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0:04
SPEAKER: Welcome back, future data analyst. As a budding analyst, you'll be exposed to a lot of data. People learn and absorb data in so many different ways, and one of the most effective ways that this can happen is through visualization. Data visualization is the graphic representation and presentation of data. In reality, it's just putting information into an image to make it easier for other people to understand. If you've ever looked at any kind of map,
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whether it's paper or online, then you know exactly how helpful visuals can be. Data visualizations are definitely having a moment right now. Online we are surrounded by images that show information in all kinds of ways. But the history of data visualization goes back way further than the web. Visualizing data began long ago with maps, which are the visual representation of geographic data. This map of the known world is from 1502. Mapmakers continue to improve their visualizations
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as new lands were charted, new data was collected about those locations, and new methods for visualizing the data were created. Scientists and mathematicians began to truly embrace the idea of arranging data visually in the 1700s and 1800s. This bar graph is from 1821. And it doesn't look too different from bar graphs that we see today. But since the beginning of the digital age of Data Analytics in the 1990s, the scope and reach of visualizations have grown along
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with the data they graphically represent. As we keep learning how to more efficiently communicate with visuals, the quality of our insights continue to grow too. Today, we can quantify human behavior through data. And we've learned to use computers to collect, analyze, and visualize that data. As an analyst in today's world, you'll probably split your time with data visuals in two ways, looking at visuals in order to understand and draw conclusions about data, or creating visuals from raw data
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to tell a story. Either way, it's always good to keep in mind that data visualizations will be your key to success. This is especially true once you reach the point where you're ready to present the results of your data analysis to an audience. Getting people to understand your vision and thought process can feel challenging, but a well-made data visualization has the power to change people's minds. Plus, it can help someone who doesn't have the same technical background or experience as you
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form their own opinions. So here's a quick rule for creating a visualization. Your audience should know exactly what they're looking at within the first five seconds of seeing it. Basically, this means the visual should be clear and easy to follow. And the five seconds after that, your audience should understand the conclusion your visualization is making, even if they aren't totally familiar with the research you've been doing. They might not agree with your conclusion, and that's OK.
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You can always use their feedback to adjust your visualization and go back to the data to do further analysis. So now let's talk about what we have to do to create a visualization that's understandable, effective, and most importantly, convincing. Let's start from the beginning. Data visualizations are a helpful tool for fitting a lot of information into a small space. To do this, you first need to structure and organize your thoughts. Think about your objectives and the conclusions
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you've reached after sorting through data. Then, think about the patterns you've noticed in the data, the things that surprised you, and of course, how all of this fits together into your analysis. Identifying the key elements of your findings helps set the stage for how you should organize your presentation. Check out this data visualization made by David McCandless, a well-known data journalist. This graphic includes four key elements, the information
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or data, the story, the goal, and the visual form. It's arranged in a four part Venn diagram which tells us that all four elements are needed for successful visualization. So far you've learned a lot about the data used in visualizations. That's important because it's a key building block for your visualization. The story or concept adds meaning to the data and makes it interesting. We'll talk more about the importance of data storytelling later, but for now, just remember
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that the story and the data combined provide an outline of what you're trying to show. The goal or function makes the data both useful and usable. And the visual form creates both beauty and structure. With just two elements, you can create a rough sketch of a visual. This could work if you're at an early stage but won't give you a complete visualization because you'd be missing other key elements. Even using three elements gets you closer, but you're not quite finished.
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For example, if you combined information, goal, and visual form without any story, your visual will probably look fine, but it won't be interesting. On their own, each element has value, but visualizations only become truly powerful and effective when you combine all four elements in a way that makes sense. And when you think about all these elements together, you can create something meaningful for your audience. At Google, I make sure to develop visualizations
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to tell stories about data that include all four of these elements. And I can tell you that each element is a key to a visualization success. That's why it's so important for you, as the analyst, to pay close attention to each element as we move forward. Other people might not know or understand the exact steps you took to come to the conclusions you've made, but that shouldn't stop them from understanding your reasoning. Basically, an effective data visualization
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should lead viewers to reach the same conclusion you did but much more quickly. Because of the age we live in, we're constantly being shown different ways to view and absorb information. This means that you've already seen lots of visuals you can reference as you design your own visualizations. You have the power to tell convincing stories that can change opinions and shift mindsets. That's pretty cool. But you also have the responsibility to pay attention to the perspectives of others
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as you create these stories. So it's important to always keep that in mind. Coming up, we'll start drawing connections between data and images to create a strong foundation for your visual masterpieces. I can't wait to get started.
