 What's going on everybody? Welcome back to the Power BI Tutorial series. Today we're going to be working on our final project. Now this is our final project of the Power BI Tutorial series. So if you have not watched all of those videos leading up to this, I recommend going and watching those videos so you can make sure that you know all the things that we're going to be looking at in today's project. I am really excited to work on this project with you because I think it is a really good one and it uses real data that we collected about a month ago where I took a survey of data professionals and this is the raw data that we're going to be looking at. And so I think it's just really interesting that we collected our own data. Now we're using it for a project. We're going to transform the data using Power Query and then we'll actually create the visualizations and finalize the dashboards as well as create a theme and a different color scheme to kind of make it a little bit more unique. Without further ado, let's jump on my screen and get started with the project. All right. So before we jump into it, I wanted to let you know that you can get the data below. It is on my GitHub. You can go and download this exact file that we're going to be looking at. Now, in the past several projects, we have been using this fake apocalypse data set. You know, it was fun. It was, you know, whatever this data set is real. There's a real data set. It was a survey that I took from data professionals. I posted on LinkedIn and Twitter and all these other places and we had about 600, 700 people who responded to the questions. So before we actually get into it and start cleaning the data and doing all this stuff in Power BI, I just wanted to show you the data. All right. So this is the CSV that I downloaded from the survey website that I used, and this is completely raw data. I haven't done anything to it at all. But let's go through the data really quickly and we'll kind of see what we have and we are not going to make any changes at all in Excel. We're going to do all of our transformations or at least a few transformations in Power BI because again, this is a Power BI tutorial and project. So I want you to kind of learn how to use that and not use Excel because you can go through my Excel tutorial if you want to do that. So let's just look at it in Excel and then we'll move it over to Power BI and actually start transforming the data. So we have this unique ID. These are all the people that actually took it. That we have an email, which this is completely anonymous. I didn't collect any data or user data on this. Then we have the date taken and let's get into the actual good information. Then we have all of these questions. So we have question one, which title fits you best and they can choose things. Now let's add a filter really quickly that we can look at this. Now you had the pre-selected ones, which were like data analyst, architect, engineer, but then there was an option where you could say other and you could specify what that was. So if you look in here, we're going to have all these different other please specify with different titles, right? And there were a lot of them. Now, typically what you want to do is really clean this up. And we're not going to be doing a ton, ton, ton of data cleaning, but we are going to do some in Power BI, but none in here. But typically with this amount of data and the way that it's formatted, we would do so much data cleaning with this one. I mean, there's a lot of work to be done like this current year salary. This is one that I would absolutely be cleaning up because it's a ranges and it has a dash and a K and all these numbers. This is something that I would be cleaning up and using. But we're not going to be cleaning this up right now. So anyways, let's just get into it. Let's see what questions we asked. We have the yearly salary. What industry do you work in? Favorite programming language? Then there were a lot of different options. This is like one question where they picked multiple options. So is how happy are you in your current position with the following? You have your salary, work-life balance. Then we have coworkers, management, upward mobility. Learning new things and they could rank it from zero to 10. So some people ranked upward mobility at 10. Some ranked it a zero or a one. And again, they can answer however they want. How difficult was it to break into data? Very difficult, very easy. If you're looking for a new job, we have, you know, what would you be looking for, remote work, better salary, et cetera? We have male female, which country are you from? And then this is more like demographics. So if you're a male, how old you are, and this was in a range. So this is like a sliding bar. So you can slide it to the exact age you had. There's some people who are apparently 92, which if that's true, I mean, good for you, man or woman. Actually, really quickly, I'm going to see, just while we're here, I'm going to see if this is a male or a female. That's a female from India. Very cool. So we have all this information and it is a lot of information. When you have something like this, I mean, there is so much data cleaning that can be done. I mean, I already see like 20 plus different things that I would need to do to make this a lot better. Um, and we also have date taken and the time taken as well as how long they took on it, like the time spent. Really just really interesting data. But again, this is a beginner tutorial series. This is the beginner project. So we're not going to get, do anything too crazy. I will be using this exact data set in a future video, doing