 three's buzzword chat GPT. It's the word on everyone's lips. And we're super grateful to welcome Alex Breeberg back to the channel. I know that Alex's crowd are watching over on YouTube. And we've also got a lot of people watching a career foundry too. So welcome to everybody. Whilst I'm here, I'm William events and communications lead here at career foundry. But everybody joining, let's just give a couple of moments for people to join. But on big market, you can see that we've got the chat on the right hand side, maybe because this is going to be all about chat GPT. Just drop why you're interested in chat GPT, where you're joining from and what your name is. Also if you're watching on LinkedIn, also if you're watching on YouTube, I know that Alex always brings his crowd. We love to see them. We've hosted lots of workshops in the past and their presentation. So thankful to everybody to join this evening. And this is all about seven ways to use chat GPT for data analytics. As you can see on big market, we've also got some emojis. Do press those. Let's know you here. We want to make this as interactive as possible. Before we get started, let me just introduce career foundry very, very briefly. And career foundry is the online school for your career change into tech. And we guide you from complete beginner to job ready professional in data analytics and help you land your job in the field. We're not any old school, our programs are so flexible that you don't need to quit your day job. You get regular mentorship for not one, but two industry professionals. So that's a mentor and a tutor. That's our dual mentorship model. And if you don't land a job within 180 days of graduation, we refund your tuition info. So that's career foundry's job guarantee. But I don't want to talk too much about career foundry. If you're watching on YouTube, and you're interested in doing a career boundary program, we've added a book, a cool link in the bottom to one of our program advisors. So if you're interested in the curriculum, if you're interested in career boundary, do book a call with a program advisor. And at the end of this webinar, we will be having a live Q&A. So drop your questions on Big Marker, LinkedIn, YouTube. I've got a lot of team in the background, and we're going to be having a great Q&A session at the end. Alex, I don't really want to say anything anymore. This is all about years, all about chat, GPT. I'll leave the floor over to you. Awesome. Thank you so much. And yeah, thank you to career foundry for putting this stuff on. It's always really fun. This is like, I think my third time, my fourth time being here. So I just absolutely love it. We have a really important topic today. And I'm, of course, going to talk about, you know, how we can use chat, GPT and data analytics, but also talk a little bit about some other topics around chat, GPT as well. And I'm sure at the end during Q&A, I already know some of the questions are going to come up. And I will stay a little extra a little longer if we have lots of questions on that. And so let's go ahead. Let's kind of dive into it. I'm going to preface this by saying I've definitely fallen in love with chat, GPT a little bit. If I ramble on a little bit too long, I apologize. But I'll do my best to keep it short. Well, basically how we're going to do this, I'm going to have a little, a few slides, walking through some of the ways that we can use it. I'll talk through it. And then I'll show you just a really quick demo on chat, GPT of how you can actually do these things. I won't, again, I can't do like a full deep dive demo. But I'll just show you a few things. And I have a full video on how you can do that if you want to check it out on my channel as well. But this is me, I'm Alex. And let's talk about chat, GPT. So if you don't know about chat, GPT at all, you're just hearing it for the very first time. It's pretty incredible. It's basically a chat where you can talk to this computer and it's going to give you answers in a really simple sense. But to be a little bit more specific, it's built off of a large language model. These LLMs are pretty relatively new, only going back four or five years, but have just recently started booming because of, you know, chat, GPT back in November of last year. It really is meant to help understand a lot of things just talking naturally to it. It's been integrated to a lot of different things, even chat, GPTs put out something called whisper AI where you can actually talk and communicate via just using, you know, your voice, which is pretty amazing. But most people are using it right now just kind of typing in the chat and doing it that way. And it has a ton of different uses. Now these three uses are like the tip of the iceberg. There are literally thousands, but these are three that I think everyone could, you know, pretty easily, you know, see. You can use it for language translation, almost like a Google translate. You can do that in chat, GPT. You can use it for things like content creation. That's something I use it for. You know, I kind of think, you know, here's a topic and then I asked chat, GPT, hey, what other topics on this could I talk about? And it just gives me ideas. So it helps me, helps create content and then data analytics, which will, of course, will be focusing on today. Let's talk about how it works. So I'm going to mostly talk on the user side, although if you really want to, like, have your mind blown, you know, look up how it works on the back end of on how these large language models work because they're insane. They're super cool. Basically how it works is, is you have this chat window and you prompt it to do something. Now you can ask it a question like a fact. You say, you know, in 1997, how many people lived in the United States and it can, you know, probably retrieve that information. Or you can do something a little bit more advanced was that you give it some context. You say, hey, you are a data analyst. You are an expert in Python. I want you to write this code or something like that. So you're prompting it to do something. You're giving it some context, giving it some information. Now chat is going to take that information. It's going to process it. It's going to do some type of reasoning on the back end. It's going to generate an output. And what's really great and what's become such an important part of chat is it has some type of memory, a working memory of previous conversations. So it's not just asking it a question. You're having a conversation with it. And you're saying, hey, three questions ago, I gave you this information. I have a follow-up question on that and it keeps track of all those things. And it's pretty incredible. So we're going to talk about these seven ways. And again, I want to preface this by saying these seven ways are very specific to data analytics, the more technical side of things. I will mention at the end some more ways that I have been using it. And I've been using it a lot. So I feel like these are good ways to really start using it, things that you can definitely start using. But then I'll give you some other ways you can use it as well. The first one is just teaching yourself. When you're first starting out and you're trying to learn, you're taking courses which I highly recommend doing, sometimes you get stuck and you're like, the way that this person's explaining it doesn't exactly make sense to me, but you have the code, you can ask ChatGBT to kind of guide you through this. Walk me through how this code works and it'll explain it to you. And you can even ask it to create kind of like a lesson plan. Like I'm trying to learn SQL. What should I start with? I don't want to ask Alex. He doesn't know anything. I want to ask ChatGBT because it knows everything. So you just ask it for a personalized plan. You say, I'm a brand new beginner in data analytics. I want to learn SQL. How can I do that? And it will kind of create that for you. And you can ask it for links and places where you can study certain things. And again, it'll offer those links and resources. Pretty amazing. I'm sure a lot of people are kind of getting into analytics or taking courses or doing all these things. So this is just another resource to start learning or even if you're looking at more advanced things even can be really great for teaching yourself. The next thing is explaining code. This one is pretty, it's really, really cool, honestly. Because even for me, even though I feel like I'm pretty advanced on a lot of writing SQL and Python and even some