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We've arrived at what I think is
a good spot to take a long break and
0:00
let all of these new concepts sink in.
0:03
But before you go off to the next course,
0:06
let's take a few minutes to recap
everything we've learned so far.
0:08
While we did implement two algorithms
in this course in actual code, much of
0:12
what we learned here was conceptual and
will serve as building blocks for
0:16
everything we're going
to learn in the future.
0:20
So let's list all of it out.
0:22
The first thing we learned about,
and arguably the most important,
0:24
was algorithmic thinking.
0:28
Algorithmic thinking is an approach to
problem solving that involves breaking
0:30
a problem down into a clearly defined
input and output, along with a distinct
0:35
set of steps that solves the problem
by going from input to output.
0:39
Algorithmic thinking is not something you
develop overnight by taking one course.
0:44
So don't worry if you're thinking, well,
0:48
I still don't truly know how
to apply what I learned here.
0:50
Algorithmic thinking sinks in after
you go through several examples in
0:53
a similar fashion to what we did today.
0:57
It also helps to apply these concepts
in the context of a real example,
0:59
which is another thing we will
strive to do moving forward.
1:04
Regardless, it is important to keep in
mind that the main goal here is not to
1:07
learn how to implement
a specific data structure or
1:12
algorithm off the top of your head.
1:14
I'll be honest, I had to look
up a couple of code snippets for
1:16
a few of the algorithms myself
in writing this course.
1:19
But in going through this,
you now know that binary search exists and
1:22
can it apply to a problem where you
need a faster search algorithm.
1:27
Unlike most courses where you can
immediately apply what you have learned to
1:31
build something cool,
learning about algorithms and
1:35
data structures will pay
off more in the long run.
1:38
The second thing we learned about is
how to define and implement algorithms.
1:41
We've gone over these
guidelines several times, so
1:46
I won't bore you here again at the end.
1:49
But I will remind you that if you're often
confused about how to effectively break
1:51
down a problem in code to
something more manageable,
1:55
following those algorithm guidelines
is a good place to start.
1:58
Next, we learned about big O and measuring
the time complexity of algorithms.
2:02
This a mildly complicated topic, but
once you've abstracted the math away,
2:07
it isn’t as hazy a topic as it seems.
2:12
Now don't get me wrong,
the math is pretty important, but only for
2:14
those designing and analyzing algorithms.
2:18
Our goal is more about how to
understand and evaluate algorithms.
2:20
We learned about common
runtimes like constant,
2:25
linear, logarithmic and
quadratic runtimes.
2:28
These are all fairly new concepts.
2:31
But in time, you will immediately
be able to distinguish
2:34
the runtime of an algorithm
based on the code you write and
2:37
have an understanding of where
it sits on an efficiency scale.
2:40
You will also, in due time, internalize
runtimes of popular algorithms,
2:43
like the fact that binary search runs
in logarithmic time and constant space.
2:48
And be able to recommend alternative
algorithms for a given problem.
2:53
All in all, over time, the number of
tools in your tool belt will increase.
2:57
Now next, we learned about two
important search algorithms and
3:02
the situations in which we
select one over the other.
3:06
We also implemented these
algorithms in code so
3:09
that you got a chance to see them work.
3:12
We did this in python, but if you are more
familiar with a different language and
3:14
haven't gotten the chance to check
out the code snippets we've provided,
3:18
you should try your hand at
implementing it yourself.
3:22
It's a really good exercise to go through.
3:25
Finally, we learned about
an important concept and
3:27
a way of writing algorithmic
code through recursion.
3:31
Recursion is a tricky thing, and depending
on the language you write code with,
3:34
you may run into it more than others.
3:39
It is also good to be aware of, because
as we saw in our implementation of binary
3:41
search, whether recursion was used or
not affected the amount of space we used.
3:46
Don't worry if you don't fully understand
how to write recursive functions.
3:51
I don't truly know either.
3:55
The good part is you can always
look these things up and
3:57
understand how other people do it.
4:00
Any time you encounter recursion
in our courses moving forward,
4:02
you'll get a full explanation of how and
why the function is doing what it's doing.
4:06
And that brings us to
the end of this course.
4:11
I'll stress again that the goal of this
course was to get you prepared for
4:14
learning about more specific algorithms by
introducing you to some of the tools and
4:18
concepts you will need moving forward.
4:22
So if you're sitting there thinking,
I still don't
4:25
know how to write many algorithms or how
to use algorithmic thinking, that's okay.
4:27
We'll get there.
4:31
Just stick with it.
4:32
As always, have fun and happy coding.
4:34
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