Course 6‐4 - Forestreee/Data-Analytics GitHub Wiki
Google Data Analytics Professional
[Share Data Through the Art of Visualization]
WEEK4 - Developing presentations and slideshows
In this part of the course, you’ll discover how to give an effective presentation about your data analysis. You’ll consider all aspects of your analysis when creating the presentation, as well as how to create clear, accessible, and convincing data visualizations. In addition, you’ll learn how to anticipate and respond to potential limitations and questions that may arise.
Learning Objectives
- Describe best practices for addressing the question-and-answer section of a presentation
- Consider the caveats and limitations associated with the data in a presentation
- Differentiate between strong and weak presentation content
- Describe how junior data analysts are expected to use their presentation skills
- Explain principles and practices associated with effective presentations
- Identify appropriate responses to presentation objections
The art and science of an effectice presentation
Pulling it all together
Welcome back. Now that we're in the share phase of the data analysis process, it's time to show other people what we found.
You've already learned about creating data visualizations and how to use data-driven storytelling. Now it's time to talk about actually presenting the data. Maybe the idea of presenting your findings of stakeholders makes you nervous, or maybe you're getting excited just thinking about it. Either way, these upcoming videos will get you ready to present like a pro. Coming up, we'll learn about the art and science of presentations, some best practices you can use for future presentations, and how to bring multiple data sources together to tell the whole story. As a data analyst, it's important to find answers and make new discoveries during your data analysis, but it's just it's important to share those findings with other people. So if you're ready, let's get started.
Presenting with a framework
Hi again. Earlier in this program, you learned how to keep your audience in mind when communicating your data findings. By making sure that you're thinking about who your audience is and what they need to know, you'll be able to tell your story more effectively.
In this video, we'll learn how to use a strategic framework to help your audience understand the most important takeaways from your presentation. To make your data findings accessible to your audience, you'll need a framework to guide your presentation.
This helps to create logical connections that tie back to the business tasks and metrics. As a quick reminder, the business task is the question or problem. your data analysis answers. The framework you choose gives your audience context to better understand your data.
On top of that, it helps keep you focused on the most important information during your presentation.
The framework for your presentation starts with your understanding of the business task. Raw data doesn't mean much to most people, but if you present your data in the context of the business task, your audience will have a much easier time connecting with it. This makes your presentation more informative and helps you empower your audience with knowledge. That's why understanding the business task early on is key.
Here's an example.
Let's say we're working with a grocery store chain. They've asked us to identify trends and online searches for avocados to help them make seasonal stocking decisions.
During our presentation, we want to make sure that we continue focusing on this task and framing our information with it. Let's check out this example slide presentation. We can begin our presentation by framing it with the business task. Here, in this second slide, I've added goals for the discussion. It starts with "share an overview of historical online avocado searches." Under that, a more detailed explanation: "We'll cover how avocado searches have grown year over year and what that means for your business." Then we'll "examine seasonal trends in online avocado searches using historical data." This is important because "understanding seasonal trends can help forecast stocking needs and inform planning." And finally, "discuss any potential areas for further exploration."
This is where we'll address next steps in the presentation. This clearly outlines the presentation so our audience knows what to expect.
It also lets them know how the information we share is going to be connected to the business task. You might remember, we talked about telling a story with data before. You can think of this like outlining the narrative. We can do the same thing with our data viz examples. If we're showing this visual graph of annual searches for avocados, we might want to frame it by saying this graph shows the months with the most online searches for avocados last year, so we can expect that this interest in avocados will fall on the same months this year. That can even be used in our speaker notes for the slide. This is a great place to add important points you want to remember during the presentation ahead of time. These notes aren't visible to your audience in presentation mode, so they're great reminders you can refer to as you present. Plus, you could even share your presentation with speaker notes ahead of time to make the content more accessible for your audience. Using this data, the grocery store can anticipate demand and make a plan to stock enough avocados to match their customers' interests. That's just one way we can use the business task to frame our data and make it easier to understand.
You also want to make sure you're outlining and connecting with your business metrics by showcasing what business metrics you use. You can help your audience understand the impact your findings will have.
Think about the metrics we use for our avocado presentation. We track the number of online searches for avocados from different months over several years to anticipate trends and demand. By explaining this in our presentation, it's easy for our audience to understand how we used our data. These data points alone—the dates or number of searches—aren't useful for our audience, but when we explain how they're combined as metrics, the data we're sharing makes so much more sense. Here's another potential data viz that we want to use. We can frame it for our audience by including some of our metrics. There's an explanation of what time period this data covers: "Our data shows Google search queries from 2004 to 2018." Where we gathered this data from: "Search queries are limited to the United States only." And a quick explanation of how the trends are being measured: "Google trends scores are normalized at 100." So now that our audience understands the metrics we use to organize this data, they'll be able to understand the graph more clearly.
Using a strategic framework to guide your presentation can help your audience understand your findings, which is what the sharing phase of the data analysis process is all about.
Coming up, we'll learn even more about how to weave data into your presentations.
Question: The purpose of a framework is to create logical connections that tie back to the business task. It also gives your audience context about your data and helps you focus on the most important information.
Weaving data into your presentation
Hey, great to have you back. So we know how to use our business tasks and metrics to frame our data findings during a presentation.
Now let's talk about how you work data into your presentations to help your audience better understand and interpret your findings.
First, it's helpful for your audience to understand what data was available during data collection. You can also tell them if any new relevant data has come up, or if you discovered that you need different data.
For our analysis, we used data about online searches for avocados over several years. The data we collected includes all searches with the word "avocado," so it includes a lot of different kinds of searches. This helps our audience understand what data they're actually looking at and what questions they can expect it to answer. With the data we collected on searches containing the word avocado, we can answer questions about the general interest in avocados. But if we wanted to know more about something specific, like guacamole, we'd probably need to collect different data to better understand that part of our search data.
