 Hi everybody, welcome to another Career Foundry event this evening, where I'm joined once again by Alex Freberg, who is a analytics manager, but you may know him as the face of Alex the analyst, which is a fantastic YouTube channel, which you should definitely check out. I'm William, events and communications lead here at Career Foundry. Now we've got a little, we'll just do this here. I know we've got a lot of people joining this evening. So if you just want to drop in the right hand side, maybe where you're joining from, why you're interested in data analytics, and also maybe some study hacks that you have yourself. We want to make this as interactive as possible. So do drop your comments on YouTube or Big Marker. We'd love to hear from you. Now, let me just introduce Career Foundry very briefly. Career Foundry is the online school for your career change into tech. We provide lots of different programs, but the one that you're probably most interested in tonight is the data analytics program. So do check that out on the Career Foundry website. We offer a dual mentorship model, which has a mentor and a tutor, but we also have a job guarantee. So if you don't find a job within six months of graduating, we refund you your tuition fee. And yeah, that's almost all that I have. Alex is going to be doing the presentation this evening. We're going to also be doing a live Q&A. So do drop your questions in the sidebar for the end. I'm going to disappear and when I reappear, I'm going to pose some of those questions, some of the best ones, but as many as we can for Alex. And Alex, I think that's about everything from me. I'm going to pass it over to you. Thank you so much for joining us once again. We loved your webinar last time and looking forward to the study hacks for data analytics students. Thank you. Thank you. I'm reading. I don't think I'm supposed to be doing this, but I'm reading the chat and I see everybody joining in from literally all over the world, which is insane. I'm seeing like Nepal and Mexico and I think I saw Argentina like it's in New York. It's crazy. There's so many people out there. So thank you guys so much for joining. You've gone to Brazil. Gone, I mean, it's everywhere. Thank you guys so much for joining and thank you for Career Foundry for hosting this. I love doing stuff like this. And you may not have noticed, but I have my camera over here, my computer over here. So if you see me looking back and forth, I'm just making sure I'm not missing anything while we're going through this. To preface a little bit of this, when Career Foundry asked me to do something like this, I was like, okay, study hacks. I was like, here's what I do. Here's how I study. And as I was actually writing it down, I was like, man, the way I study has changed dramatically over the years. Like I used to be in college and studying at the library for like 12 hours straight. And now that I've gotten older and things have changed, I've kind of gotten more professional about it. I'm realizing that the way I used to study was absolutely horrible. And that's probably why in certain classes I didn't do as well. But it's, you know, really, really important skill, especially when you're first starting out to be able to know how to study. And it's kind of overwhelming. And I'm going to talk a lot about my techniques and the things that I've learned over the years about studying specifically data analytics, but just, you know, study habits in general. But as you guys know, if you guys have watched my channel at all, you know, when I first started out, I was just studying for hours on end and just like trying to absorb as much as I possibly could. And I know I wasted a ton of time doing that. And I haven't really talked about it on my channel, which is why it's such a great topic now because I've never covered it on my channel. So this is like something for not just the career foundry people, but also for my YouTube where it's also streaming live. So yeah, this is our study hacks for data analytics students. Are we on slide two now? Am I on the wrong one? Let me see. I'm not sure if I'm actually changing this. Did I change that? Will isn't here to help me. Okay, I hope you're seeing actually, let me go to the audience for you real quick. I'm sorry guys, give me a second. I want to make sure I'm not like showing you something or going too far ahead or whatever. Give me one second. Okay, perfect. I was trying to go too far when I click that next slide. It goes to the next slide. Correct. Exactly. We can all see that. Thank you. I appreciate it. Okay. I'll introduce myself just a little bit in case you haven't seen me. If you're, you know, on the career foundry side and you've never heard of who I am. I'm, I call myself a YouTuber for the people on YouTube, but I'm mostly analytics. I just kind of do YouTube as like a really fun side thing that I've grown quite a bit. But as of right now, I just quit my job about four weeks ago as an analytics manager. I was a big fortune 10 company. And I quit my job to kind of start my own consulting business. And it's, you know, it's gone really well. And I'm super excited about it, but that's a big change. I literally had to change this about me section like 30 minutes ago. Cause I'm like, wait a second. I just quit my job. I got to redo that. But I work a lot with a lot of technical skills, a lot of cloud platforms. That's kind of like what I've been specializing in as well as sequel. If you guys know me, I love sequel. And yeah, that's my YouTube channel. If you want to check me out, please do. So let's go on to number one. Now, as I was doing all of this, and I was just talking about this before we started, I was like, you know, a lot of these things I've been doing for a while. And I've, I've started to kind of build these habits and I know what they are. And I, I just didn't have a name for it. And so as I'm like, you know, this is how I study, is there a name for this? This is kind of how I came up. I found this. It's called the chunking method. Now, if you read right here, it literally says the process of taking individual pieces of information and grouping them into larger units. Now, if you've ever done any of my tutorials or you've taken a course on like you to me or something like that, it's very similar to that style. It's very similar to it breaks it up into little chunks. And the issue that I used to have is I just used to go nonstop. I used to just try to absorb as much information as I possibly could. Let's take sequel, for example. Let's say you're learning the basics of sequel, like I did. I would try to learn the basics of sequel and like as quickly as I possibly could and just keep going and never stop. The issue with that is that I never really retained anything. I really had an issue, especially when I was first starting out, because I didn't apply it or I didn't do anything else. I had a huge issue with just kind of almost getting burnt out and getting myself kind of lost in the process. So I was seeing a lot of information. I wasn't absorbing it. So this chunking really helped me absorb the information a lot better. So this one is probably one of the more simple ones. One that anybody can do, anybody can apply by just taking individual topics and grouping them together. And people tend to remember better this way. I mean, there's a lot of studies on this. Now, I usually try to get into some details when I do stuff like this, but I don't have all the time in the world. But I was reading about this chunking method. And there's a lot of studies that have been done on it. And it genuinely just helps people remember better. And so this is something that everybody should be able to apply pretty quickly. The other thing that I think I had a huge issue with was trying to learn everything at once, not just in SQL, but I would switch from SQL to Excel to Tableau to Python to all these different things in one night. And I remember being very confused when I first started out because I'm like, I don't understand how all of these things fit together. And so what I tried to do or what I do now and what I started to learn how to do was I need to kind of compartmentalize these skills. I need to learn them each individually. How can I use them individually? And then eventually they will come together as I apply them. And so something like SQL, I had no idea how it worked with Excel or why that was important. I had no idea how it worked with Tableau, but I was trying to learn all at one time and I just was not going well. So this chunking method is, I think, a really, really good one. Let's go into number two. This one I think you can imagine I've done for a long time, several, several years. It's called the protege effect. Now I'm going to read it and I'll explain kind of how I've used it. It's a psychological phenomenon where teaching or pretending to teach or preparing to teach information to others helps the person learn that information. Now, if you guys know me, I literally have a YouTube channel where I teach analytics. Like I do tutorials, I do all these different things. And I'm going to be completely honest with you. There are times where I don't know 100% of the stuff that I'm teaching. Now let me take a step back. I know probably 90% of it. And as I'm actually preparing my notes, I'm preparing my information, I'll run into something and I'm like, wait a second. I kind of knew about that, but I didn't really know about it. So then I had to then dive into that and teach it and learn it more so that when I taught somebody, when I put it up on my channel, then everybody