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Armstrong Subero

Codeless Data Structures and


Algorithms
Learn DSA Without Writing a Single Line of Code
Armstrong Subero
Basse Terre, Moruga, Trinidad and Tobago

Any source code or other supplementary material referenced by the


author in this book is available to readers on GitHub via the book’s
product page, located at www.​apress.​com/​9781484257241 . For more
detailed information, please visit www.​apress.​com/​source-code .

ISBN 978-1-4842-5724-1 e-ISBN 978-1-4842-5725-8


https://doi.org/10.1007/978-1-4842-5725-8

© Armstrong Subero 2020

This work is subject to copyright. All rights are reserved by the


Publisher, whether the whole or part of the material is concerned,
specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other
physical way, and transmission or information storage and retrieval,
electronic adaptation, computer software, or by similar or dissimilar
methodology now known or hereafter developed.

Trademarked names, logos, and images may appear in this book. Rather
than use a trademark symbol with every occurrence of a trademarked
name, logo, or image we use the names, logos, and images only in an
editorial fashion and to the benefit of the trademark owner, with no
intention of infringement of the trademark. The use in this publication
of trade names, trademarks, service marks, and similar terms, even if
they are not identified as such, is not to be taken as an expression of
opinion as to whether or not they are subject to proprietary rights.

While the advice and information in this book are believed to be true
and accurate at the date of publication, neither the authors nor the
editors nor the publisher can accept any legal responsibility for any
errors or omissions that may be made. The publisher makes no
warranty, express or implied, with respect to the material contained
herein.

Distributed to the book trade worldwide by Springer Science+Business


Media New York, 1 New York Plaza, New York, NY 100043. Phone 1-
800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-
sbm.com, or visit www.springeronline.com. Apress Media, LLC is a
California LLC and the sole member (owner) is Springer Science +
Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a
Delaware corporation.
To all those who tinker to keep the world running.
Introduction
There is one question beginners and even good self-taught developers
ask me all the time: “How do I learn data structures and algorithms?” I
have had to answer this question so often that I thought I would write it
down in a book so that anyone interested in this topic could follow
along and understand the process.
The thing is, anyone can learn the basics of data structures and
algorithms. There really isn’t much to them. The hardest part of
teaching someone this topic is that they are likely to be using any
language from the current “zoo” of languages. They may be using
Python, Java, C++, or even C or Rust. Whatever the language they are
using, it is important to be able to understand data structures and
algorithms at the fundamental level. As such, I decided to make my
book “codeless.” This is a book on algorithms and data structures that
doesn’t use a single line of code from any programming language.
As someone who must regularly change between different
programing languages, take it from me, once you understand the
concepts behind these algorithms in a codeless way, then you will be
able to apply them to whatever language you are using.
There are also some people “devout” in their programming
language who are unwilling to look at any material that isn’t in their
language of choice. As such, I have written this book without offending
anyone’s “beliefs.” I think there are enough books on “data structures
and algorithms in X language” that are thousands of pages detailing
programs and all their nuances for the language you are using. I think
such books would be fine complements to this one, as this book will
give you the “big picture,” and then you can look at whatever book you
want for the rest. You can learn about a concept here and then go to
that book to learn the programming details.
Everything is explained in plain English, and each chapter is short
and to the point. You will learn a lot without burning your brain out.
Who Is This Book For?
This book is for people who want to understand data structures and
algorithms but don’t want unnecessary details about quirks of a
language and don’t have time to sit and read a massive tome on the
subject. This book is for people who want to understand the concepts of
algorithms and data structures in plain English. I assume, though, that
you have knowledge of at least one programming language, preferably a
C-based one such as C, C++, Java, C#, or Python. The types and
terminology used in this book are biased toward people who have used
one of these languages.
I assume you are good at thinking abstractly and at least have basic
knowledge of a programming language and of the basics of computer
science (what a bit is, what a byte is, etc.). I also assume you know basic
mathematics, at least algebra, though this book is by no means a “math-
heavy” book. I use math concepts only where they are necessary and
explain them clearly.

