 Felly rwy'n gwen i gyd yn fan ar y gwrs helper tynnu ar y gwaith. Yng ngtw wrthwyng arddangos cyfreath cyreudd ar y cyfreet o 2023. Dwi gyd yn gwneud i fan y gwaith dim ein cyfio ar y cyfarwyr. Wrth roi i gyd yn gan ymgwrs ffordd. Gweithio'r amserol meddwl â'r gweithreth yw'r gwaith yng nghymru. Rwyf ych chi'n cyfraedd rydw i gwaith cyffredinol, ac yw'r cyffredinol mewn cyfraedd yw'r cyfredinol i gael gwaith ynghyrch yn cyfraedd Cyflawni Gweithgawr Gweithgawr, Jyllwch i'n gweithio'r cyfrannu o gyllideb. A gweithio'r gyfrannu'n Gweithgawr, Alex i'n gyfalatio. Wrth gwrth dweud yr hyn o'r fathion, rhaid i'n meddwl i'n gweithio'r cyfrannu. Rhaid i'n meddwl i'n meddwl i'r cyfrannu i'r cyfrannu a'r gweithgawr Alex i'n meddwl i'n meddwl i'n meddwl i'n meddwl i'n meddwl i'n meddlunio i'r cyfrannu. ynglyn â ymddangos, rydw i'n ni i gweithio i mi. Rydw i'n William, ymddangos, ac ymddangos cymunigau i'r bwysig yma yw ymddangos yma, ac ymddangos ymddangos ymddangos ymddangos ei ei gweithio'r gweithio gyda'r cyffredin iawn. So rydw i wedi gweld i'n hyffredin am y gweithio cymdeithasol ar gyfer y cyffredin iawn i ddechrau ac erwyddon i ni'n gweithio'r gweithio'r gweithio. We're not any old school. Our programs are so flexible. You don't need to click your day job to change your career. You get regular one to one mentorship for not one but two. Industry experts, that's a mentor and a tutor. And if you don't land a job in 180 days of graduation, we refund your tuition in full. That's our job guarantee. For anybody who's watching on YouTube and is interested in the career boundary program, just click in the description below and there is a link to book a call. If you want to book a call with a program advisor and ask questions about our curriculum or anything that we offer, I recommend doing that. Just a couple of house rules this evening. We will be doing a live Q&A at the end. So if anyone's got any questions for Alex, it's your time to get those questions answered. Just drop them on Big Mark or YouTube or LinkedIn. We are screaming across numerous platforms this evening and we'd love to get your questions answered. Alex, I think that's everything. Senegal, it's a super international crowd and I'm going to hand it over to you. Alex, thanks so much for joining us again. Thank you, thank you. I saw somebody from North Carolina, Jermaine. I just want to give that person a shout out. That's where I grew up. So I grew up in North Carolina. I love seeing when people put where they're from because it's like, it's pretty wild. I mean, we got people from Guatemala, London, Bromwick in the UK, just amazing. Thank you guys so much for joining. Thank you guys for hosting this Career Foundry huge shout out. I love this topic. It really has a special place in my heart because I remember six years ago. Let me see. Yeah, six years ago now. Wow. In 2017, when I was trying to make this transition into becoming a data analyst, I had absolutely no idea what I was doing. There are now a lot of resources online on YouTube and posts and blogs and whatnot, centered around how to break into analytics and TikToks and things like that that I don't do. But there's a lot of resources now. When I was first starting out, there was not many resources, which as a lot of you guys know, that's why I started my channel. We're going to talk all about breaking into data analytics. I have a step-by-step playbook on what we're going to be doing. We have a lot to cover. I always like doing a Q&A at the end. I'm going to try to not, I could talk for hours. If you guys know me, I could literally talk for three or four hours on this, but I'll try to keep it succinct and we'll make sure we cover every step. Let me see what this next one is. That's me. We don't need to read this, but I like data analytics. Let's jump right into it. We're going to start with creating a career plan because you have to start somewhere. You have to create a plan. What I like to do with people, because I had a mentorship program for a long time before I started my analytics consulting company, and what I would always tell people, I'm like, you can be any step in this process. You can be anywhere. You could be at the very beginning and have never even heard about what data analytics is and you want to join this path, or you could have already learned all the hard skills and now you're trying to get a job. You can really apply this anywhere. I'm going to start from the very beginning, but realize where you're at and what you need to do. Hopefully that's really helpful to you. Here's what we're going to do. Identify where you're at in the process, create an action plan, and then at the very end, I'll talk about timeframes on how long you should try to, or how long it might take you for each of these steps. Let's go on over to the first one. The very first thing I always recommend people doing is learning the skills. It's really, really hard to become a data analyst if you don't have the data analytics skills. It's really tough. These are the skills that I recommend people starting with. Now, as you're reading through it, as you're looking at it, you don't have to learn all of them. That is not a requirement. In fact, I don't recommend you try to learn all of them because that just would take forever. Really what I would start with, I would really recommend starting with SQL. I just think it's so fundamental. It's where data is going to be stored. It's in some type of database, and you can query the data. I always recommend you start with SQL. Learn things like Excel and a Power BI tool as well, like Power BI or Tableau. These BI tools are meant to just visualize the data. With SQL, Excel, and something like a BI tool, you can go a long, long way. In fact, when I got my very first job as a data analyst, I only knew SQL. That's really all I knew. I learned on the job a lot of the stuff I know in Excel now, a lot of the Tableau that I know. In my next job, I learned Cloud platforms like Azure, and I learned Python. I didn't learn all these things at the beginning. I taught myself over the years as they became more relevant in my job. The last thing on this list is chat, GBT, and AI tools. I want to take a quick second just to highlight this one because these AI tools aren't going away. They're speeding up a lot of these things. They're making them a lot faster to use. Take, for example, something like Tableau. Tableau just had a conference. They're announced that they're going to be integrating AI into Tableau. This is going to be a huge part of analytics in the future, just having these AI tools integrated into these different types of systems. Here's what I will say. I don't recommend diving right into AI immediately. Although it's on this list, I highly recommend learning the basics first. Learn the basics of SQL. Learn the basics of Tableau, Power BI, Excel. Learn the basics because what's going to happen is these AI tools are going to go from 0 to 100 really quick. If you don't know what you're doing, if you don't know what it's telling you, it's going to be really hard to actually put everything together and do what a data analyst does. You want to learn the basics first, then start using the AI tools. You can even use some of these AI tools to teach you. ChatGBT can be a really good mentor. That's the first thing that you should be doing, learning the skills. The next thing, learn these skills. I can just say go learn the skills, but it doesn't really help if I don't tell you where to go. Let's take a look at this. The first one is self-learning. You can do this on a lot of different platforms. Ones like YouTube or IT State Analytics. You can learn from ChatGBT, Khan Academy and a few others. You can do paid courses. Typically what I talk about is places like Udemy or Coursera where you can go and buy a course or buy a subscription, and you can actually take it as you go. You can self-pace within a customized course. Then there's boot camps and online schools. This is where something like Career Foundry falls into place, where you're going to pay for a boot camp. What I always tell people on this is I always recommend somebody finding one that has a job guarantee, like Career Foundry. I don't take sponsors very easily. I have a very specific process that I go through. Career Foundry is one of the very few that I like