understood it. Now, I didn't know that that's what I was doing. I didn't know it was called the protege effect, but I know these technical skills 10 times better than I probably did three years ago when I first started out just because I'm now teaching it. I'm forcing myself to do this. I do this now very intentionally, but I used to do it very unintentionally. When I first started learning these things, I'd go to work and I used to sit next to somebody who's very experienced who literally had 10 years on me. And I'd be like, hey, have you ever done this? Let me show you how to do this. And he was super patient, just a great guy. And he'd be like, yeah, show me how to do that. And I would teach him and then he would be like, well, have you tried doing this? And then he'd show me a different way because he had done something different. You can actually teach somebody or you can pretend to teach someone, but if you have almost like a sounding board when you're doing this teaching, it can go extremely, extremely well. This guy I sat next to him for probably two years until COVID hit. And he was just a great sounding board. He was 10 times more experienced than I was, but of course I'm in this learning, building mode. And so I'm always coming in with different ideas, different things, trying to not necessarily teach him, show him, hey, here's what I've learned, this is how I apply it. So I was trying to teach him in a way. Now, how can you apply this to yourself? Well, hopefully you may have someone like that who you're learning with. It's very beneficial to study with someone or with a group. That is very helpful. So if you can actually teaching somebody that, that's really, really beneficial. If you cannot, you can even just type up notes, create some type of PDF. One thing that I've seen a lot of people doing, something that I've done myself, but one thing that I've seen a lot of people doing, especially on LinkedIn is they will teach their LinkedIn audience. So they will go and they will prepare like a PDF or they'll prepare some notes so that they can share. And then they'll put it up on there and explain it to them. Almost every time I'll go into the comments and I'll see somebody who'll be like, well, what about this? And the person will reply, that's a really good point. I should have thought about that or I should have included that. And it doesn't make them look stupid. It doesn't make them look dumb. It makes them look like, hey, I'm learning more information. So by teaching, you're absolutely learning. And this is something that, you know, especially since I've been doing my channel for several years, this is something that I can 100% attest to has helped me grow my skills, especially in Python. You know, that's one skill that when I started my Python series, I was like, okay, I know most of this, but it's extremely technical and I'm more in depth than kind of what I'm used to. So I had to kind of really go in depth and learn it a lot better to make sure I didn't look, you know, I didn't look dumb when I was presenting it. That was kind of my hope with that. And just like that next point, teaching something forces you to dig deeper into that topic and understand the code better. It just, it's forcing you, like, you don't, everybody wants to come off as intelligent and you don't want to like go and tell somebody something that is completely wrong. All right, so let's go on to the next one. The next one is build projects and this is, this is one that I really push hard on my channel. I just, in personal, when I work with people one-on-one, I almost always tell them the same thing and I usually get some type of pushback. For whatever reason, people are a bit nervous to really put their skills to the test with projects. That's what I've seen. It's more, it's not about the drive to do it like they want to do it, but they're nervous to start. That's where a lot of this comes in. So the first thing is that building a project forces you to encounter errors you wouldn't get in a course. Let's say you do the career foundry course, right? You get told a lot of things, you follow a curriculum, but until you actually start applying it, it's going to be fairly straightforward. They do a lot of heavy lifting for you. Building something with your own two hands and actually going and doing it, you're going to run into a lot of issues. For example, one of the hardest things for me when I was first starting out was actually getting my SQL server to run. I couldn't even get it set up. I was just having issues. It took me days to even figure out that one issue and I'm like, okay, this is a bad sign. I can't even get it going. And then finally I got it up and running. I figured out the issue. I Googled a thousand different things and I got it running. And now I know how to do it every single time. I know all the errors that I could encounter when I'm starting up my SQL server, whether it's a name, a password on the back end, in a net connection, whatever they could be. I've ran into all of them. And so you really learn better by making mistakes as painful as it is. Now, how does this relate to building projects? Well, building a project, and there are even courses and videos and whatever that will teach you how to build projects from scratch. And I highly encourage them. I have a whole series. But a lot of people will say, when I'm watching them and me and myself included, is when you're watching them, they're doing the work for you. You are watching and you're absorbing, which I think is a fantastic first step. You need a place to start if you've never done it before. So you're absorbing that information. You're doing it with them. Most of the time you'll encounter very few issues. You'll encounter one or two here. There's a data type that's different. This is some small difference. But when you go and you take your own data set and you start building it, there is just something about doing it on your own that makes you make 100 mistakes. And it can be slightly discouraging, like I was saying at first, because you're so used to kind of getting it fed to you. And that's why I recommend building projects so much because you're going to get out of your comfort zone. You're going to get out of, what did I call it? It's called, I didn't write it down. I should have wrote it down as a note, but it's called course purgatory. This is a video I made a long time ago. I called it course purgatory because you get caught in these courses and you eventually need to break out of it and start making mistakes because you just can't go any further. The next point says you'll be forced to problem solve and use the things you've learned in a different way, to help you remember it better. I'm going to give you an example because this was one that I think helped me learn Python, some of the most when I was first starting out. I was learning Python and Python to me was incredibly tough. It took me, I would say, vastly longer than the average person learned Python. Like it probably took me about four months to learn the basics of like a for loop and a while loop and stuff that I feel like I should have learned a lot faster. But I was mostly doing like really beginner tutorials. I wasn't branching out and trying my own stuff. So I decided to build this project where I was doing web scraping and this was new to me, but I really like was really passionate about it. I really wanted to do it. So I built this project and I'm actually wearing the watch that I'm going to be talking about. I wanted to buy a watch and I knew that Amazon was about to have their Black Friday sale and I wanted to know immediately like when it went live and there are apps for this all now, but when I did this like six years ago, it wasn't the apps weren't as easy to access or popular. So I was trying to do it myself. And I set up this web scraper that was going to scrape it in the back and what I needed to do is when it reached a certain point, like when it reached a price of under $90, it would send me an email and say, hey Alex, you got to go buy this right now. I ran into probably 1000 different issues with that. Every issue you could possibly imagine I ran into it. And so me now making tutorials on web scraping, I'm bypassing a lot of those issues. I'm telling you the way that I know how to do it without ever you encountering those issues. So by then you taking my project, you're not running into those issues and learning it as good as I did when I was starting out. So you have to then branch away from someone like me teaching you how to do a web scraping and you have to go build it yourself and make those mistakes. Again, that's a really uncomfortable one for a lot of people. I was saying that at the beginning, it's an uncomfortable one because you feel happy feeling like you know it, but until you apply until you build something, you don't really know it. So that is building projects. Let's go on to the next one. Next one is getting rid of distractions. I used to lie to myself a lot around this topic specifically because I used to be the guy who would put the office on, it used to be on Netflix. I had it on Netflix. I had the office on is one of my favorite shows if you've ever seen it. I used to put it on in the background. I used to say I learned better when I have background noise. I learned better when I am listening to something. Now I had seen the office a thousand times. I could quote it. So I was like, okay, I'm not actually watching it. I'm just listening. And so I did that for years, you know, especially in college. That was probably one of my