a lot more data cleaning and creating a much more advanced visualization with what we have and what we're looking at right here. But for this video, we're just going to be doing a pretty simple visualization and dashboard that you can use to practice with or put it on your portfolio. If, you know, that's where you're at right now. So let's get out of here and let's put this into Power BI. So let's exit out. And let's come right over here to import data from Excel. We'll click on Power BI, final project and open. Give that a second doing this all in real time. We only have the one, so we'll do be, we won't be practicing any don't joins or anything, but we're not going to load it. We're going to transform this data. So let's put it into power query editor. And now we have all of our data in here and it should look extremely familiar. Now, when I'm looking at this, when I start looking at this information, I kind of need to know beforehand what I want to get out of this. Do I need to clean every single column? Do I just need to clean a few of them? Do I need to get rid of columns? That's kind of where my head's at. And so right off the bat, I can already tell you that there are columns that we can just delete to get out of our way. So we're going to do that at the beginning so that we don't have to do that later on or they're just in our way. So I'm going to click on browser and then I'm going to hit shift and I'm going to go over here to refer. And we're just going to go up here to remove columns and everything that we do is going to go over here to the supplied steps. If you've been following the series, you know, we can remove things, add things, but anything we do will show up right over here so we can track it and go back if we need to. Now, one column that I know for sure that I'm going to be using quite a bit is this which title fits you best in your current role because I specifically wanted to do a breakdown of different people's roles and how much they make and different stuff like that. So I know that I want to use this, but as we saw before, there's kind of the issue is it's not very clean, right? It has data analysts, data architect, engineer, scientist, database developer, and then all like 100 different options and then a student or none of these, right? And so for the purpose of this video right here, we are not going to take every single one of these options because this involves a lot more data cleaning. Let me give you an example. This says software engineer. This also says software engineer with AI. These two would typically be combined or standardized to software engineer, but it's not very easy to do that in Power BI. We could do that in Excel, but not really in Power BI or even SQL if we pull this from a SQL database and you can find lots of different options that we have data manager and data manager. If we separated these out, these would be different options when we created our visualizations and we don't want that. So what we are going to do and this is going to be kind of an easy way out to just make sure that this is pretty clean and doesn't, we don't have a thousand different options. We're going to create this to other. So we're going to simplify this a lot and then we're going to use this. So we'll have maybe six or seven options instead of the, you know, let's say 50 that we would have if we actually did the harder work, which is to break it out, standardize it and clean it up that way. So what we're going to do is we want to click on this right here. I want to go up here to split column in this ribbon up top. We'll go to split column and we want to do it by a delimiter. And if you notice, let me see if I can move this over, if you notice we have other and then we have this parentheses and in no other option or way is there parentheses. So what we're going to do is we're going to use a custom and use this open parentheses. What that's going to do is it's going to separate it by this parentheses. It's going to leave the other and it's going to create separate columns, just one separate column for each of these. And we can do that at each occurrence or we can do the left most and really we only need it for the left most because there's only one of these left handed or left sided brackets or whatever this is called. And then let's go and click OK and it should create another column. So it's going to have point one, point two. And now we have we click on this. Now we only have these options. We have analyst, architect, engineer, data scientist, database developer, other and student looking or none. That is what we want. It makes it so much simpler and it's not perfect, but again, I'm trying to show you what we are able to do in Power BI. So now we're just going to remove that column and we're going to go and do the exact same thing to this one as well, because I know that we want to use this and I really wanted to use this one as well. But if we look at this one also, there's a lot. So I said, what is your favorite programming language and people there were pre-selected answers like JavaScript, Java, C++, Python, R, things like that. And then there was an other option and in this other option, I mean, it was free text so they can fill it in as they want. I mean, there's four, five, six different ways that people would SQL. That is something I would standardize and, you know, that would be the way I cleaned it, but that's not how we did it. So we're going to do the same thing. We're going to keep that other. So we're going to split this column again, reuse the delimiter. And for this delimiter, though, we're going to use a colon. So we're going to say we're going to