just because it's a good price and they also have a job guarantee. Some of these other ones look into them, but there's a lot of boot camps, online schools that you can look at, but just make sure that they have good mentors because that's really what you're paying for and that they have a job guarantee. That's something you need to look for. The next thing is getting a university degree. This one is by far the biggest investment in both time and money, but can have a big payoff because you're getting more accredited. You're taking a long time to get your education. Here's what I will say, and I've been thinking about this a lot over the past several months. In three, four, five years, will university degrees be caught up with a lot of these new AI tools and advancements that are happening in analytics right now? Probably not. You may get a degree and you may not be as job-ready as some of these other ones. If you took self-learning or a paid course or a boot camp, that has newer information. You really need to look into those things because that is a problem with the higher education as a whole at the moment, as they tend to fall behind. Those are your options for where to learn. I'm going to preface this one. This one actually has multiple reasons why, and building projects is so important. What does it mean to actually build a project? Building a project just means, as a data analyst, you work with data. You have to make insights out of it. You have to make it actionable. You're building this data, and then you're saying to a stakeholder or an employee or your boss or whatever, you're saying, hey, here's what you should do based off the data. That's what building projects is. It's basically giving you some experience without actually having experience. You're practicing these data analytics skills that you just learned, and now you're building projects. Now, why do you need to build a project? There's three different reasons. First, you probably shoot things, you run into issues, and you figure it out, and then you work through it, and you're like, man, I am better because I built this project. The next one is to showcase your skills to potential employers. Now, this number two and number three, because if you look at number three, it says you'll have something to talk about during your interviews. It has two different pieces. One, projects are really great to place on your resume to help you get into an interview, because it can show that you're working on these things. You can show the tools that you're using. Honestly, I've seen a lot of resumes in my day, back when I was an analytics manager, where I was like, man, this is a really interesting project, and I went onto their portfolio website and I checked it out and I'm like, this is really good. The next thing is, especially if you don't have any experience, what's going to happen when you get asked something like, tell us how you know SQL? That's a tough question to answer when you're first starting out. It was tough for me. I would be like, well, I've been practicing and I took a course and I think I'm good enough in SQL. That's a terrible answer. It was, but what I learned is when I started building these projects, I could say they'll ask me the same question. What do you know about SQL? I'll say, well, I know how to do joins and window functions and all these things, and I just built a project taking this data set and I built this project. I can walk through and I can point to that as some experience, as I built it using real data and I built this project. That's the three reasons why I recommend somebody build a project and then put it into a website, which is really good as well. This is the portfolio website that I was talking about. Once you built a project, that's fantastic, but it doesn't help if you don't put it somewhere. What you need to do is you need to take projects multiple and you need to place them somewhere. You can do that for free on a lot of different places. I have a whole video on how you can put it up on GitHub, so you can do that. You can put it up on Wix, on Kaggle, Medium, LinkedIn. These are all places you can place your projects and LinkedIn even has a place where you can go onto your homepage and you can add projects to your profile. When a potential employer or a recruiter goes onto your LinkedIn, they can see those projects. I'm a huge fan of LinkedIn, so I definitely recommend that. The next thing you need to do is build a resume. Building a resume is always difficult. Give me one second while I take a sip. I woke up with a sore throat, so I apologize. Building a resume is always difficult. I have found it probably one of the most frustrating things about just work in general. You have to apply to a job and you have to have a resume. Resumes are extremely tough when you have no experience, because then you have half a page filled up and you're like, I can't send this. This looks bad. You need to fill that out and you also need to make it a data analyst-focused resume. How do you do that? The first thing that you need to do is highlight your skills. Highlighting your skills means you want to put SQL, Excel, Python, whatever skills you have on your resume a lot. You want that up at the top, especially if you don't have any experience. I have a degree in recreational therapy, so I don't want to put my recreational therapy degree at the top, because then they're going to see that and they're going to be like, this degree is worthless, which for this field it kind of is. I put that at the very bottom and I just call it a bachelor's of science. I don't even say that it's a recreational therapy. But I put my skills at the top and then I have, if you have experience, you put experience, then you put at the next level and put your projects. This is another place where your projects are going to come in handy. You can have your projects in two different areas and I want to just highlight this because I'm a big, big, big believer in it. Your projects can be an actual section where you say, here's what I did and then underneath it you have bullet points of here's the tools I used or the insights that I got out of it. You can also put in your header a link to your portfolio website. Highly recommend this because then an employer can go and look at your actual code that you're writing or your visualizations that you're creating. They can go and actually look at it. So it's pretty awesome. So those are two different ways. Another thing that I want to mention is if you're coming from a different field, so let's say you're a nurse or you're an accountant is really helpful if you're able to repurpose your experience to be more data analytics focused. Now, what do I mean by this? I worked with a lot of, because I came from healthcare. So I worked in healthcare for a long time, then I became a data analyst. So what I did when I was first starting out and I kind of learned this over the years is that that's actually valuable experience. I didn't know it at first. I thought it was pretty useless. It's actually really valuable. So what I started doing was, I've worked with what are those called? HR? No. Healthcare database systems. They're called something. I can't think of it off the top of my head. But I would say I have experience with these databases on the healthcare side. So then when I come in and I'm working as a data analyst on the back end, I can say I already have experience with those systems. I know how to use them. That's really, really useful information. You can do that with a lot of different things. You usually really have to sit down and think how is this relevant data analytics and then try to use that. It can be tough for some jobs legitimately, but a lot of jobs, there is some trends. Now, let's go on to the next one, which is certifications. I'm going to mention certifications and I put this as an optional one here and I just want to touch on it. I personally, I have no certifications that I talk about because I don't think they're 100% relevant or I've ever helped me land a job. So I've taken the Microsoft Power BI data analyst certification. That's one that I actually took when I was in my job and my job paid for that certification. I don't put it on my resume because I don't think it's, it was that helpful back three years ago when I was