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I like to welcome up our
first speaker of the day.

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She is a senior engineer at DEV,
where she builds community and

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helps improve the software
careers of millions.

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She enjoys building and breaking code,

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the last creating empathetic
engineering teams a whole lot more.

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She is the creator of base CS and
base DS, two writing series exploring

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the fundamentals of computer science and
distributed systems.

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She also co-hosts the base CS podcast, and

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is a producer of the base CS and
bite size video series.

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Please welcome Vaidehi Joshi.

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Thank you.

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>> I'm so excited to be speaking
at the Treehouse festival and

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to be here with all of you.

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And I'm especially excited to be
sharing a little bit about one of

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my favorite topics, computer science.

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So, a little bit about me to start.

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I'm a senior software engineer at Forum,
the open source software that powers DEV.

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And I also think it's worth mentioning
that I did not go to school for

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computer science and
I don't have a CS degree.

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In fact, my degree is in English.

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So I learned to code at a boot
camp about six years ago, and

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I have been working professionally in
the tech industry ever since then.

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And when I was first learning to code,

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I didn't really know what
computer science was.

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It was only when I started interviewing
for my second engineering role that I

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started to hear a few computer science
terms and concepts being thrown around.

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And they came up in technical interviews.

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And in these technical interviews,

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I started to realize that these concepts
were coming up again and again, and

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I didn't really know that much about
what people were talking about.

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I didn't really know the terminology and
the jargon that they were using.

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And it was that experience that made me
want to learn more about computer science,

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and it made me want to fill in
those gaps in my knowledge.

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I'm someone who really doesn't
like being out of the loop, and

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not knowing what's going on.

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So, that was a pretty
good motivating factor.

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And when I decided that I wanted to fill
in those gaps in my knowledge, I decided

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that the easiest way to do that would
just be to teach myself computer science.

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Which is a pretty bold idea but definitely
doable, a lot of people have done it.

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And the way that I went about doing this
was sort of like creating a schedule for

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myself.

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And I decided that for the whole year
I was going to learn one new computer

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science concept every single week for
the entire calendar year.

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And I decided that I was going to write
about every new concept that I was

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learning during that year, too.

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And the culmination of this project
was something called base CS,

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a year long writing series that included
all of the computer science topics

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that I taught myself throughout that year.

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So, if you've ever heard CS terms or
concepts being used in a conversation and

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found yourself nodding along, but you
didn't really know that much about them.

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Fear not because you are not alone.

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And that is exactly the way
that I started out, too.

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And my hope is that by the end of this
talk, you'll have a better sense of

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what computer science is, and some of
the core concepts within the field,

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and how to go about learning it, too.

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And the other thing I wanna note is
that it's impossible to cover everything

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about computer science in just 25 minutes.

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It's a huge field, and
25 minutes is really not a long time.

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But hopefully this is going to be
a helpful first step in your journey in

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learning more about computer science.

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So let's start with the basics.

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What on earth even is computer science?

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Well, if you look up the definition of
the fields, you'll see that computer

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science is defined as the study
of computation and information.

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If we look up the definition
of computer science,

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we can see that it's actually not just one
field, there's all these subfields and

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specific discrete topics
within that field, too.

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But in my opinion,
computation and information,

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those two words really do encompass the
way that I think about computer science.

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If you think about it,
computation really just means calculation.

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And information really just means data.

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Computer science is a combination of
making calculations and dealing with data.

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If you look at CS with this lens,
with this perspective.

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You start to see that understanding
some fundamentals of computer

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science helps us think about and interact
with, and manipulate information and data.

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And oftentimes, we're making
calculations about the data, and

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figuring out how to modify
it to fit our needs.

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Now, if you're learning to code,
it can be hard to know how these two

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things computation and information,
how they fit into your life.

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And that can make it hard to know where
to start learning computer science.

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But the reality is that you've
probably already thought about both of

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these concepts in some capacity
whether you realize it or not.

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If you've ever thought about
how to structure some data,

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you were brushing up
against computer science.

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If you've ever thought about
how to sort through or

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find some data,
you were dabbling in computer science.

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And if you've ever thought about how
you can make your code just a little

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bit faster, and a little more efficient,

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you are flexing your computer
science muscles then, too.

