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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:01,150 --> 00:00:05,614 When investigating a possible new career path, one of the most important things to 2 00:00:05,614 --> 00:00:08,371 consider is its outlook and potential for growth. 3 00:00:08,371 --> 00:00:12,915 Predictions about careers related to data analysis show that there is no shortage of 4 00:00:12,915 --> 00:00:15,160 need for professionals in this field. 5 00:00:15,160 --> 00:00:19,359 Over the last decade, data focused careers have surged. 6 00:00:19,359 --> 00:00:22,166 According to estimates by Linkedin, 7 00:00:22,166 --> 00:00:28,450 the data science field grew by over 650% between 2012 and 2017. 8 00:00:28,450 --> 00:00:32,970 Many experts believe that we have not yet seen the full potential of these careers. 9 00:00:32,970 --> 00:00:37,913 In fact, the US Bureau of Labor Statistics stated that data science is one of 10 00:00:37,913 --> 00:00:42,783 the fastest growing career fields in the United States projecting a 30% 11 00:00:42,783 --> 00:00:45,710 increase over the next decade. 12 00:00:45,710 --> 00:00:47,823 Among the data science professions, 13 00:00:47,823 --> 00:00:52,379 one of the fastest growing is artificial intelligence and machine learning, and 14 00:00:52,379 --> 00:00:56,490 we've seen significant advances in these areas in recent years. 15 00:00:56,490 --> 00:01:01,278 At its core, artificial intelligence or AI is the development of computer 16 00:01:01,278 --> 00:01:06,560 systems able to perform tasks that normally require human intelligence. 17 00:01:06,560 --> 00:01:11,580 Thanks to growth in the data sciences, AI is now becoming more commonplace. 18 00:01:11,580 --> 00:01:14,628 These technologies will continue to evolve and 19 00:01:14,628 --> 00:01:18,065 provide more accurate results and richer insights. 20 00:01:18,065 --> 00:01:22,735 And as AI increasingly becomes an essential component of data work, it's 21 00:01:22,735 --> 00:01:27,642 important to be aware of the human bias that can be imprinted within your work. 22 00:01:27,642 --> 00:01:32,164 To counter this, organizations benefit most from building diverse teams of 23 00:01:32,164 --> 00:01:36,769 professionals from different backgrounds and different life experiences. 24 00:01:36,769 --> 00:01:39,689 Incorporating a wide range of perspectives and 25 00:01:39,689 --> 00:01:44,580 worldviews promotes wider representation and yields more accurate results. 26 00:01:44,580 --> 00:01:49,274 As we study the future of the data professions, I want to emphasize that data 27 00:01:49,274 --> 00:01:55,230 professionals have yet to realize the full potential of artificial intelligence. 28 00:01:55,230 --> 00:01:58,994 As these types of technological innovations continue to evolve, 29 00:01:58,994 --> 00:02:01,615 we can expect that organizations will grow and 30 00:02:01,615 --> 00:02:04,318 adapt their business practices accordingly. 31 00:02:04,318 --> 00:02:08,007 With wider and wider adoption of data analysis techniques, 32 00:02:08,007 --> 00:02:11,350 the most likely area for growth is in specialization. 33 00:02:11,350 --> 00:02:16,780 And we expect to see further subdivision of roles within data focused teams. 34 00:02:16,780 --> 00:02:20,134 Ultimately, what I want you to keep in mind is this, 35 00:02:20,134 --> 00:02:23,990 the world is generating more and more data every year. 36 00:02:23,990 --> 00:02:28,783 So it's reasonable to expect labor that extracts business value from it to be 37 00:02:28,783 --> 00:02:30,194 able to earn its keep. 38 00:02:30,194 --> 00:02:34,631 More data means more demand for the three main activities covered by the data 39 00:02:34,631 --> 00:02:39,910 professions, statistical inference, machine learning, and data analytics. 40 00:02:39,910 --> 00:02:45,630 So those skills will stay very relevant though their names might evolve over time. 41 00:02:45,630 --> 00:02:49,965 In addition, constant innovation in the field offers you the opportunity for 42 00:02:49,965 --> 00:02:52,801 perpetual learning, growth, and development. 43 00:02:52,801 --> 00:02:57,355 As you may already know, being a data professional means that your growth and 44 00:02:57,355 --> 00:03:00,990 success in this field depend on a desire to keep learning. 45 00:03:00,990 --> 00:03:05,489 In fact, that just might be the reason you enrolled in this program, 46 00:03:05,489 --> 00:03:07,826 and for that, I'm so proud of you. 47 00:03:07,826 --> 00:03:11,956 Continue to explore opportunities to evolve throughout your career, 48 00:03:11,956 --> 00:03:16,907 be proactive in acquiring new skills, keep growing, and you will always be ready for 49 00:03:16,907 --> 00:03:17,684 the future.4731

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