What Will I Need for This Book?


You won’t need anything except an open mind and time to read and
understand the concepts being presented. I wrote this book as if we
were having a coffee and I was explaining these concepts to you from
the top of my head. For that reason, you won’t need any compiler or text
editor, just a listening ear.

What Will I Learn in This Book?


You will learn quite a lot in a short space of time about data structures
and algorithms. You will learn the concepts of data structures and
algorithms with a focus on the most relevant ones in simple, plain
language. After completing this book, you’ll understand which data
structures and algorithms can be used to solve which problems and will
be able to confidently follow along with discussions involving
algorithms and data structures.
PART I – Data Structures
Chapter 1 goes into what algorithms and data structures are, and
we discuss primitive types and Big O notation.
Chapter 2 looks at the linear data structures of arrays and linked
lists; we also discuss stacks and queues.
Chapter 3 delves into trees and tree-based data structures.
Chapter 4 introduces you to hash-type data structures.
Chapter 5 briefly covers the basics of graphs.
PART II – Algorithms
Chapter 6 introduces two common algorithms, that of linear search
and binary search.
Chapter 7 explores sorting algorithms, including bubble sort,
selection sort, insertion sort, merge sort, and quick sort.
Chapter 8 presents some search algorithms; we look at breath-first
search, Dijkstra’s algorithm, and the A∗ algorithm.
Chapter 9 introduces clustering algorithms, specifically the K-
means algorithm and K-nearest neighbor and get a taste of machine
learning and neural networks.
Chapter 10 teaches some basics about the concept of randomness.
Appendix A provides resources for going further.
Upon finishing this book, you will have a solid foundation in
algorithms and data structures and will be able to use this knowledge
when writing and designing your own programs.

Errata and Suggestions


If you find any errors in this text, have suggestions, or want to ask a
question about your own project, you can contact me
atarmstrongsubero@gmail.com ; please, no spam.
Acknowledgments
I want to thank my family.
I want to thankeveryone who ever said anything positive to me or
taught me something. I heard it all, and it meant something.
I want to thank God most of all, because without God I wouldn’t be
able to do any of this.
Contents
Part I: Data Structures
Chapter 1:​Intro to DSA, Types, and Big O
Chapter 2:​Linear Data Structures
Chapter 3:​Tree Data Structures
Chapter 4:​Hash Data Structures
Chapter 5:​Graphs
Part II: Algorithms
Chapter 6:​Linear and Binary Search
Chapter 7:​Sorting Algorithms
Chapter 8:​Searching Algorithms
Chapter 9:​Clustering Algorithms
Chapter 10:​Randomness
Chapter 11:​Scheduling Algorithms
Chapter 12:​Algorithm Planning and Design
Appendix A:​Going Further
Index
About the Author
Armstrong Subero
started learning electronics at the age of
8. From then on he got into
programming and embedded systems
development. Within the realm of
programming, he has a special interest in
algorithms and data structures and
enjoys implementing them in different
programming languages and on different
processor architectures, particularly
resource-constrained systems. He
currently works at the Ministry of
National Security in his country, and he
has degrees in computer science and
liberal arts and sciences from Thomas Edison State University. He is the
author ofProgramming PIC Microcontrollers with XC8 (Apress, 2018).
Part I
Data Structures
© Armstrong Subero 2020
A. Subero, Codeless Data Structures and Algorithms
https://doi.org/10.1007/978-1-4842-5725-8_1

1. Intro to DSA, Types, and Big O


Armstrong Subero1
(1) Basse Terre, Moruga, Trinidad and Tobago

Every journey has a beginning. In this chapter, we will begin our


journey by talking about what data structures and algorithms are. I will
introduce basic types, and I will show you how easy Big O notation is to
understand. If you have ever read a dull, overly complex book on data
structures and algorithms (abbreviated to DSA), you will love how
quickly this chapter teaches you things!