Next, you'll want to establish the initial hypothesis. Your initial hypothesis is a theory you're trying to prove or disprove with data. In this example, our business task was to compile average monthly prices. Our hypothesis is that this will show clear trends that can help the grocery store chain plan for avocado demand in the coming year. You want to establish your hypothesis early in the presentation. That way, when you present your data, your audience has the right context to put it in. Next, you'll want to explain the solution to your business tasks using examples and visualizations. A good example is the graph we used last time that clearly visualized the search trend score for the word avocado from year to year. Raw data could take time to sink in, but a good example or visualization can make it much easier for your audience to understand you during a presentation.
Keep in mind, presenting your visualizations effectively is just as important as the content, if not more. And that's where the McCandless Method we learned about earlier can help. So let's talk through the steps of this method and then apply them to our own data visualizations. The McCandless Method moves from the general to the specific, like it's building a pyramid.
You start with the most basic information: introduce the graphic you're presenting by name. This direct your audience's attention. Let's open the slide deck we were working on earlier. We've got the framework we explored last time and our two data viz examples. According to the McCandless Method, we want to introduce our graphic by name. The name of this graph, "yearly avocado search trends," is clearly written here. When we present it, we'll be sure to share that title with our audience so they know where to focus and what the graphic is all about. Next, you'll want to answer the obvious questions your audience might have before they're asked. Start with the high-level information and work your way into the lowest level of detail that's useful to your audience. This way, your audience won't get distracted trying to understand something that could have easily been answered when the graphic was introduced. We added in the information about when, where, and how this data was gathered to frame this data viz. But it also answers the first question many stakeholders will ask, "Where is this data from, and what does it cover?" So going back to the second graph in our presentation, let's think about some obvious questions our audience might have when they see this graph at first. This data viz is really interesting, but it can be hard to understand at a glance, so our audience might have questions about how to read it. Knowing that, we can add an explanation to our speaker notes to answer these questions as soon as this graph is introduced. "This shows time running in a circle with winter months on top and summer on bottom. The farther elements are away from the center, the more queries happened around that time for 'avocado.'" Now some of the answers to these questions are built into our presentation.
Once you've answered any potential questions your audience might have, you'll want to state the insight your data viz provides. It's important to get everyone on the same page before you move into the supporting details. We can write in some key takeaways to this slide to help our audience understand the most important insights from the graphic. Here we let the audience know that this data shows us a consistent seasonal trend year over year. We can also see that there's low online interest in avocados from October through December. This is an important insight that we definitely want to share. Even though avocados are a seasonal summer fruit, searches peak in January and February. For a lot of people in the United States, watching the Super Bowl and eating chips with guacamole is popular this time of year. Now our audience knows what takeaways we want them to have before moving on.
The fourth step in the McCandless Method is calling out data to support that insight. This is your chance to really wow your audience, so give as many examples as you can. With our avocado graphs, it might be worth pointing to specific examples. In our monthly trends graph, we can point to specific weeks recorded here. "During the week of November 25th, 2018, the search score was around 49, but the week of February 4th the search score was 90. This shows the rise and fall of online search interest, with the help of some of the very cool data in our graphs." Finally, it's time to tell your audience why it matters. This is the "so what" moment. Why is this insight interesting or important to them? This is a good time to present the possible business impact of the solution and clear action stakeholders can take. You might remember that we outlined this in our framework at the beginning of our presentation.
So let's explain what this data helps our grocery store stakeholder do. First, they can account for lower interest in avocados between the months of October and December. They can also prepare for the Super Bowl surge in avocado interest in late January/early February. And they'll be able to consider how to optimize stocking practices during summer and spring. There's a little more detail under each of these points, but this is a basic breakdown of the impact. And that's how we use the McCandless Method to introduce data visualizations during our presentations.
I have one more piece of advice. Take a second to self-check and ask yourself, "Does this data point or chart support the point I want people to walk away with?" It's a good reminder to think about your audience every time you add data to a presentation.
So now you know how to present data using a framework, and weave data into your presentation for your audience. And you got to learn the McCandless Method for data presentation.
Coming up, we'll learn some best practices for actually creating presentations. See you soon.
Question:
An initial hypothesis is a theory you’re trying to prove or disprove with data. You want to establish your hypothesis early in the presentation
Learning Log: Review a slide presentation (Reading)
Brittany: Presentation skills for new data analysts
My name is Brittany, and I'm an Analytical Lead at Google. One of the tips that I have is to try to keep things "kindergarten simple." And what that means is, keep the concepts that you're presenting as simple and as straightforward as possible. Whenever you enter a room, there are going to be people within that room of varying interest levels, varying knowledge levels. They have different levels of subject matter expertise. Nobody wants to present to a room whose eyes are glazing over.
My pet peeve about seeing certain presentations with data is that they often will include what I like to call "eyesore charts." And what an eyesore chart is, it has way too much data, has way too many colors, it just looks busy, and you just really can't figure out what the presenter is actually trying to say.
Another tip that I have is to make your presentation fun. So nobody wants to be in a room where you are talking for a full hour, and the only voice that you're hearing is your own. One of the things that I try to do to break it up is I try to think of little fun games or quizzes, or I'll play a video or ask questions to the audience just to make sure that they're fully engaged and that they are talking back to me.
Another tip that I try to incorporate into my presentations is storytelling. Everybody loves a good story, and when you do it right, you are able to connect and make your audience engage in a way that they probably wouldn't if you weren't telling that story.