R, I get confused sometimes and somebody will send me code and I'm like, okay, what's going on here? And I'll have to really dissect the code and understand it. Now, I do that sometimes and it takes me 30 minutes an hour or I can just put the whole block of code into ChatGBT, ask it, and then I can get a much quicker overview. I've noticed that sometimes I would spend 30 minutes or an hour trying to understand somebody's code that they're sending me. I'll put it into here and I'll understand about five minutes or so. It's a huge time saver. Not only is it a time saver, but when you're working in it and you're trying to learn it, understanding what logically is going on in your code is also just really helpful because it helps you have a better perspective on how the code is functioning, how it's operating, and what it's actually doing. Give me one second. I need a sip of water. So ChatGBT also can offer other solutions. So when you ask it to explain the code, oftentimes it'll go above and beyond. That's just something that's kind of hardwired into ChatGBT is it tries to answer questions that maybe you didn't exactly ask. But sometimes when you ask it to explain code, it'll say, okay, I see your code, but it might even be better if you do this. And here's why. Pretty incredible. It's almost like the first one. It's like having your own little personal assistant who's like an expert at coding there alongside you. Pretty cool. The next thing is generating code. And I will say this is one that I've probably been using the most. I do a lot of tutorials. I also do a lot of real work where I'm like, okay, here's what I want to do. Here's what I want to build. And I'll give it a prompt. I'll say you're an expert at Python. You are just the best Python coder in the world. And I want you to write a script that performs this function. And it'll go and it'll do it. Now, it does not always get it correct. And I think I wrote it somewhere. It gets you about 80% of the way there. And sometimes it even gets it wrong. And I've been doing it enough to realize that you need to not like if you're using Python, you need to know Python to really use ChatGBT well. And the reason for that is it's going to spit out code. Almost every time it's going to be like, okay, I understand what you're asking. It's going to give you code. But if you don't understand what it does and you try to implement it, you try to use that code, I can almost guarantee you it's going to be very, very, very tough to actually use that code. What is really beneficial is understanding already how Python works, visually seeing what ChatGBT is giving you, so that when you put it in your script or your program or whatever you're building that you know what's going to happen kind of beforehand and you can make any changes or fixes to what you have. Let me see. Yeah, another thing that I wanted to mention is it doesn't just have to be in one programming language. It could be in Excel. It could be in SQL. It could be in R. It could be in Scala. It could be in JavaScript. It can be in almost any programming language. And I didn't think I mentioned this here at all. But one thing that I've really been using it for a lot is translating code. I'll have a really tough, really difficult MySQL code. And I'm like, okay, I want to kind of change the pipeline. I want to use Python or I want to put this through somewhere else. Let me change it to Python. So I'll say, you know, convert this MySQL code into, you know, Python code. And it does a really good job or at least it gets you like 80 to 90% of the way there. And it just saves you a ton of time, which is pretty great. The next thing is answering domain questions. Now, I worked in healthcare for many years. I love healthcare. I think it's fantastic. And for data analysis, it's actually, I would say somewhat underutilized data analysis is underutilized in healthcare. And what's been really great is just this this understanding that chat GBT is not know everything. But they probably know more than you. So for example, I worked with claims data. And if you don't know healthcare in the United States or you're in another country, the healthcare system, it's not great. But what I will say is, you know, I worked with claims data for a long, long time. And a lot of people would come to me and ask me, Hey, how do you? What do I need to know about claims data for data analysis? Because there's this data analyst job that's open for claims data. And I would say a ton of different things, like I could talk all day about healthcare and claims data. I know way too much about it. And now, you know, instead of doing that, I'll say, you know, let me real quick, let me ask chat GBT, I'll get a prompt and I'll say here are the actual things and here's kind of some resources and it'll provide all of those things for me. And I'll still talk with them and I'll still like help them. But at the same time, you know, it can give you a lot of context around how different domains use their data. Healthcare is just one example. But let's take finance. You can say how do you do, you know, stock price analysis using, you know, Python or using my SQL using this kind of data or that kind of data. You can really use it as just an expert in the field. And again, it's not perfect. Sometimes it gets things slightly wrong. But from my, from my messing around with it and like trying it out, especially with healthcare, which I'm really knowledgeable in, it's, it's pretty good. It's pretty accurate. The next thing is commenting your code. Now, if you're just starting out, nobody's commenting their code, like no, but nobody's commenting code. That's because you're just starting out. But when you get into a real workplace, I promise you, people will get mad at you if you don't comment your code, at least they did on my team. Because we pass around code a lot. You know, I would write my store procedure in my SQL and they get pretty advanced. And then I would just like pass it off to a database developer and he's like, what on earth did you do? He's like, this is this spaghetti code over here. And I'm like, yeah, well, I'm a data analyst. So like, you do your job. And then, you know, he would fix it up. But almost every single time, like it was a requirement as part of the team to comment your code, chat dbt does a really good job at this, where you can put in your block of code. And you can say, add comments to this. And it's almost like it's it when I was talking earlier about explaining the code. It's like it's instead of explaining the code to you, it's now adding comments of explaining the code in your actual code. So then you can just copy and paste it. Again, really, really helpful. And what I will say is, you know, commenting code, it's kind of a it's kind of a look down upon like nobody takes it really seriously. But, you know, I think it's important enough to where you should look at how to do that. Chat dbt can do a lot of it for you. So there shouldn't be any excuses going forward. Right. We should all be commenting our code. I'm trying to really push that in the data analyst community because I think it needs to be done more. All right, let's go to the next one. Next one is data cleaning. Now, data cleaning is where you're taking like raw data from a source and you're cleaning it up to make it more usable for the end product or the end analysis. So if you haven't done that before, it's okay. It's really just taking kind of messy data, making it more usable. And I have genuinely spent weeks of my life cleaning one or two datasets from customers because it gets really complex. That doesn't all just come from the data being messy, right? It doesn't mean the data is all misspellings and all bad formatting. Sometimes it's more business rules where the client is saying, yes, do that except when this and these business rules are ones that typically chat dbt can't really do. It doesn't it's not going to be able to understand those business rules like you do because you're working with the client, you're understanding how they're inputting their data into the source. But what it is really good for is doing the more obvious data cleaning that you're going to have to do anyways, like spelling mistakes or formatting issues. Those are things that, you know, are just time consuming to have to search through and find and use or in fix. And so, you know, right now, you can do it really on a small sample of data if you're using the chat gbt window on openai.com. Now, right now, I'm going to go off on a really small tangent. Stay with me