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These are the user uploaded subtitles that are being translated: 1 00:00:05,080 --> 00:00:06,250 Welcome to this lesson. 2 00:00:06,250 --> 00:00:09,820 So we're going to continue our case study just looking at the coronavirus. 3 00:00:09,820 --> 00:00:13,930 So in the previous lesson I showed you where I download the data from and also showed you that the World 4 00:00:13,930 --> 00:00:17,230 Health Organization also has some of their own data analysis. 5 00:00:17,490 --> 00:00:22,600 Now, this is a report that I put together kind of just on a global view to get an overview of what 6 00:00:22,600 --> 00:00:25,090 was happening at the time with the data. 7 00:00:25,240 --> 00:00:29,890 So if we start in the top left hand corner and just remember when you're putting your reports together, 8 00:00:29,890 --> 00:00:34,660 it is most often that people will start in the top left hand corner when they're looking at the data. 9 00:00:34,660 --> 00:00:40,020 So it's normally a good idea to have your key metrics, your key information in that top left hand corner. 10 00:00:40,030 --> 00:00:41,890 So what I did was just make it simple. 11 00:00:41,890 --> 00:00:45,130 We just wanted to show what was the new cases and new deaths for the day. 12 00:00:45,130 --> 00:00:49,180 And then at that point in time as well, what was the total number of cases and the total number of 13 00:00:49,180 --> 00:00:52,600 deaths that had been recorded through the World Health Organization? 14 00:00:52,600 --> 00:00:57,790 So this was as at that point in time, if we go down a little bit, you can see then we break out those 15 00:00:57,790 --> 00:00:59,650 numbers just by the different regions. 16 00:00:59,650 --> 00:01:03,310 So these are the regions that the World Health Organization use. 17 00:01:03,310 --> 00:01:04,690 I just kept the same codes. 18 00:01:04,690 --> 00:01:08,410 They're normally quite easy to actually figure out, like you've got Europe and you've got Africa, 19 00:01:08,410 --> 00:01:09,280 for example. 20 00:01:09,280 --> 00:01:14,530 So you do have those regions just showing you what the breakdown is then for each of the regions. 21 00:01:14,710 --> 00:01:20,050 And I wanted to show some sort of key trends so you can sort of see what was the new cases by the dates 22 00:01:20,050 --> 00:01:22,240 and also how is the cumulative cases. 23 00:01:22,240 --> 00:01:27,880 So as you can see at this point in time, the number is close to three fourths of a billion people who 24 00:01:27,880 --> 00:01:33,400 have been infected, over 750,000 million people who have been infected. 25 00:01:33,730 --> 00:01:38,350 Although to be honest, if a lot of you have seen the different steps, then you may see as well a number 26 00:01:38,350 --> 00:01:42,610 of countries maybe not reporting as many cases as they did previously. 27 00:01:43,190 --> 00:01:43,380 Okay. 28 00:01:43,450 --> 00:01:45,580 Then we've got another trend graph down here. 29 00:01:45,580 --> 00:01:50,140 And this is basically to show the number of deaths by the data and the cumulative deaths as well. 30 00:01:50,140 --> 00:01:53,950 So I just wanted a couple of easy to see trend graphs that you could see. 31 00:01:53,980 --> 00:01:58,720 Now, you may not down here that I've empty using a ribbon graph and the idea of this was especially 32 00:01:58,720 --> 00:02:03,430 in the early days, was to see which regions were actually getting the most cases and using your ribbon 33 00:02:03,430 --> 00:02:07,420 graph for this, I'm actually going to move this into full focus mode so you can just have a look at 34 00:02:07,420 --> 00:02:11,920 that and you can sort of see as we go back in time, you can see that the numbers were pretty low and 35 00:02:11,920 --> 00:02:15,580 then around the beginning, interesting in January 2020 had quite a spike. 