Data Visualization: Design Better Charts in PowerPoint | Randy Krum | Skillshare

Data Visualization: Design Better Charts in PowerPoint

Randy Krum, DataViz Designer, Author, Instructor

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10 Lessons (44m)
    • 1. Introduction

      3:04
    • 2. Your Project

      1:56
    • 3. Design Principles

      5:48
    • 4. Starting with the Templates

      4:27
    • 5. Key Message and Title

      4:49
    • 6. Remove the Chart Legend

      6:05
    • 7. Reduce Visual Noise

      3:31
    • 8. Use Color With Purpose

      4:45
    • 9. Adding Chart Elements

      6:23
    • 10. Wrapping Up

      2:42
27 students are watching this class

About This Class

Most PowerPoint charts suck! Your company spends a huge amount of time and resources on research and data analysis, but when it comes time to present your results, the default charts from PowerPoint are nothing special. Learn how to apply core data visualization design principles to create charts that clearly make your audience go “Ah-Ha!”

If you’re just using the default chart templates in PowerPoint, you’re making a big mistake. Your charts will look like the same default charts your audience sees in every other presentation, and it makes you and data look generic. Those default chart are only meant to be the starting point (you have to start somewhere), but you need to customize your charts to effectively communicate your own insights and key message to your audience in a unique, memorable way.

This class will focus applying five data visualization design best practices to charts created in Microsoft PowerPoint.

  • Choose a Key Message
  • Write a Good Title
  • Reduce Visual Noise
  • Use Color with Purpose
  • Add Chart Elements

Who is this class for?
Product managers, data scientists, analysts, engineers, designers and researchers are often expected to effectively communicate their own insights to non-data professionals; customers, executives, co-workers or the general public. This audience doesn’t want to see the raw numbers or the detailed analysis, they just want to understand the insights from your data. Good Data Visualization Design means going beyond the charting templates and designing charts and visualizations that reveal insights and tell stories to your audience.

Materials/Resources:
In this class, I will be focusing on applying data visualization best practices to charts in Microsoft PowerPoint. Students are expected to have a working understanding of PowerPoint, getting around in the interface, and already know how to create and edit basic charts.

I’ll be demonstrating these in Microsoft PowerPoint for Mac which is part of Office 365, but these design principles also apply to PowerPoint for Windows and even earlier versions of Microsoft Office. You can easily apply these lessons to earlier versions of PowerPoint, but Microsoft has changed the names and locations of the customization options over the years. Some of the menus, ribbons and formatting options may look different in older versions.