just like a data analyst. I didn't think it was that exceptionally helpful in helping me find a job. But some of you guys may really want certifications. It helps could potentially help build a little bit of credibility. You have an actual certification. I just am not a huge proponent of it. So if you like it, go for it. You can get that certification. If not, you know it's not a big one, but these are three of the ones that I think are actually useful, are actually worth getting. Although there are thousands of certifications out there, they're just not all really that great. So, you know, take it with take it with a grain of salt. The next one, step number five is to start applying. And this is a very difficult part when I first started, when I was when I was trying to get my very first data analyst job, I can't remember the exact number, but it was over a thousand jobs I applied for. And I was doing that mostly on zip recruiter. I'm trying to zip recruiter and LinkedIn. I think is mostly where I applied and I applied to over a thousand jobs. I think it was like 1200 and I got callbacks for 20 of them and I got offered a job on one of them. And that was a process. I had a whole spreadsheet. It was it was a very dark time in my life, very very disappointing if I might say because it took months and months and months to get there. Now there's a better way to do this. Don't be like younger me who did it the worst way possible. And I'm sure a lot of that's what a lot of people do because it's very inefficient. Most of these job websites where you're just applying and applying and applying, they get thousands of entries some they don't even look at it, some they just automatically scan your resume and you're like no don't have the experience they get tossed out. So it's very very tough to get a job with no experience or you know, if you're transitioning from a different career, very tough. Here's how I would do it if I were you. The first thing that I would do is always work with a recruiter. Now recruiters are interesting because most of the time they're working in metropolitan areas. They're not going to be working a lot in these rural areas or it's like farm country which is kind of where I grew up in Minnesota like in the middle of nowhere. You're not going to find a lot of jobs there. You can now find remote jobs which is great but even then a lot of these jobs want you to be somewhere near a metropolitan area. So if you don't want to do that or if you don't if you want to stay where you are you can look for remote jobs. There's a lot of companies out there that you can look for online just search remote recruiters for tech and what you need to do is you need to get on their radar. You need to talk with them, you need to hunt them down and you need to find them. Now I'm going to tell you what I did when I first found this life hack which is called recruiters. I went and worked with every recruiter who would work with me at the peak of working with recruiters after my first job I had a year experience. After that first job I was working with about seven or eight recruiters and all of them I was messaging every single week every single one of them I was calling once a week just to see if there were any open jobs. I was a bit relentless because I knew they weren't going to do it for me I had to do it myself. I couldn't rely on them to just message me out of the blue with a job offer. It just doesn't happen so I took it upon myself. You don't have to be like me although I kind of think that's it's a very efficient way to be on people's radar consistently and get new job updates when a new job is available. So if you don't know what a recruiter is I'm going to break it down super quick. A recruiter is usually somebody who's paid for by a company so let me see where I am on my camera. So here's the company. They pay this recruiter to go find an applicant. Now this recruiter just works with the company. They usually don't work they're not inside of the company, they're not part of that company. They're just a third party a third party. Now they're going to come to you and they're going to say hey we have this job at this company for an entry level data analyst position would you be interested and you're like yes of course I'd be interested. So you go into Ply and they pay you let's say $50,000. The recruiter is not going to take any of your money. The company is going to pay the recruiter let's say 10% or 5% of your salary. It's just a lump sum saying hey thanks for getting us an employee that we needed. We don't have to do that and that's what a recruiter is. You can cold email and you can cold call as many recruiters as you possibly can. I recommend doing that. I think that's even more efficient than just blindly applying on LinkedIn and zip recruiter. Another thing you can do and this is kind of that second life hack that I found later on maybe like two to three years ago which is using LinkedIn. LinkedIn has been like it is one of the best tools for job searchers. Is that the right word job searchers? People who are searching for jobs. It is one of the best tools because you can go and you can find recruiters through LinkedIn by just searching for recruiters at this company and I actually have a whole video on how to do that. It is amazing because you can search any company. I can go on Amazon right now and I can find ten recruiters who work at Amazon. I could send them a personal message with my resume attached and I can say hey I saw this specific job and you put the job in there. Say I found this job I think I'm a really good fit. Here's my resume let me know. You can do that with hundreds of recruiters online. It is incredible the access that you have to recruiters. All of those things are what I would be doing supplementing also applying on LinkedIn and zip recruiter on those other websites. I just would not only be doing that because it is a tough path to go down. Let's go to the next step. Now we are going to look at timeframes. Give me one more second. Timeframes are really important because you don't want to be doing this process all the things that we just discussed for the next two years. That's a really long time. Most of the time you're looking you're hoping to do this within three to six months. That sounds like a long time but if you really think about it I've been in this field for six years now and it has paid off really well. It's just taken me into a lot of places even before the YouTube stuff. So long term you got to think six months of my life really doing these things to get to where I want to be for the rest of my life. There's a big payoff. The first thing is learning the skills I think is going to take the longest or most likely will take the longest for most people. If you just focus on the ones that we talked about like SQL, Excel and a visualization tool and you're starting from absolute scratch like you know nothing which I've been there it's tough that can take you about two to three months. It's a really good a good handle on it. There's lots of courses and practice websites that you can use. Those are great places to look and to go use and utilize. If you're trying to learn all of those skills that can take you like six months. If you're trying to learn Python or R or if you're trying to really learn how to use chatGBT or some like an AI tool those things can take you all to really get good at. So be easy on yourself but right around three months or so to learn all the skills and start applying. The next thing is building projects. I don't think you need to spend two three months building projects. Most people can do a project a week. I usually recommend at least having three projects. Five is perfect. So if you can do one project a week and they don't have to be when you're first starting out, they don't have to be complex. Keep it simple. I have lots of guided projects on my channel. You can just watch a guided project and put that on. It doesn't have to be like a senior level analyst or a worthy project. So keep it simple. Keep it small and then you can build from there. That should take about three to six weeks. One week for three to five projects about three to six weeks. Next we have building a portfolio and really what I mean by that is just taking all your projects getting into one place, making it