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These are the user uploaded subtitles that are being translated: 1 00:00:00,630 --> 00:00:03,420 Instructor: HR managers can also become overwhelmed 2 00:00:03,420 --> 00:00:05,280 with the amount of data science terms 3 00:00:05,280 --> 00:00:07,530 and buzzwords flying around. 4 00:00:07,530 --> 00:00:10,560 This means they sometimes label job positions 5 00:00:10,560 --> 00:00:12,360 in a misleading way. 6 00:00:12,360 --> 00:00:14,490 Thus, you can end up confused 7 00:00:14,490 --> 00:00:17,700 about how to match a job title with a discipline. 8 00:00:17,700 --> 00:00:19,560 So what are the job positions 9 00:00:19,560 --> 00:00:23,160 for each of the activities you see on the infographic. 10 00:00:23,160 --> 00:00:25,650 Data architect and data engineer 11 00:00:25,650 --> 00:00:29,490 and big data architect and big data engineer respectively 12 00:00:29,490 --> 00:00:32,009 are crucial titles on the market. 13 00:00:32,009 --> 00:00:34,110 A person in these roles is regarded 14 00:00:34,110 --> 00:00:37,020 as a very important part of the entire process 15 00:00:37,020 --> 00:00:40,830 of solving a data science or a business task. 16 00:00:40,830 --> 00:00:44,550 A data architect creates databases from scratch. 17 00:00:44,550 --> 00:00:46,860 They design the way data will be retrieved, 18 00:00:46,860 --> 00:00:49,080 processed and consumed. 19 00:00:49,080 --> 00:00:51,780 The tasks of the data engineer step on the work 20 00:00:51,780 --> 00:00:53,520 of the data architect. 21 00:00:53,520 --> 00:00:55,530 His primary job responsibility 22 00:00:55,530 --> 00:00:57,960 is to further process the obtained data 23 00:00:57,960 --> 00:01:00,510 so that it is ready for analysis. 24 00:01:00,510 --> 00:01:02,070 So the result of his work 25 00:01:02,070 --> 00:01:05,340 is something analysts and people in analytics positions 26 00:01:05,340 --> 00:01:10,020 will heavily rely on: a clean and organized dataset. 27 00:01:10,020 --> 00:01:11,670 Great. 28 00:01:11,670 --> 00:01:15,360 In fact, the data in a database is not created once 29 00:01:15,360 --> 00:01:16,590 and for all. 30 00:01:16,590 --> 00:01:19,890 You have a certain flow into and from the database. 31 00:01:19,890 --> 00:01:23,130 And there is a person who handles this control of data. 32 00:01:23,130 --> 00:01:25,980 Her position is database administrator 33 00:01:25,980 --> 00:01:28,860 and she mainly works with traditional data. 34 00:01:28,860 --> 00:01:31,950 Needless to say, administration of big data 35 00:01:31,950 --> 00:01:34,470 is usually automated. 36 00:01:34,470 --> 00:01:35,763 Fantastic. 37 00:01:36,720 --> 00:01:39,630 A BI analyst will do analyses and reporting 38 00:01:39,630 --> 00:01:41,940 of past historical data. 39 00:01:41,940 --> 00:01:46,170 What a BI consultant does exactly is vague though. 40 00:01:46,170 --> 00:01:50,010 BI consultants are often external BI analysts. 41 00:01:50,010 --> 00:01:52,890 Many firms outsource their data science departments 42 00:01:52,890 --> 00:01:55,590 as they don't need or want to maintain one. 43 00:01:55,590 --> 00:01:58,440 BI consultants would be BI analysts 44 00:01:58,440 --> 00:02:00,030 had they been employed. 45 00:02:00,030 --> 00:02:02,460 However, their job can be more varied 46 00:02:02,460 --> 00:02:05,913 as they hop on and off different projects. 47 00:02:05,913 --> 00:02:09,000 Finally, a BI developer is a person 48 00:02:09,000 --> 00:02:11,640 who handles more advanced programming tools, 49 00:02:11,640 --> 00:02:14,940 such as Python and especially SQL 50 00:02:14,940 --> 00:02:18,330 in order to create analyses specifically designed 51 00:02:18,330 --> 00:02:19,800 for the company. 52 00:02:19,800 --> 00:02:22,860 It is the third most frequently encountered job position 53 00:02:22,860 --> 00:02:25,290 in the BI team of a firm. 54 00:02:25,290 --> 00:02:26,123 Lovely. 55 00:02:27,630 --> 00:02:30,420 Now we must say that the remaining terms 56 00:02:30,420 --> 00:02:33,090 are somehow mixed and the line between the activity 57 00:02:33,090 --> 00:02:36,390 of one and the other is very thin. 58 00:02:36,390 --> 00:02:39,390 A person who employs traditional statistical methods 59 00:02:39,390 --> 00:02:41,610 or unconventional machine learning techniques 60 00:02:41,610 --> 00:02:43,170 for making predictions 61 00:02:43,170 --> 00:02:45,663 could still be labeled a data scientist. 62 00:02:48,840 --> 00:02:51,480 Moreover, even if the last two columns 63 00:02:51,480 --> 00:02:54,390 are about forecasting future values, 64 00:02:54,390 --> 00:02:56,370 data analyst is the job title 65 00:02:56,370 --> 00:02:59,910 for those who prepare more advanced types of analyses 66 00:02:59,910 --> 00:03:02,010 and do the basic part of the predictions 67 00:03:02,010 --> 00:03:03,813 of the data science team. 68 00:03:05,130 --> 00:03:07,731 Finally, a machine learning engineer. 69 00:03:07,731 --> 00:03:12,030 This job is tough to do but easy to classify. 70 00:03:12,030 --> 00:03:14,340 It refers to those who are looking for ways 71 00:03:14,340 --> 00:03:17,310 to apply state-of-the-art computational models 72 00:03:17,310 --> 00:03:19,680 developed in the field of machine learning 73 00:03:19,680 --> 00:03:24,063 into solving complex data science and business tasks. 74 00:03:25,050 --> 00:03:27,390 We hope you enjoyed watching this video. 75 00:03:27,390 --> 00:03:28,223 Thank you. 5733

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