biggest downfalls in college. I always felt like I was better studying with something in the background. And a little bit I'll kind of get to some alternatives. But for me, Netflix and just watching TV was a big, big, big distraction. I didn't really think it was. And so I would spend 10 hours studying, but I'd probably really only studied for six. And then I didn't actually do any grouping. So I was just missing everything together. And I just was, I was bad at studying. And it really is a skill that you can hone in and get better at. So distractions is one that you kind of, you just need to get better at. We need, as a whole, most people do. Now, I apologize for the mistake. That was my writing. But, oh, I literally even wrote it. Netflix in the background doesn't help. It didn't help me. I thought it did. It doesn't. So what do I recommend? Well, I actually think that background noise is not a bad thing. So listening to some music or something is not a bad thing to do. The thing is, is for me, I would then look up at my screen. And then I would completely like, stop studying and get out of that mental zone. So I was like, in this zone of studying, I'd be doing really well. And then something funny would happen on the screen. I'm like, let me, let me watch that for a second. Then 15 minutes goes by. And I'm like, all right, I got to get back to study. And then I have to like reevaluate where I am, what I'm, what I'm learning, what I'm studying. So getting rid of these distractions is, it was a tough one for me. I would say I did that for probably like, maybe five years all the way through college and then out of college. When I first started learning, then as my time became more, I don't want to say it, not as valuable. I'll say valuable. I was trying to break into data analytics. And I was finding that I was getting distracted. And I was like, I, I have, I got kids. I have a wife. This time that I'm spending studying is valuable. Like I need to be able to focus. I was like, I have to start turning it off. So I eventually logged out of the Netflix account on my computer, on my phone and on our TV to just not have it anymore. The next thing that then came in was YouTube. So then what I had to do was I had to literally log out of YouTube. So there's a lot of distractions and everybody has them. Another one I said, another tip is putting your phone in your bag. If you're anything like me, I have two people that text me. That's it. There's no one else. It's my wife and probably my mom. There's only two people. Well, I have like a friend. I have one of two friends, very, very few people, but I would text them pretty consistently. And every time they texted me, I was like, okay, I got to answer. I know there's only two people texting me. My mom or my wife, I got to, I got to focus. I got to like respond to them. What I started doing was just text them. Hey, I'm about, I'm going into study mode, you know, just so you know, I'll get back to you a little bit. If it's an emergency call me and I had that setting on my phone. So this one just putting your phone in your bag really references like getting rid of it as a distraction. I now spend and I get the, I have an iPhone. So it kind of tells me how much screen time I have. I used to spend about 10 hours a day, like even not so long ago, just cause now I do a lot of my work on my phone and social media, but I used to probably spend 10, 11 hours on my phone. It was horrible. I'm down to about five hours, which still feels like a ton, but you should check them on your phone. It's, it's insane. So then the last tip is going to be dedicate your time to focusing only on what you're learning. And it's going to come a lot faster. What that means is, is I have learned that I work a lot better in the mornings. I, if I get up early and I, now it's just work, but when I was in study mode, when I would get up and study in the morning, I actually learned that I was, I had much less distractions cause nobody was awake yet. So no one was texting me. Um, you know, I was still like waking up. I was drinking my coffee and, and I could just focus better. So that became my dedicated time to study. Now, once I got married and had kids and a full-time job, nighttime became my time to study, but I set that time apart once all my kids went to sleep. Once my wife went to sleep, I would stay up from, I think it was an 8 30 PM until 11 30 PM every single night studying and sometimes longer. But that was like when I was first starting out, I had to have, I had to make that time count. I had to make it useful. And so it can be really tough. Again, these tips are not like the easiest things in the world. Kind of have to push yourself a little bit, but getting rid of distractions is a really important one. I think helped me a lot. It's going to be the last one, uh, taking breaks. I, I've said this for almost all of these. I, these are all of these tips, all of these, um, best ways to studies are all ones that I kind of had to break myself out of, um, because I used to never take breaks. I, it just wasn't something I did. Um, and so I just had to like all of these tips. I'm telling you are like after years of doing it the wrong way, I've, I've, I now know how to do it. I'm really good at it, but it took me like three, four or five years to learn and know how to study right. This one taking breaks was kind of counterintuitive to me. I have very little time. I have three hours at the end of each, each night. Um, and I'm working, playing with my kids, doing other stuff during the day. So I have a very limited time. So why on earth would I take a break? That sounds like a terrible idea. Um, well, what was happening, especially, uh, when I, before I got my first state analyst job, I had those three hours that I would do those three hours straight. And I was doing breaking all of these rules. So every rule that you see here, I was breaking and I was really hindering myself. I was had Netflix on, um, excuse me. I, I wasn't, you know, learning skill by skill or doing that chunking method. Uh, and taking breaks was not, uh, not at the top of my list. Uh, so I was doing all of these things wrong. Now I'm very aware that if I go anywhere past about an hour and a half, now, let me see if I say, if I go out. Yeah. Okay. So I said, if I go four hours straight, I remember an hour of it. This is, this has become, it started to become an issue when I was actually in my real job, because what I would do in my real job as well as what studying on the side, cause I did both, um, all the time was I would do my real work for like 70 hours straight and I would get up and I'm like, okay, I do not remember what I did. I don't, I feel exhausted. I didn't feel good. I would need a coffee to like focus. Like it was just a lot of bad things. So this taking breaks became a lot more important as I progressed further in my career. Cause I'm like, okay, you know, I'm starting to get to a place professionally where this stuff, this isn't working anymore. Like I need to take breaks. So when I studied what I was usually doing was about an hour and a half. Like break of that three hours into an hour and a half. So I do an hour and a half. I take about like just even a five minute break was so refreshing. I would just go get a drink of water, a little snack because I don't know if it's me and it could just be me, but I get exhausted studying. Like I'm at the end of it, I'm like, I need water. I need a snack. I have not, I'm like dehydrated. I'm hangry. Like I'm just, I'm a different person if I don't take breaks. Um, and my wife can attest to this. Like I'm just a different person. I need, I need breaks with my work and I need breaks when studying. And so just something to, something to think about. Uh, the next point is breaks, allow your mind to absorb and remember what you've learned. So not only is it just good physically, you know, you can then start like process things and think about things. The issue with going long or going, studying for a long time that I found is that I, I don't really take a second to really think about it. I just go. Um, this was especially prevalent when I was learning Python, which took me like four months, which it should not have taken me four months. I would just go, go, go, go, go, go, go. And I never stopped to stop to think. Um, and then when I started taking breaks, I'm like, wait, if I just stop for a second and actually think about what I'm doing. Um, and really like just give myself a brain, give my brain a second to think, I can remember this stuff a lot better. And so not only was it helpful just for my body, uh, to actually step away from the computer. My eyes get a break. My brain gets a break. I can rehydrate because when I start studying, I don't remember to like drink water. Um, give me a second. That was on purpose. I wasn't actually thirsty. I just wanted to do it to make a point. Oh, it's good to take a break. Um, and just drink some water. Cause I never remembered to do that when I was studying. I would just go like, I would just non-stop. I was a somewhat semi-abs substantive. And then the last one we kind of talked about already, which is, uh, take a break to snack, stretch, refill your body to study more. I'm not telling you not to study. If you have eight hours to study, use seven of that to study. Use an hour to take chunks to kind of break it up because I have done that many times, especially in college. I just, I, my body was just, I was always exhausted. I was always tired. So it didn't really, I don't think it really was beneficial to me in the long run. I know it