 Welcome to Power BI. My name is Jordan McCarthy. I am the data analyst and storyteller at Tech Impact. Once we've found and shaped and loaded our data, they are brought to a blank canvas, if you will, and our data fields have been loaded over here on the right-hand side. This is sort of our paint palette. Each table that we brought in will have its own sort of folder, and if you expand the folder, then you'll see all of the variables or column headings that you used to know them laid out there for your use. Values that have particular attributes, like those that are classified as numbers, will show up with a little sum side next to them. We're going to start with our duration time stamp. One really nice thing about the composition window in Power BI is that you can just drag a variable onto your canvas, and Power BI will decide what kind of visualization makes the most sense for that kind of variable. I'm just dragging the time variable in, and I release it, and I get a blank card. It's blank because the only thing I've dragged in so far is year information. You can see already that Power BI did actually make some pretty smart decisions. It's treating this thing like a time variable, and it's allowing me to actually drill down into various levels of time, which I know because these two little arrows would not appear for any other kind of data, as it turns out. You cannot do something really fancy to make any kind of numerical data zoomable like this, but for dates, it's done for you by Power BI. So if I click the drill down, I can see that this data can be handled at the year level, at the quarter level, at the month level, or even at the day level. But of course, I don't have any other data in this card yet, so let's fix that. I think a good first visualization for us to make would be a graph of some total of donations that this organization has gotten over time. So I'm going to go ahead and drag donation total in to the same graph. And there we go. That's all there is. I can reshape the graph to make it bigger, which I definitely want to do. And as you can see, I can make it wider, I can make it taller, I can shape it however I want. There's really no limitations. Now we have donations over time aggregated by day, right? And remember, we drilled down to the lowest level of data that was available in the donation time stamp variable. And so this is showing us a graph of independent of year or quarter or month, but by day of the month, when do we get at most of our donations? Let's drill up a little bit and see what we have. Huh, another interesting trend. Looks like overall this organization gets far fewer donations in the summer months than it does in the fall. Let's drill up again. We see essentially the same trend we saw before, this time displayed by quarter, and then here is our graph over time by year, which shows a really nice reassuring trend, except for 2015, which seems to be a problem, except it's really not because it turns out that this data point is not quite valid because this data set was pulled before 2015 was complete. And for that reason, I might advocate excluding 2015, because again, it's just not useful to us in an apples to apples comparison. To manipulate what you see on your screen here, you have this panel kind of in between your canvas and your list of variables. And there's a fair bit you can do here. You can drag your variables directly into specific places. So for instance, if I wanted to do something really fancy like splitting this up by state, I could actually do that. It would be very messy, as you can see. Now each state has its own little subline and there's this massive key that has 50-something entries in it. So terrible idea in this particular case. But I can also filter, and I have all of these filtering options, and then saying we want to view everything, but not 2015. And there we go. We've now removed 2015, and now we have a really nice, clean graph. It does tell a more accurate story about what's going on, and we can add 2015 back in later when we have the full data for 2015. So hopefully that gives you a sense of how to bring in your variables, create visualizations, interact with the visualizations, and shape them a little bit further using filters and or other parts of the configuration panel here, like the legend, which allows you to split things up. And then the value in this case, of course, is just the donation total which is creating the bars in the first place. But the bottom line is that you should very much explore and try things out and zoom in and out in terms of time, add different kinds of filters, add different kinds of legend variables, the things that split up the bars into multiple parts, and really just get your hands dirty because that is the way that you learn how to do the exact kind of analyses you want to do in Power BI and tell the most compelling stories possible.