An Overview of Data Structures and Algorithms


“Talk is cheap. Show me the code.”
—Linus Torvalds, Finnish software engineer and creator of
Linux

Linus said this while replying on the Linux Kernel mailing list on
August 25, 2000. This reply has become a famous quote among
programmers. It is used by developers whenever they don’t want to
read anything and instead just jump into coding. This approach is
particularly taken by novices and poorly self-taught programmers.
They don’t plan, they think they know more than everyone, and they
think programming is all about the code. This couldn’t be further from
the truth. Code simply expresses your thoughts to solve a problem.
Nothing more. Therefore, the more you know, the more you can apply
to solve a problem.
Data structures and algorithms are simply more things to know to
apply to solve your problems. Despite some people using them
interchangeably, data structures and algorithms are actually very
different things. It is possible to learn data structures and algorithms
without touching a programming language. Programming essentially
consists of thinking algorithmically and knowing the syntax of the
programming language to solve problems. In this book, we will focus on
thinking algorithmically and avoid learning the syntax of any
programming language.
Before we discuss data structures and algorithms, I think we should
talk a little about data. Data can mean different things depending on the
discipline you are currently occupied with. However, data within the
context of this book refers to any information that is stored on your
machine or that is being handled or processed by it. Data should not be
confused with information, which is data that has been processed;
however, within the context of computing, many developers may use
these terms independently to mean the same thing.

Data Structures
A data structure is a concept we use to describe ways to organize and
store types of data. Data structures are important because they not only
provide a way of storing and organizing data but also provide a way of
identifying data within the structure; additionally, they show the
relationships of the data within the structure. It is best to illustrate
what a data structure is with an example.
For example, let’s say we have some footwear, as depicted in Figure
1-1. We have two boots and two shoes arranged alternately.

Figure 1-1. Two types of footwear

We can think of each side of the shoe as a unit of data. If we needed


a way to maintain this data arrangement, we would need a mechanism
to provide some ordering and identification of these shoes; this is what
we may call a data structure. A data structure may provide some
mechanism to organize and store this data, for example, by separating
boots and shoes as shown in Figure 1-2.

Figure 1-2. Separating boots and shoes


A data structure may also be able to take each item and, regardless
of whether it’s a shoe or boot, assign an identifier, for example, a
number as shown in Figure 1-3.

Figure 1-3. Assigning identifiers

So, if I wanted the “second shoe,” instead of wondering if this means


from the left or from the right, I can simply tell the data structure, “Give
me the shoe at index 2,” and I will get exactly what I wanted.
As basic as it may seem, this is all a data structure does, though
there are much more complex methods of storing, identifying, and
showing the relationships within data. This explanation provides a
good grasp of what a data structure does. If you still aren’t clear what a
data structure is, it is best to think of a data structure as a container for
data.

Algorithms
An algorithm is a method of solving a problem by applying a sequence
of steps that will always work to solve the type of problem it was
designed to solve. Another way of saying this is that an algorithm is
simply a method of solving a problem in an ordered manner. We can
even shorten it further to say an algorithm is a procedure. Many people
may have expanded or diminished perspectives to describe what an
algorithm is, but this definition will work for our understanding.
One thing everyone will agree on is that algorithms are logical steps
to accomplish a task. To accomplish this, an algorithm must be simple,
precise, and unambiguous. Though some programmers focus on using
esoteric features of programming languages that make an algorithm
hard to read for other programmers, the simpler and more intuitive an
algorithm is, the more powerful and useful it will be.
We can describe algorithms with natural languages such as English,
pseudocode, or a programming language. We can discuss algorithms at
great length; however, it is best to see how algorithms work with a good
example. In our example, we can show how algorithms operate with a
pure English description, with no code needed.
We can gain an understanding of algorithms by looking at one in
action. Let’s say you need to put some fruit on a plate. You would, of
course, grab a plate and put some fruit on it, right? Well, imagine you
were describing these same steps to a robot. How would you do it? You
would tell the robot to do something like the following:
1. Go to the cupboard.

2. Open the door.

3. Take out a plate.


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