breadth-first search (Opens a modal)
- Analysis of breadth-first search (Opens a modal)

## Further learning

## About this unit

## Introduction to Computational Thinking Problem Solving, Algorithms, Data Structures, and More

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## Introduction to Computational Thinking: Problem Solving, Algorithms, Data Structures, and More

- Length: 670 pages
- Language: English
- Publisher: Apress
- Publication Date: 2021-07-31
- ISBN-10: 1484270762
- ISBN-13: 9781484270769
- Sales Rank: #8382059 ( See Top 100 Books )

- Think in a computational way
- Acquire general techniques for problem solving
- See general and concrete algorithmic techniques
- Program solutions that are both computationally efficient and maintainable

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## Enhance your purchase

- Think in a computational way
- Acquire general techniques for problem solving
- See general and concrete algorithmic techniques
- Program solutions that are both computationally efficient and maintainable

- ISBN-10 1484270762
- ISBN-13 978-1484270769
- Edition 1st ed.
- Publication date July 17 2021
- Language English
- Dimensions 17.81 x 3.86 x 25.4 cm
- Print length 657 pages
- See all details

## Product description

## About the Author

## Product details

- Publisher : Apress; 1st ed. edition (July 17 2021)
- Language : English
- Paperback : 657 pages
- ISBN-10 : 1484270762
- ISBN-13 : 978-1484270769
- Item weight : 1.15 kg
- Dimensions : 17.81 x 3.86 x 25.4 cm

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## Introduction to computational thinking : problem solving, algorithms, data structures, and more

## More options

## Description

Creators/contributors, contents/summary.