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[MUSIC PLAYING]
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Earlier, we talked about why data visualizations are so important to both analysts and stakeholders. Now we'll discuss the connections you can make between data and images in your visualizations. Visual communication of data is important to those using the data to help make decisions. To better understand the connection between data and images, let's talk about some examples of data visualizations and how they can communicate data effectively.
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You've maybe come across lots of these in your daily life, and we'll explore them a little bit more here. A good place to start is a bar graph. Bar graphs use size contrast to compare two or more values. The horizontal line of a bar graph, usually placed at the bottom, is called the X-axis. In bar graphs with vertical bars, the x-axis is used to represent categories, time periods, or other variables. The vertical line of a bar graph, usually placed to the left, is called the y-axis.
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The y-axis usually has a scale of values for the variables. In this example, the time of day is compared to someone's level of motivation throughout the whole work day. Bar graphs are a great way to clarify trends. Here, it's clear this person's motivation is low at the beginning of the day and gets higher and higher by the end of the workday. This type of visualization makes it very easy to identify patterns. Another example is a line graph. Line graphs are a type of visualization
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that can help your audience understand shifts or changes in your data. They're usually used to track changes through a period of time, but they can be paired with other factors too. In this line graph we're using two lines to compare the popularity of cats and dogs over a period of time. With two different line colors, we can immediately tell that dogs are more popular than cats. We'll talk more about using colors and patterns to make visualizations more accessible to audiences later too.
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Even as a line moves up and down, there's a general trend upwards. And the line for dogs always stays higher than the line for cats. Now let's check out another visualization you'll probably recognize. Say hello to the pie chart. Pie charts show how much each part of something makes up the whole. This pie chart shows us all the activities that make up someone's day. Half of it's spent working, which is shown by the amount of space that the blue section takes up. From a quick scan, you can easily
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tell which activities make up a good chunk of the day in this pie chart and which ones take up less time. Earlier, we learned how maps help organize data geographically. The great thing about maps is they can hold a lot of location-based information, and they're easy for your audience to interpret. This example shows survey data about people's happiness in Europe. The borderlines are well defined, and the colors added make it even easier to tell the countries apart.
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Understanding the data represented here, which we'll come back to again later, can happen pretty quickly. So data visualization is an excellent tool for making the connection between an image and the information it represents. But it can sometimes be misleading. One way visualizations can be manipulated is with scaling and proportions. Think of a pie chart. Pie charts show proportions and percentages between categories. Each part of the circle, or pie, should reflect its percentage
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to the whole, which is equal to 100%. So if you want to visualize your sales analysis to show the percentage of your company's sales that come from online transactions, you could use a pie chart. The size of each slice would be the percentage of total sales that it represents. So if your online sales accounted for 60%, the slice would be 60% of the whole pie. Now, here's a misleading pie chart. It's supposed to show opinions about pizza toppings, but each slice or segment represents
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more than one option. And they all add up to well over 100%. There's lots of ingredients listed below the image that are not even included in the visual data. And all of the segments are the same size even though they're supposed to be showing different values. If a visualization looks confusing, then it probably is confusing. Let's explore another example where the size of the graphic components comes into play, this time with a bar chart. In a truncated bar chart, like this one,
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the values on the y-axis don't start at zero. The data points start at 9,100 and are at intervals of 100. This makes it seem like the data, let's say, it's for novel clicks per day on different website links, is fairly wide ranging. In this view, website E seems to clearly receive way more clicks than website D, which receives more clicks than website C, and so on. While the graph is clear and the elements are easy to understand, the way the data is presented is misleading.
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Let's try to fix this by changing the graphs y-axis so that it starts at zero instead. Now, the difference between the website clicks per day don't look nearly as drastic. By making the y-axis start at zero, we're changing the visual proportions to be more accurate and more honest. Some platforms always start their y-axis at zero, but other programs, like spreadsheets, might not fix the y-axis. So it's important to keep this in mind when creating visualizations.