wasn't beneficial to push myself that hard, um, and get as exhausted as I did. It just maybe wasn't the best method. So, uh, these are my five tips. Now how, how, how are you going to actually use these? Start small. Like when you're reading through these, when you're listening to these, I am sure that it, almost everybody out there can be like, okay, yeah, I watched the office two in the background, Alex, that's me, but I'm not giving it up. I don't want to do it. That could be one thing, right? That could be just one thing. So don't, you don't have to apply these all at once. Most of these came, came at points where I didn't have a choice. I like had to do them or I was getting so exhausted, I needed to do them. Um, so identify one of these that you can do and try to start small and just build from there because studying is kind of a lifelong thing. I, I have to do a lot of research and study a lot of things for my kind of work as new data sets come to me, I have to research different domains and different things. So I still apply it to this day. Even though I'm, you know, a professional who isn't studying to get into the field anymore, I still study all the time and all of these things I do, I wouldn't say a hundred percent perfectly. I definitely make, I definitely, there are times where I forget to take breaks or, you know, I have my phone on and my wife texts me and I get distracted. But more often than not, I'm applying these almost, almost every time. It's just, you have to think about it. You have to make a concerted effort. And so a lot of these things are fairly, um, are fairly easy to do. Um, if you set your mind to it, like building a project, you, you get to a certain level and it's time to start building projects. It's time to push yourself to make yourself uncomfortable. Taking breaks is something you can just schedule with your phone. The protege effect, you can, you know, find somebody to talk to, to mentor, to even go on discord or Slack or somewhere where you can get into a group where you can try to like teach or put it on LinkedIn or you can do the, the chunking method which is just don't learn everything at once. Just chunk it up by different skills. And so all of these things have their place. They can all of the implement, you just have to do it. And that's tough. So start small, build big. Um, I think I'm a little undrawn time, but I think that's okay because what I believe we're going to do is open up to a Q and A time where you guys can ask questions or they may have questions already, but I didn't get to read any of the chat during this time. So I'm sure Will's going to, oh, hey Will, I'm ready for you to come back. He told me to say that. So I'm, I'm sure we have a lot of questions or questions that can be asked and I am ready to answer just about any question you have within reason, of course. Alex, thank you so much for the presentation. Great study hacks. Anybody watching in the audience, this is now your chance to ask your questions to Alex. So anything about data analytics, we're opening up the floor. So just drop them on big market or those watching on YouTube. I've got some colleagues in the background who are going to be taking YouTube questions as well. Um, just to kick this off Alex, I've got a couple of questions. Um, let's, let's just play this informal. Um, what would, um, one piece of advice that you would give your younger self regarding your career in data analytics if you could. So just in data analytics as a whole. In data analytics as a whole, yeah. Um, give my younger self. Um, I want, I have a thousand things, but maybe I'll try to give like one or two and I won't talk too long. I want them because I could talk about these forever. Um, the first thing would be apply to jobs before you feel ready. Um, that's something that, uh, I think I waited slightly too long. I just, I was, I had in my head that a everyone else was going to be way, way smarter and better than I was. And so I was like, okay, I got to make sure I know this really well before I apply. Uh, and that's, you know, that's somewhat true. You need to know it, but I mean, I have seen it and I have hired people who are not, don't have 10 years of experience or five years of experience. You know, you just have to do it. I wish I had started applying earlier. Um, before I felt like ready. Um, the next thing that I would do is, and this would have saved me months and I, that's not an exaggeration, actually months of studying. Um, which is identifying what I actually needed to know. I started learning a lot of like machine learning, artificial intelligence, natural language processing. I like deep dove in that even though I wanted to stay in analytics, I had no, I had no intention of going into like data science. Um, I just thought it was interesting and I started like deep diving into it. And if I had just identified what I needed to know, that was a time where I just, I wasn't sure what I needed. Um, I didn't, I didn't have a mentor at that time or anyone who I was like learning from. So I wasted probably like two or three good months of studying like stuff that I almost have not used almost ever in my career. So that would be probably my, some of my two biggest tips like quick tips, but I could go on forever. You know, if you want me to name 20, I could probably do that fairly easily. Another question following on from what you just said, do you find it with data analytics jobs? I know that happens in UX and UI because we do a lot of workshops on that side is that when job postings are put out, um, companies put in everything they want to have. So they put in so much information. They make the job super fancy. Um, is it the case that you should just apply even if you don't have all those skills that they're looking for? Or is it a case that, um, okay. And I'll tell you this because I was a hiring manager and I was on a hiring manager before, even when I wasn't the hiring manager. So I have like three years of, you know, understanding this a lot more in depth, um, which was really eye-opening when I first started. So what you're basically saying is, is, you know, when they're creating these job descriptions, which I have, you know, do they just put everything on there to answer your question in, in, uh, in just one word is mostly. Yes, that's true. Um, I, we had a few vacancies, one for, uh, database developer and then one for a data analyst on my team that I was at my previous job. And when we made these postings, I was like, okay, here's, here's my must haves. I was like, they need to know service now, uh, which is a Microsoft, uh, product. And they need to know SQL and Excel. Like that's it. That's all they need to know. Like, even if they have just a bit of experience in these, I would take it. More would be better, of course. But if they just have the basics of this, this would be great. But because this was a mid-level position, I couldn't just put that. I, my boss would not let me. He was like, okay, yes, that's all you need. But you need to put on more to fill out, fill it out, you know, make it more competitive, make the people who have more skills apply. That was kind of the, like, thought process from him. I didn't fully agree with it. I didn't really like that. I just wanted people who have those skills. But that is a very real thing. Um, and it usually comes from the top down. You want the bad, and the logic makes sense. And I'm not, like, mad about how we did it. But you want the best, the best value for your money. So if I'm going to pay a, you know, a data analyst 75 or 85,000, like, we want the best we can get for that money. So we'll put more things on there, which then we put things like Power BI, and Tableau, and Excel, or, you know, some other skills on there that we did use as well, but weren't as important. So then when somebody would apply and they're like, hey, we have a lot of Power BI, and I'm like, okay, yeah, I know that was on the listing, but, you know, I only needed these three skills. And so, yes, I 100% think that people should apply before. Because oftentimes it's much like that situation where you know SQL, you know Excel, that's really all they want. They have all these extras that are kind of nice to have. And you don't know exactly what it is on that thing that is 100% need to have and isn't important. So you don't know. It's better to just apply. It's also really good to work with recruiters who know it better, who then can tell you, hey, these people really just need to know SQL, like you're all right. And then they'll help you. So yeah, that's kind of, that's kind of my thoughts on that. It's kind of a slightly deceptive, and I'm not a huge fan of it. But yes, don't apply if you know all of them. If you know, I usually cut it off at like 30%. If they ask for 10 skills and you know three of them, I would apply. That's my rule of thumb. Great advice. Just for the people watching online, we are recording this session and do pop your questions into the Q&A on Big Marker. I can just see some comments coming through. I'm going to get round to that. There is a great question over on YouTube though from Albert Bellamy. And I didn't know this, but apparently Alex, you play guitar. I don't know if you've got another YouTube channel for that. But in terms of study hacks and stress relief, do you use music to unlock other areas of your brain or just a stress release? Do you find like playing an instrument, does that help? Hey Al, how's it going? Me and Al go way back. He's a good guy. Do I use it as a stress relief? I literally have my guitar sitting right over