- 1 Introduction 1Models of the world and formalising problems . . 4What is computational thinking? . . . . . . . . . 6Computational thinking in a broader context . . . 12What is to come . . . . . . . . . . . . . . . . . . 152 Introducing Python programming 19Obtaining Python . . . . . . . . . . . . . . . . . 20Running Python . . . . . . . . . . . . . . . . . . 22Expressions in Python . . . . . . . . . . . . . . . 22Logical (or boolean) expressions . . . . . . . . . . 26Variables . . . . . . . . . . . . . . . . . . . . . . 30Working with strings . . . . . . . . . . . . . . . . 32Lists . . . . . . . . . . . . . . . . . . . . . . . . 36Tuples . . . . . . . . . . . . . . . . . . . . . . . 41iiiSets and dictionaries . . . . . . . . . . . . . . . . 42Input and output . . . . . . . . . . . . . . . . . . 44Conditional statements (if statements) . . . . . . 47Loops (for and while) . . . . . . . . . . . . . . . 50Using modules . . . . . . . . . . . . . . . . . . . 543 Introduction to algorithms 57Designing algorithms . . . . . . . . . . . . . . . 62Exercises for sequential algorithms . . . . . . . . 81Exercises on lists . . . . . . . . . . . . . . . . . . 874 Algorithmic eciency 95The RAM model of a computer and its primitiveoperations . . . . . . . . . . . . . . . . . . 97Types of eciency . . . . . . . . . . . . . . . . . 107Asymptotic running time and big-Oh notation . . 116Empirically validating an algorithms running time 1355 Searching and sorting 141Searching . . . . . . . . . . . . . . . . . . . . . . 142Sorting . . . . . . . . . . . . . . . . . . . . . . . 147Generalising searching and sorting . . . . . . . . 182How computers represent numbers . . . . . . . . 1866 Functions 197Parameters and local and global variables . . . . . 203Side eects . . . . . . . . . . . . . . . . . . . . . 210Returning from a function . . . . . . . . . . . . . 215Higher order functions . . . . . . . . . . . . . . . 221Functions vs function instances . . . . . . . . . . 227Default parameters and keyword arguments . . . 230Generalising parameters . . . . . . . . . . . . . . 234Exceptions . . . . . . . . . . . . . . . . . . . . . 239Writing your own Python modules . . . . . . . . 2517 Inner functions 253A comparison function for a search algorithm . . 256Counter function . . . . . . . . . . . . . . . . . . 261Apply . . . . . . . . . . . . . . . . . . . . . . . . 265Currying functions . . . . . . . . . . . . . . . . . 269Function composition . . . . . . . . . . . . . . . 274Thunks and lazy evaluation . . . . . . . . . . . . 276Decorators . . . . . . . . . . . . . . . . . . . . . 281Eciency . . . . . . . . . . . . . . . . . . . . . . 2888 Recursion 291Denitions of recursion . . . . . . . . . . . . . . 291Recursive functions . . . . . . . . . . . . . . . . 293Recursion stacks . . . . . . . . . . . . . . . . . . 297Recursion and iteration . . . . . . . . . . . . . . 307Tail-calls . . . . . . . . . . . . . . . . . . . . . . 316Continuations . . . . . . . . . . . . . . . . . . . 324Continuations, thunks and trampolines . . . . . . 3359 Divide and conquer and dynamic programming 343Divide and conquer running times . . . . . . . . 355Dynamic programming . . . . . . . . . . . . . . 371Representing oating point numbers . . . . . . . 39210 Hidden Markov models 399Probabilities . . . . . . . . . . . . . . . . . . . . 399Conditional probabilities and dependency graphs . 410Markov models . . . . . . . . . . . . . . . . . . . 412Hidden Markov models . . . . . . . . . . . . . . 421Forward algorithm . . . . . . . . . . . . . . . . . 425Viterbi algorithm . . . . . . . . . . . . . . . . . . 43311 Data structures, objects and classes 439Classes . . . . . . . . . . . . . . . . . . . . . . . 441Exceptions and classes . . . . . . . . . . . . . . . 448Methods . . . . . . . . . . . . . . . . . . . . . . 453Magical methods . . . . . . . . . . . . . . . . . . 460Class variables . . . . . . . . . . . . . . . . . . . 464Objects, classes, meta-classes, and attributes . . . 471Return of the decorator . . . . . . . . . . . . . . 494Polymorphism . . . . . . . . . . . . . . . . . . . 500Abstract data structures . . . . . . . . . . . . . . 50412 Class hierarchies and inheritance 507Inheritance and code reuse . . . . . . . . . . . . 516Multiple inheritance . . . . . . . . . . . . . . . . 524Mixins . . . . . . . . . . . . . . . . . . . . . . . 53213 Sequences 537Sequences . . . . . . . . . . . . . . . . . . . . . 538Linked lists sequences . . . . . . . . . . . . . . . 540Doubly linked lists . . . . . . . . . . . . . . . . . 560A word on garbage collection . . . . . . . . . . . 579Iterators . . . . . . . . . . . . . . . . . . . . . . 587Python iterators and other interfaces . . . . . . . 590Generators . . . . . . . . . . . . . . . . . . . . . 59814 Sets 607Sets with builtin lists . . . . . . . . . . . . . . . . 612Linked lists sets . . . . . . . . . . . . . . . . . . . 618Search trees . . . . . . . . . . . . . . . . . . . . 620Hash table . . . . . . . . . . . . . . . . . . . . . 648Dictionaries . . . . . . . . . . . . . . . . . . . . 66315 Red-black search trees 669A persistent recursive solution . . . . . . . . . . . 670An iterative solution . . . . . . . . . . . . . . . . 71216 Stacks and queues 739Building stacks and queues from scratch . . . . . 745Expression stacks and stack machines . . . . . . . 748Quick-sort and the call stack . . . . . . . . . . . . 761Writing an iterator for a search tree . . . . . . . . 763Merge sort with an explicit stack . . . . . . . . . . 768Breadth-rst tree traversal and queues . . . . . . 77517 Priority queues 779A tree representation for a heap . . . . . . . . . . 782Leftist heaps . . . . . . . . . . . . . . . . . . . . 786Binomial heaps . . . . . . . . . . . . . . . . . . . 794Binary heaps . . . . . . . . . . . . . . . . . . . . 814Adding keys and values . . . . . . . . . . . . . . 825Comparisons . . . . . . . . . . . . . . . . . . . . 842Human encoding . . . . . . . . . . . . . . . . . 84618 Conclusions 853Where to go from here . . . . . . . . . . . . . . 855.
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## Introduction to Computational Thinking: Problem Solving, Algorithms, Data Structures, and More 1484270762, 9781484270769

English Pages 670 [661] Year 2021

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## Computational Thinking for Problem Solving

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## Syllabus - What you will learn from this course

Pillars of computational thinking.

## Expressing and Analyzing Algorithms

## Fundamental Operations of a Modern Computer

## Applied Computational Thinking Using Python

## TOP REVIEWS FROM COMPUTATIONAL THINKING FOR PROBLEM SOLVING

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## Introduction to Computational Thinking: Problem Solving, Algorithms, Data Structures, and More

## About this ebook

- Think in a computational way
- Acquire general techniques for problem solving
- See general and concrete algorithmic techniques
- Program solutions that are both computationally efficient and maintainable

## Thomas Mailund

## Related categories

Software Development & Engineering

## Reviews for Introduction to Computational Thinking

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## Book preview

Introduction to computational thinking - thomas mailund.