here. I apologize because now once I get rolling, I can't stop. Right now, Microsoft's integrating a lot of this chat gbt into their systems. I'm sure you've seen it with Bing, their search engine, but they're also going to be doing a lot with Azure. If you're using anything with Azure, like their Azure Data Factory, Azure Data Lake, Azure Data Warehouse, all those things are going to have this kind of stuff integrated into it in the very near future, if not some of those products already. And so on a small scale, you can do it now, but you will be able to do that on a large, large, large scale in the future. Probably not too long where you can say, hey, I have these million rows, not just 10 rows, which you can do in like chat gbt. But I have these million or 10 million or a billion or if you're working with big data, and you can say, hey, you know, what are some things I should be identifying to clean in here and then you can ask it to clean that data. There's a lot of other factors that go into cleaning data, like keeping the raw data using staging databases, a lot of different things, but that's a whole different webinar on that process. So let's keep going. Let's keep I'm trying to keep it focused. I have so much to get through. I've already been talking for almost 20 minutes. I have so much to get through. So now we have debugging code, you know, code just doesn't work all the time. I can, you know, if you've ever watched any of my tutorials on online, I'll do these long tutorials and throughout it, I'll make mistakes and I'll have to like figure it out. Chat gbt can do a lot of that for you. And actually, it's been really helpful for me. I've been doing some a lot more advanced coding in Web Dev, as well as, you know, creating a lot more complex projects in web scraping and things like that using Selenium and and a few other libraries. And it's been really complex even for me. I'm like, I'm like, okay, I'm pretty good at this, but pagination and there's some issues on certain types of websites and there's it's difficult. It really can get complicated. And I have found that when I put this really advanced code or advanced for me, let me just say that it's advanced for me when I put it in there, I'm like, like, what is wrong with this? Like, I don't understand why I'm not getting the output or it's not working how I want. I'll just ask it, hey, debug this code. I'm hope I was expecting this, but I'm getting I'm getting an error and here's the error code. When I prompt it with this information, oftentimes like, you know, maybe 70% of the time it can actually help me fix that issue very quickly, which when you're working by yourself on these projects like typically I'm doing, you know, I don't have someone next to me who I can do, you know, down the hall or on my team who I can just ask to kind of help me with this, you know, the chat DVD has kind of been like a little personal coder helper for me, especially with the more advanced stuff. And I've been really, really impressed with how it's solved a lot of my issues that I had. And what's really interesting is also if I'm generating the code, I can take that code, try to implement it into my current code block that I have. And if I get an issue, I take it back and I put it back and then it debugs it and I get it back. So it's, I really feel like I'm having a conversation with chat GBT, generating my code, explaining my code, debugging my code, it's really helpful. It's really, really fascinating. And, you know, if you haven't used it yet, if you're just starting out coding again, you know, it could help you initially start learning these things. But when you really start getting into these larger projects, you know, this is such a great resource. I I've been using the free one for a lot, the free version for a long time and you can get, you can get a ton done with even the free version of chat GBT, which is just really great. As we go to the next one, I'm going to take another sip of water. So here's some considerations. And those are the seven things. Again, I'm doing all right on time. I also have a demo to get to. So here's some considerations. Chat GBT is definitely not perfect. And I think that's a good thing. Because if it was perfect, that'd be a bit freaky. But it's not perfect and it makes a lot of mistakes. And there are something called hallucinations. And if you haven't heard of that term yet, it's definitely like, it's becoming very popular because chat GBT will quote unquote hallucinate facts, it'll hallucinate numbers, even basic calculations, it can get wrong. And that's actually something that the chat GB, the open AI is really working on is for it to do basic calculations because it's just it has a really tough time with it. And it can't run code for you, right? So you have to have some type of ID or you have there are some limitations on what chat GBT can actually do. The hallucinations are actually really important. And I don't know if they're going to be going away anytime soon. You really need to fact check, fact check what what you're putting in there. For example, if you're asking more factual information, if you're saying, hey, you know, explains me what this doesn't sequel. And it gives you an output. You know, I've had times where I'm even like, I don't think that's correct. And then I'll, you know, I'm like, because I've used it enough, I'm like, that's not exactly what it does. And I'll go look it up. And it's like just ever so slightly wrong. But chat GBT has a way of writing it and the way that the the interfaces where it makes you seem it makes it really believable. And so sometimes it's like 90% of the way they're accuracy wise, that last 10% is wrong. And you really need to look out for that. And so I want to, you know, just get your expectations. It's not a perfect system. The next thing is don't put any sensitive information into chat GBT. Don't put your, you know, personal information don't put your company's personal information. Microsoft or not Microsoft. Who was it? It was some company that just had a big, a big article written about them because they're putting like trade secrets and documents in there that they should not have been. And so, you know, that stuff gets potentially leaked and it gets into the system because, you know, on a really simple scale, it's this reinforcement learning. It's learning and it has a feedback loop and it's taking in more data. And it could be taking your sensitive information and use it for different information. And so you don't want that. So the last thing that I want you to think about or remember is that chat GBT is really only as good as you make it. If you give it a super simple prompt like, what is this? It's going to give you a fairly simple answer, although sometimes it can do really well. But the more context you give it, the more information you give it, it will actually give you a lot better responses. And so the more specific you can be, the more information you can defeat it, the better context it has as a whole and the better kind of output it'll give you. So with that being said, we're going to jump on to my screen. I'm going to pull up chat GBT. I'm going to show you what it looks like if you haven't seen it before. And I'm going to give it a little bit of just a little demo of how to use it. We're not going to go through every single thing that we talked about all seven points. So that'll take way too long. But let me pull my screen really quickly. All right. I hope you can see my screen right now. Let's see if you can. OK, so I'm going to assume you are. And if you aren't, that's not good. Let's assume you are though. This is the interface for chat GBT, at least they're on their website chat.openai.com. Now, these are all my previous conversations. Don't look at this one right here. I was looking up John Sidious and his background information seeing what chat GBT knew about it. I read the forums, but you know, I wanted to see what chat GBT said. Anyways, right now I have I have actually paid for the plus. Now I have access to three different models. The default and it gives you some information over here. Some different models. Now I'm going to use chat GBT for which is their latest model. I'm going to go ahead and click on that. And let me hide this real quick. What I'm going to do is I'm going to give you a demonstration. I'm going to go over to this Excel and this is some dummy data, although this is accurate data. This is some Tesla information. Go ahead and copy this and I'm going to not paste it just yet, but I'm going