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Introduction to Big Data

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Wrapping Up

2:39 with Craig Dennis and Jared Smith

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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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