The last tip that I have is make sure that you have an ally in the room. Oftentimes before I'm giving a really big data presentation, I will find one or two people that I know are going to be in the room and present my content to them ahead of time. And what that does is it allows me to not only get feedback, but it also allows me to make sure that someone else is nodding their head and aligned to the numbers that I'm about to present. And I can't even tell you how many times that I've been in presentations where those allies have really come to my rescue. When the room asks a lot of questions or are potentially trying to poke holes in the analysis, those allies are there to speak up, and they really are going to have your back and lend credibility to what it is that you're presenting.
The most challenging part of my job would be the fact that I am there to convince people to do something that they might not be fully confident that they should be doing. And a lot of times, it takes multiple conversations, multiple rounds of convincing, for someone to actually come around to what I was trying to articulate or get them to do. When you have spent maybe six months or a year building an analysis and building a story and building a narrative for someone to apply to their strategies, and they actually come around and they actually do it, that makes the challenges worth it.
Step-by-step critique of a presentation (Reading)
Connor: Messy example of a data presentation
So we're going to dive into specific examples that we have. So we've built what we call a messy example of a data presentation. We'll walk through each slide and the presentation as a whole to understand why it doesn't actually work well for explaining a specific analysis. We have a title slide: "The Relationship Between Health and Happiness Around the World." Right off the bat, there is a very generic picture about the world. It is a very lengthy title. We know what we're going to be talking about, but there's nothing here that's really compelling about the presentation.
The first slide, when we are looking at a data presentation, we have immediately put a lot of data in front of them and a lot of text in front of them. Right now they don't know what they're looking at. There was no statement of purpose. We don't have an introduction slide. They don't know who I am. They don't know why they're there. What are we talking about? Why are we talking about it? What should they walk away with? There's none of that—we've just immediately gone into the specific data visuals that we are showing them. Now, an important aspect of every slide is also to have a title. Now, title, subtitle, these things help people understand exactly what this slide is going to be discussing so that they know what they're trying to understand as you are talking. So immediately getting here, the audience is going to be lost. They're going to be trying to read the slide. They're going to be trying to decipher what the visuals mean. It's important for you to make sure there's not too much going on.
Now if we move on to the next slide, what we're looking at here —the visual is better, it's easier to understand. There's not more than one of them. We have a map. We have visual colors to represent the numeric values within them. But again, there's nothing for them to really understand. Now this is where you can explain within the speaker notes. But you also have, again, a lot of words, no title. What is it that they are really trying to get from this slide? v Part of a good presentation, as well, is the theme that you have or a consistent theme. So you've now switched sides of the specific visual. You have the text on the other side. Doesn't mean you can't do it, but what you're really trying to do throughout a presentation is build some familiarity, especially with data analytics. You're building familiarity with the visuals that you're showing them—the data. By the end of the presentation, they should understand the data or the concept as much as you do.
Finally, we have the conclusion slide. This one does have a title: "Laughter is the best medicine." We understand again what it is that we're looking at, but there is no logical flow and how to get here. Was this overall presentation compelling? We put two slides on there. We had too much text. We didn't really explain anything about it. Again, there's awkward placement on where all of these things are within the slide itself. When you're thinking about building a presentation, you should think about it from the audience's point of view. The only thought that's going through their head is, "Where should my focus be? As I'm trying to listen, as I'm trying to comprehend, where should I be looking?" If you have slides like we just showed you, they don't know where they should be looking or they're going to spend their time reading and trying to comprehend while you're also talking. It's very important that you are directing their gaze and directing the audience so that they know exactly what they should be listening to, what they should be trying to understand, and you are guiding them through to the overall conclusion.
So to sum up in terms of what is wrong with this overall presentation and not just what you're going to be talking about or what you are trying to conclude, but just the overall placement of the data visuals and the visuals that you chose. The main thing is there was no story, no logical flow. You started with a bunch of scatterplots and a lot of text and you moved on to the heatmap of the happiness scores, but without somebody presenting something, without any idea of the concept behind what they are trying to conclude. You didn't have titles, there's too much text, it's very difficult to understand, and it was uneven and inconsistent. Even if you had a really good explanation on each slide, you might have lost the audience because what they were trying to do, what they were trying to understand, is what was the slide trying to tell them? Finally, the most important part of any data analytics presentation is the recommendation or conclusion slide. You had that, but there was no title. They didn't know that this was the end of the presentation, that this is where they should be trying to put all the pieces together.
Coming up, we're going to discuss how we can improve this presentation, as well as dive into what the presentation will actually look like when we're trying to explain how health and happiness are correlated.
Connor: Good example of a data presentation
So now that you know what not to do, I'm going to walk you through how I would tackle a presentation.
So to start, you can see the title slide, it's a lot simpler. We have a title, we have who is presenting, and we have when it occurred. Now, I do want to talk a little bit about the date at the bottom, which is an important factor that you shouldn't forget to include. You may come back to this presentation a few months later, even a year later or this may be disseminated across your company. It's important to know when this analysis took place, and why, and what were the circumstances of it? And the big part of that is, what were the circumstances of the company at the time that this was actually presented?
So the next slide is giving an idea of what you're going to be presenting to everyone and when. So you start with your purpose statement. What you're going to be discussing, what are we talking about? The next aspect is, where you actually tell your story. And that's an important concept is this overall presentation is a story with data. And finally, you have your conclusion slide. You're going to be very clear that this is the conclusion. This is where you're going to add recommendations if this is in a business context. And then you'll have your appendix, where you can have additional information on data, data visuals, as well as overall context for the presentation that may not work within the overall flow itself.
So, our transition slide, what are we talking about?
