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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.
Explore
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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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We greatly value your opinions and
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would really love to see what you would like to learn more about.
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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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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.
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