1 00:00:00,790 --> 00:00:04,440 Wow, I feel like we threw enough terms at you during this course that we should 2 00:00:04,440 --> 00:00:05,980 consider it big data. 3 00:00:05,980 --> 00:00:07,780 Nice job ingesting all that. 4 00:00:07,780 --> 00:00:11,260 That was a whirlwind tour of the current world of big data problems, 5 00:00:11,260 --> 00:00:14,770 tools, frameworks, and companies working in that space every day. 6 00:00:14,770 --> 00:00:16,100 Way to stick with it to the very end. 7 00:00:17,150 --> 00:00:19,515 Now that you have a broad understanding of the ecosystem, 8 00:00:19,515 --> 00:00:22,082 let's set you up with a few next steps for 9 00:00:22,082 --> 00:00:26,430 things that you can do to prepare yourself for the world of big data. 10 00:00:26,430 --> 00:00:29,910 Whether that be in a position as a data scientist, a software engineer, or 11 00:00:29,910 --> 00:00:31,760 a manager who wants to understand the landscape. 12 00:00:32,950 --> 00:00:34,658 One thing that you should do for 13 00:00:34,658 --> 00:00:38,282 sure is check out the data science competitions at kaggle.com. 14 00:00:38,282 --> 00:00:40,540 I dropped a link in the teacher's notes. 15 00:00:40,540 --> 00:00:44,680 On Kaggle, you can participate and compete in competitions where you're 16 00:00:44,680 --> 00:00:48,500 given a large amount of real data, and problems to solve with that data. 17 00:00:48,500 --> 00:00:50,782 There are very large prizes. 18 00:00:50,782 --> 00:00:54,034 Kaggle is where many data scientists and software engineers wanting to work 19 00:00:54,034 --> 00:00:57,230 on the machine learning and big data get their start. 20 00:00:57,230 --> 00:01:01,400 You should also browse through existing open data sources on sites like GitHub and 21 00:01:01,400 --> 00:01:05,890 try checking out the public data sets available on Amazon Web Services. 22 00:01:05,890 --> 00:01:06,790 With AWS, 23 00:01:06,790 --> 00:01:11,280 you pay only cents per hour to deploy your own large clusters of machines. 24 00:01:11,280 --> 00:01:14,040 This allows you to play around with the tools that we've talked about 25 00:01:14,040 --> 00:01:16,030 using the data they already have on their platform. 26 00:01:17,200 --> 00:01:20,950 Make sure you check out some pre-made Jupiter notebooks on introductory data 27 00:01:20,950 --> 00:01:24,756 science topics, and extend them with languages like Python to solve different 28 00:01:24,756 --> 00:01:26,877 problems with available public data sets. 29 00:01:26,877 --> 00:01:29,650 We've dropped links in the teacher's notes that should keep you busy for a while. 30 00:01:30,730 --> 00:01:36,060 Remember, big data, data science, machine learning, and the related fields comprise 31 00:01:36,060 --> 00:01:41,740 a massive world of new topics and skills to be explored, learned, and mastered. 32 00:01:41,740 --> 00:01:44,510 Even the most seasoned of big data experts take 33 00:01:44,510 --> 00:01:46,710 years to feel comfortable with the tools we've presented here. 34 00:01:47,750 --> 00:01:50,711 I say this, so don't get discouraged. 35 00:01:50,711 --> 00:01:54,645 Not only are you now familiar with the ecosystem, but you can start with big data 36 00:01:54,645 --> 00:01:58,463 no matter how large or small your company is, just by going through some of our 37 00:01:58,463 --> 00:02:02,660 recommendations and being excited about this hot new area of software technology. 38 00:02:03,670 --> 00:02:06,860 Please, let us know what you'd like to see here on Treehouse by making 39 00:02:06,860 --> 00:02:07,780 your voice heard. 40 00:02:07,780 --> 00:02:09,650 We greatly value your opinions and 41 00:02:09,650 --> 00:02:12,580 would really love to see what you would like to learn more about. 42 00:02:12,580 --> 00:02:16,350 This course here was spawned from a request made by your fellow students. 43 00:02:16,350 --> 00:02:20,360 So make sure to thank them and pay it forward by requesting even more content. 44 00:02:20,360 --> 00:02:23,160 Also, please make sure to leave us some feedback on what you 45 00:02:23,160 --> 00:02:24,490 thought about this course. 46 00:02:24,490 --> 00:02:27,092 Check out the discussions of the community for more, too. 47 00:02:27,092 --> 00:02:30,037 Now finally, I'd like to give a huge shout out to Jared Smith, 48 00:02:30,037 --> 00:02:32,831 who did a ton of heavy lifting and research for this course. 49 00:02:32,831 --> 00:02:36,690 And I think, and you probably do too, he did an amazing job. 50 00:02:36,690 --> 00:02:37,550 Thanks for hanging out. 51 00:02:37,550 --> 00:02:38,260 We'll see you next time.