So this is where you let the audience know what we're talking about, what are we trying to tell them? What are all the slides that are following this going to be driving towards? So when we look at this slide, I'm trying to identify if there are geographic, demographic, and/or economic factors that contribute to a happier life. That is the purpose of the overall presentation. So everybody now in the room knows this and that is what they're going to be thinking about as you present all of the data to them.
Next section on our table of contents, present the data. It is important to mention these will probably have different titles as you build them out, but this is the topic that we're moving into. So you'll recognize this visual from the messy slide, but it has a different color context. That's not as important but what is important is when you get here, you have a title on the slide, you have a visual, but there is no text. And this is an important aspect to it is what we're trying to do is walk and introduce the audience to the overall data that you're going to be using. Now, this is the first slide that has any form of content on it, so it's important that you introduce them to the underlying data. And seeing as the data is all about geographic, demographic, and economic data points per each country, it's important that the visual represents that. If I were to be the presenter on a slide like this, I would start by getting to this slide and explaining the process and data that we're looking at. So, we analyzed the data set, consisting of data collected from residents of European countries between 2015 and 2017. The data contained demographic and economic data for individuals within each country, including population, GDP, or Gross Domestic Product, and the happiness score per person. So I've now introduced them to the data set. There's still no text, so they know that they should be looking at the visual and listening to me.
Now the next aspect which can be over utilized but I've also seen underutilized is using animations in your presentation. Animations can be used as a way to direct your audience's attention as you speak. A way to say, look over here at this area of the slide as I'm talking. It also allows them not to get too bogged down or distracted as you're introducing new concepts to them. Because remember, as you're introducing data the technical components may be new to a lot of people. And finally, another way to do this is through annotations on top of visuals that can be used as another form of directing their gaze and their overall attention. So putting these together, we can have something like an annotation appear as you're discussing it. So, if we were trying to explain what the visual is showing, we have an annotation that pops up that says "happiness score" and points to the score within the specific country, and we can explain exactly what the visual is showing. So in this way, we could say something like, we began by creating a heat map of the happiness score for each country. Where the number within each country represents the overall score and the colors represent how high or how low the score is on a scale. So, the darker blue the country is, the higher the numeric happiness score for that country. The deeper red that the country is, the lower the happiness score and overall numeric value.
So what we've done before any text has appeared on the screen is explain the visual, explain the overall data that they're going to be looking at throughout the presentation, so that they now can understand when we dive into this specific analysis. So it's important that you only use text on the screen in a short and concise manner to highlight the main points that you're discussing. So after I introduce the visual, I can now dive into the analysis. So, we have our first bullet point: Happiness levels vary widely by country. So with this as it appears, my speaker notes can be something along the lines of: "However, as high and low scores are spread sporadically throughout the map, there is little correlation that we find between geographical location and happiness. Finally, we concluded that the geographical location alone was not a strong indicator of happiness." So as you can see as I'm discussing and as I'm explaining what we are looking at within the data, the overall text on the screen only populated as I began to discuss it, so the audience knew exactly where to look and exactly what to be listening to when I'm talking. A very important aspect of the flow of the overall presentation is the transition from one slide to the next. So as I'm discussing this, you can use a bullet point, you can use your speaker notes. Either way, there should be some transition from one slide to the next so that the audience knows that this part is over and they know what's coming next. So, for this slide, I used my speaker notes. So I'm going to explain the transition, something like: "Our next step was to identify the demographic and economic differences between the higher and lower countries to isolate the correlated features between them."
So, we get to the next slide. Very common theme, it may be a different visual, but the overall title and where the text is going to show up is going to be in the same place. So we familiarize them with the overall theme of the presentation within three slides. Now, the title immediately tells what we're going to be discussing. The previous one was geographic; this one is all based on population. As we move through this slide and as you saw in the messy example, we use a lot of scatterplots, and scatterplots may not always be the best option because they are rather difficult for people to follow within presentations. But if you explain it to them once so that they understand, you can use them throughout the presentation because you familiarize them. So, because it's the first time it popped up, it's important that you explain the visual in-depth and all the features of it that you will be talking about later throughout the presentation. We use animations again. We talk about what are the axes on the scatterplot. We created a scatterplot in which we plotted countries based on their happiness score and the population to see if there was a correlation between the two. The higher up something is on the scatterplot, the happier the country is. The further to the right that the country is plotted, the larger the population. And the line that goes between the two is testing for correlation or if these two different points are related to one another. So these annotations and these animations are there to clarify what the chart is plotting. Now, the overall purpose is that we are attempting to identify if there is a relationship between the population size of the country and the overall happiness score. So, now that you have explained what this visual is, you can now dive into the results of it.
Now this slide itself has one bullet point. It is the results of the overall analysis that you can find just based on the data visual. We found that there was little to no correlation between happiness and population based on the analysis that we ran. So all discussion and in-depth explanation of the visual is kept in the speaking notes besides the overall annotations.
And again, the transition is very important to the next slide. So, you can say something like: "So next, we dove into the specific demographics of each country to see if we can identify the features that separate or correlate with the overall happiness of the country." Again, same thing. We have the title, we know what we're going to be talking about now. This is how the health of each country and how it correlates with happiness. We have a scatterplot again, except the good news is, you've already introduced what the scatterplot is and what you are comparing on there. So now the audience has been familiarized with the data set. You don't have to go through and explain exactly what the visual is representing.
