Course 6‐1 - Forestreee/Data-Analytics GitHub Wiki
Google Data Analytics Professional
Share Data Through the Art of Visualization
WEEK1 - Visualizing data
Data visualization is the graphical representation of data. In this part of the course, you’ll be introduced to key concepts, including accessibility, design thinking, and other factors that play a role in visualizing the data in your analysis.
Learning Objectives
- Explain the key concepts involved in design thinking as they relate to data visualization
- Describe the use of data visualizations to talk about data and the results of data analysis
- Discuss accessibility issues associated with data visualization
- Explain the importance of data visualization to data analysts
- Describe the key concepts involved in data visualization
Communicating your data insights
Introduction to communicating your data insights
Hey there, it's great to see how far you've come in this Google Data Analytics certificate. First of, I want to say congrats on your achievement. Second, welcome to your next course. It's all about the art of data storytelling through visualization. As a data analyst, you can do all the necessary work of planning, collecting, cleaning, and analysis. But you also need to show stakeholders what your data means in a compelling way using visuals. We're here to show you how that's done.
With my experience as Director of Analytics at Google, I hope I'll be a pretty good guide. My name's Kevin, and I'll be your instructor for this course. This part of your training is particularly meaningful to me because I love data storytelling.
I can't think of anything in today's business world that's more vibrant, more exciting, and more rewarding. With the amount of data we have around us, data analytics plays a key role in pretty much every part of business. In my opinion, there isn't a skill that's more important to you as an analyst than being able to effectively communicate the stories you want to covered to stakeholders.
Stakeholders usually lack the time, access to data, or expertise needed to find those stories by themselves. That's why we'll focus on visualizing data to help you better share Data Stories.
Which means we're now at the share phase of the data analysis process.
We'll start with the basic concepts of visualization and why visualizing your analysis is such an important part of Data Analytics.
From there, we'll discuss how to plan for and start building effective visualizations that are inclusive, accessible, and consider the audience first.
Afterwards, we'll explore one possible tool you can use for data visualization, Tableau. Tableau helps us create visualizations from our analysis so that we can share our findings more effectively.
We'll show you how data visualizations, including visual dashboards, can help bring your data to life.
We'll also explore how you can use visualizations in your presentations and slide shows to continue telling a story with data. We'll discuss the art and science behind effective presentations.
Finally, you'll learn how to anticipate and answer questions from stakeholders and respond to their feedback.
Throughout the course, I'll guide you through what I think is the most exciting part of the data analysis process. By the end, you'll have everything you need to plan, create, and present an effective and compelling data visualization. Now that we've gotten to know each other little, it's time to get down to business. Coming up, we'll talk a bit about the history of data visualization and why visualizations mattered so much today. We'll also discuss the methods for using imagery effectively and what you can do to make the most out of your visuals. See you there.
Course syllabus
Course content Course 6 – Share Data Through the Art of Visualization
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Data visualization: Data visualization is in many ways the culmination of the data analysis process. In this part of the course, you will be introduced to the concepts involved in data visualization. You will learn about accessibility, design thinking, and other factors that play a role in visualizing the data in your analysis.
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Data visualizations with Tableau: Tableau is a tool that can help analysts create effective data visualizations. In this part of the course, you will learn all about Tableau and its uses. You will also explore the importance of creativity and clarity while visualizing your findings appropriately.
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Stories about your data: Connecting your objective with your data through insights is essential to good data storytelling. In this part of the course, you will learn about data-driven stories and their attributes. You will also gain an understanding of how to use Tableau to create dashboards and dashboard filters.
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Developing presentations and slideshows: In this part of the course, you will discover how to give an effective presentation about your data analysis. You will consider all aspects of your analysis when creating a presentation and learn how to use multiple data sources in the data visualizations you will share. In addition, you will learn how to anticipate potential limitations and questions that might arise and how to provide useful answers to stakeholders.
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Course Challenge: At the end of this course, you will be able to put everything you have learned into practice with the Course Challenge. The Course Challenge will ask you questions about key principles you have been learning about and then give you an opportunity to apply those principles in two scenarios.
Learning Log: Reflect on data visualization
Link to learning log template: Reflect on data visualization
Reflection
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Which data visualizations have been particularly effective in communicating data? What do you think made them effective?
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Have you ever seen a data visualization that was very unclear or confusing? What do you think might have been the problem with it?
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How do you think your visualizations might complement the data you’ll work on?