here. I love playing it. But I think music as a whole, I've started, I've kind of got a, I've gotten kind of meta with it. I started listening to classical music in the background, which I never used to do. But I've personally found that I was, I kept reading about it. They're like, play Bach, play Beethoven. And so I play like, well, I'll play classical music now. And I think it does help. It keeps me more calm. Like I get very tense. I like, you don't see it when I'm making my videos, but I like, we'll be like this. And it helped. I think it just helps me be more relaxed, which I think I'm looking over here because I'm looking at you. But I should be looking right here. It just helps me be more relaxed. So music, music has been a huge part of my life, but for studying, I try to keep it super simple. I don't actually like, ever play and like, try to like, call myself down. I just like, maybe we'll put some on in the background to kind of like, help keep my tension low. That's about it. That's some ACDC. Thanks for that question. I'm just jumping into the big market questions. Mary Cruz, I think has asked a great question. Will data analytics assist into veering into a path to a career in data science? Will it assist it? Or will it hinder, what it was the? Will it assist? So data analytics, would it assist into a career into data science? Yeah. And, you know, that's a super popular question. I've, and I made a few videos on that actually, but it at 100% will. You know, there is definitely overlap in kind of some things that you, that you use and you work on, things like Excel and working with data sets and, you know, understanding the business side of things. These are things that you'll learn in data analytics. There's a lot of crossover. To give you some context to why I'm pretty confident about it is I was working on a data collection team and I was a data analyst. That's what I did, but we were part of a larger data science team. And as I was in about two, three years, I started working with the data scientists and I got really good at it and they wanted me to become a data scientist within my team. They were like, hey, you really know this stuff. We wanted you to come on and it just wasn't my thing. Like I was like, I don't mind working on this with you, but I don't enjoy this. Like it wasn't my thing. But 100%, it was a great launching pad to break into that. If that was something I wanted to do. But data analytics can be an end place or it can be a springboard into, you know, kind of a different career. Data engineering, data science, you know, mostly those two, but also business analytics and a few different stuff. But I absolutely think this is a fantastic place because you get introduced to a lot of different things and then if you're working on a team, you're going to be working with database developers, data engineers, data scientists, all these different things and you can pick up from them, you know what you need to learn and then internally, if you want to move internally in the team, you know, if they're using like Azure data factory or data lake or whatever, you can then learn that with them and be like, hey, I really know this stuff. You know, you guys have, you guys have any openings I'd love to apply. So yeah, 100% it can be a great launching pad into a data science career if you want to go that path. Awesome. For anybody from the career foundry audience who maybe joined a little bit later, do check out Alex's channel, Alex the Analyst. There's some great videos and great content over there. Definitely, if you want to learn about stepping into a career in data analytics, do check that out. And at the same time, I'm not going to leave career foundry's video team out. I know they're in the background. Do check out career foundry's YouTube channel too. We're also streaming live on LinkedIn tonight and we've got a great question coming from Vladimir. Alex, is it really possible to learn data analytics in four to eight months? Yes. Yes. 100%. I have a video coming out. I think it's in two weeks. So you can look for that. But it's literally how to become a data analyst in 2023. And I look, I talk about timeframes. On the low end, you're looking at about four to five months if you have a lot of time to dedicate to it. There's just, and I say yes, but there are a lot of factors, right? Your own, you know, just personal life. Do you have a full-time job? Do you have kids? Do you have free time to actually study and learn the skills? I imagine it's somebody who has like a completely non-related degree or is like right out of college and wants to completely switch careers, which is exactly what I did. I, it took me about a year from start to finish to follow that path. But I had no mentor. I had no guide. I had nothing. I learned it all my, my own. It took me about one year to make that switch. And that's including like applying and finding a job. But with, just in the past five years, it's gotten so much easier to like have a learning path ahead of you, have tips and tricks to apply. Like the, when I was first starting out, I couldn't find anything like that. Excuse me. And so yes, I think six months is definitely doable for, for a lot of people. It's just very dependent on how much time you have to dedicate. But yes, I do think it's possible for sure. And just to briefly mention here, the career foundry data analytics program, if you were going to be working, if it assigned 15 to 20 hours a week, you go through the career foundry data analytics in eight months. So that's just a little bit of a, a kind of like a, I'll just keep that on your radar. And if you've got any questions about that, do book a call with a program advisor. And following on for that, Alex, I've got a great question here on big Margaret from ACPOR. How do you actually cope with studying while working in a full-time non-data role? So how do you balance the two? Yeah, that's a good question. The role I was in before was kind of like a caretaker is how I like to put it. It was called a resident advocate. So I worked at a, to give some context, I worked at a nonprofit. I was, it was for people who were abused in relationships. So they would flee their abuser. They'd come in like literally live in that place and I would go in, help them go apply for jobs, help them do paperwork, feed them food. Like that's what I used to do. So during that time, you know, I would do that for eight hours. I'd go home and I had just gotten married. My wife had had a child from a previous relationship. So I was already a dad. I'm a dad. I'm a husband. I'm, I have a job. So how do I, how do I keep studying or keep that, I guess motivation? For me, it was, I was extremely motivated by the fact that I was earning very little money. And I had a lot to support. I kind of dove in early with marriage and having kids. I, you know, money was not as important. I was like, I just, I really wanted to be a family man. So that motivation of having kids and having a wife, I like pushed myself really, really, really hard to kind of dive into this field. I didn't know exactly what I was diving into because I didn't know what data analytics really was, but I kind of learned along the way. That was my, I just, I think I had a, an extremely huge motivation because my wife was earning like over almost triple what I was making. And I was making very little. And I was like, I got to like provide for my family. I got to step it up. So that was like, I don't know, it doesn't really help. But that was my motivation that really pushed me to study a lot when I was first starting out in, in a non-data job like at all. Awesome. Thanks for showing that, Alex. I think Mario is also asking a great question. Have you got any examples or what's it, what's it kind of like working on a day-to-day as a data analyst? What are some examples? How does your day look when you get up for that morning coffee? What does your day look like as a data analyst? Sure. I'll speak from like two, two years ago because I, I was an analytics manager before I, before I quit my job. I was an analytics manager. So I was managing a team so it's a little bit different than when I was a data analyst. So when I was a data analyst, I was on that data collection team and we would get data sources from, I worked in healthcare so it was a, you know, different hospitals, different, you know, doctors and stuff and we would, we would take that in. So usually how it went is I get in at like eight, eight o'clock, eight 30. We had our standup at nine o'clock. And what a standup is is literally just everybody comes together and you talk about all your current projects. So we used agile methodology which is basically just a work, a way of grouping work and making sure you're on track. Like, you know, Jira or Kanban or agile or, I'll come on agile. But, you know, those different methodologies of tracking your work, chunking it, making sure it gets done. So we'd get in there and talk about what progress you made on our projects, where we're at, timeframes, then I'd actually go back and do work. Now, the longer I was in my job after I was in that same position for three years or, in that same team for about three years, I started getting really meeting heavy, which, you know, that it happens, especially it's cyclical as well sometimes. Like, it's really low at, at the holiday time because everyone's gone. But then, like come January, February, I mean it's like, you know, two, three hours of meetings at least today, minimum. And then I, you know, would work on my projects. And so I'm at my computer, I'm coding, I'm sending