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Professional Motorsport Data Analysis: Data Visualisation

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Data Visualisation


00:00 - Before we get into the course material itself, we need to put in a little ground work.
00:05 Throughout the course, we'll be making use of some underlying concepts and terms that you'll need to have a good understanding of to get the most out of the material.
00:14 Although you might already have a good grasp on some or all of these topics, especially if you've just completed our data fundamentals course, there's not harm in spending the time to go through them again for clarity.
00:27 Unlike the fundamentals course, this will be more of an overview of these concepts as opposed to a how to on applying them at a basic level.
00:35 Lastly there's some math underlying these concepts, there's nothing to be afraid of here though and we'll be keeping things all very practical.
00:43 Regardless of the data analysis package you're working with, the concepts are generally the same, as is the structure that's used to help organise your analysis.
00:53 The software's user interface is generally built on a hierarchy of sorts, often starting with the project or the workspace.
01:01 Within that you might find workbooks and within workbooks you'll find the work sheets and then lastly the displays.
01:08 Think of it as though each level is the child of the one above it.
01:12 Each of these is a type of container which holds multiple of each of the children within it.
01:19 Let's start at the very top with the project or the workspace.
01:23 This can be thought of as a container that stores all the pieces of our analysis.
01:27 It's where all the settings and displays we configure will generally be stored.
01:32 Moving onto the next layer, we have the workbooks.
01:35 These are used to group together related types of analysis.
01:38 For example we might have a chassis workbook where we'd store the relevant worksheets which is something we'll be discussuing next.
01:47 These worksheets within the chassis workbook could include for example, ride height, roll, balance and grip.
01:55 Organising our projects like this makes it quicker and simpler to find exactly what we're looking for which is really important because as our experience increases, we're going to build up many different worksheets and displays and keeping them well organised means that you can find them quickly when you need them without having to make them each time.
02:14 As promised, let's now take a look at the worksheets themselves, that sit inside these workbooks.
02:20 This is where we arrange the displays that are used to visualise the data and it's normal to collect multiple complimentary displays together on a single worksheet.
02:29 This is so we can quickly cross reference them against each other without having to click to another part of the project.
02:36 We only even have so much screen real estate though and it's always a battle between displaying all the information we need to help us understand the data and each display being big enough to actually see the detail required.
02:50 In the real world, there's rarely enough room on your screen to collect all the relevant information so it'll often be necessary to switch between different workbooks on the fly.
03:01 Constantly clicking through workbooks is cumbersome and inefficient.
03:05 So we should always try to collect together the ones that make the most sense for that we're trying to achieve at any one time and don't worry, we're going to be covering some examples in detail later in the course.
03:17 The final innermost layer of our data analysis structure are the displays.
03:21 This is where we actually visualise the data.
03:24 Most commonly we'll be using the time vs distance graphs, XY plots, track maps, histograms, report tables and gauges.
03:33 But there are many more variations available, depending on your specific software.
03:38 At a bare minimum, these are the displays we should make ourselves comfortable with.
03:42 It's important to remember that there are always multiple ways to display the same data.
03:47 It ultimately comes down to what you're looking for in the data and personal preference.
03:52 So it pays to have a play around and figure out what the most intuitive way is for you.
03:57 The more time you spend familiarising yourself, the more obvious it's going to become.

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