WEBVTT 00:01.240 --> 00:04.580 Say hey welcome to the next section. 00:04.790 --> 00:08.290 So this section as you can probably gather we're still working with our books data. 00:08.290 --> 00:10.150 I hope you're not tired of it yet. 00:10.360 --> 00:12.250 If you are almost done. 00:12.290 --> 00:17.060 But it's important that we keep working with one dataset for a little bit at least so that you can get 00:17.060 --> 00:18.090 familiar with it. 00:18.140 --> 00:22.940 And the reason that that matters is that as we do some of the more advanced things we want you to be 00:22.940 --> 00:24.260 able to check your work. 00:24.260 --> 00:29.850 So in this section we're going to learn a lot about different ways of performing analysis on data. 00:29.900 --> 00:35.450 So things like finding averages or something a bunch of data together grouping things by authors and 00:35.450 --> 00:39.710 calculating average quantities or page numbers per author. 00:39.710 --> 00:45.650 These are operations that if we had 10000 books or 10000 something else it's really hard to know if 00:45.650 --> 00:46.900 you're doing it right or wrong. 00:47.030 --> 00:50.690 You get an answer just a number of let's say sixty seven point five. 00:50.780 --> 00:52.650 And how could you know if that's right or wrong. 00:52.940 --> 00:58.020 But if we're working with books and we have 20 of them and you're familiar with the page counts and 00:58.030 --> 00:59.690 the authors you'll know. 00:59.690 --> 00:59.980 OK. 00:59.990 --> 01:05.750 This author has three books that seems right versus OK that author only has two books why are we saying 01:05.750 --> 01:06.080 that. 01:06.140 --> 01:08.540 You know we have three or something like that. 01:08.570 --> 01:11.020 Terrible example but the same idea is true. 01:11.330 --> 01:12.980 We know our data at this point. 01:13.130 --> 01:17.450 We're going to at the end of this course and along the way you know we're going to keep upgrading our 01:17.450 --> 01:22.360 data to more complex structures more tables more rose complex stuff. 01:22.730 --> 01:28.580 But at the very end kind of the capstone case study we'll be working with Instagram esque data fake 01:28.580 --> 01:33.950 data for Instagram and we'll have thousands and thousands of rows and you won't actually know if you're 01:33.950 --> 01:38.600 doing things right or wrong based off the number you're getting unless you've manually checked your 01:38.600 --> 01:41.970 work by you know doing a thousand additions or something. 01:42.300 --> 01:45.910 So all this to say stick with the books if you can. 01:46.190 --> 01:51.230 And in this section we're focusing a lot on these new aggregate functions. 01:51.260 --> 01:58.620 So those are things like finding averages counting summing things together based off of grouping data. 01:58.820 --> 02:02.170 So it's a bit hard to explain and a headshot that's showing you code. 02:02.270 --> 02:04.000 So I'll let the code do that. 02:04.010 --> 02:05.420 And just a few videos from now. 02:05.630 --> 02:10.690 But the rough idea is that we take our data and there's all sorts of insights we can gain. 02:10.850 --> 02:17.000 So rather than just working with an individual row or a group of rows we can combine things into like 02:17.120 --> 02:18.040 mega rows. 02:18.170 --> 02:24.380 I can combine all of our authors and group books based off of who wrote them or group books based off 02:24.380 --> 02:28.570 of what year they're written in and then perform operations on those groups. 02:28.700 --> 02:35.930 So it allows them to do things like find the average sales that we've had per year or we could do things 02:35.930 --> 02:40.450 like find the average page number for books per general. 02:40.670 --> 02:43.990 And then we could expand that obviously to more complex stuff. 02:44.000 --> 02:48.680 If you're working with advertising data or let's say our Instagram data we'll be able to at the end 02:48.680 --> 02:55.270 of the course do things like find out which one of our users is a power user influencers what they're 02:55.280 --> 03:01.820 called I guess meaning that they have the most comments the most likes on each one of their posts on 03:01.820 --> 03:02.450 average. 03:02.540 --> 03:06.980 So who in our database is getting on average the most likes and comments. 03:06.980 --> 03:12.050 Or we could do things like which hash tag generates the most traction and to do that we would need to 03:12.050 --> 03:17.270 analyze all of our hashtags and then take all of our photos that have those hash tags group them together 03:17.540 --> 03:21.280 figure out which one of those hashtags generates the most likes. 03:21.470 --> 03:24.690 So there's a lot of stuff that we can do with these aggregate functions. 03:24.740 --> 03:29.820 They form the backbone of a lot of the questions and analysis that we'll do throughout the course. 03:29.960 --> 03:30.650 All right. 03:30.650 --> 03:32.640 I'm rambling now I'm going to go away. 03:32.690 --> 03:34.520 Hopefully you enjoy this section. 03:34.520 --> 03:37.590 It's important to say that a lot but it really is. 03:37.760 --> 03:42.800 And of course we'll have a bunch of exercises throughout the course or throughout the section and especially 03:42.800 --> 03:43.560 at the end. 03:43.760 --> 03:45.200 And I'm trying to keep it interesting. 03:45.200 --> 03:46.260 Look forward to those. 03:46.400 --> 03:50.120 If you don't just remember it's a database course. 03:50.120 --> 03:51.580 I'm trying my best. 03:51.740 --> 03:53.500 It's you know it's databases. 03:53.780 --> 03:54.620 All right. 03:54.620 --> 03:55.200 I'm done.