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And it's this idea of computation, and
information that maps really quite well

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to two of the core concepts in computer
science, data structures and algorithms.

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Now data structures are information
part of computer science,

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while algorithms are that
computation aspect.

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And data structures, and
algorithms were the two main

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concepts that I first started learning
when I wanted to teach myself CS.

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And I found that if you start
with these two topics first,

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you'll often run into other
related topics along the way.

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So let's start with data structures first.

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A data structure is really just
a way of organizing some data.

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If you're learning to code,

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you probably already run into data
structures in some shape or format.

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For example, arrays and objects
are actually both just data structures.

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So are sets and dictionaries.

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When you're learning to code, you
are forced to use the data structures that

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are built into whatever
programming language you're using.

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Because that's what you have to use
in order to store any data that you

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care about.

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And computer science is
really no different.

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When you study CS,

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you'll encounter different kinds of data
structures that let you store your data.

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The only difference is that you might have
not seen them in the programming language

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you're learning before.

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For example, you might have heard of and
use something called an array.

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But arrays aren't the only
way of organizing data.

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There's also something
called a linked list,

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which is a linear data structure
that orders data sequentially.

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Here's an example.

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Each element within this link list is
stored within something called a node.

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And each node holds that data.

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Each node also has a pointer or
reference to the next node.

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And that's sort of where
the linking of the list comes from.

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Now even if you've never seen this data
structure before, at the end of the day,

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all this is is just another
way to organize your data.

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Learning about different data structures
through the eyes of computer science can

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show us the different ways to structure
information and how to hold it.

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And it can also show us scenarios when
certain data structures are more useful,

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and situations when they're less useful,
and maybe not the right tool for the job.

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For example,

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the Ruby programming language does
have an array data structure built in.

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But what it doesn't have is a built
in binary tree data structure,

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which is what's illustrated right here.

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Now, you might not have seen a binary
tree data structure before, but

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keep in mind all it is is just another
way of structuring and holding data.

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Immediately off the bat, even if we've
never seen a binary tree before,

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we can see that they're different
from arrays and linked lists.

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They're nonlinear, which means that their
data is organized non sequentially.

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But they still hold data.

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They have nodes and they have pointers.

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And they're just another way
of structuring information.

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And sometimes using a binary
tree might be the right choice.

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And other times using an array
might be the better choice.

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And as you learn about different
kinds of data structures,

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you also start to see how and
where they are used in computing.

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For example, binary trees like
this one illustrated here,

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are used within databases.

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So in the future if you're
ever dealing with databases,

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knowing what a binary tree is and
how it works can be really helpful.

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Some fundamental data structures
that you might encounter

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while learning computer science
are linked lists, trees,

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graphs, hash tables, and sets.

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So now that we've sort of
covered data structures,

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let's look at the other main
concept which is algorithms.

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Now an algorithm can seem
like a scary word, but

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really all it is is a set of instructions.

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And those instructions are used for
deciding how to perform a calculation.

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In other words, an algorithm is
basically just a function or a method.

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And we use that function or
method to help us compute something.

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Algorithms actually couple pretty
well with data structures.

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Because every time we have
to interact with some data,

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we usually have to perform
some computation on it.

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Which means that we're relying
on an algorithm to do that.

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For example, remember that binary
tree we just saw a minute ago?

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Here's an illustration of how to
insert an element into that tree.

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Now inserting data into a data
structure needs a set of instructions.

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There's no way for the computer or
for a program to know how to do this.

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So we have to give it instructions
to tell it how, and guess what?

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Those instructions are just an algorithm.

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So in this case, the instructions
to add some data to a binary tree

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are the insertion algorithm for
this data structure.

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And you can imagine how just as there's an
insertion algorithm for this binary tree,

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there's also a deletion algorithm for
removing some data from the binary tree.

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Anytime that we deal with inserting,
deleting, searching through,

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sorting through some sort of data, we're
dealing with an algorithm to do that.

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And as you learn more about data
structures, you will naturally

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learn about the algorithms that
are specific to that data structure.

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There are also some algorithms that
are engineered to solve a very

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specific problem.

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For example, in this illustration,

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you can see a binary search algorithm
that's being performed on a data set.

