1 00:00:07,930 --> 00:00:09,540 Welcome to the course! 2 00:00:09,540 --> 00:00:14,240 You've begun one of the most complete overviews on data science tooling that you’ll currently 3 00:00:14,240 --> 00:00:15,930 find on the internet. 4 00:00:15,930 --> 00:00:20,359 This doesn’t mean that we cover each and every tool, but later in the course we’ll 5 00:00:20,359 --> 00:00:26,150 introduce a comprehensive list of tasks a data scientist needs to perform and give you 6 00:00:26,150 --> 00:00:31,390 the top two or three open source and commercial tools available to complete them. 7 00:00:31,390 --> 00:00:37,850 We also explain how the tools overlap in functionality, what their pros and cons are, and how these 8 00:00:37,850 --> 00:00:41,000 tools can address the whole data science pipeline. 9 00:00:41,000 --> 00:00:43,239 Let’s start with data. 10 00:00:43,239 --> 00:00:46,760 Data is obviously central to data scientists. 11 00:00:46,760 --> 00:00:53,090 In this course, we’ll show you how to manage, extract, transform, analyze, and visualize 12 00:00:53,090 --> 00:00:54,090 data. 13 00:00:54,090 --> 00:00:59,160 Now, you might be able to survive data science without programming skills if you use the 14 00:00:59,160 --> 00:01:00,790 right set of tools. 15 00:01:00,790 --> 00:01:05,880 However, we highly recommend getting familiar with programming and the related programming 16 00:01:05,880 --> 00:01:08,289 frameworks for data science. 17 00:01:08,289 --> 00:01:12,710 To help you along, we’ll introduce you to the most commonly used programming languages 18 00:01:12,710 --> 00:01:16,320 and frameworks available for data science. 19 00:01:16,320 --> 00:01:21,240 That said, there is a lot of automation available in the latest tooling that a data scientist 20 00:01:21,240 --> 00:01:22,550 can use. 21 00:01:22,550 --> 00:01:27,840 In this course, we’ll explain how to make use of those tools to save time and uncover 22 00:01:27,840 --> 00:01:29,710 inspiration. 23 00:01:29,710 --> 00:01:32,920 Visual programming is available in many tools. 24 00:01:32,920 --> 00:01:37,400 In this course, you’ll learn how visual programming can be used to speed up development 25 00:01:37,400 --> 00:01:42,470 time and to help non-programmers enter the field of data science. 26 00:01:42,470 --> 00:01:47,950 Open source software is leading the field of data science, but its total costs of ownership, 27 00:01:47,950 --> 00:01:54,240 or "TCO," can be higher at times due to configuration, customization and maintenance costs. 28 00:01:54,240 --> 00:02:00,560 As a result, commercial software also has its place, especially since the new generation 29 00:02:00,560 --> 00:02:07,590 of commercial data science software leverages open source software and open standards. 30 00:02:07,590 --> 00:02:13,120 This makes it easy to migrate between tools and can reduce overall TCO. 31 00:02:13,120 --> 00:02:18,500 In this course, we’ll introduce you to both open source and commercial software and point 32 00:02:18,500 --> 00:02:22,299 out their strengths and weaknesses for data science. 33 00:02:22,299 --> 00:02:27,189 We'll also show you ways that you can take advantage of their respective strengths. 34 00:02:27,189 --> 00:02:33,200 Finally, we'll show you how cloud computing can be used to further speed up and facilitate 35 00:02:33,200 --> 00:02:35,319 data scientists' work. 36 00:02:35,319 --> 00:02:41,450 We'll introduce you to the most commonly used and newly emerging cloud tools for data science. 37 00:02:41,450 --> 00:02:46,620 In addition to lectures, this course, has numerous labs to make you more familiar with 38 00:02:46,620 --> 00:02:50,879 the material and get hands-on experience. 39 00:02:50,879 --> 00:02:53,169 There are also multiple quizzes to test your learning. 40 00:02:53,169 --> 00:02:54,169 Nothing more to say than we’re glad to have you in the course and happy learning. 41 00:02:54,169 --> 00:02:55,169 In case you have trouble in any way, please don’t hesitate to contact us in the discussion 42 00:02:55,169 --> 00:02:56,169 forum. 43 00:02:56,169 --> 00:02:57,169 There's nothing left but to begin! 44 00:02:57,169 --> 00:03:00,769 We're very happy to have you with us as you start your data science journey. 45 00:03:00,769 --> 00:03:05,670 If you have any trouble with any of the course material, please don’t hesitate to contact 46 00:03:05,670 --> 00:03:07,919 us in the discussion forum. 47 00:03:07,919 --> 00:03:08,849 Let's get started!