You can dive into the overall differences or analysis that you're going to be presenting on this slide. You can have something explaining that we found a positive correlation between happiness and health, or overall life expectancy of the country. Now we found this because the correlation coefficient between the two different factors being happiness and health was 0.50. Now, you just introduced a new concept. This is where you have to now explain the new concept because otherwise you may lose people in the room. This is a technical component to your overall analysis and it is an important component so it is critical that you do explain what it is, but in a simplified way so that everybody understands. So, you can say something along the lines of: "A correlation coefficient is a measure of strength and direction of the linear relationship between two variables. So in other words, the closer to one that the number is, the more positively correlated they are. Meaning, when one of the variables goes up, so does the other one. The closer to negative one that the number is, the more negatively correlated they are. Meaning, as one of the variables, such as happiness, goes up that the other variable like health would go down. And the closer to zero it is, it means they are not correlated at all, which is what we saw between population and happiness, and means that they have no relationship together." So we've now explained exactly what it is that we use as an analysis on this specific slide. And it's important again that we discuss the transition to the next. So: "We did find that there is a positive correlation between happiness and health, but the question remains, are happy people healthy or are healthy people happy? We know that they are related but we don't know what causes the other. And finally, what contributes to a longer life expectancy? If we know that longer life expectancy is related to happiness, what is it that helps create longer life expectancy within a country?" Now these are the two questions that we need to answer before the end of the presentation moving on from here. So again, we are creating a logical flow as we move through this presentation.
Now, we are looking at a new concept: wealth within each country.
Now that you are using such as the scatterplot are familiar with the audience, it's okay now that you add in additional ones. So, you can say something along the lines of: "We then analyzed how GDP or the overall economic status of the country relates to the overall health of the country. Because if we know that GDP is related to health and we know that health is related to happiness, then we can infer additional information through that. So, we found that there is a strong correlation between gross domestic product and the overall health of a specific country with a 0.7 correlation coefficient, so higher than the overall correlation coefficient for health and happiness. Next we found an even stronger correlation between GDP and happiness. So, whereas we first looked at health and happiness and then GDP and health, we're now looking at GDP and happiness and found that it has the highest correlation coefficient between all three of those comparisons. So we have a conclusion within just this slide, which is, we found that richer countries have a higher average happiness level."
This is a good transition to the overall conclusion of now your entire presentation.
So again, you're directing your audience through just presenting the text that you want them to look at. Your first conclusion from your overall presentation. Wealthier countries and ones that have sustained economic growth tend to have a higher average happiness level. Your second conclusion: healthier countries also tend to have a happier population. However, healthier countries also tend to be wealthy. And finally, this is where you take it home.
So our evidence suggests that wealth, health, and happiness all go together. It's important to also discuss any caveats or future analysis that needs to be ran to answer the questions that may come up based on this analysis. "So we have said that the evidence suggests that wealth, health, and happiness all go together, but that does not mean that one causes the other, So there needs to be future analysis to understand any causal effects between them."
And then you have your final slide, and this is where questions would come in. So it's important to remember that data storytelling is an art. What we've given you is some high-level overview and examples of what not to do and an improved version, but don't be afraid to put yourself in there.
The overall presentation style is going to come from your personality and skill set within data analytics. You can use the tools that we use to help you build the layout of your presentation, but it's up to you to really put a lot of yourself into it, and a lot of your own skills to help people understand the overall analytics that you've run.
Putting evaluation of presentations into practice (Discussion Prompt)
Test your knowledge on effective presentations (Practice Quiz)
Identify presentation skills and practices
Proven presentation tips
Hey there. So far we've learned about using a framework to guide your audience through your presentation and how to weave data in.
Now I want to talk about why these presentation skills are so important and give you some simple tips you can use during your own presentations.
As a data analyst, you have two key responsibilities: analyze data and present your findings effectively.
Analyzing data seems pretty obvious. It's in the title "data analyst," after all. But data analysis is all about turning raw information into knowledge. If you can't actually communicate what you've learned during your analysis, then that knowledge can't help anyone. There's plenty of ways data analysts communicate: emails, memos, dashboards, and of course, presentations. Effective presentations start with the things we've already talked about, like creating effective visualizations and organizing your slides, but how you deliver those things can make a big difference in how well your audience understands them. You want to make sure they leave your presentation empowered by the knowledge and ready to make decisions based on your analysis. That's why strong presentation skills are so important as a data analyst. If the idea of giving a presentation makes you nervous, don't worry—a lot of people feel that way. Here's a secret: it gets easier the more you practice.
Now let's look at some tips and tricks you can use when giving your presentations. We'll go over some more advanced ones later, but let's start with the basics for now. It's natural to feel your adrenaline levels rise before giving a presentation. That's just because you're excited to be there. To help keep that excitement in check, try taking deep, controlled breaths to calm your body down. As a bonus, this will also help you channel all that excitement into a presentation style that shows your passion for the work you've done. You might remember we talked earlier about using the McCandless Method to present data visualizations. Well, it's also a good rule of thumb for presentations in general. Start with the broader ideas, the obvious questions your audience might have, and what they need to understand to put your findings in context. Then you can get more specific about your analysis and the insights you've uncovered.
Let's go back to our avocado example and imagine how we'd start that presentation. After we introduce ourselves and the title of our presentation, we have a slide with our goals for the discussion. We start with the most general goals and then get more specific. We might say our goal for today is to first provide you all with the state of the world on online avocado searches. Then we'll examine the opportunities and risks of seasonal trends in online avocado searches. We'll move into actionable next steps that can help you start taking advantage of these opportunities, as well as help to mitigate the risks. Finally, we'd love to make the third part a discussion with you about what you think of these next steps. What you'll want to notice here is how our presentation focuses on the general interest in avocados online before getting into specifics about what that means for our stakeholders.
We also learned about the five-second rule. As a quick refresher, whenever you introduce a data visualization, you should use the five-second rule and ask two questions. First, wait five seconds after showing a data visualization to let your audience process it, then ask if they understand it. If not, take time to explain it, then give your audience another five seconds to let that sink in before telling them the conclusion you want them to understand. Try not to rush through data visualizations. This will be the first time some of the people in your audience are encountering your data, and it's worth making time in your presentations for them.