Transcripts

1. Introduction : Hi, I'm Randi Crumb. I'm a data visualisation and infographics designer, author of the book Cool, Infographics and Host of Date of his TV. What we do when we design data visualizations is we help people communicate their data and information visually, make it easier to understand and make sure that we're communicating effectively to an audience. Now, designing data visualizations is a skill, the skill that not only do we all use, but we should all practice like every other skill you need to learn it. You need to develop it and you need to practice, practice, practice. The more you practice designing data visualizations that better, you're gonna get data visualizations, air used everywhere. They're used in presentations, of course, but also reports infographics, social media graphics, white papers, dashboards and a whole bunch more. So it's a skill that applies to all these other formats for communicating information. So as I mentioned in the descriptions, the problem is your charge. Probably suck if you just use the chart buttons in PowerPoint and Excel and accept all of the defaults that are built into the charts. Your charts have too much stuff, and they probably look like everybody else's charts. Now, you probably have some great data and some unique insight and learning that you're trying to communicate from your data. But when you're charged look like everybody else's charts, you're not actually communicating a clear message, and you're really struggling to get your data to come across as something that's unique and interesting and valuable to your audience. In this course, you're gonna learn a handful of basic data visualization design principles. We're gonna go through five of them, and you're gonna be able to then look in Power Point. We're gonna bring up the software. And how do you take those design principles and apply them to charge that are designed in Power Point now, Once you designed those charts in Power Point, they could be used anywhere, even though Power Point is primarily for presentations. But we use charts designed and PowerPoint and excel in infographics in social media graphics that we used these charts all over the place. So that's really the focus of the lessons that we're gonna go through in this course. Of course, the requirements for this course are using Power Point. I'm gonna be using Power point on the Mac but these apply equally well to both power point on the Mac and Power Point on Windows. I'm using the latest version of Office 3 65 but the's air pretty common design principles. So even though I'm using what is today the most current version of office, these design principles and things you could do to improve your charts applied to older versions of offices. Well, I have a lot of people are on very different versions of office all over the place. So the buttons, maybe in different places, the configuration options may be in different places. But there, there, you're gonna be able to apply these no matter what version you're using. All the sample files that I'm using in these lessons are gonna be available for download as part of this course on skill share. And I'm hoping that you will follow up and post your own projects in the project space below. We'll talk about that in the next video. Please post questions. We'll take a look and be able to respond to questions and apply them and feel free to follow me on skill share or on social media 2. Your Project : the project for this course is going to be fairly simple. I want you to take a chart and start with the default tart that Power Point creates and apply all the data visualization principles we cover in the lessons to create a much better , more effective chart at the end. A really encourage you to try using a chart of your own. Start with your own data set. Create one of the default charts in Power Point. But a lot of people have only confidential data and wouldn't be able to share maybe data or charts that they create for work. So I'm also alternatively going to provide a sample data file just for your project that the new chart just created as a default in power point that you can then take and apply the data visualization design principles as part of your project. When you post your project, I love to see how it started. So the original default chart, how it ended, of course, the final result of what your designs are and then some description of how you apply the principles in the interim and maybe even some images of what it looks like. A each step along the way. Post those down in the project section below, and I will take a look and provide feedback and even, maybe even asking questions as well. So when you're doing your project, some of the tips I want you to follow from the lessons certainly focus on a key message. Write a really good effective title. Reduce the visual noise as much as possible. We're gonna talk about doing that in a handful of different ways, to be very selective about how you use color in your charts on and at the very end. You actually may want to add back in some descriptive elements to help make sure that your chart is understandable to your audience. So the delivery bols choose a chart. Start with it is the default charts. Let us see what that default chart looked like to begin with. And, of course, the final chart at the end is what we would like to see that communicates your message effectively without any additional description. Like the chart should be able to speak for itself. Post those in the put project section below um, and hopefully we'll get some feedback between me and other people that are taking the course 3. Design Principles: So let's talk about data visualization for a minute, so data visualization can be anyway. You use visual design to share data or information. Of course, charts are the most basic type, but there are also maps, diagrams, illustrations, icons. Data visualization applies to visualising data in any way that you can. Using colors and fonts is part of data visualization design. But data visualization design is not about making your charts pretty. There's a common misconception about what data visualization designers do. Our primary goal when we design a data visualisation is to