Kevin: The power's in the data viz
Data Analytics is the collection and analysis and then use of data to tell stories, using charts and visualizations, so that businesses can make better decisions. So I've always loved numbers and have always enjoyed math and calculus and those sorts of things. So data came really easy to me. I was working at a consultancy before and using lots of numbers, and I enjoyed it for sure. But it wasn't until I worked at an advertising agency where I saw the creative expression of numbers and how data could drive that creativity. That it really all fell into place for me and I realized that doing analytics in a marketing and in an advertising setting was exactly what I wanted to do. And I found that I just really enjoyed when we put those two things together.
I think the visuals that come out of data analysis and data analytics are really beautiful, but what is even more interesting for me are the stories behind them.
If you look at a big block of text or a big block of numbers, those stories are in there, but they really have to be found. And so it takes certain skill to pull those things out. And I find that skill, that analysis really exciting and interesting. But it ultimately then ends in a beautiful visualization, which I find very gratifying.
There are great data visualization thinkers, and you see these visuals all over the place. You can find inspiration from looking at news outlets today and seeing the visuals that they present and how they tell stories that way. Visualizations have become so important that you can find them everywhere, and you could take great inspiration from that. But I also take inspiration from unlikely sources like photography and art and others and seeing how composition is created, how color is used. I think that's really important. And I try hard to bring those sorts of elements and those sorts of influences into the visualizations I create. So I know this course is pretty intense. We're throwing a lot at you. Your brain is probably overloaded. You're probably fried. Just stick with it. It is all going to start to piece together and make sense. The biggest thing that I think you can think of is just how important these skills really are. This is the new way of doing business, involving data, using the analytics tools and techniques that we're talking about to make decisions. So there's a great big reward waiting for you at the end of this course. I'm Kevin. I'm director of analytics here at Google.
Meet and greet
Refresher: Your data analytics certificate roadmap
Understand data visualization
Why data visualization matters
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, whether it's paper or online, then you know exactly how helpful visuals could 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. Map makers continued to improve their visualizations 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 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 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 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.
In 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 okay. 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 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 help 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 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 a 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 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.
For example, if you combine 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 of these elements together, you can create something meaningful for your audience. At Google I make sure to develop visualizations 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 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 could change opinions and shift mindsets. That's pretty cool. But you also have the responsibility to pay attention to the perspectives of others 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.
Effective data visualizations (Reading)
Connecting images with data
Hello again. 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.
Now let's check out another visualization you'll probably recognize. Say hello to the pie chart.
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.
By following the conventions of data analysis, you'll be able to avoid misleading visualizations. You always want your visualization 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.
Coming up, we'll check out how to make those visualizations reach your target audience. See you then.
The beauty of visualizing (Reading)
A recipe for a powerful visualization
Hey, there. You're back and ready to learn how to create powerful data visualizations. Coming up, we'll explore how to take our findings and turn them into compelling visuals. 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 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.
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, which could be days, weeks, months, or years. You can set your visualization to show only the time period relevant to your objective.
This visualization shows the search interests in news story topics like environment and science and social issues. The viz is set up to show how the search entries change day to day. The bubbles represent the most popular topic on each day in a given part of the US. As new 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 change the interactive feature 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 a negative one to a 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, other histograms would be more effective, like this one about the length of dinosaurs. Here the bins or buckets of data values are segmented. 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 are ranked, with bars arranged in ascending 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 database included a lot more metals.
Correlation charts can show relationships among data, but they should be used with caution 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.
If two variables look like they're associated in some way, we might assume that one is dependent on the other. That implies causation, even if the variables are completely independent. 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 lines for a specific date of search traffic, June 15th. The data is automatically correlated because both lines are representing the same basic information. But the chart also shows one big difference. When a football match or soccer match for Americans began on June 15th, 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 will help figure out which of these common visualizations to choose. Or you may want to check some other kinds of visualizations out there. There are also glossary visualizations that you'll be able to reference later. That wraps up our lesson on creating visualizations.
Coming up next, we'll add some more layers to your planning and execution of visuals. Hang on tight.
Question:
Correlation and causation (Reading)
Dynamic visualizations
Hey, great to see you again. 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. 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. 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. 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. 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. 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 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 about what kind of data vis 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.
The wonderful world of visualizations (Reading)
Data grows on decision trees (Reading)
Self-Reflection: Choosing your visualization (Practice Quiz)
Practice Quiz: data visualization
Design data visualizations
Elements of art
Hello, and welcome back. 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 are 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 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 a 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.
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, 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 data viz.
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.
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.
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 color's 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 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 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 we'll 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. 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. Coming up, we'll continue exploring ways to add meaningful creative expression to your data viz. Bye for now.
Question:
Principles of design (Reading)
Data visualization impact
Welcome back, let's jump in. 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. As you complete your analysis, you'll have to decide which visualization serve your needs and your audiences 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. Let's say you want to highlight the differences among the age groups to compare them or directly, for that you might use a positive negative bar chart like this.