emails and data sources with questions and issues and, you know, trying to learn the data really well. And so the data collection team, I love data collection. That's one of my, a lot of people don't like it, but I loved it because I got to do a lot of data cleaning. I worked a lot in SQL, in Azure, in Databricks. I got to use some Python. So, those are kind of the, the things that I used and, and kind of the type of work I was working on. Awesome. We do a, we also do a live workshop with Dr. Humaira, who's a machine learning expert and a mentor. And, we do find that a lot of people love data cleaning. Data cleaning is also, it has a massive fan base. So, if that's up your street, I think, data analytics will be, will be a career for you. Just moving forward a little bit, looking at skills and in kind, in terms of like core skills that you use as a data analyst. What, what are kind of the core skills that the top two or three skills that you need to be a data analyst? Sure. You know, again, I just made a, I'm just made the video. It hasn't come out yet, but I'm going to give you insight into where I think it's actually going in just a little bit, even though you didn't ask me. I'm going to do it anyway. That's okay. The core skills haven't changed too much. Like when I say core skills, I mean the things I think everyone should know, but when you get into a team, then they have like their own stack, their own tech stack, which is breaks, usually breaks away from the core skills. I recommend everybody learning SQL. It is a, something that is taking my career way farther than I thought it ever would. I kind of guessed that SQL would be useful, extremely useful. 100% you need, I think everyone needs to learn it. Then I'd say a BI tool and Excel, BI tool, like Power BI, Tableau, Looker or something like that. There are a ton of other ones. Now, I'm going to get into what I think is important in like the coming years. Right now though, like you learn those skills and then you look and apply and there are ones that have it on there, but then there are other ones. You'll see things like Redshift or you'll see, you know, different BI tools that you've never heard of and there are a lot of them, like, Chartio or something like that, like something that you just have never seen. The reason I say stick with those core skills, because those core skills are typically transferable. You can learn Tableau, and then if your company has some random BI tool, there's a lot that can transfer to that. And so, in like SQL, like querying a database is fundamental across data. You have to be able to access your data and get to it. SQL is the most popular, but also there are other platforms that do it different ways, but you'll know the ins and outs of how to group your data and sort your data and all these things using SQL. So those are the core skills I would learn. And again, when you get into the actual job, you'll find that most jobs don't just use that tech stack. They have their own unique stuff and you learn it, but you'll be like, okay, let's go to SQL. It's kind of like Excel, you know, whatever they use. Now, what do I think is actually going to be really important to know for upcoming data analysts? I think that learning a cloud platform is going to be really important in the coming years. It was not when I first started. It was still, it was up and coming. Big companies were still using it, but smaller companies weren't, so most companies weren't. They had their own servers dedicated like servers on-prem, or on-premises, which means they bought a server, a network application. Now, cloud platform, cloud computing is becoming so cheap, I think that that is going to be a necessary skill in the next even three years, five years. Last year was kind of like tentative on saying it. Now I'm pretty confident in saying it. You know, it's just one of those things that it's becoming cheaper. It's becoming cheaper than owning your own on-prem, so even small companies are going towards it and they want you to know AWS or Azure, Google Cloud Platform. And so, these are things that I think you once you learn those core skills, that's something you kind of branch out into and learn the basics of like Azure Data Warehouse, Azure Data Factory for like ETL processes. And it gets more technical. It gets more difficult in my opinion, but I think that's where a lot of companies are starting to like they're looking for people who already know it or have used it and it's not as many people as can fill those roles sometimes. Awesome. Thanks for listing those. I think traditionally they would be kind of classically be seen as harder skills. Now, if we look at people like moving into a career in data analytics, are there certain soft skills which really kind of lend themselves to a career in data analytics if we kind of look at softer skills? Yeah, for sure. You know, I think there's a at least it was a perception of mine which I was really excited about, which did not pan out at all. I was really excited about that data analytics was a tech job and I wouldn't have to talk to people. I believe it or not, I'm not the most social person in the world. I love my own time. So I was hoping when I started working as a data analyst I wouldn't have to communicate with a lot of people. Little did I know is you communication skills are extremely necessary. So people who are good at talking, good at communicating, that is a super necessary soft skill. Another one and this is probably one of the more obvious ones which is problem solving. You can teach yourself problem solving. You don't have to be a problem solver to be a data analyst. You learn it. You can be taught and so problem solving I think is the next biggest one just because you run into so many issues in working with data. It is like that data cleaning process to me is a problem problem solving issue. I just have to probably if I think about it enough I look at it enough I can problem solve I can fix this data set and then we can use it for what we need to use it for. So problem solving and just being able to look at something figuring out a solution to solve it is a very big kind of like soft skill. I don't know if that's technically a soft skill but it's a soft skill to me. That's just one of those skills that's kind of an intangible you just have to learn how to solve it. It's like a puzzle. We actually have a lot of experience at Career Foundry have a lot of prospective students who are thinking about studying a new career and one of their kind of like main blockers is am I really going to be able to start completely from scratch but actually a lot of people forget that a lot of transferable skills apply to many positions moving forward you know the skills to present to communicate just as Alex is illustrator these skills are just as important as the hard skills so remember you're not starting from phase one you're kind of starting a little bit further down the road I'm going to take a couple sorry yeah just say one more thing because you actually prompted me to think this one other thing because you said presentation skills and I was like yeah that's really true but then what popped into my head was almost like being almost like a salesman you know being able to present the information but also be really personable I think that that got me my first job actually I know it got me my first data analyst job because I failed the technical interview and they really just like me as a person and how I talked to them and communicated with them that they gave me the this person can learn will teach them that's how I got my first job so I think I really was able to sell myself well I had a good personality in the interview and so you know that skill presentation skill that being in front of people and talking skill that is a lot harder than it seems that's a really good soft skill I just I'm glad you mentioned that one if anyone has checked out the career foundry programs too we do have a fantastic career services team at career foundry who are behind you every step of the way looking over your portfolio products but also helping you look at your CV look at your LinkedIn resume to ensure that you get your dream career in tech just a little plug but you're not alone in the career change I'm going to take a couple more questions there are so many questions going through Alex you really got your communities on fire tonight thank you everybody for the engagement lovely to see everyone joining let's just have a look at portfolios and projects I think Tom's got a great question on big market how many projects how many projects should we do before applying for a job without any background sure yeah projects highly highly highly recommend I usually recommend three to five and one in you can do it on I prefer to do one on each skill so you know the follow up to that is is why do you even need projects why not just do the projects and not show anybody why do you need three to five projects to show there's two reasons and I again I extrapolate too much just not me if I'm talking too much but the first thing is is projects what can help you get an interview and then they can help help you land the job so it can help you get the interview because you can create a portfolio website if somebody clicks on it and sees your work they're like okay this person for sure knows up to the level Excel we're looking or up to the level SQL we're looking for so then they can be more confident interviewing you then when you get in that interview job you have no experience you don't you know nothing about the actual world