do a colon right there. Just do the left most. We'll click OK. And then we have our options and it's much simpler. Now I really would have rather kept all these and because SQL is in there quite a bit, but, you know, a lot of people don't think SQL is even a programming language. So we're going to delete that column. Now, when did I just skipped and I kind of wanted to go back to is this current yearly salary? I really want to use this. Let's see if we can use it. I here's what I want to do with it. And this is not perfect. Before this video, I want to try it. What I want to do is break up these numbers, 106, 125 and then take the average of those numbers. Then we'll use some DAX in there. So we'll take 106, 125, create that into two separate columns. Then we'll create a third column that will give us the average of those two numbers. So we'll do 106 plus 125 divided by two. And then we'll have the average of that. Now that is not perfect, but it's going to give us at least, you know, an average, a kind of roundabout number because they gave us this range. They said my salary is between 106 and 125,000. So if we say that their salary was 112,000, at least gives us, it makes it usable. It's a numeric value. Instead of being this, which is text, which we really, we could use and I'll show you how to do that because we're going to keep this column. I'll create a copy of this and I'll show you the difference between this and using the average. But for, but for this data cleaning portion, let's just try it. Let's see what we can do and see if we can make it work. So first let's create a duplicate. So we're going to duplicate the column. So now we have this copy at the very, very end and we can use this one instead of having to use the original way, way, way back here. So we're going to leave that one how it is and we're going to use this one. So let's go ahead and split this one up. We're going to click on the column header, then we're going to click on split column and we'll do it by digit to non-digit. And if you look at it right here, it's broken it out kind of in the fact that now in this one, we just have numeric values. And in this one, we have K dash numeric or just dash numeric. And now this can be easily cleaned, whereas this one we can just completely get rid of because it's only K. So we'll just remove that column. And then in this one, we're going to right click, we're going to click on replace values. And so if it just has, we're just do a K or replace with nothing. OK. And then for the last one, we'll go to replace values. And we'll do the dash or the minus sign and we'll place that with nothing. And so now we have our values as well. Well, we also have a plus. Let me get rid of that because that's when some people had 250 or 225,000 plus. So for that one, the average is just going to be 225. We'll have to specify that in our DAX. But actually, if somebody has 225, let me find this plus really quick. Let me filter by it because it's a lot faster. What we actually want to do for the purpose of this one is we want to put 225 here so that when we do 225 plus 225 divided by 2, it comes out to 225. That's just what we're going to put it as. There's only two people. So I'm actually going to replace this. I'm going to do replace values. I'm going to say plus with 225. We'll click OK. Awesome. We can unfilter these, select all. So we're going to go right up here to add a column. I'm going to say custom column. And we're going to go right over here. Actually, let's make it average salary. Make it average salary. So we're going to insert this, I'm going to say. Parentheses and we're going to say plus. This insert. And close the parentheses divided by two. And it says no syntax errors have been detected. Let's click on OK. And it's giving us an error. So saying we cannot apply operator plus to types text and text, which makes a perfect sense. These aren't numbers. So let's make it a whole number and let's make it a whole number. And then let's see if this will actually work or maybe you need to try a whole another one. So let's try transfer or add column custom column. Let's try this all again. See if I can make it work. Insert this one plus this one. And we'll do divided by two. And let's try this one. And there we go. So now let's get rid of this column columns. And we can actually remove these ones as well. Because now we have this average. Salary column, which when we look at this or when we use this, we can see if I can just move this way, way, way over. All right, I might cut because it's taking forever. So if you take the average of these two numbers, you'll get 53. If you take the average of zero and 40, you'll get 20. So now we have this average salary. And again, when we get to the actual visualization part, I'll show you why this isn't as useful as having this average salary. And just a reminder, this is not perfect. I wouldn't typically do this, especially if I had it in Excel or if I was, you know, creating this survey in a different way. I would probably have a very specific value where they can do it on a slider. But this is how it is. So we've at least made it usable or more usable in my mind. And we have a few other things that we can change. Like what industry do you work in and where you can break this one out? So I'm going to go ahead and break this one out as well as. This one right here, which country do you live in? I'm going to break both of those out to where it's the country or other. I'm not going to have these other values, although there are a lot of them. There's a lot of people who live in these different countries, but we can't really do that super well in here