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And the binary search algorithm is
something that's used specifically to

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search for or find an element
within a sorted collection of data.

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As you learn more about different
algorithms, you'll start to see that many

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algorithms build on other
algorithms which is pretty cool.

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So as you grow your knowledge about
how different algorithms perform,

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you'll start to see them
pop up in the real world.

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As we learn the fundamentals
of algorithms,

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it'll become easier to learn more
complex versions of the same ideas.

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So now that we know about data
structures and algorithms,

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there's a third concept which
I didn't mention before.

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But I think it's pretty cool,
I'm gonna throw it in there.

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There's a third concept
which is big O notation.

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And to me it sort of sits right between
data structures and algorithms.

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Big O notation is a way
of measuring efficiency.

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And efficiency is something that
actually applies to data structures,

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into algorithms.

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In fact, we think about efficiency
a lot when we're writing code and

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when we're thinking about really
intense forms of computation.

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Now, what is big O notation?

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It's really just a way to
express the amount of time and

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space that something requires.

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In other words, we can use big O notation
to talk about how efficient something is.

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Now this could mean how efficient
it is in terms of space,

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it could also mean how efficient
it is in terms of time.

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Something to note is that we
always talk about efficiency and

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big O notation in the worst
case scenario context.

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So we can use big O to talk about
how well something performs and

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decide on if we can improve on that, and
make it do better and perform better.

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And we can talk about Big O in terms
of both data structures and algorithms.

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So, whenever we ask ourselves, how much
time does it take to run this algorithm?

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What we're really asking is what's the big
O time complexity of this algorithm?

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Or how will this algorithm perform
in the worst case scenario?

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All three of those questions
really are equivalent.

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And they're all talking about
how efficient this algorithm is.

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So when we evaluate how an algorithm runs,
we want to know how much time

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it will take for
that algorithm to run as its input grows.

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If we give the algorithm
more elements to process or

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perform computations on,
what does the algorithm do?

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Does it slow down?

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Does it remain the same?

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That's how we can evaluate how it behaves.

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And amazingly efficient algorithm is
one that runs in constant time or O(1).

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Now this time complexity just means that
the algorithm will always take the same

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amount of time to run regardless
of how its input grows in size.

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So an algorithm that is running in
constant time behaves the same if you

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give a 10 elements or
if you give a 10 million elements.

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It's always still just as fast,
which is pretty cool,

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but these algorithms are a little
bit hard to come by.

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More likely what you will see
in the real world is some

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algorithm that runs in linear time,
those are a bit more standard.

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And we can say that
those run in O(n) time,

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where n is the variable that
represents the size of the input data.

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So n could be 10, if you're
passing algorithm 10 elements, or

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could be 10 million if you're
passing the 10 million elements.

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And when something runs in linear time,

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what that means is that as
our input data increases,

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our algorithms runtime increases linearly
in proportion to that input data.

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On the other hand, a terribly slow
algorithm might run in something

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called quadratic time or O(n-squared).

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Now, you can imagine if
n is the input data,

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how that algorithm runs
as the input data grows.

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Basically an algorithm that runs in
quadratic time is one that gets extremely,

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extremely slow as its
input data increases.

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Its performance is directly proportional
to the square of the size of its input.

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And you can see this kind of algorithm
a lot when you write code that has lots of

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nested loops within it.

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Those are often algorithms that
run in quadratic time, and

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those are things to avoid.

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Now you know what they look like.

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But as I mentioned, we don't just need
to be efficient when it comes to time.

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We also care about efficiency
when it comes to space too.

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We usually wanna know how much space or
memory an algorithm will use up.

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So if you find yourself asking, how
much memory will this algorithm require?

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You're really just asking what's the big
O space complexity of this algorithm?

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Another way of phrasing
that same question is,

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how much memory will this algorithm
require as its input grows?

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And measuring efficiency doesn't
just apply to algorithms,

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it applies to data structures too.

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If you find yourself asking how much
space does this data structure take up,

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then you're really asking about the big O
space complexity of that data structure.

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So you can see how you can talk about and

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consider efficiency with lots
of different perspectives.

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Big O notation is pretty cool because
it allows us to evaluate the space and

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time complexity of data structures and
of algorithms.