communicate effectively to the audience. Oh, certainly, at the end, we may want to spend some time making it aesthetically pleasing. But the most important part of a data visualisation design is that it effectively communicates a message or a number or a statistic or whatever that information is to your audience. Now there's a really big distinction that I really have all sometimes a hard time explaining to designers. And that is we use data visualization for two different purposes. The 1st 1 is for discovery, right? We may visualize data because we're trying to learn from that data. We're trying to find a trend a cluster outliers in the data. Whatever that insight is, we're trying to look for that insight and learning from the data itself. So we may use data visualization as a tool when we do that afterwards. Now we have found our insight. Now we need to communicate what we've learned toe others. And so then that's a different use of data visualisation and is often a completely different data visualization design. So you may have your insight, and you may want to communicate to potentially a non date, a professional audience who isn't gonna be interested in digging into all the analysis you went through and seeing all of the data. And so discovery can be messy. You may visualize the data and a handful of different ways, and really not spend a whole lot of time cleaning up that visualization because you're just trying to find something a Z, your insight, communication, though you really do need to clean up your design, try to make it as clear and simple and easy to understand for your audience, and so that's very different. The example I like to use is something like Where's Waldo? Which is a find and seek kind of visual game. So if you look at this original, where's Waldo? Image? It looks like raw data, right? It's messy. There are different colors. You might start to see clusters. You might to start to see different areas and that draw your attention. But the goal is the insight. So you have to actually find the Waldo character in this visual mess. There's a whole bunch of visual noise here in this world, although example. So this is what discovery and using data visualization discovery looks like. Now if we're gonna change that and say, Look, we found Waldo and now we need to communicate that to our audience and our audience might be customers might be co workers or executives in the company. It might be just publishing information to the general public. And so now we take that where's Waldo example? And we change it, and we redesigned it for the purpose of communication. We have found Waldo, which is our insight in the data, and now we want to show this to an audience and get them to quickly understand what we're talking about. And so in this, where's Waldo? Example that's redesigned for communication. You can see that I've taken all the mess, all the visual noise and made it into a sort of a grayscale background so that Waldo is the only character that's in color. And it draws your attention very quickly to Here's my insight in the data. Here's what I want to talk about. We have found Waldo in the data, and now we want you to focus on that as we go forward in our presentation. Now don't confuse data visualization with simplification. Part of data visualization design is removing all that visual noise, something that we just did in the Where's Waldo example, but not all of it. Sometimes we actually have to add information, text or data back into the design, whether it's a call out an arrow, maybe adding the data values. So it's a process of removing the unnecessary parts but also adding back in parts that help enhance understanding and help your audience very quickly. Understand what you're trying to communicate with your chart. So data visualization design is all about improving understanding, and so sometimes it's removing things and simplifying charts, and sometimes it's adding information back in to make them easier to understand. So there are five data visualization principles that we're gonna run through in these lessons. The 1st 1 is to choose a key message for your chart, your presentations lighter without, however you're using your data visualization you wanted to communicate one clear message to your audience, and all the other design principles are going to be focused on communicating that one key message. The second thing is to write a good title. Titles really matter in charts and on presentation slides. And so that title should be. What is it? You want your audience to get out of this information out of the data and out of the data visualization, not simply a description of. Here's what the data is on. We'll talk through that, reducing visual noise like we did in the Where's Waldo example we wanted. There are a handful of different ways that we can reduce visual noise that we're gonna talk through. Fourth would be using color very selectively and very methodically. We don't want it to be a huge rainbow of colors because that distracts the eye all over the place. We want to use color to draw the audience's attention to our insight in the data. And finally, we want to add back in some descriptive elements, so you may need add some text you may need to add in some call outs or pointers. You may need to add in data values, so there may be things that we need at back to the chart to make it easier to understand. All right, so those are the five principles we're gonna run through in the lessons, let's go ahead and dive in. 4. Starting with the Templates : Now we're gonna be working in Power Point. But these lessons apply to charts built in Excel as well. Office 3 65 package. They actually use a common shared charting engine. So all of the same charts, all the same customization options are available in both Excel and Power Point. It's worth mending and just is gonna be a short lesson. But the idea is that the chart templates that defaults or where you have to start, but you want to make sure that that's not where you end. Right? So the chart templates are not meant to be your whole design. They're just meant to be a starting point for you to build and craft your story and your message with your data and with your chart. Understand that Microsoft, when