because, again, the same issue kept happening. Argentina, Argentina, Argentina, Australia. So we can't normalize those values unless we spend just a copious amount of time doing that. So I'm going to go ahead and do these. I'm going to fast, I'm going to fast speed this so it goes a lot faster. So I'm just going to go silent and let this happen really quick. And then we'll get to the end and we'll actually start building our visualizations. All right, so we've split them up. And as you can see, we have all these options as well as other. And I think, you know, there is, let me tell you, there is so much more that we could do with this. I mean, just so many other things. But this is like what the bare minimum of what we need for this project. So let's go ahead and close and apply this. And if we need to come back at any point and actually fix anything or change anything, we can. So it's not like that's permanent. So as you can see, we have everything over here. We have all of our data as it is transformed in here as well. And now we can start building out our visualizations. Let's go back to our reports. And let's start building something out. All right, so let's add a title to our dashboard. Make this right at the top. We'll call this the data professional survey breakdown. And let's make that quite a bit larger and make it bold. Why not? And we'll put that in the center. And now let's let's add some effects. Let's change that background to something like that's too dark. Something like this, I'd not like that bold. Let's take that off. There we go. So something like this just has a quick title to what we're about to do, what we are about to build. So we're going to start off with the most simple visualizations that we're going to do and we'll kind of work our way towards kind of the harder ones. So the first one that we're going to start off with is a card. And the cards are obviously like just super, super easy. They usually just display one piece of information. So we're going to go right over here to the very bottom at the unique ID. And we're going to select it. And we're going to say a count of distinct or a count, it doesn't matter. It says 630 count of unique ID. Now we're not going to keep that as is. We're actually going to go right over here. I want to say rename for this visual. It says count of unique ID. But we're going to say count of survey takers. And you can say whatever you want here. But in general, that is what it is. We're counting how many people, you know, took this survey. And that's just a kind of a total, maybe I say total amount of survey takers. But you can say count of survey takers, how many people took the survey. So let's click out of there. Let's click on card. Let's make it about the same size. We're going to drag it up here and try to make them about the same. We will in a little bit, we'll make them the same size. But for this one, we're going to look at age. So we're going to look at current age. So I'm going to click on that and we'll say the average age. So our average age taker is almost 30 years old. So let's go right over here. We're going to say rename for this visual. We'll say average age of survey. This might be too long. Average age of survey taker. Again, name it whatever you'd like. So again, these are meant to be high level numbers. So when somebody is looking at your dashboard, they can just really quickly glance at this and know exactly what it is instead of like some of these other visualizations that we're about to create. They don't really have to dig into it. Look at the X axis, the Y axis, the different legend colors and whatnot. They can just see these high numbers and get a really quick glance at the data. Now let's create our first visualization. And what we're going to do for that one is a clustered bar chart. So let's go ahead and click on the cluster bar chart and create as small or as large as we'd like. And for this one, we're going to be looking at the job titles. Now, remember, we kind of change the job titles or, you know, transform those, if you want to say that. So we're going to get job titles. And then we're going to look at their average salary. And if you remember, we transformed that one as well. We have allege average salary. Now this one is, it looks like a text right now. So it may not work properly. And what we're actually going to do is go over here. I want to see the average salary. So let's click on the average salary and see if we can change this data type from a text to a decimal number. Let's click yes. I forgot to do that when we were transforming it. And there we go. This is perfect. So now we can go back and we can select our average salary. And as you can see, it has this function symbol. And so now we can click on it and it'll look a lot better. And although this says average salary as the title, it's actually doing a count or the sum. So we can click average right here. And what we want to do is actually break this down by the job title. And so now we can see data scientists are making the most by far. They're making average of 93,000, at least from the survey takers that took it. Then we have our data engineers making 65,000 data architects are making 63. And then we're the data analysts, data analysts are right here making 55. So again, we had 630 people take this survey. And so the vast majority of them were data analysts. So this one's probably the most accurate out of all of them. And I actually don't like how this looks as the cluster bar chart. Let's try the stacked bar chart. But this has the legend. That's more what I was going for. I don't know. I didn't want as skinny because when you're doing this