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[MUSIC]

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Hi, my name is Pasan.

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I'm an instructor here at Treehouse and
welcome to introduction to algorithms.

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Whether you're a high school or college
student, a developer in the industry, or

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someone who is learning to code, you have
undoubtedly running to the term algorithm.

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For many people,
this word is kind of scary.

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It represents this body of knowledge
that seems just out of reach.

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Only people with computer science
degrees know about algorithms.

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Now to others, this brings up
feelings of impostor syndrome.

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You might already know how to code but

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you're not a real developer because you
don't know anything about algorithms.

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Personally, it made me frame certain
jobs as above my skill level,

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because the interview
contained algorithm questions.

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Well, whatever your reasons are,
in this course, our goal is to dispel

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all those feelings and get you comfortable
with the basics of algorithms.

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Like any other subject,

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I like to start my courses with
what the course is and is not.

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In this course, we're going to cover the
very basic set of knowledge that you need

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as a foundation for
learning about algorithms.

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This course is less about
specific algorithms and

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more about the tools you will
need to evaluate algorithms,

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understand how they perform,
compare them to each other, and

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make a statement about the utility
of an algorithm in a given context.

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Now don't worry,
none of this will be theoretical and

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we will learn these concepts by
using well-known algorithms.

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In this course,
we'll also be writing code.

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So I do expect you to have
some programming experience if

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you intend to continue with this topic.

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You can definitely stick around even
if you don't know how to code but

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you might want to learn the basics
of programming in the meantime.

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In this course, we will be using
the Python programming language.

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Python reads a lot like regular English
and is the language you will most likely

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encounter when learning
about algorithms these days.

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If you don't know how to code or if you
know how to code in a different language,

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check out the note
section of this video for

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links to other content that
might be useful to you.

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As long as you understand
the fundamentals of programming,

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you should be able to
follow along pretty well.

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If you're a JavaScript developer or
a student who's learning JavaScript,

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for example,

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chances are good that you'll still be able
to understand the code we write later.

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I'll be sure to provide links along the
way if you need anything to follow up on.

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Let's start with something simple.

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What is an algorithm?

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An algorithm is a set of steps or
instructions for completing a task.

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This might sound like
an oversimplification but

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really that's precisely
what an algorithm is.

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A recipe is an algorithm, your morning
routine when you wake up is an algorithm,

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and the driving directions you follow to
get to a destination is also an algorithm.

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In computer science, the term algorithm
more specifically means the set of

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steps a program takes to finish a task.

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If you've written code before,
any code really, generally speaking,

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you have written an algorithm.

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Given that much of the code we write
can be considered an algorithm,

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what do people mean when they say,
you should know about algorithms?

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Now, consider this.

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Let's say,
I'm a teacher in a classroom and

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I tell everyone I have an assignment for
them.

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On their desks,
they have a picture of a maze and

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their task is to come up with a way to
find the quickest way out of the maze.

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Everyone does their thing and
comes up with a solution.

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Every single one of these
solutions is a viable solution and

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is a valid example of an algorithm.

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The steps one needs to take
to get out of the maze.

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But from being in classrooms or
any group of any sort,

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you know that some people will
have better ideas than others.

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We all have a diverse array of skillsets.

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Over time, our class picks
the best of these solutions.

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And anytime we want to solve a maze,
we go with one of these solutions.

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This is what the field
of algorithms is about.

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There are many problems
in computer science but

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some of them are pretty common, regardless
of what project you're working on.

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Different people have come up
with different solutions to these

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common problems.

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And over time, the field of
computer science has identified

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several that do the job well for
a given task.

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When we talk of algorithms,
we're referring to two points.

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We're primarily saying there's
an established body of knowledge on how to

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solve particular problems well.

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And it's important to know
what these solutions are.

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Now why is it important?

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If you're unaware that a solution exists,

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you might try to come
up with one yourself.

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And there's a likelihood that your
solution won't be as good or efficient,

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whatever that means, compared to those
that have been thoroughly reviewed.

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But there is a second
component to it as well.

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Part of understanding algorithms is not
just knowing that an algorithm exists but

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understanding when to apply it.

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Understanding when to apply
an algorithm requires properly

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understanding the problem at hand.

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And this, arguably, is the most important
part of learning about algorithms and

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data structures.

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As you progress through this content, you
should be able to look at a problem and

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break it down into distinct steps.

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When you have a set of steps, you should
then be able identify which algorithm or

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data structure is best for
the task at hand.

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This concept is called algorithmic
thinking and it's something we're

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going to try and cultivate together
as we work through our content.

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Lastly, learning about algorithms
gives you a deeper understanding about

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complexity and efficiency in programming.

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Having a better sense of how your code
will perform in different situations is

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something that you'll always
want to develop and hone.

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Algorithmic thinking is why algorithms
also come up in big tech interviews.

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Interviewers don't care as much that
you're able to write a specific algorithm

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in code but
more about the fact that you can break

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a seemingly insurmountable problem
into distinct components and

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identify the right tools to
solve each distinct component.

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And that is what we plan on doing.

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In this course, though, we're going to
focus on some of the tools and concepts

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you will need to be aware of before we
can dive into the topic of algorithms.

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If you're ready, let's get started.