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And it gives us this language to help us
standardize how to talk about efficiency.

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So all three of these core concepts, data
structures, algorithms, big O notation,

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they all build on one another.

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And as you learn a little bit about one,
you reinforce your knowledge and

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understanding about the others.

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So the question is, of course, how do
you go about learning all of this stuff?

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I have to answer this question when I
was teaching myself computer science and

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there's really no one right answer.

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But I do wanna share a couple of
strategies that really helped me when I

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wanted to learn these concepts but
I didn't really know how to start.

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So my first piece of advice is to just
pick one topic and start learning.

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From there you'll stumble
upon other related topics and

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you'll expand what you don't know.

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And that's okay because the first step to
knowing what to learn is knowing what you

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don't know.

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So just pick one topic and
focus on understanding that really well.

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And maybe that means you start with a data
structure that you've always wanted to

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know about, or maybe you learn
about a specific algorithm.

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Or you can do what I did,
which was just to learn what binary is and

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how to count in binary, which is something
I didn't even cover in this talk,

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but it's also pretty cool.

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Next, figure out how you learn and

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then find a resource that works
best with your learning style.

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If you're an auditory learner,

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maybe that means you need to
find a really good podcast.

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And if you're a visual learner,
find those books and

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blog posts that really are gonna
cater to your learning style.

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And you're somewhere in between,
maybe a video would work best for you.

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Knowing how you learn is going to help
you narrow down resources that best

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match your learning style,

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and then you're gonna be even more
effective in your own learning journey.

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Then try to find examples of
what you've learned in the wild.

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Look at different frameworks and
see if they use a data structure or

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algorithm that you just
learned about under the hood.

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Or find out where that concept that you
just learned about actually applies in

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the real world, like that example
with binary trees and databases.

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Computer science is actually
just a whole lot more

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than anything I've
really said today there.

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It's more than data structures,
it's more than algorithms,

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it's more than big O notations.

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In fact, this field encompasses
things like networks,

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computer architecture,
security, distributed systems,

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operating systems, and
that's just to name a few.

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This is all to say there is just so
much to learn, but

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I believe that if you learn
the fundamentals, you'll often find

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that you'll bump up against some of
these other related CS topics too.

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And even if you don't need
to know computer science

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concepts immediately when
you're learning to code,

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I do think it can be
helpful at some point.

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Learning CS can help you think in the
context of performance and efficiency and

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it'll help you recognize pattern, and
it will help you in technical interviews.

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And sometimes it will even
help you in conversations

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with people who are a bit gatekeepery and
like to throw around terms.

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It's pretty empowering to know
what those terms mean and

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be able to engage in those
conversations and feel empowered that

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you can understand these concepts
even without getting a CS degree.

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And a lot of this stuff can be heard at
first and it's okay not to know all of it.

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When I was teaching myself computer
science, I quickly learned that

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as an industry, we need a lot more
approachable learning materials.

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And that's why I wrote down everything
that I learned along the way, so

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that it would be easier for everyone else.

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So the one thing that I hope you
will do as you decide what to learn

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in your learning journey, whether it's
computer science or something else,

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the one thing I hope you'll do is
share what you learned with others.

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Help make it easier for them and for

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the next person that tries to
learn the same thing that you are.

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If you'd like to check out some of the
resources that I created while learning

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computer science, here are a few of them.

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There's the BassDS writing series and
a BassDS podcast,

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if that's how you'd like
to learn new concepts.

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And there's also BassDS writing series
that I wrote a couple years ago

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on distributed systems.

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And there are a couple of links to all
of these resources, including a link to

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the video series that covers a couple
of the fundamental algorithms and

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data structures that I talked about today.

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Finally, I'm working on a brand
new resource which I'm gonna be

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sharing on Twitter in a couple of months.

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00:23:50,807 --> 00:23:56,106
So if you're curious to stay up to date
with even more computer science resources,

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00:23:56,106 --> 00:24:01,273
you can follow me there and keep up with
all the new CS content that I'm creating.

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And that's about it,
thank you so much for listening.

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I hope you all end up trying to
learn computer science and enjoy it.

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And even if you decide that you
wanna learn something else,

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I hope you just have fun
along the way while doing it.

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Thanks.