look really nice having a website. If you've never done it before it can be tough. So just find a tutorial and then build that out. It should take about one to two weeks. Then the next thing we have is building a resume. Hopefully this won't take too long. You don't have to start from scratch, just take your current resume and kind of revamp it, rework it for data analytics. You should be able to get that done in about a week but two weeks kind of at the most if resumes are not your thing. The last piece is job hunting and job hunting is like a huge variable. I've seen people and I've worked with people on a job within the first month. They did all the things that I told them to do. Then they started applying and within a month they got a job. It happens but it doesn't happen all the time. It's not super common. Usually I'm seeing about three months to land a job. Now I put one to six months because sometimes it takes people six months sometimes even longer. It depends on where you are. If you're in the middle of nowhere and you're looking at very specific jobs only remote has to be within this pay range, very specific jobs that you're applying for, it's going to take longer. But if you're open to relocating if you're open to taking positions that don't pay as much right away to get some experience which sometimes you may have to do you can get a job a lot faster. There's a lot of different variables and sometimes I lived in Dallas which is really beneficial for me getting some of my first jobs because there's a huge hub of companies and need for data professionals there. So I was in a good location to be able to get that job. If I was in the middle of nowhere today I think it would be quite a bit more difficult to land that first job. Let's go to the next one. And that's actually it. So I'm going to get Will back in here in just a second and I'm sure he's coming but I know that I just kind of got that quick. But you guys can ask me any questions that you possibly could think of and want to know and I will at least have some answer. May not be a good one but I'll have an answer. Thanks so much for presenting this evening. Let's just clue back to this slide. Yeah I think just to start it off I know a lot of people watching this evening are thinking about taking their first steps in the industry junior data analyst positions. How do you think that the industry has changed in the past six years since you've been working in the industry? That's a good question. I'd like to start it off with one of the meteor questions. That's a great question. It's changed a lot. I started six, seven years ago. It was very different. It wasn't as remote focused. I think it was a somewhat lower barrier of entering. Right now is a really interesting time to be getting in because we've seen some layoffs at big tech companies but what I will say is that as a whole overall the job hiring hasn't slowed down that much except at some of these big tech companies which is not where most data analysts work. Most data analysts work at a lot of Fortune 500 companies mid-sized companies and even small companies that's where I started. That's where 90% of data analysts work. They don't work at the big tech companies. I think for a lot of junior people if they're starting out now the difference between when I started and now is that it's just more competitive. There's more remote people trying to get jobs whereas back when I first started there wasn't as big of push for remote work. There's just a little bit more competition at the entry level because a lot of people just want to work remote. If you are in person if you are in those metropolitan areas I think you even have a bigger leg up in getting those jobs because you're in person or you could work remote so you have that flexibility so you can really work where they need you which a lot of companies like. Awesome. I want to also go back to the portfolio one more question from my side talking about portfolios. If a portfolio lands on your table what excites you most when you see that portfolio land on your table? What do you like to see? I've worked with a lot of entry level people like I was a analytics manager so when we were hiring and I had a lot of consultants and a few full-time employees but when I would see one I'd look at their portfolio not a ton had it on there which I prefer them. Some people don't care some people really care but I just prefer them. I would hope they have them. I remember specifically there were a few that came across my table where I'm like this is a really interesting project I go on their website I look at this project and I'm like I have never seen a project like this before it's just really unique on some niche topic I thought those were super interesting because it showed passion it showed a curiosity for just diving into data and so I would see and I remember this guy specifically because we ended up hiring him he had this really in-depth portfolio on fantasy football stats in like part of his process it was all in Python so part of his process was also determining the best position for fantasy football drafts which positions to take first based off different factors all data analysts using data analysts tools and stuff it was just really interesting I loved that I thought it was super cool it's actually interesting you said that because we also in previous career foundry events we've had it in the past where a student has taken a career foundry project and actually changed it and done their own thing and then they've landed a job from that so I think that's great advice too this is the time to get your questions answered for those watching on Big Marker linked in YouTube I'm going to jump in apologies if I pronounce anyone's names wrong that's not intentional I think Mia's got a great question over in Big Marker Alex any insight into how AI might affect this industry in the next few years yes I just so a lot of what I'm going to say I have a I just released a video yesterday that's like an hour and 15 minutes on this exact topic and it goes really in depth but here's I'm going to give you the cliff notes of it I think that AI has a massive potential somewhat not unlimited but a massive potential to transform the analytics and just data industry in general um for job seekers like you and I who are working as data analysts or want to get into data analytics some of the potential concerns that I've seen are things like it's really good at automating things it can work at a really fast pace like a thousand times faster than a data analyst and it's very cheap those are three things that I'm I think I'm concerned about here's what I'm not concerned about here the things that I think are positive for people like you and I trying to get a job I think that AI still makes a lot of mistakes it's getting better but those hallucinations are very real and I wouldn't because I've been a manager myself right so I'm putting myself in like a business mindset I would not want to blindly trust anything that I'm telling me ever I'm almost never going to blindly trust it I'm going to need somebody to dig into the data make sure that what they're giving me is correct go back look at the code make sure the code is correct the data cleaning process you know the whole spectrum that is a that's a one of the bigger pieces that I would say is um makes data analysts extremely useful and very relevant is that as a business owner I don't I could not put multi million dollar projects which is what I would work I work on a million or two million dollar project I'm not trusting AI to do that not right now and probably not for the foreseeable future the next thing that I think is am I talking too much I need to stop no it's great I think this is great I think because I mean AI is the buzzword at the moment so I think please elaborate on this topic all right I talked about it for an hour 15 before I could I could talk longer than that so I don't want to I'm going to try to try to keep it more concise and I think I was going to be the other pieces that you have to think about most businesses and companies integrating AI into their into their world when I was managing a team of six seven people and you know I'm thinking about AI how I would have integrated it because it really wasn't a big thing when I was a manager I