Hi, my name is. I am a last year student at Sydney Business School and I'll be your tutor for this recital. In this recital, we are going to talk about how to present your findings and how to make your point to an audience. First, I give you some dos and don'ts, some basic rules you might want to follow when you present. Then we'll see some examples both of slides and visualization. I hope you enjoy this. Have a nice MOOC. So this recital aims to give you a couple of tips to get your point across during the presentation. The first thing you need to know is that presenting is all about telling your audience a story it can hear, remember, and reproduce. You have to focus on being clear, and making sure they can follow every step of your performance. To do so, there are four main things you can do. The first one, is get your audience's attention by showing them how you're relevant to their problem, that you've perfectly understood why they need you. The best way to do so is to use a pain point. Pain point is the first thing you're going to say in a presentation. Can be a thought, a number and it must introduce tension into your demonstration by defining by the stake. Once you've caught your audience's attention, it's all about keeping it. So the second you must do is connect your ideas throughout the presentation so that you don't lose anyone in the process. The best way to do that is to structure representation as a story, so that each transition makes sense. In case you do lose someone, the best way to get them back is to use action titles. An action title gives your reader a summary of the slide in one short sentence, and serves two purposes. First one, is to let someone you've lost catch [INAUDIBLE] and the second one is to help someone quick create for your slides, which is very important especially in case it's due for some management. Eventually, you have to give actionable recommendations so that your work matters and has an impact. In order to present effectively, there are some rules you might want to stand by as well. Regarding your presentation, you have to make it clear what the point of each slide is and make it easy for the audience to get the most important points. Using the action title we've just talked about coupled with a take away box, is a good way of doing so. The take away box is basically a box where you highlight for the reader, what they should keep in mind after the slide, to understand how you come to the next one. Regarding the contents, bear in mind that everything on the slide must be straight to the point, self containing, and relevant. Don't write everything, this is not the Word document but be clear, nonetheless. In order to structure your thoughts, you may use bullet points, but remember, that if everything is a bullet point, nothing really is. For instance, it is usually a good idea to avoid adding bullet points within bullet points, within bullet points. Last but not least, keep your technique in check. Remember who you are talking to and wonder whether they can understand this difficult and precise fancy technique that you've just presented. And whether they want to. When you do want to explain something a bit technical or use a method, make sure you make it understandable. Briefly explain the general method. One sentence at most, main steps, without going into details. If you use a visualization, help people read it and draw relevant conclusions for them. Remember, you don't present for yourself, but for a person listening to you or reading your slides. In data visualization, you have to remember that your data is only as good as your ability to understand and communicate it. So make sure you choose the right visualization to have an impact. The first thing you have to do, like in the presentation, is to identify the story that you find your data so that you can pick the right visualization for it. It is important to identify and understand the story you are trying to tell and the relationship you're looking to show. Knowing this information will help you select the proper visualization best to deliver your message. Data visualization also has a couple of do's and don'ts it's best to follow. For instance, use only one color to represent each category or else it gets messy and difficult to understand. Order datasets using logical hierarchy, maybe biggest to smallest. Or we've rewound or in terms of cutting these. But you have to have a hierarchy, you can use call outs to highlight important or interesting information. Visualize data in a way that is easy for the reader to compare values. And that comes with [INAUDIBLE], use icons to enhance comprehension and reduce [INAUDIBLE]. Can use high contrast color combinations, do not use on the other side. High contrast color combinations, such as read red or green, or blue or yellow. These hurt the eye, don't use 3D charts they screw up the reception of the visualization. Basically, don't get to see that data as well and it's distracting for the reader. Don't add chart junk either. Unnecessary illustration, drop shadows, or ornamentation distracts from the data as well. Don't show more than six columns singled by relevance. Once again the same point, if it's too messy or too differentiated, afterwards, it gets confusing for the reader. Don't use distracting fonts or elements. The same points still, don't use bold, italic, or underline text. So for starters, if you look at the slide, you see that we have a pain point as the very first thing we tell the audience. Presently we define the state, the inefficiency of the media strategy. And this is why we're here, we want to fix this. On this next slide, you can see that we start with an action title. That defines what we are going to talk about. AVI Scores. We also defined the method for AVI scores, and how we computed them. You can see here that we keep it very to a top and simple. Only one line, and one line also on hypothesis. Here we also have a takeaway box. Finding what we've learned at the end of the slide. This slide is also the same structure. First an action title, then the method, how did we get there, how did you do, what does this graph mean. Then as we have a graph, we'll do the title, what is it, what is the timeframe, a legend which has to be constant. So for instance, if you have two things that are equivalent but slightly different, use the same colors and use the same codes, then you try to reduce them in the different colors you use. You give the main point if, for instance, it's 100 AVI, because if it's under 100 it means it's not efficient, if it is over it, it means it is efficient. You give findings for visualization, you gave and a conclusion to a slide overall. You can also find great examples of data visualizations in the press or in other places. Viacom is, for instance, is a great source. You can see here that we have a single title, simple legend, few colors, but the axes are defined. And that we have a source, we are also given the R2 and the countries are ordered. The continents are ordered in alphabetical order. This graph is also really good because it's very to the point. You can see the home index per state from 1971 to 2013 and have evolution from that. This is about the same thing. We have a title, we have the time frame, we have a scale, ticks indicate the volume of migrations in millions. And we have the graph itself that replicates all that and that's ordered as well. This graph is interesting as well, it is very to a point. We see the airline routes across the world, basically. It is interesting to note that the airlines have been ordered in a way that's not immediately understandable. So we've actually explained why it's ordered that way. We have different colors, not too many of them, so that we can still read the graph, basically. And this one is basically one of the best one you can get. You have a title and then the axis is as defined, it shows noted cities. And the average course of buying a car and owning a family car in each city. Here are the cities, here are the costs. They split the cost in two colors, followed purchase price and running cost. They give you a scale that start at zero, which is really important for you to actually get the big picture and the evolution, so that you know the long dynamic frame. And we have a source here as well so that you can actually place some outside data source. So basically that was all. I hope it was clear, and that you understood most of what I said, and that you will be able to reuse it some time. Good luck.
