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Terms
Jupyter Notebooks -- Jupyter Notebooks are online, interactive notebooks of code, graphs, and explanations that are used for describing new data science methods, explorations of data, and other code snippets in a better way than just a commented code file.
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Wow, I feel like we threw enough terms
at you during this course that we should
0:00
consider it big data.
0:04
Nice job ingesting all that.
0:05
That was a whirlwind tour of
the current world of big data problems,
0:07
tools, frameworks, and
companies working in that space every day.
0:11
Way to stick with it to the very end.
0:14
Now that you have a broad
understanding of the ecosystem,
0:17
let's set you up with a few next steps for
0:19
things that you can do to prepare
yourself for the world of big data.
0:22
Whether that be in a position as a data
scientist, a software engineer, or
0:26
a manager who wants to
understand the landscape.
0:29
One thing that you should do for
0:32
sure is check out the data science
competitions at kaggle.com.
0:34
I dropped a link in the teacher's notes.
0:38
On Kaggle, you can participate and
compete in competitions where you're
0:40
given a large amount of real data,
and problems to solve with that data.
0:44
There are very large prizes.
0:48
Kaggle is where many data scientists and
software engineers wanting to work
0:50
on the machine learning and
big data get their start.
0:54
You should also browse through existing
open data sources on sites like GitHub and
0:57
try checking out the public data sets
available on Amazon Web Services.
1:01
With AWS,
1:05
you pay only cents per hour to deploy
your own large clusters of machines.
1:06
This allows you to play around with
the tools that we've talked about
1:11
using the data they already
have on their platform.
1:14
Make sure you check out some pre-made
Jupiter notebooks on introductory data
1:17
science topics, and extend them with
languages like Python to solve different
1:20
problems with available public data sets.
1:24
We've dropped links in the teacher's notes
that should keep you busy for a while.
1:26
Remember, big data, data science, machine
learning, and the related fields comprise
1:30
a massive world of new topics and skills
to be explored, learned, and mastered.
1:36
Even the most seasoned
of big data experts take
1:41
years to feel comfortable with
the tools we've presented here.
1:44
I say this, so don't get discouraged.
1:47
Not only are you now familiar with the
ecosystem, but you can start with big data
1:50
no matter how large or small your company
is, just by going through some of our
1:54
recommendations and being excited about
this hot new area of software technology.
1:58
Please, let us know what you'd like
to see here on Treehouse by making
2:03
your voice heard.
2:06
We greatly value your opinions and
2:07
would really love to see what you
would like to learn more about.
2:09
This course here was spawned from
a request made by your fellow students.
2:12
So make sure to thank them and pay it
forward by requesting even more content.
2:16
Also, please make sure to leave
us some feedback on what you
2:20
thought about this course.
2:23
Check out the discussions of
the community for more, too.
2:24
Now finally, I'd like to give
a huge shout out to Jared Smith,
2:27
who did a ton of heavy lifting and
research for this course.
2:30
And I think, and you probably do too,
he did an amazing job.
2:32
Thanks for hanging out.
2:36
We'll see you next time.
2:37
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