would take a lot of precautions because you know I we had a lot of important data that made important decisions and so I would be very careful with implementing it and that could be a year long plus process so I think just in terms of actually getting it into a lot of workplaces at a company level is going to take a long time I think chat GBT has done a pretty good job of integrating at a personal level to speed up workflows but integrating these AI tools into an actual business is I think going to be a lot more complicated than I think people understand especially if you're not a data person right if you don't understand how these tools work how the data flows the data infrastructure all the nuances of the data and you just try to plug and play like I don't see it working well and so I think those are all those are some of the bigger things I think is going to keep a lot of data analysts very busy some of my predictions for the future of AI I think freelancing in AI freelancing as a data analyst is going to become really big and I said that and then a day later Tina if you know her she's a data scientist YouTuber made almost the exact same prediction I messaged her I said hey you just made like the same prediction I did she's like I recorded that video a week ago we had no idea but we had the exact same prediction which is a lot of smaller companies are going to need to start using these AI tools and they do not have the data infrastructure built up they do not know how to use AI tools there's some Joe Schmo in a business and they're like I need to use this to stay afloat so I think freelancing as a data analyst and knowing how to use these AI tools is going to be a huge business you'll see a lot of things like Fiverr and Upwork or small little niche businesses like mine pop up and people are just going to you know start their own businesses so I think freelancing is going to be a big thing because a lot of small companies that have never used this stuff are going to want to use it a lot like hundreds of thousands of companies around the world are going to start having to use these to keep up with the pace so that's one of my predictions another one of my predictions and look at this real quick because I haven't pulled up in front of me real quick so data freelancing I think this was another interesting one that I had thought about that I don't know if anyone else is talking about it but I think it's cool I think that as AI tools get into different departments within a company you'll see more nuanced data professionals something that a data analyst would do except now it's nuanced into that specific department whereas they wouldn't have had that before so something like I wrote this down and I sounds kind of weird but something like an HR AI analyst or an HR AI specialist something that may not even have the word analyst in it but it does that similar work where you're taking HR data and you're finding insights where they may never have done that before but now they feel like you know we need to get into this so you'll see I think you'll see a lot of that popping up in within companies a lot more data centric AI people so that's one of my predictions is that the data analyst title is going to change over the next five years it's going you're going to see more AI analyst you're going to see more data analyst using AI you're going to see people like AI professional or AI AI data I wrote it down the other day but you're going to see a change in the job titles as these tools get integrated and I think they'll come slowly I actually don't think it's going to happen like super quick you'll see like one or two pop up every so often but I think in five years it'll be a pretty good mix of just a data analyst business analyst you know a marketing analyst and then like data AI analysts who you need to know how to use this AI tool or know how to use these types of AI tools to work at that position I'm just predicting AI Alex the analysts popping up on the YouTube is a we'll see but there was one interesting point I think you made there on the having the expertise before and this is something which has come up in some skills workshops that we've done to is that for instance I could write something in chat GPT we know what is nuclear fusion what is this what is that and you know for me with no experience it does look it looks right but you need to have the expertise in the field to be able to reflect on actually what the answer is and you know what's what's the truth and what's not the truth so I think I think you pulled up a really important point there is actually need to be a little bit of a level ahead to see what's wrong and what's right and so let me touch on that actually because what's really interesting is right now you see people like developers who are using chat to build products here there's it's super interesting I've been seeing it everywhere everyone's like I could be a dev tomorrow and they're not that wrong here's why because you can ask chat to build something you can plug it in to whatever ID you're using and you just need some base knowledge of how to use that and you can visually see that your product is working now if you want to go further and you want to make alterations and get out of backend database and do all these other things you have to know what you're doing right but at an entry level or not even entry level but at a very base level a lot of people can plug and play and build something like a dev but once you start getting to the more complex stuff chat you can only kind of recommend things it can only help you can't build that database for you and connect it to your to your front end and build all the UI and it just can't do that stuff yet maybe it will in the future analytics is going to be very similar in the fact that you can build something but something that's different than even dev work is that devs can see it they can visually see this is correct it is working analytics is very in my opinion is a little bit more nuanced because there's a lot of different business use cases so we're going to see people will be able to generate a lot of stuff but is it right that's when you need somebody who knows how to dig into the data be able to use chat gbt in these different tools to validate that these things are actually working and are correct and are not just giving me some random information that makes no sense and doesn't help the business at all so there's going to be a lot of that going on but yes knowing the base tools how they work how analytics works understanding that process is still going to be extremely important to know how to do and especially at the mid level senior level it's going to be you have to know those things you can't just rely on chat gbt or an AI tool and if that's not inspiration to learn data analytics I don't know what is so Alex you're fully in belief that it's a future proof career then I don't want to make that claim I don't think it's future proof in very nuanced areas I think that there are some data analyst jobs out there that should have been automated a long time ago that are just super simple they didn't need to be a data analyst job in the first place there's tools that 5-10 years ago that could have automated that away so I think in the broad spectrum of data analyst jobs very realistically I would say there's probably 5-10% that have a high likelihood of being automated in like 10 years where an AI tool will do most of it they'll just need somebody to kind of maintain it I think that 80%, 90% plus is still going to require data analysts to be hands on doing this work just because there's so I just with my domain knowledge and my industry knowledge that I've worked for the past 7 years hands on I could not imagine any business like my old company is called the Merisorysburg and my old company if they tried to implement AI right now and do that it would be an absolute disaster I could only imagine how much money we would lose from all this business that we were working with using AI and making a mistake and letting and trusting it like I just it would be a horrible move so integrating those tools and then learning how to use them scaling that up is going to be multi multi multi year process and there's going to be a lot of jobs that come up so those 5-10% of jobs that do go away I