they designed the charting tool, they have new idea what data you're gonna bring to the table, what your data looks like, what your insights gonna be. They don't have any idea what you're bringing to their software package on DSO. As a result, they create a default that shows you as much as possible that could be built into a chart. They're just giving you options and showing you everything that you can use in your chart. And then it's up to you to cut and choose and add and remove and customize the charts so that it actually tells your story with your data. Um and so let's take a quick look. So if we open up power point, So here we've got office package were in open Power point. I'm gonna use the blank presentation. Now it comes up with the title slide. We're going to change that lay. Oh, just to be a blank slide so that we've got a nice, clean white space to work with. We go to the insert ribbon Now insert ribbon over here. This is the chart list. Omits is where all of the default charts in PowerPoint and Excel live. And so we're gonna look at just the 1st 1 The column chart I'm going to do is just a standard clustered column. And so when the chart comes up, it's actually opening up the data in excel, um, and then showing you the chart and we don't need excel. So we're just gonna look at the chart itself. Now Microsoft is set up their default charts to show you like I said everything. So we've got the title. We've got grid lines, We've got Borders, We've got the Y Axis labels. We've got X Axis labels for the Siri's. We've got a chart legend for color coding and then every single data. Siri's has its own color to differentiate it within the chart. And so this is the default. Now The challenge is Microsoft has no idea what you're bringing to the table. But a lot of people that use PowerPoint and Excel have not been trained in data visualization design, Right? So they assume, Hey, Microsoft has been company offices. This really popular, mature software products, So these must be good charts. These must be what good charts look like. What happens is Microsoft doesn't know what you need. They throw everything in, and a lot of users don't know what they really want to do and don't want to spend time designing a chart. So they just accept all the defaults that Microsoft created. They're a couple downsides here. One is your charts can be messy, complicated and have too much stuff in them, so it actually makes it harder for your audience to understand what's in your chart and what's in your data. It also, by just using the defaults, makes your data your insight as amazing and insightful as it maybe makes it look generic. Makes your chart makes your data look kind of boring because it looks like every other chart that somebody else just used the defaults as well. And you really want to make sure that you're telling a nice clean message with your data and that your message, your insight, stands out. Now the last thing when we're looking at Power Point on the screen, the's ribbons across the top give you most of the options that you have to edit the data across here in this chart designed ribbon, we can also move over to the chart format ribbon. They're a handful of MAWR options here, but personally, I like to right click and open up the format pain. And now this format pain on the right side will change, depending on which chart element we're looking at. So whether off I'm looking at a data Siri's, it'll give us a handful of options here. If I'm looking at, say, the legend is giving me different options grid lines, theme axes. As you select different pieces, they changed the options that are available and give you full customization over in the right hand pain. So this is really we're gonna dive into, um, and spend some time customizing a chart so that it actually makes it more meaningful teary audience. 5. Key Message and Title : in the data visualization design principles lesson. Earlier, I talked about the difference between designing data visualization for discovery and designing data visualization for communication. These lessons are all focused on the communication side of that design. You've already analyzed your data. You've already found that insight that you want to communicate to the audience. So now all of these design principles are focused on How do you take what you found and put it into this chart to this chart Communicates that to your audience. Whoever your audiences might be customers might be the general public might be shareholders . I don't know who you're communicating your data too. Now, the first principle is choose a key message. You've already done the analysis. You've already got your insight. What is that key message? That you want to communicate your audience And I say that I mean one key message, not two key messages and certainly not five or 10 key messages. And this is a struggle that a lot of people have when they designed presentations or church that are gonna be used in PowerPoint or Excel. If you have five or 10 messages or insights that you're trying to communicate your audience . You really should design those five or 10 separate slides with separate charts that highlight those separate messages. If you try to highlight too many messages in one chart again, it just makes your chart busy, complicated and confusing to your audience. And that's the last thing you want is to confuse your audience. Knowing your audience is something a lot of people say, and it's certainly part of knowing your key message. But I think, more importantly, is knowing what decisions your insight is trying to inform. So just to say my audience is my customers doesn't help. As much as I'm trying to help my customers choose between the products that we offer on our website, that's the choice you're trying to inform, and that's the insight or information you want to convey. And that's hugely important. Now I'm gonna combine in this lesson. We're gonna move on to not only your key message but also writing a good title. We're gonna do both of those, so let's take a look at a sample chart. So I've got a couple of sample files here. These are the ones that are available for download one. We're gonna use first is is one called the U. S. Population. So we're gonna