Bummer! This is just a preview. You need to be signed in with a Basic account to view the entire video.
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.
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
You need to sign up for Treehouse in order to download course files.Sign up