[MUSIC] Welcome back. Before we dive into screen cast demonstrations of telling the story of your data using Tableau, I'd like to set the stage by reminding you of five important story considerations. These will apply to all your data stories. There may be more, but these are the five that I think are essential. The first is trends, the second is rank ordering, the third is is comparisons, the fourth is counter-intuition, and finally, the fifth is relationships. After this lesson, you will be able to recall these five essential story considerations, and use them to guide you whenever you have a data story to tell. So let's get started. First, trends are almost always time-based. So one of the fields or variables should be also time-based. Since it is time-based, it is often depicted in a line or a bar graph, depending on the data. If it's non-continuous data, and you're doing counts, sometimes you may want to do a bar graph. But sometimes you may want to do a line graph. Here is an example. This is from the sale Superstore data set. And it's an example of a trend in a story. The second way to tell stories with data is rank ordering. As you see in this bar graph. You've done something very similar to this. This tells you who had the most sales. Here I hovered over this to give you an example. We can see that Ken Lonsdale is at a sales rank of six. So he is ranked sixth from the top. If you count down you see one, two, three, four, five and the bar with the outline representing Ken Lonsdale's sales is indeed the sixth bar down in this bar graph. That's an example of rank ordering. The third way to tell stories with data is comparisons. This is an example from earlier in 2016 showing the comparison between the two major presidential candidates in the United States in 2016. This example should be somewhat familiar to most people, even outside of the United States. The example shows both a trend and a comparison to see how the trend of each candidate across time is. And also comparing them against each other. The fourth way to tell stories with data is the counter-intuitive visualisation. Here is an example. In that category, you also have the unusual outlier. This example is a classic one, so you may have seen this around. It's the incarceration rates in the OECD, or the Organization for Economic Cooperation and Development. Looking at these incarceration rates you'll notice that the Unites States has, by far and away, the highest incarceration rate. It's so high that, in fact, if you took that out it is actually higher than many of the other countries' incarceration rates combined. It's 266 for Chile, the next biggest, and 710 for the United States. That's a very good example of an unusual or an outlier. So we can go and see why that might be. Why is it that the United States has a higher incarceration rate? It may be difficult to answer but at least you have that baseline to work with. Finally, the last and fifth way to tell stories is through relationships. This is a classic scatter plot which you actually did in the course earlier. Here, the scatter plot reveals that there is a relationship between sales and profit. And that's going to be our story. Why is that relationship not as firm as it could be? Why are some people who are doing high sales not earning profit? We're going to go back and look at these outliers and see why these exist. Finally, as we're wrapping up the course and as you get into the real world, let me share some data driven storytelling tips. Let's start with the first. Check your facts. This is the biggest one of all because you're going to spend so much time actually doing a lot of the data analysis ahead of time, that the story itself turns out to be very straightforward in the end. It's the preparation beforehand that often takes a lot of time. Before you start crafting the story design, check to make sure the facts of the story are based on reality, and that they are right. The second tip I offer is to focus on one key statistic, and that's what we're going to do. How do we drill down to find that one piece of information that might be very interesting or intriguing for people to figure out within the bounds of what we've been asked to do? While doing analysis be on the look out for that one key statistic that can become a focal point. The third tip is to use visuals. I mean, this is, of course, a data visualisation course so, of course, the visuals are very important. To tell the story well, it often makes it a lot easier to be able to tell that story through a visualisation. The fourth storytelling tip is to make the presentation of your data insightful. If it brings insight to your audience then that's interesting. You should give the information in a way that makes your insight obvious to the viewer. Don't leave them guessing, just tell them the information and tell it in an insightful way. Finally, make it relatable. Oftentimes you will use a representative person, a representative piece of data or a representative category to tell a story of the data. Facts and figures have more impact when they are presented as a person or situation in a story that has relevance for your audience. So those five data driven storytelling tips are the types of things you should should think about as you move through your process to tell your data story. And that concludes this lesson. In our next lesson we will dive into the story itself. Thanks.