think will actually be highly will be overly repurposed into different jobs in the future so that 5-10 that goes away I think it will be about 20% upswing with all these smaller businesses freelance jobs and everything in the future so I don't want to just say it's foolproof I just think you need to know the skills know the tools and know your audience and your market so that you correctly align with that so you do have a job and a place to work in the future Excellent answer and I also think I'm just harking back to the AI conversation we were having earlier for anyone who is learning data analytics it is worth while just keeping up to date with everything that is changing in the industry like the career foundry blog just the shameless blog there but also just keeping up and seeing AI as a kind of something to work with as opposed to something to just ignore because it's not going away so it's good to start and for anyone who has been inspired this evening to start their own journey in data analytics I am going to post a link to the career foundry short course on big marker if you are thinking about taking those first steps it's a free start to 6 day short course which will take you on those first steps going back to the questions there was a great question here from Alan over on big marker for those without a technical background what do you think are some of the most important transferable skills that lead to a success as a data analyst So I worked I was blanking on the name for a second I worked with a guy named Sergio and if you follow me on LinkedIn you may have seen Sergio Ramos love the guy I worked with him, he was my mentor he worked in a warehouse using forklifts and stuff like that and he was like I really want to be a data analyst I was like let's do it so he became my very first mentee that I ever had and now he works at PayPal as a data analyst and I say that because it's very much a similar story and here's what I recommend here's what I did with him I said first and foremost you have to learn the skills so he's like alright so he busted his butt just trying to learn those skills learn the skills really well I was really impressed I was like hey you learned this really well in like 2-3 months great job I was like alright build projects so he hustled to build those projects then I was like apply this is where he really stood out he did exactly what I told him to do and what I wish I had would have done and what I kind of talked about earlier which is reach out to as many recruiters as you can bug them until they help you find a job and you have to be kind of like a little bit shameless and he did that and he literally was like every week he'd be like hey just messaged all my recruiters just messaged all my or just called all my recruiters and what not and eventually he got his first job as just a consulting job then he at 6 months after that job he applied for another job got another 6 months he had a year and then applied to PayPal got a PayPal job all using recruiters so if I were you if I were just starting out you don't have any relevant technical experience and he literally had none he didn't even have a college degree he literally just had got out of high school started working in a warehouse is that your passion and your drive and your just your ability to keep going through these steps is really important so he just had a really positive outlook on it he didn't let it get defeated he just kept pushing and when he landed that first job I mean he just worked and he was a hustler like I call Sergio he's a hustler is he just kept hustling and so I think part of his like just a I think learning the skills good resume and working with recruiters super important the other thing I will say especially somebody who is good at interviewing or practice you can practice interviewing have a good personality that goes a long long way especially with recruiters when you're working with them to make sure they don't blacklist you or getting interviews and like talking with a hiring manager your personality goes a really long way so those are the things that I think are most important those are the things that Sergio did that made him really really successful even with almost no qualifications It wasn't the same Sergio Ramos who used to play for Real Madrid was it? No, no different one Sean has got a great question over on big marker if you're looking into becoming a data scientist would being a data analyst be a good starting point? Yeah I get asked this a lot I think that it definitely can be and in fact my third year as a data analyst I was working at Amerisor's Burg they're like the Fortune 5 on the Fortune 500 list they're a huge company and I was working in this data analytics team I was a data analyst on a data science team that was really good at what I did and I started working with the data science team and after about a year there they asked me if I wanted to transition to becoming a data scientist and so it absolutely is possible now I turned that down because I saw the work they were doing and it just didn't interest me I was like I don't want to do this on the rest of my life and it wasn't my thing but a lot of people love data science and so I know for a fact you definitely can do it you kind of have to I don't know if I want to say up skill but you have to change your skill sets as you go so like analytics is very focused on the data collection on the data cleaning pre-processing and then visualization as well whereas data scientists have a lot of data collection and pre-processing for their stuff but they also need to know some of the machine learning models so you have to skill up in different areas but it absolutely can be definitely a good stepping stone I've seen a lot of people do it and message me and say hey I was a data analyst became a data scientist after two years and I was like that's awesome and you know if that's what you want to do go for it and there's some people who become data analysts for life like me I don't see myself changing I've been working closely with data engineers and database developers and really loved building pipelines but you know it just wasn't something I was as interested in that moment so there are you can definitely start with analytics and go from there there are people who have been data scientists and become data analysts it's just different highways you get on and off and you can kind of choose what you want and what skills you want to learn awesome just reading some of the comments YouTube everyone watching on YouTube also Alex's audience we love to see always bring great energy Mandy said that she just saw a chatbot data analyst role so it just goes to show that there's lots of roles going on it's already happening a great question I think here from Salma and it harks back to the recruiters questions that you were answering earlier how do you actually find good recruiters? so recruiters are I'm going to give you a story real quick really quick story and then I'm going to answer that question when I first was becoming a data analyst somebody told me to work with a recruiter never heard of a recruiter didn't know what that was I was very very like perplexed I was like what on earth is this so a guy tells me he has a job offer or a job interview for me I'm like is a data analyst job I was like alright cool this pays pretty good I'm going to meet me at this garage that's next to the building and I'm like oh my gosh I'm going to get murdered and I told my wife where I was going what time I was going if she didn't hear from me in an hour from that time to call the police that is actually what happened I looked back and it was really funny but I didn't know anything about recruiters they were very scary to me it was just a different world I wasn't in the tech world I was in healthcare so just a different world you have to understand that you need to be cautious with the recruiters there are some you need to make sure they're legit but recruiters they can be anywhere I find a lot of recruiters I found a lot of recruiters on LinkedIn so there's a lot of recruiters on LinkedIn like I was saying before you can go online look at a company and search for recruiters in LinkedIn find them and message them so if you find a job a job posting that you really like and you're like oh this