one, it typically they have multiple options per X axis. And so I think that's why it was that little skinny line. But this one is more what I was looking for. But let's make that smaller and let's definitely change that title because good night. This is like incredibly long. So let's go over here to this format visual. We'll go to the general, the title. And we're just going to say average salary by job title. Just like that. And this looks a lot better. Now we're not going to kind of format all our whole dashboard yet. We're going to create our visualizations and then we're going to kind of organize everything and kind of play Tetris with it to make it look the best. So we're just going to minimize this and put it right up here for now. But we will go back and kind of make everything look better at the end. And actually, while we're here, I also want to change this as well. So rename for this, we're going to say job title. Why did I do that? Job title and for this one, we're just going to say average salary. There we go. Looks much better, much cleaner, took away a lot of the anxiety that I was feeling about two minutes ago when we first put that up there. So let's go on to our second visualization. The next one that I'm interested in is actually what programming language people were using the most. So we have salary. There's a thousand different things we can look at in here. But I want to know, you know, what is people's favorite programming language? So let's take a look at that. So we have favorite programming language. Let's find that. So we have our favorite programming language and we also have how many people actually took it or the unique people. So right now this is columns. We don't want that. Let's, um, let's do a clustered column chart. Click on this right here and it looks like here we go. That is kind of what we're looking for. And instead of count of unique ID, we'll say count of, let's do count of voters. And for favorite programming language, we'll say favorite, favorite programming language and get rid of that as well. And then we're going to go into here also and change the title and say favorite programming languages. Favorite programming language. Just like this. Now let's make this a lot bigger so you can see it, but really quickly at a glance, you can see Python is by far the most popular are other C plus plus JavaScript Java. Now, all we're seeing is they count. So it's all the same. It's just blue. We can see how many people voted for each one. But if we wanted to break it out, similar to how we did with the job titles, we could still do that. So all we'd have to do is break it out or bring this job title down to the legend. And now it breaks out like this. And that's not exactly what I was going for. I was going more for something like this, where we can see the still the whole count, but now we can see who is actually voting for these things. So I'm just not a huge fan of the colors that are pre-selected here and kind of the whole theme of this dashboard. At the very end, we're going to completely revamp this, change a bunch of colors, the background and make this look a lot nicer rather than just a white background like we have it. And so for now, let's just make this a lot smaller and put it into this corner. These will not be staying there, but we need to we need room to create our next visualizations and just a cleaner space to do things. Now, the next thing that I really want to include is a way to break down where they're from their country, because especially something like salary is very dependent on your country. Whereas the average salary in the United States for a data analyst may be like 60,000 in another country. It could be 20,000 that could bring down the average quite a bit. So we need a way to be able to break that down. Now, we can do something like a filled map and there's no problem with that at all. But, you know, for what we're building, what we're creating, it's not probably going to work out the best. I mean, this looks OK. We could stick it in the corner of something and you can do that. And that's perfectly fine. I think what I'm going to do is something like a tree map, which I don't use a lot. But I want something where they can just click on it. They can look at the values. Stinked. They can look at the values and just click on it, and it'll be right there for them. So they don't have to filter it out on their own or no geography and look at this map. They can just read Canada, other United Kingdom in the United States and click on that. And so, for example, let's click over here on United States. The numbers change quite a bit. Now the average salary for a data scientist is one hundred and thirty nine thousand. For data analysts, it's 80. And if we look at India, you know, the average salary for a data scientist is 68. The average salary is 26 for a data analyst. That doesn't mean that they make less money in India. That just means that the cost of living is probably lower in India. Therefore, they don't need the higher US dollars salary. Because again, this was all done in US dollars. So just something to think about. Let's click out of that. So we'll keep that one as well. So now let's create our next visualization. This is one that I do not get to use enough in my actual job. So we're going to use it in this project and it's going to be this gauge right here. So let's add that one. Put it right over here. We're going to add two of those. Go ahead and add another one while we're at it. We're going to have them kind of like right here right next to each other. The first one and these ones are really good for kind of looking at these kind of