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These are the user uploaded subtitles that are being translated: 1 00:00:05,670 --> 00:00:11,580 Hello everyone and welcome to the lecture discussion on what is data science and this lecture will briefly 2 00:00:11,580 --> 00:00:17,610 be discussing three main topics we'll talk about the growing interest in data science is a field. 3 00:00:17,610 --> 00:00:23,100 The growing demand for data scientists in the job market as well as an overview definition of what is 4 00:00:23,100 --> 00:00:25,290 data science. 5 00:00:25,370 --> 00:00:30,930 A simple Google Trends search for the term data scientist reveals a recent explosion in popularity for 6 00:00:30,930 --> 00:00:33,710 people searching for the term data scientist. 7 00:00:33,720 --> 00:00:37,650 This is because data science can affect so many fields and domains. 8 00:00:37,650 --> 00:00:42,180 You may already be familiar of domains that work closely if data science such as machine translation 9 00:00:42,330 --> 00:00:45,970 speech recognition robotics search engines etc.. 10 00:00:46,080 --> 00:00:52,980 However they do science can affect other fields such as biology health care humanities finance Medical 11 00:00:52,980 --> 00:00:59,010 Informatics business economics and so much more because data science is applicable to so many fields 12 00:00:59,010 --> 00:00:59,920 of study. 13 00:00:59,970 --> 00:01:05,310 The explosion of popularity and the ability to use data science has grown just in the past few years 14 00:01:05,450 --> 00:01:06,520 tremendously. 15 00:01:06,810 --> 00:01:12,450 Searching for job trends on indeed dotcom also reveals Eytan exploding demand for data scientists in 16 00:01:12,450 --> 00:01:13,640 the job market. 17 00:01:13,890 --> 00:01:20,190 Over the past four years there's been a 1600 percent growth in job postings for the term data scientist 18 00:01:20,190 --> 00:01:20,510 . 19 00:01:20,610 --> 00:01:25,920 In fact data scientists has been called the sexiest job of the 21st century by the Harvard Business 20 00:01:25,920 --> 00:01:33,450 Review McKinsey and Company project a global excess demand of 1.5 million new data scientists needed 21 00:01:33,450 --> 00:01:33,980 . 22 00:01:34,050 --> 00:01:37,420 This is going to lead to a huge skills gap that you can fill. 23 00:01:37,470 --> 00:01:42,900 Allowing data scientists the man very generous compensations for their new skill sets. 24 00:01:43,500 --> 00:01:48,720 The explosion of the popularity of data science can be attributed to many factors but there's four main 25 00:01:48,720 --> 00:01:50,220 driving factors. 26 00:01:50,220 --> 00:01:54,120 One is that there's more data being created than ever before. 27 00:01:54,210 --> 00:01:58,860 The second being large computing power is easily available. 28 00:01:58,860 --> 00:02:04,230 Things such as Amazon Web Services and Google's cloud computing platform have allowed us to have large 29 00:02:04,230 --> 00:02:06,870 computing power at our fingertips. 30 00:02:07,080 --> 00:02:12,870 The third being new programming tools tools such as the our programming language to help you quickly 31 00:02:12,960 --> 00:02:16,650 analyze data and perform statistical analysis on it. 32 00:02:16,650 --> 00:02:22,770 And finally this huge skills demand has led to generous compensation for data scientists meaning more 33 00:02:22,770 --> 00:02:28,500 and more people are willing to jump into the new field will learn a lot and demand those higher salaries 34 00:02:28,500 --> 00:02:30,270 . 35 00:02:30,270 --> 00:02:36,570 Now let's talk about a definition of data science we can think of data science as an intersection of 36 00:02:36,570 --> 00:02:42,960 fields the intersection of computer science math and statistics knowledge and then general domain knowledge 37 00:02:42,960 --> 00:02:43,280 . 38 00:02:43,320 --> 00:02:46,690 We can look at the intersection of just two of these fields. 39 00:02:46,830 --> 00:02:52,440 For instance the intersection of computer science and math and statistics leads the field of machine 40 00:02:52,440 --> 00:02:53,510 learning. 41 00:02:53,520 --> 00:02:58,230 You should note that machine learning is not the same thing as data science instead of machine learning 42 00:02:58,290 --> 00:03:01,170 is part of data science. 43 00:03:01,170 --> 00:03:06,090 If we were to cross computer science and general domain knowledge of a specific field you would get 44 00:03:06,090 --> 00:03:12,390 software development meaning use using computer science knowledge develop software very specific domain 45 00:03:13,130 --> 00:03:18,270 and then if you combine math and statistics with domain knowledge you'll get classic research such as 46 00:03:18,270 --> 00:03:21,580 research performed in social sciences in academia. 47 00:03:21,750 --> 00:03:27,270 It's the intersection of all three of these topics where you get data science as an outcome in this 48 00:03:27,270 --> 00:03:27,750 course. 49 00:03:27,770 --> 00:03:31,790 You'll learn how to use computer science programming with the R programming language. 50 00:03:31,950 --> 00:03:37,710 The math and statistical analysis available in our programming language and apply it to various domains 51 00:03:37,710 --> 00:03:42,650 of study in order to fully understand the full data science lifecycle. 52 00:03:43,010 --> 00:03:47,490 All right let's start learning all these new skills with the rest of the course. 53 00:03:47,490 --> 00:03:49,470 Thanks everyone and I'll see at the next lecture 5919