would be my dream job don't just apply you apply then you find the company you reach out to the recruiters and say hey I just applied for this tattoo resume I think it would be a really good fit I'd love to chat about this if you have time just a quick note and that can put you on their radar they're going to have 50 other people who apply you might be on their radar and no one else is another thing that I did search for your area so I lived in Dallas so I just Googled data analyst recruiters in Dallas now I quickly found out that sometimes they're called different things they're called technical recruiters they're called there was another term for it but a lot of it was called technical recruiters for the Dallas area that's what I found and then I started reaching out to them and that works as well and you can email, you can call them so LinkedIn and cold calling and cold email are the best ones now kind of a this is something that somebody else did I am not supporting this Will, don't get mad at me if somebody gets mad at you but here's what someone else did he was living up in Canada and he wanted to move to Minnesota which Canada to the United States the job that he had required that he live in the United States so when he was talking to the recruiter what he did was he went online searched for in Minneapolis recruiters that live in Minneapolis where he wanted to go then he reached out to a bunch of them and said hey I live in Minneapolis I want a job he got a remote job from Minneapolis and then he moved so he just went ahead and said I already live there I'm going to be there and he applied and talked to recruiters as if he lived there so you know I'm not saying you should do that but it did work for him awesome awesome also for anyone watching and considering a career foundry data analytics program you're not on the program alone you do get the full support as I said at the beginning of a mentor and a tutor that's our dual mentorship model and we also have a team of career specialists and the job preparation course all behind you to prepare you for getting that dream job in tech so you're not on your own on the career foundry data analytics program I want to pick up on one comment on YouTube which I think is lovely from Hania I just want to thank you Alex I got my first job and your videos helped me a lot we love that positivity and congrats Hania on landing that job now I know a lot of people watching this evening are considering jumping into data analytics or taking their first steps I think this is a very very good question from Cering is it necessary to be good at maths in order to be successful at data analytics at math math yeah you guys say maths which always throws me off I say you guys that's a lot of people they say maths so here's what I'll say I was pretty good at math in high school and I took the algebra in college here's what I found and I'm trying to be super realistic if you know a lot of basic math and you'll learn a lot of it as you try to you learn the skills to become a data analyst if you know basic math you can understand a lot of percentiles you can understand division, multiplication, pendos the basic stuff for an entry level role that's usually enough usually you don't need to know a lot you won't need to know things like linear regression or just some other more advanced stuff typically now in the financial world if you're looking at banking or other institutions within the like fintech they usually rely more heavily on math but I worked in healthcare so my math that I had in high school college was enough for me I haven't really gone way above that except for some personal use cases so I think if if you know the basics you'll learn a lot of functions within excel, within sql within tableau those are if you know those you're going to you'll either learn it or you will not it's not I wouldn't say it's crazy tough understanding data is not always about math understanding data is sometimes a lot more about like the context the domain understanding of how the data flows and then usually the end math that you're using is not crazy complex or if it is there's other people there to help you make sure it's correct so I wouldn't be you don't have to go and get a degree in like statistics in order to be a data analyst at a lot of places although like I said there's a lot of quants which is like extreme it to the nth degree with with needing no math and there's a lot of fintech finance data analyst that do need to know that level of of math so but it's not it's definitely not the majority awesome we've got someone on YouTube Joseph who's doing his algebra homework and wants to now I'm just kidding there's a great question there's a great question here from a circa why does a company hire a data analyst instead of a statistician you know you can do it I've worked with statistician so I worked with two statisticians they were working in our data science team and they were extremely knowledgeable in in creating machine learning models statistics that I mean way above my head in some cases they taught me a lot of stuff but for a lot of the other work they would just be overkill I mean in in fact statisticians at least from because I worked with statisticians at my job and I company that I consulted with not too long ago had a statistician they are just very specific to at least from what I found to making sure fine tuning creating hyperparameters for these models fine tuning parameters creating new equations that will help with so it's very math heavy not that's not always but a lot of statisticians don't have the base level data knowledge or maybe industry knowledge that you might have with just a regular data analyst like me who really knows healthcare extremely well and then I became a data analyst whereas they know numbers really well and then they kind of work with the healthcare data so they're just coming from a different side and wherever that job aligns they would align that if it's a very math heavy job they might hire a statistician if they need somebody to know data cleaning and the domain knowledge they'd hire a data analyst Thanks Alex It's an interesting question that we do get through and from Steven I had 33 years in IT and want to come out of retirement I did some analytics for 15 years of my career can a senior citizen 69 have a reasonable chance of landing a job Yeah it's a really interesting question I just had this talk with my father-in-law so I'm a tiny tangent I'm going to come back, it's going to be on point though My father-in-law is 67 years old and for most of his career he worked with logistics so he worked in just a few different companies working with transportation logistics super smart guy his experience allowed him later in life when he was 65 to work for two years consulting with I think it was like PWC or Bain or one of those big consulting companies he had a lot of really good experience and so they hired him when he was 65 years old and they wanted him to keep working for the next 10 years I say that because I could see somebody like you potentially being in a similar position if you have a really really in depth knowledge in IT and data analytics you really know what you're doing there are going to be people who value that experience even like in consulting companies who really just want somebody who knows the ins, the outs how system should work things that new people coming into the field even senior analysts wouldn't know how to do as in depth as you would know so maybe you don't even go for just a regular data analyst job maybe you go for a chief data officer job at a small company or some higher level manager position at a smaller company where you're more managing than hands-on and fit in I think more that would be my very quick analysis of your situation for those of you also watching on bigmark I'm just going to post a link to career foundries diversity equity and inclusion report and you can have a look if I can post it on bigmark here we go send because I'll post it in the wrong channel in the report you can see the breakdown of our student body I do recommend checking out for anyone who's interested in doing a career foundry program I've just posted a link over in bigmark so do check that out I'm just going to jump into the questions again on bigmark let me go down let's