Data Visualization With Power BI - Stacked Charts (No Coding Development, Branding, Microsoft Excel)

1. Introduction: Data visualization and modeling through Power BI is very powerful, specifically creating visuals such as stacked column charts in which you have the ability to add layers of graphically designed themes on top of the dataset that you have created through columns in order to add some level of insight and visualization and modeling, which in turn will help you figure out what's going on with your data. Sometimes we're taking a look at datasets. You have no idea what are the connecting points between the pieces of information and the hidden raw data. A CEO, an entrepreneur, as an engineer, data drive decision making. And it's very crucial to be able to find the patterns within data. And in this current class, I'll be teaching you how to create stack column charts through Power BI to help you make up informed decisions, regardless of your industry, whether you are working in marketing, analytics, data science, accounting, finance, project management, the list goes on, you will find this class very, very helpful. 2. Your Project: Your project for the class revolves around utilizing Power BI in order to create stacked column charts in which I'll be providing you with raw dataset that you're going to be uploading on Power BI in order to help you build up your stacked column charts and feel free to tinker with it. Add some graphic design elements, some themes, your own branding, try to get creative and innovative with the process. After which you'll be sharing your stacked column charts with the rest of the community for feedback. 3. Data Visualization with Stacked Column Charts as part of Marketing, Branding and Development: Look another visual that you could use, which is the stacked column chart. So I'm going to be adding the stacked column chart and the same way I'm going to extend it to help us understand how it's used and how could we modify it. So once we go for the stat column chart and you click on it, notice what happens over here, you are able to see the same representations. For every single visual that you click, it has its own field that you're going to populate. You go to the table and feel free to alter whatever parameters that you have, and you can take a look at the resources section as well. If you feel that you're ready actually to start tinkering with this, download the dataset that I've provided you with, upload it, and try to mimic the actions that I'm doing. That way, you're able to understand how to navigate PowerBI. Before building the report, before building the dashboard, we're equipping you with the skill set from the ground level up. So I'm going to try now to go for the total sales, let's say, for the Y axis. However, I'd like to have the developer or the console. Here we go. So if you notice, for example, we got the sum of total sales. I got this for the Y axis and the console and the X axis. If you take a look at the consoles, you have PS three, X 360, ps four, and you're able to see the sum of the total sales for every single one of these consoles. But you might be wondering, how could I use the data, right? So let's add some legends to it, add fields here. How about we add the publisher. Notice what I've added at the top, how it altered the visual representation. Think about the legend as the visual filter in that sense. Visual insight. Here, have the PS three, X 360, all of them compiled, right? But the companies or the publishers who are publishing for the PS PS three or Xbox 360, PS four, they are different companies. So how would I be able to segment my data? So I'm able to add, for example, let's say, the game titled the developer who developed the game as legend. Look what happens, for example. You get the developer at the top, and you're able to see the color code for these developers, which adds further level of complexity to your data. You could add the genre, for example. See what happens. I take a look at the color green, these are the shooter games first person shooters like Call of Duty, for example, you can see that they accompany for the majority of the sales, and this what we call as the tool tip, by the way. See the icon. Once I go over here and I hover at the data, and I'm able to see that a pop up will come up on the surface of the talents we'll call as a tool tip, right? So I'm able to see the genre of the data. I'm able to select, by the way, this. When I select a certain legend, I'm able to filter the data. Let's say I'm interested in learning about sports games. So if I click on this, I could see the sales based on console for every sport game in that case. So this adds another level of drilling, let's say, for the data. Where you have certain parameters, and you could layer another one as a legend, which helps you make sense of your data even more and to actually get more out of the data. Try to get more out of the surface levels of just simply total sales and console. Let's say you'd like to learn about not just simply the sales and the console, but what is the genre of the games based on that data? Or how about the critique score? This is very powerful. Here we go. Take a look at the critique score. These are the various critique scores. If you'd like to filter data, games which got a critique score more than nine. You click on this, and you notice you got these segments of the games based on these consols who made such sales, for example. So it's very powerful to use the stacked column chart to layer up your data. We build up the X axis, you build up the Y axis, and you add the legends which are clickable in order to navigate further insights of the data. So this concludes it for our understanding of stacked column chart. 4. Wrapping Up: So what do you think? I truly hope that you found the class helpful if it helped you level up your knowledge in terms of data visualization and data modeling, but at least 1% is a job well done. I look forward to receiving your feedback on the current class and make sure you follow my profile for the latest releases and updates, and I'll see you in the next class.