open up this U S population chart. So before we start customizing this chart, we need to know what our key messages so that we can write a good title and then start customizing the chart itself for our purposes. I'm going to assume that the main point we want to make in this chart is that Gen X is the largest population group, right? So these bars represent the total population in each of these age groups in the US and our messages that Gen X, this green bar in the middle of this tallest bar is now the biggest population group in America. That's our key message. And so that's what we want to communicate. Now titles really matter. They make a huge difference on how your audience interprets your data and how quickly they understand your message. A lot of generic charts just put the description of the data into the title. For example, I could go in here and just add US population age groups. Right? There is my title on Yes, that's what the chart is, but that's not what my Insight is so it's describing the data but not communicating our insight. What we really want to do is take this title that we've written, which is a description of what the data is and move that down to the X Axis label. Now, in this case, it's gonna make us edit this title in the Excel data file. I'm gonna move this, that you could see it. So here's our data and Exelixis where we edited and it's taking it directly from this first column where it says Category one, I'm gonna paste in our title there and then close it. So now its added that title as U. S population age groups. Now we want to go up here and change our title to something that's our actual insight. So we're gonna make this You're an ex is the largest age group, right? So this is our insight. This is our finding, and this is what we want to communicate in our title. And now this title should impact us as a designer. And everything we do to this chart should help support the insight that we're communicating in that title. So we've chosen a key message and Now we've written a good title for this chart that clearly communicates our message. Let's move on and actually start editing the chart next. 6. Remove the Chart Legend : I'm going to cover the design principle of reducing visual noise over the span of two lessons. This 1st 1 is so important. I wanted to focus this entire lesson on just removing the chart legend. One of the main features or elements that power point almost always includes by default is this chart legend or color key over to the side or underneath the chart, and most charts don't need it. It is certainly helpful for complex data, but most of the church with simple data sets with a couple easy design steps. You can remove the chart legend and actually make your chart easier to understand. As I mentioned earlier Power Point, Excel. Throw in this chart legend with every default chart, just in case your data is gonna need it. And it's up to you to use some of these design principles to remove it and make your chart more impactful. So let's go back and take a look again at our U. S. Population chart. So here's where we left the population chart. We've changed the title, but we haven't made any changes to the chart itself. Yet some versions have this chart legend over on the side. Some versions of the default charts have it on the bottom. Doesn't matter either way. We want to eliminate it and remove it. There are a number of things that we can do to improve this chart, but let's just focus in this whole lesson on removing the chart legend itself. The easiest way is to select it like we have here and press the delete key you'll notice very quickly. The chart actually got bigger when we removed the chart legend. And from a design perspective, that's great because it gives us more space to work with as we continue to improve the effectiveness in the design of this chart. Now we still need to identify the columns for our audience, but we want to do that within the audiences field of view. We want to do that in the chart itself, not over to the side. We want to help the audience avoid having to look back and forth between the chart and the legend and the chart and the legend to determine what each of these columns represents. So our best option to do this is we're gonna change the data set itself using the Microsoft Office terminology, we will change the layout of the data so that all the values are included within one data. Siri's instead of five different data Siri's being shown here. So to edit the data and excel, we right click on the chart and choose edit data in Excel. So here's our data set. This is the data numbers that are behind the chart we're using right now, and you'll notice they're all in one row with multiple column names here. And this is how the data was originally. It loaded into the default chart. So we want to change that. So we want to take all the titles from Gen Z over the silent Jinbei, selecting all of them. We're gonna copy those, and we want to turn those into categories. So we want to move those over two column A. But we need to have them stacked. Um, and so when we paste them, we're gonna do a special paste, um, called transpose, and so it will place them now into a column instead of being a row of titles. Now we want to do the same thing with the data. We want to change the data into a single column, so we select all of it. But if we're gonna transpose it, we can't overlap where we pace the data on the original data set. So we actually have to temporarily do a transpose somewhere off to the side. Let's make that large as you can see the data, and then we'll copy that column of data and move it into column B. It just doesn't let you overlap when you do the transpose function. So now we no longer need this column. Well, just eliminate it, and the last step in cleaning it up is to we need to move our charts. Selector handle, use. Noticed this small carrot or controller down here in the bottom right corner of this cell. We want to grab that and on Lee Select column B as the data to be included in our chart, and that's it. So if we close that and go back to our chart, here's what's changed. You'll notice it's all one color blue now for all the columns. That's because it's all within one Siris of data in