working in it and have experience using these skills that is just a fact that's where I used to be but projects allow you to answer questions like you never built a project and you have no experience you really can't answer that you'll say well I learned it through XYZ you know I've used Microsoft SQL server I've used my SQL and I just practiced not a great answer projects allow you allow you to answer it like this well I was actually just building this project using SQL where I took data from Excel I imported it in I did the data cleaning process and I did some exploratory data analysis using SQL and I found these insights so you it just sounds so much better when you talk like that and you're you're pointing out specific projects and you can say I did a project on you know this data set or I did a project on this issue and if you have projects that are catered to your industry mine is healthcare so then I did projects on healthcare and they would ask me questions about things like ICD codes do you know anything about ICD codes and I'd be like yeah I did a project on ICD codes being able to break those codes so projects can be extremely helpful so three to five you know with different skills if they want Tableau make a project in Tableau so it's kind of some again I could talk about that stuff for hours so I'm just going to stop there but that's my general tips on like projects cheers Alex now a buzzword I think this year and at the end of last year was chat GPT and a couple of questions are coming this evening how do you think AI platforms would change the future future of data roles or data reporting no I love chat GPT I've been using it actually a lot more in the past two weeks and it's been I mean I've had that exact question so like this is new to me but I can already see in my head how it's going to change things I think it's going to have actually a slightly bigger impact than most people would think and not in the way that most people are thinking a lot of people they worry they say okay chat GPT is coming out they won't managers or companies will not need data analysts because they'll just go and ask chat GPT which is eventually going to be connected to their data like that's what people imagine there's a lot of issues in that thinking from an actual technical standpoint so I don't think just to you know I don't see that happening right off the bat I don't see it replacing fully a list of work not in the near future definitely not like the next 10 years it's just you know most people don't understand how technically behind companies are there are very even my company that used to be that was behind they just chat GPT even if it became it could solve all your issues tomorrow it would still take most companies 15 years to catch up so you know it's not going to happen but in terms of that because Microsoft is already starting to play a big game and you know I don't want to get into all the politics behind it but the technology itself if integrated with a lot of different products can be massively useful I don't think it's going to replace data and data analysts I think what it will do is enhance a lot of the work and potentially down the road 10 years down the road instead of needing you know five data analysts you only need to have a team potentially but that's really speculative and I think at most companies that won't actually be true just because again of how behind they are but it's fascinating and I have used it myself for sequel and Python and creating formulas and stuff like that it's insanely helpful and it generates it stuff that I can't find on Google I Google so it's an incredible it's a it's a mind blowing software I've been messing around with it I'm going to make a video on it eventually about like my thoughts how to use it and just because it's it's pretty it's pretty revolutionary I'm I'm I've been pretty blown away by it it's pretty amazing I read a fantastic post on LinkedIn this morning which said that you should treat chat GPT as the company intern into your into your work it's not going to be perfect every time but just you know as a backup as an intern a company intern definitely a good sorry I don't know who posted that but I just read it in the digest this morning but yeah do check it out and I think that was a great answer I'll make one more note I'll make one more note I think chat GPT is actually it's slightly dangerous and here's why I will say that I have five years experience five years experience five years experience as an analyst but I you know I have a lot of years experience or many years experience just trying to search things online right it's a skill Googling and using as a skill the issue with something like chat GPT is if you're just starting out and you try to use it you don't know what is good and what is bad what is right and what is wrong so when I'm using it now I know sequel probably to about a level I worked with a lot of developers I think I'm about it at like a developer level so I know the ins and outs of like how sequel works the back end the front end everything for the most part not like to do my own horn but if you're just starting out and you don't know those things and it gives you this output and you just kind of trust it because you're like well it's chat GPT it should know the issue with that is I've actually been using it for the past two weeks and I'm like okay it's kind of giving me what we need but I can tell by looking at that code and so it can be a bit misleading because it's not perfect and so if you don't know the skill well and then you try to use it like I was saying I don't think it's going to replace data analysts because you have to chat GPT is going to be a skill just like any other skill you're going to have to know how to answer it how to interpret it how to actually use it and they're going to want someone to do that that's where a data analyst is going to do they're just going to want you to use that tool so it can be dangerous because if you don't know what you're looking for you don't know that skill well enough and you trust it you could it could lead you down just a path that's going nowhere so knowing the skill before you start using it for that skill or incorporating it in writing SQL code it can be dangerous that's I you know just a just a note that I was thinking about No I think that's great advice and I think if anyone's starting out you know don't just leap into chat GPT or use some other sources but you know it could be a tool that you're learning to so do keep an eye on it and in terms of looking around and like keeping up to date with all the latest resources do you have some places where you keep up to date with all the latest data information or three favorite blogs or it's different for me because everybody sends me things they I get LinkedIn messages every single day emails every single day hey check this out check out this article so I get it from my audience now so it's a little different but mostly they'll send me articles from like medium they'll send me articles from towards data science I can't I can't I'm like this is my like focusing face towards the science medium and sometimes like Reddit which will take us to like a different different websites but yeah and then I do a lot a lot on LinkedIn so like on LinkedIn I get a lot of information or articles that I'll like then go and read some of those I think this is a great moment to say that we've also got a lot of editors in-house at Career Foundry so on big market I would just post a link to the Career Foundry data analytics blog there's some great specific articles there about things that we discussed this evening but there's also some more general articles about career change and I think editors would be a little bit annoyed if I didn't post it so do check that out they do write some great articles so much time I'm just going to ask a couple more questions but for you what is the best part about being a data analyst why do you love this career? Sure yeah no we can run over my wife's not home so I got all the time in the world let's see the best part about being a data analyst you know it's funny because it's counter-intuitive I was saying something earlier I said I really wished it was like a you worked on your own I've actually found if you're working with the right people it can be fantastic you know when I first got into analytics people just like helping for the most like if you find the right people people like helping you so you know if you have a problem you can help them and work with them to problem solve and I found myself being a very much more collaborative person than I thought I was and you know I love that aspect of it I like that you can work with somebody who has really technical complex problems and when you figure it out it's like it is extremely rewarding you feel really good about yourself and so I like working with I like working with people I have found that to be very fun the other side of that is I get to work with really cool tech stacks you know I just really like I like cloud platforms I like python I like sequel and then there are different variations and flavors and you just have to figure it out and I like to dig I like to dig into technical things and really understand them well and so you know that's something that I really like because I get to do that for my job I don't just have to do it in my free time I get paid to do it awesome just talk about the community as well it's fantastic to see on your channel Alex that you've really developed a community there which support each other and that's something that