36 00:02:15,610 --> 00:02:17,560 Europe had quite a big spike at that time. 37 00:02:17,560 --> 00:02:22,510 And at this time, January 2023, I think China did a lot of reporting of numbers as well. 38 00:02:22,510 --> 00:02:24,820 That showed a huge spike in terms of things. 39 00:02:24,820 --> 00:02:29,320 But the idea of this graph was really just to see with the regions, you can see how the colors change 40 00:02:29,320 --> 00:02:30,640 using our ribbon graph. 41 00:02:31,640 --> 00:02:36,800 If I go back to our view, you'll also see that I have put a slicer at the top just for your region. 42 00:02:36,800 --> 00:02:42,230 So if you did want to focus on a specific region, if you wanted to look at Europe, for example, then 43 00:02:42,230 --> 00:02:43,340 you could just see that. 44 00:02:43,340 --> 00:02:47,570 And then obviously that would just filter all of your different different information. 45 00:02:47,750 --> 00:02:52,100 Well, for example, you just shown the countries now you've shown the trend graphs just for that region. 46 00:02:52,110 --> 00:02:54,560 So it gives you the totals just for that region alone. 47 00:02:55,750 --> 00:02:56,020 Okay. 48 00:02:56,020 --> 00:03:02,380 So you could have a specific region and for example, could again look at Asia over here, parts of 49 00:03:02,380 --> 00:03:04,030 Asia, Japan, China. 50 00:03:04,630 --> 00:03:06,370 Recording of those numbers. 51 00:03:07,300 --> 00:03:08,790 I'm going to turn off the region. 52 00:03:08,790 --> 00:03:11,420 Just go back to show all of the information. 53 00:03:11,430 --> 00:03:15,600 The other view that I did want to show was the ability to have the countries. 54 00:03:15,600 --> 00:03:19,140 And the idea here was to have total cases of new cases, total deaths and new deaths. 55 00:03:19,140 --> 00:03:22,980 So if you wanted to, you could easily sort in this to see which was highest, which was lowest at that 56 00:03:22,980 --> 00:03:23,740 point in time. 57 00:03:23,760 --> 00:03:28,200 So at this point in time, if we looked at the total cases, we can see that the United States is over 58 00:03:28,200 --> 00:03:29,580 100 million at this point. 59 00:03:29,700 --> 00:03:32,240 China closely followed 98 million. 60 00:03:32,250 --> 00:03:37,260 But because we can use that cross filtering that we covered in the course, you could click on China, 61 00:03:37,260 --> 00:03:41,100 for example, and it would now filter the other views to be able to see that. 62 00:03:41,100 --> 00:03:46,740 And as we can see, pretty much most of the cases all were towards the end of 2022, towards 23. 63 00:03:47,010 --> 00:03:52,950 Again, if we look at India, India did have some high spikes towards the beginning of around 21 and 64 00:03:52,950 --> 00:03:53,340 so on. 65 00:03:53,340 --> 00:03:57,450 So you could go by country by country if you wanted to look at the details of this. 66 00:03:57,450 --> 00:04:02,190 So that's really what I was looking to do with this report was just give an overview and just give a 67 00:04:02,190 --> 00:04:05,400 way of being able to get an understanding of what the trends are. 68 00:04:05,490 --> 00:04:08,160 What were the new cases, what were the new deaths, for example. 69 00:04:08,160 --> 00:04:11,910 And people could then do their data analysis from here. 70 00:04:11,910 --> 00:04:18,779 What we did was we then uploaded this into the PA by service and we then embedded it into a public website 71 00:04:18,779 --> 00:04:21,000 so that people could have access to this. 72 00:04:21,000 --> 00:04:25,290 And again, the link to the public website is in the Udemy course notes for you to go to. 73 00:04:25,320 --> 00:04:28,890 So in the next lesson, I'm going to be looking at the embedded report. 74 00:04:28,890 --> 00:04:33,860 We're going to just have a look at creating an embed code and then seeing how we created that. 75 00:04:33,870 --> 00:04:36,630 So we're going to conclude this lesson and we'll see you in the next one. 8153

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