surveys. And I don't get to work with surveys enough. But we can see, you know, how happy are they in terms of work-life balance? So we can add that. We're going to add work-life balance. And right now it's doing a count. And if we don't have minimum or maximum values in there yet. So it's going to look kind of weird. But we're going to look at the average rate or the average score of these. Then we're going to pull this over to the minimum value. We want to put that at the minimum and pull this over and add the maximum value. So now it actually has zero to 10. And it shows that the average person is happy with, which one was this? Their average person is happy with their work-life balance. They rate about a 5.74 overall. Now let's really quickly change the title of this because this is ridiculous. I want to say happy with work-life balance. So this is their rating, you know, change it to whatever title you want. That's what I'm going to do. And we'll also do happy with their salary. Looks like it's on salary. We'll add that to minimum and we'll add the maximum value as well to make sure that we know how to use that and then we'll take the average. So not many people are happy with their salary. I'm just finding out. I mean, this is a real survey. This is real data. So I mean, it's pretty interesting. Let's go to the title. Let's go to happy with, or maybe it's happiness. Happiness with salary. Maybe that's what we should make it. And I'm going to change that over here as well because I think it sounds better. Some of this I've already planned out. Some I haven't. This is not something I've planned out. So, so we're going to say happiness with work-life balance. Happiness with salary. Really interesting. Um, we may go back and tweak these just a little bit in the future, but the very last visualization that we're going to do is male versus female. Kind of got to have that in there. Um, I don't typically like pie charts and doughnut charts, but, uh, you know, I'm feeling, I'm just feeling it. So let's try it. Um, and we will do, let's see, let's make this larger. So you have male, female, and what do we want to look at? Like, what do we want to measure? So we have a male versus female. We can measure anything, um, but maybe what we'll do is the average salary. Again, I mean, we've kind of only looked at salary once in this one right here. Um, and a little bit of like how happy they are, but we'll look at the average salary between males and females, and then we'll look at not the current age, oops, I meant average salary. And then we'll look at the average. And it looks like the average salary is actually really close versus males versus females 55 for female versus 53 for male. So actually the females are a little bit higher. Uh, congratulations. So they're just a little bit higher in terms of pay. So now we need to start organizing all of this, cleaning it up, making it look a lot better than it does right now. It looks great, uh, you know, but we can do a lot more with this. So I'm going to, we're going to keep these are all these kind of over on this left-hand side, I'm going to put this, I want this up here. You also need to change that title. I want this up here. Um, and again, we're going to kind of change the theme as we go. I just want to format it right. We'll have it just like this. Let's change the title of this title. I'm going to say country of survey takers. Uh, I'm not the survey takers. I'm not really stuck on that. If you find something better or you think of something better, I would go with that, but, um, you know, it definitely doesn't look bad. And where did this, where are my other visuals? There it goes. Um, I think this one, I want to make kind of more tall. Um, so I might move it this way. Geez. This is such a, I hate, I hate having a lot of visualizations on here. It just really, uh, is annoying to me. So we're going to do that, step this to the side, put this to the side as well. Make it to where it's just, okay. I didn't want it to cut off. We'll do that. We might make these bigger actually. I want it to kind of match the size. Like right there, match this, perfect. This one, I kind of want to bring over here and bring it down a little bit. Maybe something like this. Maybe I'm not sure I'm not, I'm not sold on that. Um, I added a few different visualizations that I didn't have in my original. So now I'm kind of having to do this on the fly. So, um, I might fast forward some of the parts where I'm like really thinking about it or taking too much time on it, but I'm going to bring this down a little bit actually, because I don't like how close that is to, um, the, the text above it. But one thing we do need to do, I'm going to put this up kind of like this. I think that looks fine. I think I'm going to put this at the very bottom. So let's make some room for it. Uh, right. It's just like that. Stretch it to the side and we'll lower it. And I think we'll keep that as is. Like this. Um, okay, there's a lot going on in here and there are some things I'm just noticing as we're walking through this that I kind of missed. Um, like I need to change some titles and stuff like that. So let me go ahead and change some of those things. So we're going to do title, do average salary by gender or by sex. Like that average salary by sex. I also don't like that it's in the middle. Um, I don't like that it's on the outside. I want them on the inside for this. So let's go to the details. Let's go to inside and see if that looks any better. Oh, that looks terrible. Um, let me see if I can change that. Maybe I don't know. I definitely want it. Um, I guess we'll do outside. I, you can't even