we've seen looking at the channel from Korea Foundry's perspective but it's great to see everyone each other advice and jumping in love to see that on your channel so do check out Alex's YouTube channel Alex the Analyst and taking a couple more questions from Big Market I think this is a great one coming in and I do apologize if I pronounce anyone's names wrong but Vanille on Big Market is saying you know what is the future of data analyst roles what's the future of data analytics the future it's hard to predict the future but I can give Mike and Basil a guess you know as technology even since I've been in the past five years but especially in the past I would say ten years since the really the explosion of data science there's a lot of undefined roles even especially in analytics you may not think it but I've seen it firsthand like you know you go to Silicon Valley and you are you a data scientist is what a data analyst does for some jobs there's a lot of mishmashing of jobs I think that's going to happen even more so in the future where you know they'll want a core data analyst who also can do some data pipelines or you know they'll want somebody who they'll want they're going to call a data analyst but it's going to be melded with these different positions I think we're looking at more hybridization over the next ten I always I always shoot about like ten years because it takes a long time for adaptation but I have a lot of friends who work in Silicon Valley I have a lot of friends who work in a tech space and I've only seen it more since I've been in the space is that you know most companies look for hybrid roles they don't only look for sequel they want sequel in this they don't only look for a business analyst they want business analyst in this or data scientist in this so really get more hybridization potentially in the next you know ten years for large companies that's kind of like a prediction of mine that I've had for the past year or two I'm like I just have noticed a lot of overlap and responsibilities and kind of these hybrid roles definitely we see that too and another question that we get often from prospective students thinking about doing a course at Career Foundry is salaries it's a question that's on everyone's lips especially as we've been talking about recessions a lot recently and what's the kind of like the wage development like for a data analyst or what a seller like for data analysts yeah I'll talk about I'll talk about it in terms of the United States because I know the United States much better than I know any other country now how much you make is very dependent on several factors one is location I say that was one of the biggest ones so like if you live in the Bay Area in Silicon Valley then you know you're gonna inflate those prices 100% of other areas if you live in New York or a major metropolitan area they're going to be higher so for me I was living in Dallas, Texas and that's a that's a it's a slight inflated I'd say about 20% more than other places I started out at 63,000 that was my first data analyst job was at 63,000 so you account for inflation you're looking at around like high 40s low 50s just for your first job now the and that might be low for some people or that may be good for some people I don't know but the great thing about tech and this is something that I didn't know getting into it but the great thing about tech is that your salary goes up so fast or it can if you play your cards right because you start out like for me I start out at 63 within two years after that I was no, three years after I was making 100,000 so you know the starting salaries I you know they're important but if you have the the thought process of like looking ahead even just two years that's where you start making what I would consider very good money you start making 75, 85 and then you go up from there so starting salaries I would usually give the band of around 45 to 75 depending on where you are just depends on location depends on the kind of company like if you work at a small company you're going to make less if you work at a big company you're going to make more so you know there's just a lot of factors but yeah 45 to 75 and then even after a year you can look at whatever you're looking at probably about a 15 to 20% bump because now you have experience and experience is gold in the tech world and just data analytics you get a year's experience and you can bump that up by 20% and you can demand that and especially as inflation continues which is very much a real thing right now so salaries are going up because cost of living is going up and the demand is going up so you know that may even next year those could be low maybe looking at probably like a 7 to 8k bump on both of those ends so like low 50s like 80 thanks a lot for that as well and also for those watching on Big Marker I'm just going to post a link to a blog article which is a salary guide put together by our editors at Career Foundry which also looks at median salaries too so that's just going to you know show what it's like in the European market I think there's also some more information there too so do check that out thanks a lot for that Alex and another question that a couple of people have asked is a couple of people in the audience are thinking about career change but they're a little bit older so end of 30s beginning of 40s is it still possible to change careers when you're older going into data analytics? Yes but there are some small caveats I do I just stopped my mentorship program because I just started my own business started doing that full time so I took a small break on mentorship but when I was mentoring I worked with about three people who are over the age of 40 and well it was exactly three people who were over the age of 40 two of them were able to get jobs now the reason why they were able to get jobs and what I helped kind of coach them as what I'll call to do is when you're at that age it is much tougher if you don't have any transferable skills much tougher if you have a job like one of them was a nurse so he was a nurse for the past like 20 years and he's like I want to get into tech I don't like like the long nights I don't like doing over night work like he's like I just he just got married he's like I've been married for two years he's like the most important thing that I think I helped him with was changing his mindset to seeing his background as bad to seeing his background as really helpful he eventually got a job at a healthcare company and I was like you need to sell yourself on that and say you're an expert in healthcare and you have the data analyst skills the other guy I worked with was a pharmacist and now he's a data analyst at a healthcare company both of those were over a certain age but I was like you need to look at your experience as a data analyst what industry can you transfer those skills to that they'll be like this is really valuable this is valuable that that person has that background and now has the technical skills to combine with it now again it's it's tougher you're older you may be just trying to get that first job so if you have like 20 years experience in nursing and he's making you know 130 I'm like I'll build that up over time back to where you were so you know it's tougher but 100% can be done and I've seen it myself and so yeah definitely definitely possible awesome thanks Alex just post one I feel like I'm posting a lot of blog articles but another blog article looking at exactly that question for those of you I knew a couple of questions came through from the audience I'll just link that here Alex I am mindful of the time thank you so much thanks Alex and also elaborating a little bit further on data analytics as a career all your tips and tricks we love your channel so do check out anyone who's watching from the career foundry audience once again do check out Alex's channel Alex the analyst over on YouTube for anybody who is considering a career change into data analytics after listening to the talk this evening we are currently offering a New Year's scholarship and all you need to do to book that is a book call with one of our program advisors I think I'll just change the slide over yep so we are currently offering a New Year's scholarship off of our programs but if you've also got any questions about career foundry's curriculum maybe jobs in your locality or how our dual mentorship model works do book a call with the program advisor because they're on the lines to talk about the specifics of the program and help you out but also talk about the New Year's book call with them and yeah and that's pretty much all I've got from this evening one more thing to say is that we do have a lot of exciting events upcoming Alex is returning again to the channel I think later this month Alex yeah the 24 fantastic and I know that we're going to be looking at data analytics portfolios so if you want to check out portfolios and data analytics portfolios and remember this is going to be analytics Alex is going to be reviewing some portfolios having a look what's good what's not so good what to put in so do join that and also check out the career fund events page for all future events info sessions about the programs but I also do a great skills workshop with Dr. Humira we do an intro to data analytics to take your first steps and that's pretty much everything from my side Alex thank you so much for joining and for all those questions that we had on big market tonight as well so thanks Alex and thank you to everybody out there and we'll see you again very very soon