Power Point. You'll notice that the titles for the columns are now in the chart itself. We don't need that separate chart legend to tell us what the individual columns mean. Now. They're now the X axis labels and we've eliminated the X axis label we had before, which was our data descriptor. Um, so if we still want that, we have to add it as a separate text box. So if we add a text box and now we type in U S population age groups, um, this is now a separate text box separate from the chart that describes what data is being shown. Another thing you'll notice that these columns are a lot narrower because it's expecting clusters. We originally chose the cluster column chart, and so is expecting you to have a need for room for additional columns, but we're not gonna need that. So if you select any single column, you'll notice that the entire data Siri's is selected. And if we open up our format pain, this gap with in the chart options is what controls this white space in between the columns . So 219% means that this with is 219% the width of a column, and we can just grab the slider. And by reducing that with, we can give us much wider columns and eliminate that unnecessary waste face. So now that's it. We even remove the chart legend. We've moved the titles of those columns into the chart itself. So now it's all within one field of view. Um, and we had to create this new data descriptor down beneath the chart, but we did that with a separate text box, and that's with it. So we've removed our chart legend, and it makes this chart easier to read. Now we're gonna move on to the second lesson about other ways to reduce visual noise. 7. Reduce Visual Noise : now this is the second lesson in reducing visual noise. We've already removed the chart legend, but they're still a number of lines and numbers and text and mawr of what I call visual clutter. Still, in this chart, anything that is on the page that doesn't immediately lend itself to helping your audience understand the data becomes extra unnecessary and visual noise that we want to reduce that as much as possible to provide clarity with our chart. So let's look again at our U. S. Population chart. So here's where we left our population chart. We've changed the title. We've eliminated the chart legend and move those labels down here into the X Axis labels. But we really haven't touched much of the chart itself yet. Um, and still a lot of stuff going on here that a lot of numbers There are a lot of grid lines , things that we can continue to remove to remove mawr of that visual clutter or visual noise . So let's talk about this. Why access? That's why access We've got 10 steps leading up from 0 to 100 million, so these air 10 million steps. That's a lot of steps to show and display. It's a lot of digits being shown in the values here, so let's look at how we can reduce that over here in the format access paying this major units This value is what determines each step along the way. So it shows what the steps are in the Y Axis labels as well as the grid lines themselves. So if we change that to say, 50 million, which would be changing this to a 5.0, right. So now we have reduced that only show the midpoint and the endpoint. This may have gone too far. I mean, it's pretty clean. But with with this reduced scale, you have a hard time determining just visually what the difference between some of these interim columns are. So let's go back. Let's change that again. This time, let's make it 2.5, so it's actually 25 million, or 22.5 means 25 million, so four steps that cleans it up a little bit and a little bit easier to read Now. This is really important because sometimes revising a chart to reduce visual noise might mean adding elements back in and you have to be careful not to remove so much that that data is no longer understandable. Tree audience Remember, the whole objective of data visualization design is to convey or communicate a clear message to your audience. You need to make sure you don't remove too much. Now we can save some space by reducing the number of characters shown here on the Y axis by changing what Microsoft calls the display units. So while these labels on the Y Axis air selected, you'll noticed over here I have this choice for display units. In this case, if I reduce this, I can say thousands, and so this would be 100,000 thousands. Or if I go one more step, I can change it to millions. And so now I've shown smaller steps and change the scale of what's being shown without actually changing the values and excel. So you can see this now goes from 0 to 100 million, and it's much cleaner without all those extra digits, um, in the numbers being displayed on the Y axis, So this is getting better, but we can keep going and make this even better in the next couple lessons. Let's move on to using color with purpose now that we've removed more visual clutter now and make sure in the end we don't remove too much and make sure that the chart is still understandable to our audience. 8. Use Color With Purpose : So let's talk about the use of color and let me help you be more purposeful in your use of colors in your chart. What I mean by that is that color will draw the attention of your audience to a specific place in the chart, and you want to use that very selectively. What you don't want is a big rainbow of colors where everything on the chart is a different color, because then every color on the chart is fighting for your audiences attention. You want to use color to draw your audiences attention specifically to the data that supports your key message. Now to do this, let's talk for a minute about what are called pre attentive attributes, pre attentive attributes or visual elements that your brain can process very quickly without the need of your higher brain functions. You noticed the types of elements within nanoseconds, and they immediately draw your attention to whatever it is you're looking at. You can see there are a few different types of pre attended attributes here, but we're gonna focus specifically on color in this lesson. Now take a look at this grid of numbers. How many fours can you find in this grade, I'll give you a few seconds here. It usually takes people between 15 and 20 seconds to