to give it first some context. And then I'm going to paste it. Now I'm going to refresh this actually really quickly just to make sure it's fresh. And it's working. So here's what we're going to do. I'm going to say you are a data analyst. You are going to analyze data. For your company Tesla. So this is a data analyst at Tesla. That's the context. We're giving it at least. And I'm just going to I'm going to keep it simple because again. He's going to he's going to give us a response. I'm calling it a key, but chat. He's going to give us a response. I'm going to stop. It's going to keep going. But I want to show you how you can use it. Now it's going to understand that that's what it does. It's even to say as a data analyst for Tesla, my main goals is this. It's going to give you some information. Now I'm going to feed it in a data set. I'm going to say here is a data set you're working with. And I'm going to paste in this information and it's doesn't seem formatted. Well, let's just see what it says on this. And so it's going to be given the data site. We can derive some insights and perform analysis on various aspects. So it's again taking on this persona of he is a data analyst. Here is the data that I'm given. And as a data analyst, when I'm working with data here are some things that I should be doing. Now oftentimes when you're working with you input some information, it'll build out this little table that it'll remember for future conversations. So right now he's you know, or he but Chachi BT is you know, going to give us some output. I have not prompted it to do anything besides exactly what it is doing right now, which it's going to kind of just do its thing. Now while it's talking or while it's typing, I'm going to talk to you about what I'm going to do in just a little bit. I'm going to ask it to write some code for us. And because this was in Excel also have it write some Excel formulas for us. And I'm going to keep it fairly simple just so you know, because if I get to advanced it will take a long time for it actually to work. It could take you know, as you can see it's taking a while to write all these things out. So if I give it a long prompt, it could take a long time to generate the code. So I'm going to go ahead and stop it here. It's giving us actually some somewhat generalized information, but also some specific information. So here's what we're going to ask it. We are going to say these and I always like to be kind, please write a MySQL, a MySQL query to see which vehicle made us the most profit after expenses. And let me spell that right. So I'm saying I wanted to give us this information. Now I'm going to let it type, but I want to go back up really quickly. So we have our car price, the cars sold, so how many cars were sold and how much it costs to produce these vehicles. These are actually accurate. This is actually interesting and accurate information. I believe this is based on 2022, though, but it's going to, we haven't given any information about these columns, what data is in there. Let's go back down and see what it's giving us. So it's going to assume that the car or the table is called Tesla sales and it's going to infer information based off the data that's in the columns as well as the column names itself. So just generated this and this profit right here is exactly how I would have done it. Super simple. We have the car price. That's how much we're selling it for minus how much it costs to produce times the amount of cars sold. And that is our profit for that actual vehicle. So either the Model S or the three or whatever. So if I plugged this in, I would just copy this code. I plug it into a SQL database. If this was in SQL. Now let's say I had it in like a pandas data frame or a Polar's data frame. Let's say I wanted to convert this code into Python pandas and I'm going to go ahead and run this. And we're getting the same and this happens a lot with chat GT. I'm just going to let you know this is when you're using it. If you don't ask it right away. It sometimes doesn't work properly. Now this is a good example that it remembers this conversation because I had to refresh, but it kept this conversation in memory. And now I'm continuing this conversation saying convert this code into Python pandas. So it's going to go ahead and do that for us. Now this one is going to take a little bit. Usually imports pandas creates the data frame and then it's going to create the code using a data frame. So give it just a second. But the very last thing that I'm going to do is have a convert into an Excel formula. We'll take that Excel formula and we'll put it back into our Excel that we had. And that'll be the last thing that I'm going to demonstrate. Again, it's I can't express enough how just the board is pretty it's magical. It's just incredible. It's pretty it's pretty awesome to watch visually. It happened, but also to think about how it's working on the back end. It's incredible. And as a data analyst using stuff like this or not using this, you're going to spend a lot more time writing out this code. You're going to spend a lot more time possibly understanding your data. And so it's just again, it's a it's a it's an enhancer of your ability as well as a it just can allow you to do so much more. So this is the code. If I just glance at this, I'm going to take a look. Yeah, this looks perfectly fine because you're taking you're taking this data frame, you're taking that column, you're saying minus the production cost times that this all looks good. And then we're even it's even sorting it for us. So yeah, you know, it's it's going above and beyond even in this case and creating you know, this most profitable vehicle and that's printing it out. I didn't ask it to do that. Sometimes it does that. I didn't ask it, but that's what it did. Now I'm going to say, can you convert this formula into an Excel formula? I don't know if that's the best way to say it, but let's see if it understands it. Let me take a sip of water really quick. Now what's really great is it's ability context without you giving a context because you know, oftentimes and this is actually a really great example of it making mistake, but what's really great is it's going to say, you know, OK, if it's in Excel, it's starting in cell a one. So it's kind of it's just really, really interesting. Now, OK, it's it's it's giving us this really I believe it's converting this formula from this right here, which because it's using, let me see, we're figuring this out on the fly. This is, you know, this is part of using chat ABT, but it's using the profit. Let's see. Yeah, this this formula is not 100 percent correct. But what we're going to do is I'm going to take it anyway. Yeah, it's OK. It is it's converting. Actually, it's converting this Python code into a formula. So it didn't work exactly as I was actually hoping I was just wanting it to give it give us a really simple one. But it's me. There we go. Now, oh, it's formatted run. Now, I'm not going to I'm not going to try to get this to work because I don't want to waste any time. But if I had prompted it based off of my SQL one, I think it would have worked properly, but it did it based off of the the Python code. So, you know, it keeps everything in context keeps everything in memory. I asked it to convert this formula. And I'm sure it was thinking of this one. So that's some of the small limitations you need to be really specific in what you're asking and how you are asking it. Because again, if you're not very specific, it can often make mistakes like this. That was my fault, if I'm being honest, that was completely my fault. So with that being said, that is, you know, how you can use it. That's what it looks like. This is just a very quick demonstration. But there are lots of tutorials. I have some on my channel, but there's lots of ways and you can even look at their documentation on how to use it, how to prompt it, all of these things. Let me go back really quickly. I'm going to stop screen sharing. And I'm back. So that was just a quick demo. I didn't show you everything. I could have also asked it to comment the code, which maybe I should have done really quickly. But go and try out yourself. It's pretty incredible. We're not showing slides anymore. It's just me. There we go. It's pretty incredible. And the technology is just getting better and better and better. They released us back in November, and it's already made leaps. And it's already improved by leaps and bounds in such a short time. So here's what I'll just say in a