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By following the conventions of data analysis, you'll be able to avoid misleading visualizations. You always want your visualizations to be clear and easy to understand but never at the expense of communicating ideas that are true to the data. So we've talked about some effective data-driven visualizations, like bar graphs, line graphs, and pie charts, and when to use them. On top of that, we've discussed some things to avoid in your visualizations to keep them from being misleading.
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Coming up, we'll check out how to make those visualizations reach your target audience. See you then. [MUSIC PLAYING]
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Earlier, we discussed the relationship between data and images. Now we'll build on that to explore what visualizations can reveal to your audience and how to make your graphics as effective as possible. One of your biggest considerations when creating a data visualization is where you'd like your audience to focus. Showing too much can be distracting and leave your audience confused. In some cases, restricting data can be a good thing. On the other hand, showing too little
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can make your visualization unclear and less meaningful. As a general rule, as long as it's not misleading, you should visually represent only the data that your audience needs in order to understand your findings. OK, now let's talk about what you can show with visualizations. Change over time is a big one. If your analysis involves how the data has changed over a certain period, could be days, weeks, months, or years, you can set your visualization to show only the time period
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relevant to your objective. This visualization shows the search interests and news story topics, like environment and science and social issues. The viz is set up to show how the search interest changes day to day. The bubbles represent the most popular topic on each day in a given part of the US. As news stories come up, the data changes to reflect the topic of those stories. If we wanted the data for weekly or monthly news cycles, we'd change the interactive feature
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to show changes by week or month. Another situation is when you need to show how your data is distributed. A histogram resembles a bar graph, but it's a chart that shows how often data values fall into certain ranges. This histogram shows a lot of data and how it's distributed on a narrow range from negative one to positive one. Each bin or bucket, as the bar is called, contains a certain number of values that fall into one small part of the range. If you don't need to show that much data,
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other histograms would be more effective, like this one about the length of dinosaurs. Here, the bins or buckets of data values are segmented so you can show each value that falls into each part of the range. If your data needs to be ranked, like when ordering the number of responses to survey questions, you should first think about what you want to highlight in your visualization. Bar charts with horizontal bars effectively show data that's ranked with bars arranged in ascending
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or descending order. A bar chart should always be ranked by value, unless there's a natural order to the data like age or time, for example. This simple bar chart shows metals, like gold and platinum, ranked by density. An audience would be able to clearly see the ranking and quickly determine which metals had the highest density even if this data viz included a lot more metals. Correlation charts can show relationships among data, but they should be used with caution
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because they might lead viewers to think that the data shows causation. Causation, or a cause effect relationship, occurs when an action directly leads to an outcome. Correlation and causation are often mixed up because humans like to find patterns even when they don't exist. So if two variables look like they're associated in some way, we might assume that one's dependent on the other. That implies causation even if the variables are completely independent.
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And if we put that data into a visualization, then it would be misleading. But correlation charts that do show causation can be effective. For example, this correlation chart has one line of data showing the average traffic for Google searches on Tuesdays in Brazil. The other line's for a specific date of search traffic, June 15. So the data is automatically correlated because both lines are representing the same basic information, but the chart also shows one big difference.
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When a football match or soccer match for Americans began on June 15, the search traffic showed a significant drop. This implies causation. Football is a very popular and important sport for Brazilians, and the data in this chart verifies that. We've now talked about time series charts, histograms, ranked bar charts, and correlation charts. Each of these charts can visualize a different type of analysis. Your business objective and audience
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will help figure out which of these common visualizations to choose. Or you may want to check out some other kinds of visualizations out there. There's also a glossary of visualizations that you'll be able to reference later. So that wraps up our lesson on creating visualizations. Coming up next, we'll add some more layers to your planning and execution of visuals. So hang on tight. [MUSIC PLAYING]
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So far, we've shown that there's lots of choices you'll make as a data analyst when creating visualizations. Each of your choices should help make sure that your visuals are meaningful and effective. Another choice you'll need to make is whether you want your visualizations to be static or dynamic. Static visualizations do not change over time unless they're edited. They can be useful when you want to control your data and your data story.
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Any visualization printed on paper is automatically static. Charts and graphs created in spreadsheets are often static too. For example, the owner of this spreadsheet might have to change the data in order for the visualization to update. Now, dynamic visualizations are interactive or change over time. The interactive nature of these graphics means that users have some control over what they see. This can be helpful if stakeholders want to adjust what they're able to view.