carefully scan each line, identify the fours and keep a running total going in your head. It's actually quite difficult as an audience to find all the important data when it's buried in a whole bunch of other text. Okay, so now the answer is, by the way, did you get that one right? As data visualization designers, there are two ways we can use color and pretend of attributes to highlight the data we want our audience to focus on. The first way is to color the fours so it makes them much easier to find. And here you can see all eight of the fours and pick them out of the other numbers quite easily. By highlighting the fours and Onley the fours, your eyes were naturally drawn to them. The second way is to reduce the intensity or the opacity of all the other numbers, so that the four stand out from the crowd. Here I have changed the color of all the other numbers to a light gray so that the fours clearly stand out and draw your attention. Now let's take this principle of pre attentive attributes and apply it to our population chart. When we change the data set to remove the chart legend, we re arranged our data and excel toe all be within the same data Siri's. And because of that power point re colors all the bars toe all be the same color blue because they're now all part of the same data Siris in our data set. And that's fine. We're gonna change this color anyway, So I'm not really concerned by what Power Point did by default. Our key message is that Gen X is the largest ace group, so we want to use color to highlight that part of the chart. We want to use color to make it obvious to our audience. Which part of the chart supports the claim that we're making in the title. To do this, I'm going to use both pre attentive attributes. I just showed you in the number grades. First, we want to make the entire data. Siri's a light gray color. Before we specifically highlight the Gen X Column. To do this, we select the data. Siri's so with a single click. You'll notice the entire Siris of data is selected and over on the right side. In our pain, we can now edit this data. We're gonna switch over here to the fill options and change it from automatic to a solid Phil so that we can choose what color is being used Is that fill color that's done here under the color. When we dropped down our color options you may want to use, you know, whatever color you want, I am going to choose just a light gray color to be all of our data as its default. All right, so now let's add color specifically to highlight the Gen X Column. To do that, we click a second time. So there were Onley selecting that number in the data Siri's you'll notice the other columns are no longer selected, and here I'm still in my fill options over on the format pain. So we're gonna change the color here, and I'm gonna make it into a break. Blue doesn't matter what color we use. In this case, it's not relevant. You may want to use a brand color from your company or a color that may be appropriate or related to the data set that you're using. In this case, you know, Gen X doesn't have a specific color associated with them, so we're gonna use Blue and highlight it with that color. Now we have used the pre attentive attribute of color to draw attention to just the data that supports our key message on our chart is starting to look pretty good, but there are a few final things we can do to make it even cleaner and easier to understand . 9. Adding Chart Elements : in the design principles lesson I talked about not confusing simplification with the individualization design. Simplification is a big part of data visualization design, and that's what I refer to as reducing visual noise. And we covered that in the last few lessons. Now another part of data visualization design, maybe adding new chart elements to your designed to help improve the clarity or the understanding of the data that you're trying to convey. You can add at elements like text illustrations, arrows, data values, call outs, icons all kinds of things can actually hope your audience understand your data and your chart even better. So in our population chart, I want to take the principle of reducing visual noise one step too far and then add back in a chart element that makes it even easier to understand. So let's take a look. So here's our population chart, and let's start by going one step too far. The's Y axis labels and these grid lines are still visual noise that I want to remove and eliminate for my chart. So the easiest way to do that right this to select so select the data labels on the Y axis and press delete. I'm going to select the grid lines specifically not the plot area, but the grid lines. You can see the lines themselves are selected and delete those. So now we have a problem, right? We have a nice clean chart, but we've gone too far in reducing too much visual noise because no one can now tell what values are associated with these different bars. To address this, I want to add back in a new chart element called data values. I want to actually show the values of each of those bars. Most of the time. Default charts and PowerPoint don't display the actual data values themselves, but in this case, I want them to be shown. So with the chart selected, we need to go to the chart designed ribbon. And this very first button is called Ad Chart Element. Now, in here, you can find all of the chart elements that are available to you. So if you've deleted one that you want to bring back to your chart like if you wanted to bring back that y axis labels, you confined those here under access titles. But what I want is data labels So I'm gonna go to this data label section and I'm going to choose. I want to display them inside the end of my bars. And so now the actual data value was shown in each bar. This black is a little bit boring. We're going to select the labels themselves and you see it select all of the data labels for the entire Siri's go back to the home ribbon. And here we can just sort of edit the text a little bit. I'm gonna make the text. Wait. I'm gonna make it bold. Instead of 12 point, I'm gonna push that up to about 18 point right? So now it's much easier to read on this chart by eliminating the Y Axis labels. We also lost the display units of