nutshell is chat GBT is an amazing tool. That's how I see it is a fantastic tool. And if you're not using it, you're going to be wasting a lot of time. That's kind of now how I would perceive it is if you're if you're in a job where you're actively a data analyst and you're not using something like this for at least the simple stuff, you can't input sensitive information, you can't connect it to your database. But if you're not using it to for for template stuff that won't give away trade secrets or data or personal information, you know, I think you're you're using you're not using your resources effectively. I've just become such a big proponent of I'm such a big believer in it now, especially after using it since November, I've used it since it came out. It's just an incredible incredible tool. I know there's going to be 1000 questions in just a second. So I'm not going to go deep dive into a few other things that I could talk about. But I'm going to pass it over to Will. I'm going to let him talk for a little bit. And then we'll do some Q&A. And again, I'm looking forward to the Q&A because I can talk about this all day. So just I meet myself. Thank you, Alex, for that great presentation and also for the live the live work on chat GPT. I think it's great to see it working. But also how you just put in input one part and then the second part and the third part. So you get actually far removed from what you started off with. But you do see the inner workings of chat GPT. So I think it was super useful. There's been a lot of engagement on Big Market. There's also been a lot of engagement on YouTube and LinkedIn. If you've got any questions, it is now question time. So the floor is open. It's your time to ask questions about chat GPT. Or I know we have a lot of people watching this evening who are just starting their journey in data analytics. So if you've got any intro, you know, questions about data analytics, industry or career foundry, this is the time to ask the questions. You got a question, Alex? I'd like to say one thing. I am going to answer any question you guys have to the best of my ability. I promise you, but I will say I am not an expert in chat GPT. I actually use it a ton. I would consider myself an expert user, but I don't know everything about it. But I've used it for like a thousand different things and I find it amazing. But you just want to put that context out there. I'm not like claiming to be the sole source of truth on this. But I'll do my best to answer any questions you guys have. Any questions that we can't answer, we'll just refer everybody to chat GPT to ask those questions. I think actually the first question, which I think is a great one to start off with, because I know that Alex, you showed us the different versions of a chat GPT since it's been released. Bridget had a good question. Do you think paying for plus is worth it? As a beginner, no. I don't recommend it. And in fact, I didn't pay for it until maybe two months, maybe it was a month ago. It was recent. And I only did that because I wanted access to chat GPT for because I was doing more advanced things. The one of the big benefits of chat GPT for is it has a lot better responses. Let me refer to that. It doesn't have a lot better responses. It has slightly improved responses. It understands a little bit more of the context. For 90% of people out there, I think that using the free one is perfectly fine. Now, if you really want consistency and when you're using the free version, there are some data caps. If they have a high influx of volume, you won't be able to access it. That's a problem. But it's not required. I really don't think it is. But I think if you're going to use it enough, if you're using it once or twice a day, me personally, I use it multiple times a day for different things. And it's been extremely worth the money. I don't think it's necessary, but it's helpful if you have extra 20 bucks a month and you use it often. Great answer. Another question that's coming through, and I think this is a question which comes through specifically from people who are thinking about transitioning into data analytics, making a leap into the field. Do you think that CHAP GPT will replace the data analyst? That's also a question that's come through on Big Marga. Yeah, this is a question that I've been asking myself a lot recently. And I take this question extremely seriously. I in no way have I taken this question lightly because it affects a lot of people. And it's not just data analysis. It's data science, data engineering, database developers. It's going to affect every single piece of software users in some way, genuinely. So I take this question extremely to heart. I have a huge community and I want to do right by them and giving them a good response. I've been looking into this, I think more than the average person. And here's my take on it as of today, as of April 18, 2023. As of right now, I've used enough to understand a lot of its limitations. I've done enough research to understand its limitations. I think there are some very genuine limitations in what it can and will be able to do in the future. And I think at least I'm quite confident that the data that's going to be generated from these systems, the data that's actually going to be more usable because these systems, I believe, and again, this is my personal belief based off my research, is that it's actually going to cause people a lot more companies to be more data focused because they have to be, not because they want to be, but because they're going to be left in the dust if they don't. So millions of companies out there that have never had a data analytics team are now vying for data analysts who can come in and use their data because they have never done it before. I think larger, larger companies who have the infrastructure to actually really utilize it well will at least for the next 10 to 15 years is my estimate. Again, hard to say with how fast things have been improving. I don't think you'll see any real decrease or automation and jobs just because of a lot of ethical issues, a lot of data sensitivity, data protection issues. I think there will eventually be regulations from the a lot of governments around the world on use of this and within a lot of databases, public health information. There again, I've done dove into this too much to go into all the aspects, but there's some there's a lot more that is going on than I think what people think when they think automation they're just like, could it do its their job? My general genuine answer is right now it can't. And even if it could, I think there's going to be a lot more issues than just can it do your job? But how well can it do it? Is it legal? What are the ethical ramifications? There's a lot of things to consider. So again, I'm trying to give you the best answer I can without without being too confident. My gut, my research tells me that no, I don't think data analysis is going to be automated. In fact, I do think to an extent, it will actually be maybe for a short time. But even for an extent, I think it's going to be actually more in demand than before. That's my feeling of it. Now, you could take that however you'd like. I work in data analysis. I'm a huge proponent of data analysis. Everybody knows that. And you can you can take that how you'd like it. But that is my genuine belief as of today and has been for the past probably about a month since I really started diving into it. That's a great answer. And I think that aligns also with what we also invite other guests to talk a career foundry, a doctor who may I think also subscribe to that opinion, too. For those watching also Dr. Camero is doing an intro to data analytics workshop tomorrow. Just going to shame sleep like that one there. I'm just going to pick up on the regulation point that you made Alex, because we've had a great question coming on LinkedIn from Cynthia. What restrictions might be put in place regarding open AI in the future? I think it's not even I think I know a lot of companies and even countries are starting to ban it completely because they're having major data leaks. They're having data misinformation. They're having these hallucinations that are causing issues. I mean, there are so many positive and again, before I talk a little further, the positives for it to AI are are massive. They have a lot of implications for a lot of different things. There's so much upside, but there's also some downside. And some of those downsides, especially looking at a company level companies