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Let's check out a visualization about happiness that we've created in Tableau. Tableau is a business intelligence and analytics platform that helps people see, understand, and make decisions with data. Visualizations in Tableau are automatically interactive. We'll go into the dashboard to see how the happiness score has changed from 2015 to 2017. We can check this out in our 12th slide, yearly happiness changes. On the left, are the country level changes in happiness score.
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The countries are sorted by largest increase to largest decrease. On the right, there's a map with overall happiness scores. The color scale moves from blue for the countries with the highest happiness score to red for those with the lowest. If you look below the map you'll notice a year to view slider where people can choose which year's happiness scores to display on the map. It's currently set for 2016, but if someone wants to know the scores for 2015 or 2017, they can adjust the slider.
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They could then make note of how the color coding and score labels change from year to year. Other dynamic visualizations upload new data automatically. These bar graphs continually update data by the minute and second. Other data visuals can do the same by day, week, or month. So if you need to, you can show trends in real time. Having an interactive visualization can be useful for both you and the audience you share it with. But it's good to remember that the more power you give
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the user, the less control you have over the story you want the data to tell. It's something to keep in mind as you learn how to create your own visualizations. You want to find the right balance between interactivity and control. Something else to consider is a choice between using a static or dynamic visualization. This will usually depend on the data you're visualizing, the audience you're presenting to, and how you're giving your presentation. Now that we've made some decisions
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about what kind of data it is we want to create, we can start thinking about the design, which is exactly where we're going to start talking about next time. See you there. [MUSIC PLAYING]
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You probably didn't think you'd be learning about art in a data analytics course, but that's exactly what we're going to do. Both data analysts and artists use elements of art in their work. We'll introduce those elements to you here, and we'll show you how to apply them to visualizations later. The elements we'll check out our line, shape, color, space, and movement. Now, these aren't the only elements to consider, but these particular ones can add value to your data viz
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by making them more visually effective and compelling. Lines and visualizations can be curved or straight, thick or thin, vertical, horizontal, or diagonal. They can add visual form to your data and help build the structure for your visualization. These charts show some of the variety that lines can bring to your data viz. The combo chart shows two different types of lines both providing a graphic for the data. The line chart does the same but uses curved lines instead.
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Shapes are also known for their variety. Shapes and visualizations should always be two-dimensional. This is because three-dimensional objects in a visualization can complicate the visual and confuse the audience. Shapes are also a great way to add eye catching contrast, especially size contrast, to your data story. This circle, used for a pie chart, lets someone quickly understand the data in a familiar format. Shapes with symmetry are usually more familiar to people,
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so there's less work for the audience to do when viewing symmetrical data viz. But the asymmetrical shapes in this map are still instantly recognizable as countries. It's good to note that the data you're sharing with your audience will usually inform the types of shapes you want to use in your database. Next, we have colors, and colors are, well, colors. Of course, in the eyes of artists and analysts, colors can be much more complex. Colors can be described by their hue, intensity, and value.
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The hue of a color is basically its name, red, green, blue, and so on. Intensity is how bright or dull a color is. And finally, there's value. The value is how light or dark the colors are in a visualization. In more scientific terms, value indicates how much light is being reflected. Dark values, with some black added, are called shades of color, like these shades of green. Light values, with white added, are called tints, like these tints of blue.
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In this map, there are shades and tints of gray. The value of these colors help us understand the population data in the map. And varying the colors value can be a very effective way to draw our audience's attention to specific areas. Space is the area between, around, and in the objects. There should always be space in data visualizations, just not too much or too little. For example, the space between the bars of a bar graph like this one should be smaller
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than the width of the bars themselves. This will draw the viewer's attention to the bar and the data it represents instead of the empty space. Finally, there's movement. Movement is used to create a sense of flow or action in a visualization. One of my favorite examples is the data viz, The Wealth and Health of Nations. This viz showcases a correlation between the financial health and physical health of nations. It traces these elements over time,
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so you can see how the two correlated effects play out. The movement pulls in data from the 1800s all the way up until recently. The interactivity allows for a greater volume of data to be displayed and will reveal multiple stories from the same data visualization. Remember, this is something that should be used sparingly. There's a fine line between attracting attention and distracting the audience. A static image lets you control all elements of the story you want to tell.