Here's our first data viz in the avocado presentation. When we get to this slide, we want to introduce our yearly avocado search trends graph and explain the basic background we've included here. After we wait five seconds, we can ask, "Are there any questions about this graph?" Let's say one of our stakeholders asks, "Could you explain Google search trends?" Great. After explaining that, we wait another five seconds, then we can tell them our conclusion: Searches for avocados have been increasing every year. You'll learn more about these concepts later on, but these are some great tips for starting out.
Finally, when it comes to presenting data, preparation is key. For some people, that means doing dress rehearsals. For others, it means writing out a script and repeating it in their head. Others find visualizing themselves giving the presentation helps. Try to find a method that works for you. The most important thing to remember is that the more prepared you are, the better you'll perform when the lights are on and it's your turn to present.
Coming up, we'll cover more best practices for presentations and also look at some examples. Looking forward to it.
Self-Reflection: Examples of great presentations (Practice Quiz)
Guide: Sharing data findings in presentations (Reading)
Learning Log: Evaluate your presentation (Reading)
Present like a pro
Hey, good to see you again. By now you've learned some ways to organize and incorporate data into your presentations. You've also covered why effective presentation skills are so important as a data analyst. Now you're ready to start presenting like a pro.
Coming up, I'll share some pro tips and best practices with you.
Let's get started. We've talked about how important your audience is throughout this program, and it's especially important for presentations. It's also important to remember that not everyone can experience your presentations the same way. Sharing your presentation via email and putting some forethought into how accessible your data viz is before your presentation can help ensure your work is accessible and understandable. But during the actual presentation, it can be tempting to focus on what's most interesting and exciting to us and not on what the audience actually needs to hear. Sometimes, even the best audiences can lose focus and get distracted, but here's a few things you can do during your final presentation to help you stay focused on your audience and keep them engaged.
First, try to keep in mind that your audience won't always get the steps you took to reach a conclusion. Your work makes sense to you because you did it—this is called the curse of knowledge. Basically, it means that because you know something, it can be hard to imagine your audience not knowing it. It's important to remember that your audience doesn't have the same context you do, so focus on what information they need to reach the same conclusion you did. Earlier, we covered some useful things you can add to your presentations to help with this. First, answer basic questions about where the data came from and what it covers: How is it collected? Does it focus on a specific time or place? You can also include your guiding hypothesis and the goals that drove your analysis. Adding any assumptions or methods you used to reach your conclusions can also be useful. For example, in our avocado presentation, we grouped months by season and looked at overall trends. And finally, explain your conclusion and how you reached it.
Your audience also has a lot on their mind already. They might be thinking about their own work projects or what they want to have for lunch. They aren't trying to be rude, and it doesn't mean they aren't interested; they're just busy people with a lot going on. Try to keep your presentation focused and to the point to keep their minds from wandering. Try not to tell stories that take your audience down into unrelated line of thinking, and try not to go into too much detail about things that don't concern your audience. You might have found a really exciting new SQL database, but unless your presentation is about databases, you can probably leave that out.
Your audience can also be easily distracted by information in your presentation. For example, the more you include in a chart, the more your audience will need to explore it. Try to avoid including information in your presentations that you don't think will be productive to discussions with your audience, sharing the right amount of content to keep your audience focused and ready to take action.
It's also good to note that how you present information is just as important as what you present, and I have some best practices for delivering presentations. First, pay attention to how you speak. Keep your sentences short. Don't use long words where short words will work. Build in intentional pauses to give your audience time to think about what you've just said. Try to keep the pitch of your sentences level so that your statements aren't confused for questions.
Also, try to be mindful of any nervous habits you have. Maybe you talk faster, tap your toes, or touch your hair when you're nervous. That's totally normal—everyone does—but these habits can be distracting for your audience. When you're presenting, try to stay still and move with purpose. Practice good posture and make positive eye contact with the people in your audience. Finally, remember that you can practice and improve these skills with every presentation.
Accept and seek out feedback from people you trust. Feedback is a gift and an opportunity to grow. With that, you've completed another module. The presentation skills you've learned here, like using frameworks, weaving data into your presentation, and best practices you can apply during your actual presentations, are going to help you communicate your findings with audiences effectively.
Presentation debrief (Discussion Prompt)
Test your knowledge on presentation skills and practices (Practice Quiz)
Caveats and limitations to data
Anticipate the question
Hello. So let's talk about how you can be sure you're prepared for a Q&A.
For starters, knowing the questions ahead of time can make a big difference. You don't have to be a mind reader, but there's a few things you can do to prepare that'll help.
For this example, we'll go back to the presentation we created about health and happiness around the world. We put together these slides, clean them up a bit, and now we're getting ready for the actual presentation. Let's go over some ways we can anticipate possible questions before our Q&A to give us more time to think about the answers.
Understanding your stakeholder's expectations will help you predict the questions they might ask. As we previously discussed, it's important to set stakeholder expectations early in the project. Keep their expectations in mind while you're planning presentations and Q&A sessions.
Make sure you have a clear understanding of the objective and what the stakeholders wanted when they asked you to take on this project.
For this project, our stakeholders were interested in what factors contributed to a happier life around the world. Our objective was to identify if there were geographic, demographic, and/or economic factors that contributed to a happier life. Knowing that, we can start thinking about the potential questions about that objective they might have. At the end of the day, if you misunderstood your stakeholders' expectations or the project objectives, you won't be able to correctly anticipate or answer their questions. Think about these things early and often when planning for a Q&A. Once you feel confident that you fully understand your stakeholders' expectations and the project goals, you can start identifying possible questions.