using this and where they don't have specific safeguards in place, there's a lot of there could be data leaks. There can be, you know, you're opening up your systems to, you know, really advanced software. And sometimes even people at chat, you'll be T or open AI are like, we don't know exactly how this is working. There are countries like Italy, as well as some other countries are starting to ban or are looking to ban it completely because of this this exact issue. And so companies are doing the exact same thing. I've looked at I've read a lot of companies read about a lot of larger companies that are like, okay, it's here's a there's a lot of upsides. But we're now starting to realize all the potential downsides by, you know, integrating these into our systems that we're not actually, we're not actually sure if it's worth it because of the potential lawsuits, the potential data issues, the potential misinformation just within their company. So yeah, there's going to be it's I'm talking about the US government specifically, they are so slow to act. I think it'll be several years before we have any real regulatory systems in place at a federal level. But certain countries are already starting to enact restrictions on it. You know, again, there's a lot of upside, but there actually is a lot of downside as well. And most people like myself, when I first started using it, I was like, there's no downside, this is amazing. But I consult with a lot of large companies, I work, I have worked with large companies and I've talked to a lot of experts in the industry who work at these large companies and a lot of them have said the same thing. They're managers, they're even seed levels that I've talked to and they're like, yeah, our companies decided not to use it. I'm like, oh, not use it at all. They're like, no, we banded on all company computers. You cannot have it. So I know personally somebody who's already done that. And I think it's only it just it's a matter of time for people start trusting it or not trusting it. And there's so many implications. Again, I'm trying not to be crazy, specific, but there's a lot of positive and negative implications of using it in a company. So companies need to be very, very careful on how they would integrate it, how they use it, whether it's actually helpful with their current data infrastructure and business system setup in place and data pipelines like, you know, it's it's a lot more. It's not as easy as people make it seem to even implement. It's extremely complex into actual data infrastructure. I think that's a great answer. And also reading between the lines of what you've been saying this evening, Alex, I think it's worthwhile before people jump into using chat GPT also to read around the subject to like check up blog posts, you know, check in the press, read about the implications of chat GPT. Whilst you're using it simultaneously. And I think that will put you in a much better position to understand not just, you know, how it works, but also some of the security concerns and maybe some of the ethical issues also around it too. So I think that's a great answer. Alex, I love it when we invite you on. We always get your crowd on YouTube and they love data cleaning. They're obsessed with data cleaning. I find it fantastic. We love to see that. We love the data cleaning crowd. There's room for everybody. But there's a great question on YouTube. Are there any specific prompts for data cleaning? How can chat GPT help with the data cleaning? Yeah, that's a great question. I was mentioning I did a video earlier on like some ways that you can use chat GPT in data analytics. And I showed data cleaning. It was a very, very simple prompt, which was, you know, how can I clean this data? That is, that's kind of my example of you're giving it no context. You're giving it no information. The more information you give it. Now, I haven't, I know I'm not an expert, nor am I like, like, I don't I'm not trying to push chat GPT on anybody. So I'm not like I'm a chat GPT list or whatever you want to call me. I just I really like it. So I don't think I haven't like developed prompts for this. But here's what I will say. I've used it. I've used it a few times for actual data cleaning for some projects that I was doing. And what I would personally do is I would say, OK, you are a lead data analyst. Here is the data that you're working with. You're also an expert in I think I was working with. Oh, what was it? I'm trying to think there's an I'm going to say health care data, but it wasn't that it was something else I was working with. But I was like, you're an expert health care data analyst. I was like, here's the data that you've been given, but you found some issues like the formatting and I would try to prompt it. The formatting in this seems to be off in this specific column. Here's the format that I wanted in. What it would do is it would generate the code from all these different formats that was there. And it would it would say, here's how you can generate the code to actually fix it. So then I would I would try it. It wouldn't work exactly right. I'll say this format isn't working with the code you generated. Can you also fix this type? So I don't have a prompt, but the more information you give it, the better it does. And especially with data cleaning, I like to be I have been very specific like this column. I believe these be formatted like this. How can I do that? And it does pretty well. It does a pretty good job. Thanks, Alex. I hope that's appeased the data cleaning crowd. I feel like I feel like we should do maybe a live event just on data cleaning in the future. I think I loved it. I do a whole webinar on data cleaning. I data cleaning is like I did that in my job for like three years straight. So I'm I'm a big fan. Wilson Wilson Wilson. Another question coming through on YouTube. And I suppose with everything that we spoken about this evening. Alex, in your opinion, is if I was going to say today is now the right time to start thinking about a career as a data analyst in light of everything that we spoken about this evening? Yes, another really good question. So I'm going to I'm going to take you back to five years ago or no, I'm getting older six years ago. Now, when I first became a data analyst, you know, when I was when I was there, the there was not this huge. I mean, data analysis was still very popular. But there's nothing that I think in recent years, about probably the past two to three years, data analysis become more popular because data science has become slightly less popular, if that makes sense. So data analysis has had this huge boom. At the very entry level, there's still a very large amount of people. There's a large amount of people trying to get in break into into tech. So just with just that six years ago, it was easier. There was not as much competition as there is now because of remote work and all these other factors. So right now, just as a whole, it is harder to break in. But I genuinely believe that within the coming year to years, we're going to see a lot more jobs open up, especially around entry level, with people who know tools like using chat GPT or can build more advanced projects. So if I were trying to get in today, I would say, you know, there's and I almost, I don't want to say like, I worked through this, but I went through about three months ago, I went through kind of this like, I don't want to say like existential crisis of like for like my community. But I very much was worried. I was very much worried. I was like, what implications does this have from my community, my data analyst community, like, will will this, like I was talking about, will this go away? Is this like not going to be a thing anymore? The more I've used it, the more I've researched it. The more I realized that, you know, there's still a lot of opportunity here. So if I were starting out today, I would just incorporate chat GPT into my learning or artificial intelligence tools into my learning after I learn the basics, or even maybe the intermediate stuff, because you can't really do the work of a data analyst without at least knowing like the concepts really well. So then you can use chat GPT because now you understand how these things work, how they integrate, how they work together. On top of that, if you can get domain knowledge, I think domain knowledge is going to be even more important in the future. My experience as a healthcare professional, you know, I think that was helpful when I first became a healthcare analyst, they've