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When you start incorporating movement and interactivity, the story is controlled by whoever is controlling the interactivity, whether that's you or possibly your audience, if you've turned control over to them. We'll discuss this delicate balance later on in the course. When you bring many of these art elements together in a visualization like this one about sea levels, it can be beautiful and provoking. It proves that there's a place for creative expression in data analytics.
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Coming up, we'll continue exploring ways to add meaningful creative expression to your data viz. Bye for now. [MUSIC PLAYING]
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Hopefully, by now, we've developed a clear picture of data viz. We've explored everything from design principles to the types of charts you can use in your visualizations. Choosing the right visualization for your data findings can often come down to one question, which one will make it easiest for the user to understand the point you're trying to make? No matter how complex your analysis is, your audience will only care about what's in front of them and how easy they can understand it.
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As you complete your analyzes, you'll have to decide which visualizations serve your needs and your audience's needs for each task. For example, if you want to show a comparison of the different age groups of visitors to a website, a line graph with a line for each age group plus one for total users would work. Well, say you want to highlight the differences among the age groups to compare them more directly. For that, you might use a positive negative bar chart like this.
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We've touched on this before, but let's make some more connections between the data you'll have after analysis and the visualizations you'll want to use for different cases. We'll start with some charts. You've worked with some of these before and we'll cover more about charts with more examples later. You'll also discover that the best charts to suit your purposes might depend on the needs of your industry and company and the stakeholders who will be in your audience.
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For comparing data over time, we showed you how line graphs can be effective, like in this one. Bar graphs and stacked bar graphs, along with area charts, can also be good ways to visualize how data changes over time. By the way, there's a lot of charts out there. We'll give you as much information as possible about as many as we can. But doing your own research or practicing using them in visualizations will also be helpful. When you're comparing distinct objects,
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like in our example about mobile versus computer usage, ordered bar and grouped bar graphs and ordered column charts are useful. Then, there's charts that show parts of a whole. This is known as data composition, and it's achieved by combining the individual parts of a visualization and displaying them together as a whole. Stacked bars, donuts, stacked areas, pie charts, and tree maps can do all this. Now, to show relationships in your data, you might want to use scatterplot and bubble
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charts, column line charts, and heat maps. Let's revisit the happiness data viz to show you an example of this. Each of these scatter plots show the relationship between a country's happiness score and one of the factors that contributes to that score. So the health versus happiness scatterplot shows a strong relationship between the life expectancy of people living in a country and how happy those people are. Basically, as life expectancy increases, so does their happiness score.
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Speaking of happiness, a successful data visualization results in a happy audience. So it's important to understand how your audience is viewing your data visualizations since they should always be top of mind. And it all starts in the brain. When processing information, our brains try to find patterns and rely on visual context. As data analysts, we can use our understanding of the human visual system to produce better visuals. When we create visualizations, we
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can do so in a way that helps the audience process the information and helps them remember what they're seeing. Visual journalist Dona Wong proposes that effective visuals, like the data viz we've been discussing here, have three essential elements. The first is clear meaning. Good visualizations clearly communicate their intended insight. The second is a sophisticated use of contrast, which helps separate the most important data from the rest using visual contexts that our brains
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naturally look for. The third essential element for effective visuals is refined execution. Visuals with refined execution include deep attention to detail using visual elements, like lines, shapes, colors, value, space, and movement, in other words, the elements of art that we talked about earlier. The first rule in most businesses is to satisfy the customer. It's no different with data analytics. While your customers will probably be managers and other stakeholders,
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you should always think of them first when creating data visualizations. Think about the five second rule we called out earlier. If you make your data viz easy to look at and understand quickly, then you'll have done your job. And then you'll be satisfied, just like your customers. Coming up, we'll talk about design thinking and data visualizations. See you soon. [MUSIC PLAYING]
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We've covered a lot of ground in our exploration of data visualizations. We've talked a lot about how your audience should be the focus when you are making decisions about charts, colors, space, labels, and everything else that goes into a data viz. Now, let's talk about design thinking. Design thinking is a process used to solve complex problems in a user-centric way. When you bring design thinking into your work, you're trying to identify alternative strategies
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for your visualizations that might not be clear right away. You have to challenge your own thinking and explore different ways of approaching the problems and finding solutions. Airbnb is one example of a company that use a design thinking approach to help their business grow. When the company, a vacation rental online marketplace, wasn't generating as much revenue as they wanted, they decided to start experimenting. Even though the data they collected and analyzed