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 will 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.
For comparing data over time we showed you how line graphs could 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.
Okay, when you're comparing distinct objects like in our example about mobile versus computer usage, ordered bar, and group 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.
Stack 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 charts, column/line charts and heatmaps. Let's revisit the happiness data vis to show you an example of this.
Each of these scatterplots 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.
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 can do so in a way that helps the audience process the information and helps them remember what they're seeing.
Visual journalists Dona Wong proposes that effective visuals, like the database 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 context that our brains 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, 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 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.
Question:
Data is beautiful (Reading)
Design thinking and visualizations
Hey, welcome back. 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 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 uses 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 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 2 to 3 times more bookings, and their revenue nearly doubled, thanks to their new design thinking, and 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 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 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 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. If there's someone on your team who is vision impaired, you want to find a way to explain the data verbally as well.
The define phase helps you to find your audiences 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. 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.
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. 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. You could 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 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 its 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.
Question:
Design thinking for visualization improvement (Reading)
Identify data visualizations in your life (Discussion Prompt)
Data visualizations are a powerful tool for data analysts as they communicate with data. So far, you have been learning about how important data visualization can be when telling stories with data or making data-driven decisions. Based on what you have learned, are there areas of your life where data visualization has helped you tell a story or make a decision? This could include using sports team statistics, stock market trend graphs, or advertisements that used data visualization to help you make a decision.
Submit 3-5 sentences (150-200 words) answering this question, explaining how data visualization has helped you. Then, visit the discussion forum to read what other people have posted, and respond to at least two posts with your own thoughts.
Practice Quiz: designing data visualizations
Explore visualization considerations
Headlines, subtitles, and labels
Hello again. 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 five 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. 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. 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. 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 charts 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 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 force the audience to do more work, because a 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 data viz as effective as possible.
Next up, we'll figure out how to make it accessible to all. See you in a bit.
Pro tips for highlighting key information (Reading)
Accessible visualizations
Hey, great to have you back, let's dive back in. Over 1 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 color blind might look to specific labeling for more description. 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'll just have to think a little differently, it helps to label data directly instead of relying exclusively 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 can be changed into other forms people need, such as large print, braille, or speech.
Alternative text provides a textual alternative to non-text 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.
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.
Question:
Red-green color blindness is the most common and occurs when red and green look like the same color. You can avoid placing green on red or red on green in your visualizations.
Blue-yellow color blindness is less common and occurs when it is difficult to tell the difference between blue and green, or yellow and red. You can also avoid using these colors on top of or next to each other.
For more information, refer to these resources: Types of Color Blindness Web Accessibility Guidelines, Contrast & Color
Another option is to avoid relying solely on color to convey information, and instead distinguished with different textures and shapes.
Another general rule is to avoid overcomplicating data visualizations. 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 and 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 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 course of exploration of data visualization. You've discovered the importance of creating data viz that cater to your audience 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.
Question:
Andrew: Making data accessible
I'm Andrew and I'm a data and Insights Manager on the ads research Insights Team. What does that actually mean? I help my company Google, make better decisions through data and I also work with our data to tell stories for marketers. Basically data storytelling at scale. Accessibility should be built into everything we do.
Accessibility is really about making sure that you are creating data visualization, graphs, charts, tables, that anyone can interact with whether they have a long term or even as a temporary form of impairment. It could be auditory, it could be visual, could be sensory in some way. Typically the ones we talked about in data visualization have to deal with color and contrast. Or maybe they can't see. So there's a number of things you can do any visualizations as you're getting, ready to show people and bring it out into the world.
To make it easier for them to understand your graph, and understand the points you're trying to make, and just to make yourself more inclusive, you're going to create this stuff and you're not going to be the one presenting it anymore. It's going to show up in a place where you won't be the one being able to navigate the data for people and frame it for people. And that's a good thing. But as it moves further away from you, you also won't be there standing by it being able to explain it to people and be like, hey, here's the point, or, hey, maybe you can't read or see this or maybe these colors are confusing, let me make sure that you got the point clearly. It's just a way of making sure that everyone in the room is able to experience the thing you've worked so hard on and able to take away the point in a clear way that they too, can basically take action on the data that you spent all this time working with and making you know presentable for folks.
So all these things, they're great for accessibility and they're more inclusive but they're also this making you a better data analyst and a better storyteller, because they force you to become more empathetic of your audience and who's receiving your data. You're making this to move the hearts and minds of other people to convince someone else that this data is meaningful to them and they should take an action on it. Or they should know this and they should use this for the organization or for their lives or for whatever the thing may be. And so, by focusing on accessibility, by focusing on the audience, by making it more inclusive, you're making your data clear and more impactful for everyone.