got help me get my first job. In the future, technical skills won't, I don't know if those will be the main selling point, they'll be very important. But maybe like 60 40 where if you know the domain, it may be even slightly more important than learning, knowing all the technical skills, because in a year or two years, it could be quite a bit easier to use those technical skills, even at the basic level. So no, I don't want to, I don't. Again, I really have been thinking about this a lot. I really don't think that this is something where people should be like, okay, chat dbt here AI has taken over I don't data analysis is going away I don't need to be doing anymore. I do I genuinely do think a lot of people have thought that about a lot of things like software engineering data science, etc. I genuinely think it's here to stay for a long time until there are some crazy breakthroughs in the AI space. I don't I don't think actually, let me make one more note. Sorry, I could talk about this for a long time. One more note is that you have to think about this from a business perspective as well. When you have a lot of data, you do not want managers, you do not want, you know, C level exact people doing data analysis work or asking these questions and getting rote responses from AI. If you actually think about it, even from a small company perspective, you want someone who knows this data, who understands the business value of this data can interpret it and can bring it to upper management stakeholders, etc. I don't see a world at least yet where there is not that part of it where we need someone to still like let's say data analysis gets completely automated tomorrow. Like me, I am turned into a robot and they can hire me for $10 an hour. Let's say that happens. They are still not going to want to go to the Alex robot and have to manually prompt it to ask questions, mainly prompted to all these things, work through issues with the AI side, they're going to have somebody who's going to do that. I just, I even that's in a perfect world in like let's say 20, 30 years. It's just really hard knowing its limitations, knowing a lot of the things that I've seen, issues that I've seen and read about in a ton of communities and forums that I've been researching. It's just really, really hard for me to believe that that's going to happen anytime soon, where we won't need data analysts. Great answer. Also, Alex has been talking a lot about his community. For anyone who's watching on Career Foundry, do check out Alex's YouTube channel, Alex the Analysts over on YouTube. Alex is closing in on 500,000 subscribers with your help this evening. We might be able to get a little bit closer there whilst we're here and because the Career Foundry YouTube team are in the background, I will also plug the Career Foundry YouTube channel. There's some great content over there on Data Analytics too. And also Alex reviewed Career Foundry's Data Analytics program a couple of months ago. So do check that out if you want a deep dive into what the program offers. Alex, we just talked about the past. Let's look towards the future. Horizon scanning. Where do you see this going in five years, 10 years? I know it's changing so fast. But where do you see this going in the five to 10 year range? Yeah, so I think I'm a little too involved in the AI community. I'm going to give you a comparison. When crypto really became a huge thing, crypto people were like, it is going to change the world. Here's exactly how it's going to change the world. It's happening tomorrow because they were so deeply delved into it that they could see what's going to happen. That doesn't exactly happen. With AI it's a little bit different. I have been so deep dived into this AI stuff. I kind of like, oh geez, this stuff is like right around the corner. Realistically, it's many years away. I see AI getting better. I see it being more efficient, especially as Microsoft has picked it up. They are spending an insane amount of money training these models to be accurate. Sam Altman who's the CEO of OpenAI even said himself, he's like it's not sustainable to train on this many parameters. He's like, we have to figure out better ways to do it. They're going to condense it. They're going to do it more efficiently, which is good for the end user for us in the end because that just means that these models will have faster iterations. We'll see more improvement. I'm going to talk a little bit about the negative that I see and then I'm going to talk about the positive. The negative that I see is I don't see hallucinations going away. In fact, OpenAI has openly admitted that they don't know how to solve this issue. They've tried to fine tune their models to make it more data accurate. It has to come from a certain source. Even then, they're seeing almost the same level of hallucinations. That's a very real and needing to be considered thing. I don't know how to say that, but it needs to be considered when you're using it because even as it gets more advanced in five, 10 years, we don't know if these hallucinations will ever go away and that's a problem. That's part of that automation piece that is concerning. If you have a data analyst telling you facts, that aren't facts. That's always a problem. That's some of the negative that I see. The positive that I see is I see these models getting better, more accurate, more efficient. I see them being integrated into a lot more things. A lot more things. I almost see AI being integrated into almost every aspect of the internet already. That's kind of my deep dive into things. I see it integrated into all these different products. It's just like one person on the internet doing it. I'm like, you give that a year and companies are going to do that. It's moving, but it's not as fast as I perceive it. I think that's three, five, ten years away where most companies, most platforms online are going to have AI in some way be part of their platform. Just understanding artificial intelligence, understanding these large language models which are going to change over time is important. One more thing I want to mention is that it's just changing so rapidly. If you cannot keep up, you are not the only one. I cannot keep up. I am always, every single day I'm looking at new stuff. I can't keep up with it. There's too much. What I will say is learn the basics. Start with chat GBT like what we looked at here online. That to me is like the basic building blocks. I'm now personally, I just started using auto GBT. I was running that on my local machine yesterday, running agents where they can automatically go out and do tasks. They are at a very low success rate right now, like 10%, 15%, but they are going to get better. These automators, these task automators or automation in general, it's going to get better. I genuinely see, I can see the horizon where these systems aren't going to be perfect and they're just not going to, I think they're going to change the world, but I do see a lot of issues with the actual quality of the products being put out. There's just, there are a lot of limitations and as you use it more, hopefully you'll see that as well. That's just not a perfect system, you know. Definitely, definitely. I'm trying to link some of the questions that are coming from Big Marker, LinkedIn and YouTube. So I'm just trying to link something together, but one question which is coming through is, and I know that a lot of people watching this evening are thinking about taking their first steps in data analytics. Alex, in your experience, for a junior data analyst or someone who's ready to break into the industry, what are the kind of tools that you would like to see to give that candidate a competitive edge? We're talking about just like any data analyst tools or are you talking about most of like AI tools? And data, just the tool, yeah, data analyst tools. Okay. So if you're just breaking in, I still think SQL is the number one thing you need to learn, even above, even above chat GBT, you know, SQL is like just one of the fundamental skills you need to know. Highly, highly, highly recommend learning it, becoming really good at it, building projects on it. It has a very low skill cap, but it has a extreme or it has a low skill cap to start. So low barrier of entry, but it has an extremely high skill cap. Some of the best data analysts, data engineers in the world, you know, are still learning things about SQL and they've been using it for 30 years. So SQL, number one thing, then your classic Excel, you got to know how to use