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was valuable, they needed to look at their product through the eyes of the customer. They realized the photos of the places that customers were seeing just weren't very good. So they decided to help their customers replace the not-so-great photos with more professional looking ones. So they hired a photographer and went door to door to take professional photos of their New York City listings. In a week, the listings with these photos saw two to three times more bookings,
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and their revenue nearly doubled thanks to their new design thinking user-based mindset. If design thinking can work for companies like Airbnb, it can help data analysts too. And data visualization is the perfect stage of your analysis to apply a user-based mindset. If you use design thinking when planning and creating your data viz, you'll be making decisions based on the needs of the people who will be viewing them. This way your audience will be engaged and enlightened by how
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you visualize your findings. While the design thinking process comes in lots of different forms, they all have stages or phases. We'll talk about five phases that you can use when creating data visualizations. Empathize, define, ideate, prototype, and test. In the spirit of design thinking, these phases don't have to follow a set order. Instead, think of them as an overview of actions that can help you produce a user centered design in your visualizations. In the empathize phase, you think
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about the emotions and needs of the target audience of your data viz, whether it's stakeholders, team members, or the general public. Here you should avoid areas where people might face obstacles interacting with your visualizations. For example, let's say you've been working on an analysis for a pharmaceutical company about how patients have been responding to a new treatment. You're getting ready to visualize the data, so you should think about the audience, which
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will include stakeholders, like pharmacists, doctors, and other medical professionals. Maybe you're thinking of using a color scheme that you like, but you realize that these colors might be a challenge to some people. The colors might be too bright or dramatic, which might not be right for the seriousness of the data. Or the colors might not have enough contrast for people who have color vision deficiencies. By adjusting the colors, you'll be empathizing with the needs of your audience.
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If there's someone on your team who is vision impaired, you'll want to find a way to explain the data verbally as well. The define phase helps you define your audience's needs, their problems, and your insights. This goes hand in hand with the empathize phase as you'll use what you learned in that phase to help you spell out exactly what your audience needs from your visualization. You could use this phase to think about which data to show in your visualization.
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Maybe this data viz will also be presented to patients who are part of your company's study. While you'll need to meet your objectives, there might be data that could make these people uncomfortable. You can think of ways to position that data to make it more digestible. Or if you're presenting to different audiences, you can adjust your visualizations to meet each group's needs by seeking input from members of the group or colleagues who've worked with that group before.
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In the ideate phase, you start to generate your data viz ideas. You'll use all of your findings from the empathize and define phases to brainstorm potential data viz solutions. This might involve creating drafts of your visualization with different color combinations or maybe experimenting with different shapes. Creating as many examples as possible will help you refine your ideas. The key here is to always remember your audience when coming up with ideas and strategies.
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You want to think about how you can position your visualizations to meet the needs and expectations of your audience. The final two phases are prototype and test. Here, you'll start putting your charts, dashboards, or other visualizations together. If you've kept your audience in mind through all the phases to this point, then your data viz will be informative and approachable. You might want to create lots of visualizations to choose which one best meets your objective.
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You can test your visualizations by showing them to team members before presenting them to stakeholders. If you've created more than one for the same data or for different audiences, like the medical professionals and the patients from our earlier example, you can share all of your options. As always, listen to any feedback you get. Critiques, both your own and others, are key to the design thinking process. They help you keep your focus on the audience
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by integrating new ideas in your final product. The phrase thinking outside the box is used a lot, but it definitely applies here. The box, in this case, is your own usual way of approaching data and it's visualization. If you embrace design thinking, you'll be able to create super effective data viz for any audience. Up next, we'll cover more things you need to consider within your data viz. See you there. [MUSIC PLAYING]
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So we've learned data visualizations are designed to help an audience process information quickly and memorably. You might remember the 5-second rule we covered earlier. Within the first 5 seconds of seeing a data visualization, your audience should understand exactly what you're trying to convey. Five seconds might seem like a flash, but adding in descriptive wording can really help your audience interpret and understand the data in the right way.
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Your audience will be less likely to have questions about what you're sharing if you add headlines, subtitles, and labels. One of the easiest ways to highlight key data in your data viz is through headlines. A headline is a line of words printed in large letters at the top of the visualization to communicate what data is being presented. It's the attention grabber that makes your audience want to read more. Take charts, for example. A chart without a headline is like a report without a title.