millions. So we need to manually add that back into the data description in our text box at the bottom. Otherwise, these short values of 86 won't be understood by our audience to mean 86 million eso. If we go down here and in parentheses, ad million's toward data description and we'll move this centred on the chart and center that text. Okay, Now, our description explains that these air in millions of units for each of the population age groups. Now, this example works because we have a small number of bars and they're wide enough that it's easy to show the data values and clearly understand which value goes along with each bar as a personal preference. I prefer data values displayed inside the end of the bars, like we've done here, so that the text doesn't visually extend past the length of the bar itself that can potentially make the bars longer than they actually are by having text beyond the scope of the bar. So in general, I just like to put the text inside the bars instead of outside the bars. This chart is looking great, but as a data visualisation designer, I want to add one MAWR visual element that's gonna make this chart even Maury unique and memorable to my audience. I'm gonna add icons. Each of these age groups is gonna have an icon that represents the value or the age range that's represented, so that makes it that much easier for my audience to understand what these groupings represent in the U. S. Population. Now, please don't go just download icons off the Internet and steal them from somebody's website . There are plenty of legal ways to get icons that actually fulfill the terms of their licensing agreements. Let me show you the one that I use. I subscribed to a website called the Noun Project at Com that has over a 1,000,000 icons available for use. You're allowed to use these icons for free as long as you give the designers credit. When you use them in your work. Or like I do, you can subscribe to the service and get unlimited royalty for your license to use A Z. Many of these icons in your work is you need to all right, so let's take a look at our chart now. I've already downloaded the icons I want, so I'll just paste them into our slide and then we'll move him into place. A quick trick is to use the distribute function and power point. Um, if I carefully placed the last icon, so is not to cover up the numbers and the first icon. I want them to be flush, as you can see at the bottom. Then, if I move these others up to that same level. I can select all of my icons. And the distribute function will leave the 1st 1 in the last one in place and evenly distribute the remaining ones in between. So I'm gonna do in a line under the home ribbon under the arranged button align. We're gonna distribute horizontally, and then, if you need to, you can tweak just a little bit to make sure there where you would like them to be there. So now I've got icons in there that just help the audience, understand. And under the age range represented by each of these age groups in the population, you may need to add additional chart elements to your own chart. But I think that's all this chart needs. Um, I think we're ready to move on from here. So that's it. This is my final customized chart design. The last lesson will wrap up the whole entire course 10. Wrapping Up: So let's go back and look at the original chart and the chart template default. We started within powerful. This was our starting point and included all of the extra chart elements and visual noise that Microsoft includes, just in case our data was gonna need that level of complexity in our terms. And this is my final, improved design of the US population chart compared to where we started. This chart is now unique, memorable and easier for my audience to read and understand. It's ready to be used anywhere, a presentation and infographic a report or a social media post. We apply it all five of my data visualization design principles to this new and improved chart. We started by choosing our key message carefully and then writing a title to go along with that key message. We reduced the visual noise in a number of different ways, removing the chart legend, removing the grid line, simplifying the text in the Y axis labels and ultimately removing the Y axis altogether. We used color with purpose meeting. Not every data point on the chart is now in color. We have color that's drawn your attention to the focus and the key message of this turn. And ultimately we add new chart elements into the chart, whether it was the data values themselves or putting icons on top of the chart to help understanding of what those age ranges were in the turn. Everything we did is included in Power Point, except for going out and getting those icon separately. You don't need extra external design software to make great charts in power Point for your presentations for your project, I want to take your own data set, start with a default chart template and then create a new and improved chart. Using these data visualization design principles, you can use data you already have, or find a public data set on the Internet. Or, if you'd like, I am gonna make available down below a sample chart that is brand new with sample default chart that you can start with and use that as your project. If you'd like to please post an image of your original chart template, what is that? Default looked like when you started and, of course, the final new and improved chart you designed at the end. I'd love to see some description of how you apply the data visualization, design principles along the way and even some additional images if they're worth and things you try that didn't work or just images of steps along the way. Additionally, feel free to post any comments or questions down below in the community section. I will be keeping an eye there to try and respond any questions that come up. And I'm sure that other members will also have be available to answer questions or respond to comments as well. So that's it. You've reached the end, the end of our data visualization design course and using it in power Point. Thanks for taking the course and spending your time with me, and I hope this course actually inspires you to create better charts on your