WEBVTT 00:00.000 --> 00:11.000 What is data analytics? 00:11.000 --> 00:15.400 Data analytics is the buzzword you would have heard a lot about in the recent years. 00:15.400 --> 00:20.800 To put it simply, it's the process of analyzing raw data to get insights and find patterns 00:20.800 --> 00:22.800 about the information. 00:22.800 --> 00:27.560 Using various techniques and processes, the raw data is transformed into a format which 00:27.560 --> 00:31.360 could be further visualized and be made human readable. 00:31.360 --> 00:36.760 To understand the definition in an even clear way, let's take a look at this example. 00:36.760 --> 00:39.920 John, who is a digital marketing professional? 00:39.920 --> 00:44.760 It is running an online advertising campaign to promote a new smart television. 00:44.760 --> 00:48.400 John starts by targeting the group of people who might have bought the previous versions 00:48.400 --> 00:50.760 of the smart TV in question. 00:50.760 --> 00:54.480 As said campaign runs, data analytics comes into the picture. 00:54.480 --> 00:58.160 Through the data generated whenever people clicked on the published advertisement. 00:58.160 --> 01:02.480 Now, by inspecting the data about these users' interest and their buying patterns, you 01:02.480 --> 01:07.720 might come across that many of them are interested in streaming 4K, UHD video. 01:07.720 --> 01:11.720 The reason behind this might be that the new smart TV has a better streaming quality 01:11.720 --> 01:13.840 than the previous versions. 01:13.840 --> 01:16.280 Using this information, you could tune in. 01:16.280 --> 01:21.000 Your advertisements to focus on users who brought the previous version of the smart TV and 01:21.000 --> 01:24.000 are interested in getting 4K streaming quality. 01:24.000 --> 01:28.160 This data can also be used to attract new customers who did not go for the previous 01:28.160 --> 01:33.120 version of the smart TV, but are very interested in a better version of the team. 01:33.120 --> 01:36.080 Why is data analytics significant? 01:36.080 --> 01:40.440 Businesses use data analytics to optimize and grow by making better businesses decisions 01:40.440 --> 01:43.560 and finding more efficient ways of doing business. 01:43.560 --> 01:47.960 The company can build new and better products with the buying patterns and better understanding 01:47.960 --> 01:49.960 what the customers need. 01:49.960 --> 01:52.720 So, how do data analytics work? 01:52.720 --> 01:57.320 The data analytics process has some components that can help a variety of initiatives. 01:57.320 --> 02:01.520 By combining these components, a successful data analytics initiate will provide a clear 02:01.520 --> 02:05.640 picture of where you are, where you have been, and where you should go. 02:05.640 --> 02:09.040 Generally, this process begins with a descriptive analysis. 02:09.040 --> 02:12.600 This is the process of describing historical trends in data. 02:12.600 --> 02:15.920 Descriptive analytics aims to answer the question, what happened? 02:15.920 --> 02:20.600 This often involves measuring traditional indicators such as return on investment. 02:20.600 --> 02:24.200 The indicators used will be different for each industry. 02:24.200 --> 02:28.120 Descriptive analytics does not make predictions or directly informed decisions. 02:28.120 --> 02:32.320 It focuses on summarizing data in a meaningful and descriptive way. 02:32.320 --> 02:36.320 The next essential part of data analytics is advanced analytics. 02:36.320 --> 02:41.640 This part of data science takes advantage of advanced tools to extract data, make predictions, 02:41.640 --> 02:43.240 and discover trends. 02:43.240 --> 02:47.640 These tools include classical statistics and as well as machine learning. 02:47.640 --> 02:53.080 machine learning text such as neural networks, natural language processing, sentiment analysis, 02:53.080 --> 02:55.440 and more enabled advanced analytics. 02:55.440 --> 02:58.280 This information provides new insight from data. 02:58.280 --> 03:01.760 At once, analytics addresses, what if the question? 03:01.760 --> 03:05.120 Data analytics affects every aspect of our day-to-day lives. 03:05.120 --> 03:06.880 You may not even realize it. 03:06.880 --> 03:10.200 But you probably use data analytics almost every day. 03:10.200 --> 03:15.360 Here are some of the everyday applications of data analytics, social media stats, in 03:15.360 --> 03:21.000 Stanta Register, anytime there's a visitor, or a post to a page, cell phone bills can 03:21.000 --> 03:26.160 pull up months of calling data to show you patterns of usage, sensors monitor the changing 03:26.160 --> 03:30.440 weather, and report the data to you instantly on your smartphone. 03:30.440 --> 03:34.720 Sports fans collect statistics on their favorite athletes, and often use the data to form 03:34.720 --> 03:36.960 the term of fantasy leagues. 03:36.960 --> 03:40.880 Schools across the nation are involved in high stakes testing that generates voluminous 03:40.880 --> 03:43.920 data on each student's progress. 03:43.920 --> 03:48.880 Teachers then utilize data generated from extensive reports to drive the instruction. 03:48.880 --> 03:53.520 Data analytics is a growing field, and will always be a top domain to get into. 03:53.520 --> 04:00.240 The average salary of a data analyst in India is 7 LPA, and in the US, it's $70,000. 04:00.240 --> 04:04.960 There are 27,000 plues data analytics jobs available only on LinkedIn. 04:04.960 --> 04:08.360 And there are very few skilled professionals to fill that gap. 04:08.360 --> 04:09.880 That's all about data analytics.