All language subtitles for 001 Introduction to Visualization Section_en

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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:05,560 --> 00:00:06,880 Welcome to this section. 2 00:00:07,240 --> 00:00:11,680 In this section, we're going to now move into looking at our graphical views of our data. 3 00:00:11,770 --> 00:00:16,059 So, so far, we've been looking at tables and we've been looking at text views of the data. 4 00:00:16,329 --> 00:00:20,350 With graphical views, we're able to actually be able to see the patterns within the data. 5 00:00:20,350 --> 00:00:24,880 So we get a different visual representation of the data and being able to understand it. 6 00:00:25,300 --> 00:00:27,170 We're going to start with the clustered column graph. 7 00:00:27,170 --> 00:00:28,300 We're going to see the common bar. 8 00:00:28,300 --> 00:00:32,830 Graphs are really great when we want to understand how big something is or how small something is, 9 00:00:33,220 --> 00:00:35,290 how much bigger one thing is than the other. 10 00:00:35,470 --> 00:00:39,700 So we're going to start with the clustered column, and then we're going to move into the 100% and the 11 00:00:39,700 --> 00:00:40,840 stacked column graph. 12 00:00:40,840 --> 00:00:45,700 And what we're going to see is the ability to be able to see how one item contributes to the other. 13 00:00:45,700 --> 00:00:49,370 So we're going to be able to see how these items are contributing to the total. 14 00:00:49,390 --> 00:00:51,900 Then we're going to move across to our trend graphs. 15 00:00:51,910 --> 00:00:56,530 So with our trend graphs, we've got a few graphs that we can be looking at, can look at our line graph 16 00:00:56,530 --> 00:00:57,700 or area graph. 17 00:00:57,850 --> 00:01:02,110 So our line graph gives us a representation of how data changes over time. 18 00:01:02,110 --> 00:01:04,629 And this is basically our trends that we're looking for. 19 00:01:04,780 --> 00:01:09,190 So line graphs are also great when you want to understand forecasting how the things move out into the 20 00:01:09,190 --> 00:01:11,050 future, we're going to see that area. 21 00:01:11,050 --> 00:01:15,670 Graphs give us very similar view to what line graphs do, but they're coloring in the area before. 22 00:01:15,670 --> 00:01:18,910 So they give us an ability to understand how big or how small something is. 23 00:01:19,240 --> 00:01:23,860 Then I'm going to look at a your visualization for the ribbon graph, which actually gives us the view 24 00:01:23,860 --> 00:01:25,930 of being able to see how items are ranked. 25 00:01:25,930 --> 00:01:30,900 So you can see which item is biggest by following how it is ranked throughout the graph. 26 00:01:30,910 --> 00:01:33,010 But you'll see in the lesson how we do this. 27 00:01:33,010 --> 00:01:35,140 Then we're going to look at some additional graphs. 28 00:01:35,140 --> 00:01:40,510 When we look at our pie graph, look at a tree graph, and these will give you different visual representations. 29 00:01:40,630 --> 00:01:44,440 Also going to be looking at a scatterplot and the bubble plot which could allow us to be able to see 30 00:01:44,440 --> 00:01:46,120 correlations between data. 31 00:01:46,210 --> 00:01:50,650 And we're going to be looking at the decomposition tree, which allows us to see how the components 32 00:01:50,650 --> 00:01:52,540 of an item make something up. 33 00:01:53,050 --> 00:01:56,410 But yet again, let's jump into the lessons and I will see you there. 3553

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