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? 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.