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You want to make it easy to understand what your chart's about. Be sure to use clear, concise language, explaining all information as plainly as possible. Try to avoid using abbreviations or acronyms even if you think they're common knowledge. The typography and placement of the headline is important too. It's best to keep it simple. Make it bold or a few sizes larger than the rest of the text, and place it directly above the chart aligned to the left.
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Then, explain your data viz even further with a subtitle. A subtitle supports the headline by adding more context and description. Use a font style that matches the rest of the chart's elements, and place the subtitle directly underneath the headline. Now, let's talk about labels. Earlier, we mentioned Dona Wong, a visual journalist who's well known for sharing guidelines on making data viz more effective. She makes a very strong case for using labels directly
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on the data instead of relying on legends. This is because lots of charts use different visual properties, like colors or shapes, to represent different values of data. A legend or key identifies the meaning of various elements in a data visualization and can be used as an alternative to labeling data directly. Direct labeling like this keeps your audience's attention fixed on your graphic and helps them identify data quickly, while legends forced the audience
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to do more work because the legend is positioned away from the chart's data. The truth is, the more support we provide our audience, the less work they have to do trying to understand what the data is trying to say and the faster our story will make an impact. Now that we've covered how to make a database as effective as possible, next up, we'll figure out how to make it accessible to all. See you in a bit. [MUSIC PLAYING]
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Over one billion people in the world have a disability. That's more than the populations of the United States, Canada, France, Italy, Japan, Mexico, and Brazil combined. Before you design a data viz, it's important to keep that fact in mind. Not everyone has the same abilities, and people take in information in lots of different ways. You might have a viewer who's deaf or hard of hearing and relies on captions. Or someone who's colorblind might look to specific labeling for more description.
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We've covered a lot of ways to make a data visualization beautiful and informative, and now, it's time to take that knowledge and make it accessible to everyone, including those with disabilities. Accessibility can be defined a number of different ways. Right from the start, there's a few ways you can incorporate accessibility in your data visualization. You just have to think a little differently. It helps to label data directly instead of relying exclusively
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on legends, which require color interpretation and more effort by the viewer to understand. This can also just make it a faster read for those with or without disabilities. Check out this data viz. The colors make it challenging to read, and the legend is confusing. Now, if we just remove the legend and add in data labels, bam! You've got a clearer presentation. Another way to make your visualizations more accessible is to provide text alternatives so that it
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can be changed into other forms people need, such as large print, Braille, or speech. Alternative text provides a textual alternative to nontext content. It allows the content and function of the image to be accessible to those with visual or certain cognitive disabilities. Here's an example that shows additional text describing the chart. And speaking of text, you can make data from charts and diagrams available in a text-based format through an export to sheets or Excel.
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You can also make it easier for people to see and hear content by separating foreground from background. Using bright colors that contrast against the background can help those with poor visibility, whether permanently or temporarily, clearly see the information conveyed. Another option is to avoid relying solely on color to convey information and instead distinguish with different textures and shapes. Another general rule is to avoid over complicating data visualizations.
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Overly complicated data visualizations turn off most audiences because they can't figure out where and what to focus on. That's why breaking down data into simple visualizations is key. A common mistake is including too much information in a single piece or including long chunks of text or too much information in graphs and charts. This can defeat the whole purpose of your visualization making it impossible to understand at first glance. Ultimately, designing with an accessibility mindset
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means thinking about your audience ahead of time, focusing on simple, easy to understand visuals, and most importantly, creating alternative ways for your audience to access and interact with your data. And when you pay attention to these details, we can find solutions that make data visualizations more effective for everyone. So now you completed your first close up exploration of data visualization. You've discovered the importance of creating data viz that cater to your audience
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while keeping focus on the objective. You learned different ways to brainstorm and plan your visualizations and how to choose the best charts to meet that objective. And you also learned how to incorporate elements of science, art, and even philosophy into your visualizations. Coming up, we'll check out how to take all of these learnings and apply them in Tableau. You'll get to see how this data visualization tool makes your data viz work more efficient and effective. See you soon.
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TONY: Congratulations on finishing this video from the Google Data Analytics Certificate. Access the full experience, including job search help and start to earn the official certificate by clicking the icon or the link in the description. Watch the next video in the course by clicking here, and subscribe to our channel for more from upcoming
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