that. And then I always recommend people start with Tableau Power BI for visualization because, you know, it's there, they're really big in the community. Most likely, if you go to a company, sometimes they won't use it. They'll use some other tool or they're use a tool that's within there that they built themselves or some third party tool usually has a lot of the same features. And you can figure it out very quickly if, you know, Tableau or Power BI. And then after that, I'm a big proponent of Python myself. I use it a ton, especially for data analysis. It's got so many libraries and packages that are great for data analysis. And so those are usually the ones I would start with. And then after that, you know, look at look at cloud platforms. Azure is going to be Azure is already big, but it's going to be bigger with AI. I'm being integrated into it. But AWS is always a great one to learn. I don't personally recommend Google cloud platform. I don't it's not the best one, in my opinion. But organizations use it for their use cases. And so you may have to learn it. And then learn AI tools, right? That's I just post on LinkedIn last week. I or maybe it's like maybe it's yesterday. I don't even remember time flies in my world. So I posted I was like, Hey, listen, you still need to learn the basics. You still need to learn the fundamentals. Don't just go straight to chat. GVT. But once you learn those, start learning how you can integrate chat. GVT or AI tools into your, you know, kind of your tool belt. Awesome. Fantastic. And also as a junior data analyst or someone who's breaking into the industry, what's the what's the importance of a portfolio when you're going to job into these? Sure. Maybe you elaborate on that. Love portfolios that helped me land a few jobs myself. Portfolios are still extremely relevant. I really think it sets you above people who don't have them, which is a portfolio is good for two things. One, it's good for helping you get an interview. So you can send your portfolio on your resume as a link and your contact and the portfolio is going to have projects. It's going to have like tangible projects that a hiring manager or a recruiter can look at. They can look at it and they can be like, Oh, this guy has like four different projects in Tableau. We needed someone who knows Tableau. They look at your projects and they're like, these are pretty good. So that helps you get the interview. Better than that though, is it's extremely helpful for people who have no experience because you don't want to go in there and they'll ask you, you know, how have you SQL in your job and you're like, how actually I have no experience using SQL in my job because I've never been a data analyst. Terrible answer. That's not what you want. That's why you have a portfolio where you have SQL projects. That's just one tool, but you have projects in SQL. You can say, I actually just built out this project where I, you know, I took this data set and performed these things on it in SQL and I use it. You know, I worked with it with data cleaning and exploratory data analysis and, you know, automation with sort of procedures and triggers and all these things. And you can say you, you can talk to that. Instead of saying I don't have any experience, you can say I have experience building these projects. I think that is by far the biggest reason to have them as well. It will help you. It'll help you see much more credible and look more credible as well as you then have something to talk about when they ask you your experience on these skills. The other thing you don't want to say is I've taken some courses on SQL. You need to take courses on SQL. Do it. But you don't want to say that in an interview. You want to say, well, I've built these projects and here I use case statements. I know joins. I know unions. I know window functions. I know store procedures. That's what I've used. And then they're like, oh, wow, we should hire this guy because he has the skills that we need in SQL. So, you know, it's all how you frame things in interviews and you don't want to frame it that you don't know what you're doing, even though you have no experience. You do have experience if you've built projects. That is your experience until you get a job paid experience. Fantastic. And I would also say at this point that just to get back to career foundry, but the career foundry data analytics program, the key takeaway is the portfolio. So you work during the program on the portfolio and the great thing as Alex just said is that when you go into that hiring meeting, you will be able to talk through the projects that you've worked on and explain exactly what you did step to step. And it works like Alex said, it's a great, great guide to have. A couple more questions coming through Alex on YouTube, but also on Big Marker. You know, where are you keeping up to date on all this information about AI or specifically, you know, data analytics in general, how do you keep up to date with the industry? Okay, so I'm on a lot of places. I also have a big community that sends me a lot of things. So me personally, I get sent a lot of things via email, LinkedIn, Twitter almost every day, a lot. So I can't it's hard to keep up with all of it. But me myself, if I'm just scrolling, I actually have found Twitter to be the best place for AI content. And I just follow a lot of people in AI who are researchers at Google and Microsoft to who post updates as well as people who are doing open source projects in AI. I'm a huge believer in open source things. Just open source as much as you can because, you know, it allows people to have insights into these advancements that are happening live instead of keeping it behind a wall. So I follow a lot of GitHub repos as well. A lot of people on Twitter who post these GitHub repos, then I go and follow them. But I personally have found Twitter to be the best source for AI content in my opinion. Awesome, awesome, awesome. Yeah, there's loads of free content out there. Great blogs. I am mindful of the time, Alex. I don't want to take so much of your time. There was a fantastic presentation this evening. Thank you so much for everybody's questions from the audience. Great to see all the interaction on Big Marker on LinkedIn on YouTube. And thanks so much for Alex's crowd storming YouTube. And we love to see you here. And just in terms of free content, as Alex mentioned, I'm just posting in Big Marker, a link to Career Foundry short course. So if you are interested in data analytics and you want to do a free five day short course, do check that out. Also do check out the Career Foundry blog. We've got a lot of editors working in the background, writing some great blog articles on data analytics. Also some more general articles on career change. And so do check out the Career Foundry blog. Also, whilst I'm here, let me just switch over one slide. We are currently offering a Career Foundry Career Change Scholarship. So if you've listened to Alex this evening, you've been inspired by chat GPT and you've been inspired by data analytics. And you're thinking about, you know, jumping into the industry. We are offering a career change scholarship of data analytics program for the first 50 career changes. If you're watching over on Alex's YouTube channel, if you just go down, you can book a call with a program advisor, you can talk about the career change scholarship, but also whether you would be a good fit for a career in data analytics. And that's pretty much it from my end. Alex, thank you so much for joining us this evening. I think you shared, I love that you share such candid knowledge and all your different insights on data analytics industry and chat GPT. It's always a pleasure to have you on the channel. And for everybody watching who hasn't already done so, do subscribe to Alex's YouTube channel, Alex the Analyst over on YouTube. There is some fantastic content there. Alex is a rock star in the world of data analytics and do check out Alex's YouTube channel. Alex, that's it. Thank you so much for joining us this evening. And we're going to be seeing you, I think in soon, you're coming back to the channel soon. So do also check out the career foundry events page for all upcoming events and also do join us tomorrow where Dr. Humara will be doing a intro to data analytics skills workshop where we're going to be taking some raw data and taking it right the way through towards visualization and presentation. And yeah, thank you for everyone joining this evening. And we will see you again next time.