see the information. Oh, the decimal is crazy long. Um, let me go and see if I can change that decimal to just like a whole number or like 1.1, uh, because that's a problem. So maybe I need to go over here to the value. All right. So I think I want to change this one. It's just not working out exactly how I wanted. And you guys know if I make mistakes, I'm going to keep it in here so you guys can see it. I, I hope that this was going to turn out better, but it didn't. Um, one that I do want to add because it's kind of a breakdown and a nice visualization. I want to add this difficulty piece. So I want to add this, how difficult was it for you to break into data science? So let's get rid of these. And I want to click on this really quickly, see what it gives us, um, values. Okay. So now this shows us percentages, um, of how easy it was. Again, it's neither easy nor difficult, difficult, easy, very difficult, very easy. These numbers make absolutely no sense. We need to kind of order them a little better. So I'm going to come over here to slices. We have our colors over here. We want very difficult to be like the most difficult. Um, so I'm going to make that red. And then we want difficult to be maybe like an orange. Let's see if we can find an orange. There we have an orange. This does not look red enough. There we go. Oh, no, no, no, very difficult as red, difficult as orange. We have neither easy nor difficult, and that's kind of a neutral. Um, let's see if we have something neutral in here. Kind of like this yellow. I don't know. Let's try it out. Then we have easy and very easy. And these will be like our blues. So I'm going to keep that, um, keep that kind of like a dark blue ish. And then our blue for super easy is just going to be like really blue. Um, and that doesn't look bad. The, I mean, look, I'm, I'm not a color person. I, I'm not great with colors and we're going to kind of organize this in just a little bit, but this looks better to me. Um, but we need to change up some stuff as well. Like the title you do difficulty to break into data. There we go. And we're also going to change this title right here. We're just say difficulty, difficulty. This looks better to me. Um, again, not perfect. And there's a thousand different things you could have done, but that's just what we're going to do. I need to go through here and see what I need to change. So right off the bat, I can see I need to change this, um, to, let's see right here, I'm going to rename this job title, just like we did in this one right here. Uh, count of voters. That's fine. Program language breaking into difficulty, happiness, happiness, average count. Okay. Okay. So what we have here is very close to a finished product. Now it's not a hundred percent complete. I mean, I, I do want to make it look a little nicer rather than just the typical white, so what we're going to do, I'm going to go up here, we'll go to, uh, view, and we have all these different filters. And we're just going to play around with it. See if we can find something that we like. Um, this doesn't look too bad. It's not really my style. Uh, we can do this one frontier. This is pretty neat. I kind of am digging this. We might come back to it. I like the natural tones. I don't know why I said tones like that, but I did. Um, this one's not bad, but I don't, I don't, it's not, that's not my, I don't like how dark that is. Um, and so maybe it's like, you know, uh, we changed like the background color of all of these as well as match it with, um, match it with something else. Whatever you want, genuinely, you customize this however you want. I kind of like this one. It's kind of groovy, man. And, um, it's not perfect by any means, but what we can do and we can customize this current theme, we can come in here, customize this theme. However we'd like. I personally don't want color five, which is the data analyst color. I don't like it. I want to go out and change it because I don't like it, but I don't really like that color per se. You know, I might want to choose a different color. Um, but it has to be like this muted, like it has a style to it. So you can come in here and you can customize this and make it however you'd like and really mess around with it, play around with it. For me, uh, I'm just going to keep it how it is because I don't really want to mess with it and break it or anything like that. So, uh, let me just, so this is it. This is the project. I hope that it was helpful. Um, I am not joking when I say that I'm going to, because I'm going to do a different project. I'm going to go really in depth in another project. It's probably going to be like a two hour project. It's going to be crazy long. Um, well for a YouTube video, but I can see doing a thousand different things with this data, creating a really great dashboard, really cleaning the data, which is a large part of, of actually doing this. And we didn't do much data cleaning at all. There's just so much you can do with this. And so really dig into this, see what you like, see what you don't like, see what you want to clean, what you don't want to clean. You could put it in SQL. You could put it in, um, Excel and just, and just standardized the data to make it a lot more usable. Do whatever you want with it. I mean, I took this survey for you guys that we could use it. So go out and use it and make the best dashboard that you can possibly do. So I hope that this was helpful. I hope that you enjoyed this. Thank you so much for watching this video. If you like this, thank you so much for watching. If you liked this video, be sure to like and subscribe below and I'll see you in the next video.