1 00:00:00,360 --> 00:00:04,050 So here we are looking at our spreadsheet with country population data, 2 00:00:04,050 --> 00:00:06,150 and it's pretty well organized. 3 00:00:07,360 --> 00:00:10,650 I'm gonna start off by creating a line chart that includes 4 00:00:10,650 --> 00:00:11,700 all of the countries in it. 5 00:00:12,950 --> 00:00:18,710 I'm going to hold down SHIFT + CTRL, and select all the data I want to chart. 6 00:00:18,710 --> 00:00:23,674 I'm going to use my keyboard shortcuts, ALT, N for insert and 7 00:00:23,674 --> 00:00:26,120 N again for a 2-D line chart. 8 00:00:28,030 --> 00:00:30,209 And you can see that part of the chart is cut off, and 9 00:00:30,209 --> 00:00:32,720 that's because we're all the way over here at column BG. 10 00:00:34,210 --> 00:00:38,978 So I wanna get back over here, and we can see now the full chart. 11 00:00:38,978 --> 00:00:42,810 I wanna go ahead and move this to a new sheet. 12 00:00:44,660 --> 00:00:48,508 And I'm gonna call that sheet LineChart. 13 00:00:48,508 --> 00:00:53,020 Okay, we can see pretty clearly some things from this chart. 14 00:00:53,020 --> 00:00:56,770 First of all, we can see that China is always the largest country. 15 00:00:56,770 --> 00:01:01,660 And India is the second largest, and has actually caught up quite 16 00:01:01,660 --> 00:01:05,800 a lot to China in terms of population size by the very end of this time frame. 17 00:01:05,800 --> 00:01:09,950 So the furthest data points we have out are 2015. 18 00:01:09,950 --> 00:01:15,088 And there used to be a lot bigger of a gap between the population sizes of China and 19 00:01:15,088 --> 00:01:19,160 India in 1950 than there are now in 2015. 20 00:01:19,160 --> 00:01:23,992 We also can observe that the US has consistently been the third 21 00:01:23,992 --> 00:01:28,500 largest country, and Brazil is growing quite a lot as well. 22 00:01:29,990 --> 00:01:34,270 Let's take a look at what this chart becomes when we make it a stacked 23 00:01:34,270 --> 00:01:35,250 area chart. 24 00:01:35,250 --> 00:01:38,850 So I'm gonna change the chart type and do Stacked Area. 25 00:01:40,800 --> 00:01:44,926 So we can see the relative size of the different countries and 26 00:01:44,926 --> 00:01:49,310 how they stack up to create the total population for these different countries. 27 00:01:49,310 --> 00:01:53,200 But I like the line chart better, it was more clear to see the comparison and 28 00:01:53,200 --> 00:01:55,100 size of the different countries. 29 00:01:55,100 --> 00:01:58,680 But maybe that's an opinion that you don't share. 30 00:01:58,680 --> 00:02:02,730 Nevertheless, I'm going to change these back to line charts. 31 00:02:02,730 --> 00:02:05,280 I think another view of this data that would be 32 00:02:05,280 --> 00:02:09,440 interesting to see is the 100% stacked area chart. 33 00:02:09,440 --> 00:02:13,841 So because we wanna make two different charts, I wanna go back here and 34 00:02:13,841 --> 00:02:15,569 select all the data again. 35 00:02:15,569 --> 00:02:22,521 Do Alt, N, And 36 00:02:22,521 --> 00:02:28,147 100 % Stacked Area. 37 00:02:31,817 --> 00:02:33,970 Let's move this to its own sheet as well. 38 00:02:37,132 --> 00:02:40,604 And let's call it 100%. 39 00:02:42,370 --> 00:02:46,180 So this is also interesting because, in the other charts we looked at earlier, 40 00:02:46,180 --> 00:02:50,330 we saw how big each country was, and how their populations changed over time. 41 00:02:50,330 --> 00:02:55,834 Let's look at the line chart again, so you can see the relative size and 42 00:02:55,834 --> 00:02:59,338 how those populations have grown over time. 43 00:02:59,338 --> 00:03:02,682 With the 100%, we can see how the share of each country's 44 00:03:02,682 --> 00:03:05,330 population contributes to the whole over time. 45 00:03:05,330 --> 00:03:09,925 By showing line charts, we can show how populations have evolved over time and 46 00:03:09,925 --> 00:03:12,233 clearly compare them with each other. 47 00:03:12,233 --> 00:03:16,845 Adding the 100% stacked area as the second chart shows us that even 48 00:03:16,845 --> 00:03:19,970 though China has had the largest population and 49 00:03:19,970 --> 00:03:24,050 it's continued to grow in the line charts, you can see that. 50 00:03:25,200 --> 00:03:30,367 When we look at the 100%, we can see that China's percent of total population for 51 00:03:30,367 --> 00:03:35,200 this group of countries has remained fairly constant, around 39 to 40%. 52 00:03:35,200 --> 00:03:40,350 India is the country that has gained the most population share. 53 00:03:40,350 --> 00:03:43,720 There are lots of data points in each of these series. 54 00:03:43,720 --> 00:03:48,517 You could simplify things a bit by making the chart only show every 5 years instead 55 00:03:48,517 --> 00:03:50,965 of every year from 1950 to 2015. 56 00:03:50,965 --> 00:03:55,071 That's just an option to make the chart slightly less data heavy, but 57 00:03:55,071 --> 00:03:58,345 still communicate the large themes of what's occurred 58 00:03:58,345 --> 00:04:01,570 over the time frame we're looking at. 59 00:04:01,570 --> 00:04:04,670 You can go ahead and try that yourself for some extra practice. 60 00:04:04,670 --> 00:04:09,380 How you visualize your data will vary on a case by case basis. 61 00:04:09,380 --> 00:04:13,440 You are going to have to exercise your own judgement regularly. 62 00:04:13,440 --> 00:04:19,380 It's always helpful to ask the question, what's gonna tell the story most clearly? 63 00:04:19,380 --> 00:04:24,330 What visualization communicates the most information most clearly? 64 00:04:24,330 --> 00:04:28,960 In the teacher's notes, I shared some links to articles with examples of 65 00:04:28,960 --> 00:04:33,840 bad charts for some comedic relief and as a guide for what not to do. 66 00:04:35,450 --> 00:04:40,560 Well, we've arrived at the end of our Data Visualization in Excel course. 67 00:04:40,560 --> 00:04:44,370 Knowing how to visualize data is a great skill to have, and I'm so 68 00:04:44,370 --> 00:04:47,630 grateful to be able to teach you the fundamentals of it. 69 00:04:47,630 --> 00:04:52,830 Remember, as with many things, practice makes perfect. 70 00:04:52,830 --> 00:04:56,257 So take advantage of every chance you have to improve. 71 00:04:56,257 --> 00:05:01,740 Day-to-day work can become an opportunity to sharpen your data visualization skills. 72 00:05:01,740 --> 00:05:05,980 You just have to remember to approach things with the I'm always learning 73 00:05:05,980 --> 00:05:06,520 mentality. 74 00:05:07,750 --> 00:05:09,040 Have fun telling stories with your data.