A great way to identify audience questions is to do a test run of your presentation. I like to call this the "colleague test." Show your presentation or your data viz to a colleague who has no previous knowledge of your work, and see what questions they ask you. They might have the same questions your real audience does. We talked about feedback as a gift, so don't be afraid to seek it out and ask colleagues for their opinions.
Let's say we ran through our presentation with a colleague, we showed them our data visualizations, then asked them what questions they had. They tell us they weren't sure how we were measuring health and happiness with our data in this slide. That's a great question. We can absolutely work that information into our presentation. Sometimes the questions asked during our colleague tests help us revise our presentation. Other times, they help us anticipate questions that might come up during the presentation, even if we didn't originally want to build that information into the presentation itself. It helps to be prepared to go into detail about your process, but only if someone asks. Either way, their feedback can help take your presentation to the next level.
Next, it's helpful to start with zero assumptions. Don't assume that your audience is already familiar with jargon, acronyms, past events, or other necessary background information. Try to explain these things in the presentation, and be ready to explain them further if asked. When we showed our presentation to our colleague, we accidentally assumed that they already knew how health and happiness were measured and left that out of our original presentation.
Now, let's look at our second data viz. This graph is showing the relationship between health, wealth, and happiness, but includes GDP to measure the economy. We don't want to assume that our audience knows what that means, so during the presentation, we'll want to include a definition of GDP. In our speaker notes, we've added gross domestic product: total monetary or market value of all the finished goods and services produced within a country's borders in a specific period of time. We'll fully explain what GDP means as soon as this graphic comes up; that way, no one in our audience is confused by that acronym.
It helps to work with your team to anticipate questions and draft responses. Together, you'll be able to include their perspectives and coordinate answers so that everyone on your team is prepared and ready to share their unique insights with stakeholders. The team working on the World Happiness project with you probably has a lot of great insights about the data, like how it was gathered or what it might be missing. Touch base with them so you don't miss out on their perspective.
Finally, be prepared to consider and describe to your stakeholders any limitations in your data. You can do this by critically analyzing the patterns you've discovered in your data for integrity. For example, could the correlations found be explained as coincidence? On top of that, use your understanding of the strengths and weaknesses of the tools you use in your analysis to pinpoint any limitations they may have introduced. While you probably don't have the power to predict the future, you can come pretty close to predicting stakeholder and audience questions by doing a few key things. Remember to focus on stakeholder expectations and project goals, identify possible questions with your team, review your presentation with zero assumptions, and consider the limitations of your data. Sometimes, though, your audience might raise objections to the data before and after your presentation.
Coming up, we'll talk about the kind of objections they might have and how you can respond. See you next time.
Question:
A colleague test helps you assess what questions your stakeholders might have, what assumptions they might make, and what areas of your presentation might be unclear.
Preparing for the Q&A
Handling objections
Welcome back. In this video, we'll talk about how you can handle objections about the data you're presenting. Stakeholders might raise objections during or after your presentation.
Usually, these objections are about the data, your analysis, or your findings. We'll start by discussing what questions these objections are asking and then talk about how to respond.
Objections about the data could mean a few different things. Sometimes, stakeholders might be asking where you got the data and what systems that came from, or they might want to know what transformations happened to it before you worked with it, or how fresh and accurate your data is. You can include all this information in the beginning of your presentation to set up the data context. You can add a more detailed breakdown in your appendix in case there are more questions. When we're talking about cleaning data, you learned keeping a detailed log of data transformations is useful. That log can help you answer the questions we're talking about here, and if you keep it in your presentation's appendix, it'll be easy to reference if any of your stakeholders want more detail during a Q&A.
Now, your audience might also have questions or objections about your analysis. They might want to know if your analysis is reproducible, so it helps to keep a change log documenting the steps you took. This way, someone else could follow along and reproduce your process. You can even create a slide in the appendix section of your presentation explaining these steps, if you think it will be necessary. And it can be useful to keep a clean version of your script if you're working with a programming language like SQL or R, which we'll learn all about later. Also, be prepared to answer questions like, "Who did you get feedback from during this process?" This is especially important when your analysis reveals insights that are the opposite of your audience's gut feelings about the data. Making sure to include lots of perspectives throughout your analysis process will help you back up your findings during your presentation.
Finally, you might be faced with objections to the findings themselves. A lot of the time these will be questions like, "Do these findings exist in previous time periods, or did you control for the differences in your data?" Your audience wants to be sure that your final results accounted for any possible inconsistencies and that they're accurate and useful.
Now that you know some of the possible kinds of objections your audience might raise, let's talk about how you can think about responding.
First, it can be useful to communicate any assumptions about the data, your analysis, or your findings that might help answer their questions. For example, did your team clean and format your data before analysis? Telling your audience that can clear up any doubts they might have.
Second, explain why your analysis might be different than expected. Walk your audience through the variables that change the outcomes to help them understand how you got there.
And third, some objections have merit, especially if they bring up something you hadn't thought of before. If that's true, you can acknowledge that those objections are valid and take steps to investigate further. Following up with more details afterwards is great, too.
And now you know some of the basic objections you might run into. Understanding that your audience might have questions about your data, your analysis, or your findings can help you prepare responses ahead of time, and walking your audience through any assumptions about the data or unexpected results are great approaches to responding.
Coming up, we'll go over even more best practices for responding to questions during a Q & A. Bye for now.
Question:
Question2:
If your stakeholder has a concern about a problem you didn’t realize, you can acknowledge the objection and promise to take steps to investigate further.
