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Wow, I feel like we threw enough terms
at you during this course that we should

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consider it big data.

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Nice job ingesting all that.

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That was a whirlwind tour of
the current world of big data problems,

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tools, frameworks, and
companies working in that space every day.

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Way to stick with it to the very end.

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Now that you have a broad
understanding of the ecosystem,

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let's set you up with a few next steps for

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things that you can do to prepare
yourself for the world of big data.

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Whether that be in a position as a data
scientist, a software engineer, or

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a manager who wants to
understand the landscape.

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One thing that you should do for

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sure is check out the data science
competitions at kaggle.com.

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I dropped a link in the teacher's notes.

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On Kaggle, you can participate and
compete in competitions where you're

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given a large amount of real data,
and problems to solve with that data.

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There are very large prizes.

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Kaggle is where many data scientists and
software engineers wanting to work

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on the machine learning and
big data get their start.

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You should also browse through existing
open data sources on sites like GitHub and

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try checking out the public data sets
available on Amazon Web Services.

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With AWS,

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you pay only cents per hour to deploy
your own large clusters of machines.

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This allows you to play around with
the tools that we've talked about

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using the data they already
have on their platform.

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Make sure you check out some pre-made
Jupiter notebooks on introductory data

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science topics, and extend them with
languages like Python to solve different

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problems with available public data sets.

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We've dropped links in the teacher's notes
that should keep you busy for a while.

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Remember, big data, data science, machine
learning, and the related fields comprise

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a massive world of new topics and skills
to be explored, learned, and mastered.

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Even the most seasoned
of big data experts take

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years to feel comfortable with
the tools we've presented here.

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I say this, so don't get discouraged.

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Not only are you now familiar with the
ecosystem, but you can start with big data

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no matter how large or small your company
is, just by going through some of our

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recommendations and being excited about
this hot new area of software technology.

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Please, let us know what you'd like
to see here on Treehouse by making

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your voice heard.

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00:02:07,780 --> 00:02:09,650
We greatly value your opinions and

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00:02:09,650 --> 00:02:12,580
would really love to see what you
would like to learn more about.

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00:02:12,580 --> 00:02:16,350
This course here was spawned from
a request made by your fellow students.

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So make sure to thank them and pay it
forward by requesting even more content.

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00:02:20,360 --> 00:02:23,160
Also, please make sure to leave
us some feedback on what you

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thought about this course.

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Check out the discussions of
the community for more, too.

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Now finally, I'd like to give
a huge shout out to Jared Smith,

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who did a ton of heavy lifting and
research for this course.

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And I think, and you probably do too,
he did an amazing job.

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Thanks for hanging out.

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We'll see you next time.