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2021

T. MailundIntroduction to Computational Thinking https://doi.org/10.1007/978-1-4842-7077-6_1

## 1. Introduction

## Models of the World and Formalizing Problems

## What Is Computational Thinking?

## Computational Thinking in a Broader Context

## What Is to Come

T. MailundIntroduction to Computational Thinking https://doi.org/10.1007/978-1-4842-7077-6_2

## 2. Introducing Python Programming

## Obtaining Python

## Running Python

in your terminal. If you want to use Python interactively, you can write

Press Ctrl+D to leave the Python terminal again.

## Expressions in Python

Try writing some arithmetic expressions into the Python terminal, for example

k = (today + 10) % 7 # Weekday 10 days from today

But what if we want to know what weekday it was 10 days ago? Then we could do

k = (today - 10) % 7 # Weekday 10 days ago

## Logical (or Boolean) Expressions

SyntaxError: can't assign to literal

(2 == (1 + 1)) and (3 == (2 + 1))

((2 == 1) + 1) and ((3 == 2) + 1)

will be one, since (2 == 1) is False, which is interpreted as zero.

will be False since both 2 == 1 and 3 == 1 are False.

You can give the result of an expression a name. For example, if you write

in the terminal, then a will be a label that refers to four. If you then write

Python will show the value that a refers to.

You can use a variable where you would put the value it refers to:

You can reassign to a variable to change what value it will be a label for. After

the variable a now refers to five instead of four.

You can assign to more than one variable at a time using this syntax:

to switch what a and b hold. If you tried to do this in two steps

## Working with Strings

You can assign strings to variables

and you can concatenate two or more strings using +:

You can concatenate a string with itself a number of times using the multiplication operator:

This inserts 42 into the string at the expression indicated by {}.

You can insert values everywhere in a string and use any value, including other strings:

{}, {} .format( hello , world! )

{}, {} .format( world! , hello )

It is possible to name the placeholders if you also name the arguments. The syntax looks like this:

{x}, {y} .format(x = hello , y = world! )

{x}, {y} .format(y = world! , x = hello )

will give us the same result as the preceding one.

Exercise: Extract the h and the o from hello .

Exercise: What happens if the first and last numbers in a

## Introduction to Computational Thinking Problem Solving, Algorithms, Data Structures, and More

## Print ISBN 9781484270769, 1484270762

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## Enhance your purchase

- Think in a computational way
- Acquire general techniques for problem solving
- See general and concrete algorithmic techniques
- Program solutions that are both computationally efficient and maintainable

- Print length 657 pages
- Language English
- Publication date 17 July 2021
- Dimensions 17.81 x 3.86 x 25.4 cm
- ISBN-10 1484270762
- ISBN-13 978-1484270769
- See all details

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## Introduction to Computational Thinking: Problem Solving, Algorithms, Data Structures, and More

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## Introduction to Computational Thinking : Problem Solving, Algorithms, Data Structures, and More

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- Format Paperback | 657 pages
- Dimensions 178 x 254 x 34.29mm | 1,259g
- Publication date 17 Jul 2021
- Publisher APRESS
- Publication City/Country Berkley, United States
- Language English
- Edition Statement 1st ed.
- Illustrations note 78 Illustrations, black and white; XIII, 657 p. 78 illus.
- ISBN10 1484270762
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Fostering computational thinking skills: a didactic proposal for elementary school grades.

## 1. Introduction

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## Introduction to Computing Using Python: An Application Development Focus, 2nd Edition

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Chapter 1. Introduction to Computer Science

Chapter 3. Imperative Programming

Chapter 4. Text data, Files, and Exceptions

Chapter 5. Execution Control Structures

Chapter 6. Containers and Randomness

Chapter 8. Object-Oriented Programming

Chapter 9. Graphical User Interfaces

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Learn approaches of computational thinking and the art of designing algorithms. Most of the algorithms you will see in this book are used in almost all software that runs on your computer. Learning how to program can be very rewarding.

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Computational thinking is built on four pillars: decomposition, pattern recognition, data representation and abstraction, and algorithms. This module introduces you to the four pillars of computational thinking and shows how they can be applied as part of the problem solving process. SHOW ALL 6 videos (Total 44 min) 6 videos 1.1 Introduction 4m

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