Self-Reflection: Real-world objections (Practice Quiz)
Test your knowledge on caveats and limitations to data (Practice Quiz)
Listen, respond, and include
Q&A best practices
Hello again. Earlier we talked about some ways that you can respond to objections during or after your presentations. In this video, I want to share some more Q&A best practices. Let's go back to our world happiness presentation example.
Imagine we finished preparing for a Q&A, and it's time to actually answer some of our audience's questions. Let's go over some ways that we can be sure that we're answering questions effectively. We'll start with a really simple one: listen to the whole question. I know this sounds like a given, but it can be really tempting to start thinking about your answer before the person you're talking to has even finished asking their question.
On slide 11 of our presentation, we outline our conclusions. After explaining these conclusions, one of our stakeholders asks, "How was happiness measured for this project?"
It's important to listen to the whole question and wait to respond until they're done talking. Take a moment to repeat the question. Repeating the question is helpful for a few different reasons. For one, it helps you make sure that you're understanding the question. Second, it gives the person asking it a chance to correct you if you're not. Anyone who couldn't hear the question will still know what's being asked. Plus, it gives you a moment to get your thoughts together. After listening to the question and repeating it to make sure you understand, you can explain that participants in different countries were given a survey that asked them to rate their happiness, and just like that, your audience has a better understanding of the project because you took the time to listen carefully.
Now that they know about the survey, they're interested in knowing more. At this point, we can go into more detail about that data. We have a slide built in here called the appendix. This is a great place to keep extra information that might not be necessary for our presentation but could be useful for answering questions afterwards. This is also a great place for us to have more detailed information about the survey data so we can reference it more easily.
As always, make sure you understand the context questions are being asked in. Think about who is your audience and what kinds of concerns or backgrounds they might have. Remember the project goals and your stakeholders' interests in them, and try to keep your answers relevant to that specific context, just like you made sure your presentation itself was relevant to your stakeholders.
We have this slide with data about life expectancy as a metric for health. If you're presenting to a group of stakeholders who are in the healthcare industry, they're probably going to be more interested in the medical data and the relationship between overall health and happiness. Knowing this, you can tailor your answers to focus on their interests so that the presentation is relevant and useful to them.
When answering, try to involve the whole audience. You aren't just having a one-on-one conversation with the person that's asked the question; you're presenting to a group of people who might also have the same question or need to know what that answer is. It's important to not accidentally exclude other audience members. You can also include other voices. If there's someone in your audience or team that might have insight, ask them for their thoughts.
Keep your responses short and to the point. Start with a headline response that gives your stakeholders the basic answer. Then if they have more questions, you can go into more detail. This can be difficult as a data analyst. You have all the background information and want to share your hard work, but you don't want to lose your audience with a long and potentially confusing answer. Stay focused on the question itself.
This is why listening to the whole question is so important. It keeps the focus on that specific question. Answer the question as directly as possible using the fewest words you can. From there, you can expand on your answer or add color, contexts, and detail as needed. Like when one of our stakeholders asked how the data measuring happiness was gathered. We started by telling them that a survey was used to measure an individual's happiness, and only when they are interested in hearing more about the survey did we go into more detail.
To recap, when you're answering questions during a presentation Q&A, remember to listen to the whole question, repeat the question if necessary, understand the context, involve your whole audience, and keep your responses short. Remember, you don't have to answer every question on the spot. If it is a tough question that will require additional analysis or research, it's fine to let your audience know that you'll get back to them; just remember to follow up in a timely manner. These tips will make it easier to answer questions and make you seem prepared and professional.
Now that your presentation-ready, it's time to wrap up. We covered a lot about how to consider questions before a Q&A, how to handle different kinds of objections, and some best practices you can use in your next presentation. That's it for now. See you in the next video.
Question:
Ensure you answer an audience member’s question appropriately by listening to the question, repeating it, and understanding its context.
Question2:
It is best to involve the entire audience while answering a question. This keeps everyone involved in the discussion.
Asking for feedback (Discussion Prompt)
Connor: Becoming an expert data translator
Throughout my journey, and something that you'll probably move through as you become further and further along in your career in data analytics, is it's not the data itself that is really important to the company — it's the understanding of the data and the impact that it can take. A lot of business leaders don't have the technical expertise to understand the data in its raw form, so you as the analyst are the translator. So the most important part of the data is you, as the translator of the data, for the business leaders.
And I've found throughout my career that presenting data is probably the most important aspect of being a data analyst because it doesn't matter how compelling the analysis is or how accurate it is; if people don't understand it, then it doesn't offer value to the business and it doesn't offer value to anyone. I've devoted a lot of my time and a big part of my current job is really to help business leaders understand their data and taking something that's very technical and simplifying it so that people understand what it means and how they can use it to impact the business. It's an art form — it's a skill to really be able to take something that's highly technical and simplify it down so that everybody in the room understands what it's saying and how to use it.
The next aspect to a really good presentation is to keep it concise. You don't want things to be too wordy. You don't want a lot of text on the screen, and you don't want it to be too long. It's not that everybody is too busy to be in the room, but you really want to make sure that you're making your point without losing everybody's focus.
The next one is, have some logical flow to your presentation. You don't want everybody's focus bouncing around from different ideas as you're trying to make your point. You want a very concise and logical flow so that they know what's coming next and what you're talking about currently.
The next aspect to think about is making the presentation visually compelling. You want somebody to know what they're looking at and understand it. So the visuals that you choose, the theme that you have for the overall presentation should be appealing, and it should draw people in so that they know exactly what they're getting out of the presentation.
All of these things roll up to the one main concept that you should be thinking about when building a presentation, which is, how easy is it to understand. You are trying to make something that is very technical data analysis and simplify it down so that it doesn't matter what the technical background or even business background of the person is, but they understand what it is that you are trying to explain to them.