have a look so mumama's asking what career planning advice do you have for someone who is at the beginning of their career in their first data analyst job I suppose this is a pathway question if you just got at your first data analyst job first off congratulations because that's super exciting looking back I think I stumbled upon a good formula I didn't know what I was doing it just happened but if you could actually plan it out that would be better here's what I would probably try to do it depends on what you're trying to optimize for are you trying to optimize for salary do you want to make as much money as you can within a short time do you want to optimize for comfort do you want to work remote really I would be jotting down what's most important to you in the beginning of my career it was all about money because I was broke so I really needed to optimize for money but then after about three years I needed to optimize for remote work because of COVID and I had three kids so knowing what's important to you is really important but really what you can do is after about a year of working as an entry level data analyst at one year you can then start trying to apply for mid level roles now excuse me drinking too much water what a lot of people think or kind of the old school way of thinking is oh I need to work at a place three, four, five years and about five years on a mid level analyst how it works in at least this field or hasn't for probably the past ten years things move really fast and you learn a lot of the job and your skills become a lot more valuable to an employer when you have actual hands on experience at a company so once you get a year experience you can then start job hopping if that's what you want to do you can go to another company get a year there in a different field or the same field job hop and get probably 20% to 40% pay bumps if you do it right that's what I was looking at with mine I was doing about 20 to 40% for each pay bump so if you're optimizing for money that's a great way to do it but you should really be writing down what do you want to do in two, three years because it's a short lifespan a short transition to the next stages of a data analyst, mid level, senior level you can get to mid level one to five years somewhere in that range senior level four to eight years so you need to start planning what do you want to optimize for and then try to follow that that's what I'd recommend for those people who are studying data analytics and they want the highest salary possible after learning data analytics what would be the avenue to go down if you wanted to hit the highest salaries what would you specialize in this would be I don't want to say controversial I think other people have different opinions that's what I'll say my opinion is that I would optimize for an industry I think industry knowledge especially in the coming years, five, ten years it is going to be more important to know domain knowledge than ever before because of AI and knowing these tools I think domain knowledge is just going to be probably one of the more important things technical skills in your domain knowledge for sure I would be specializing in a domain finance, construction agriculture, whatever it is become really really good at it I think that will lead to more money in the long run now if you want to make the really big bucks you're looking at things like consulting working at big tech companies or really focusing on that domain knowledge so you have different avenues but I think the most the quickest way to get there as well as probably the easiest way to get there is specializing in a domain if you try to go to Amazon right away to get a big salary it's going to be really tough to break in because it's just a lot of people who work there a lot of competition so that's what I would do I would recommend finding an industry that you like becoming really good at it I think that's going to be really important in the next several years awesome another question also when you're a data analyst and how much does strategy come into play so you've got the company data you see that there's some problems you visualize it, you present it to the company do you also stand there in the boardroom or in the meeting and say look this is what's going on with the data in the company and these are potential solutions or are you just delivering the message yeah so I have different experiences at different companies in different positions I was a data analyst at a very small if under 50 people healthcare analytics company with that job I mostly reported to the clients and I mostly reported to my boss so my boss was he was the director of analytics so I mostly reported to him when I worked at the fortune 500 company when I was just a data analyst I had just broken in we worked at a larger team at the small company I was just reporting to one guy at the at the Merisores Burg and I was working as a team of about 12 people so our team would come together and I usually present to the whole team because we were a part of a long process of the data collection process, the data cleaning process transforming it doing all that stuff creating the visualizations, we had a person for everything database engineers database developers visualization specialists, we had the whole gambit data scientists and we would then report up to the program manager who then we would present that to them first then we would present that to either my manager or my manager's manager who was the senior director of data science and after that we never went higher than that. Now in my last job I was an analytics manager I reported directly up to the CIO and the senior vice president of IT so I think it depends just as a data analyst you're typically at a small company's reporting higher at smaller companies you're reporting like one or maybe two levels above and then as a manager I was reporting up to the highest and then everyone in between because of just how my role was so I was reporting to really everybody or presented to really everybody Awesome thanks Alex Just going back quickly to the career found data analytics program because some people are asking about the length. If you can devote 30 to 40 hours a week you can complete the course, the program in four months and if you can devote 15 to 20 hours you can complete it in eight months just to clarify for anybody who wanted to know that. Alex thank you so much for hosting another fantastic presentation this evening also thanks so much to Alex's crowd and I would also just like to say quickly to anybody who's watching from career foundry do subscribe to Alex's YouTube channel over it's Alex the Analyst it's currently at 470,000 subscribers and we want to get it to 500 so do go over there and subscribe we've got to get to 500 by the end of 2023 it's a combined goal but yes thank you so much for all the great questions all the engagement this evening for anybody who's watching and is interested in data analytics also recommend checking out the career foundry blog I'm just posting a link over on big marker there are some very specific topics on tools and salaries but there's also some more general topics there on soft skills transferable skills and jobs in your locality that kind of thing so do check it out we've got a great team of editors working at career foundry writing these articles and they would love for those to be read also do check out the career foundry YouTube channel you can see previous that we've done over there but you can also see members of the team who've been interviewed and people that we've interviewed alumni do check that out there are some familiar faces especially in the data analytics section so I would recommend going over there especially checking out the videos from Tom Gadsby who is the senior data scientist here at career foundry he's got some great introductory YouTube videos so do check that out and last but not least if anyone is considering a career change and is thinking about career foundry are currently offering career change scholarships worth up to $1,125 or 1,125 euros off the full price of the program to get that or to apply for that just book a call with the program advisor if you're on YouTube click the link below in the description or if you're on big market click the sticky note and speak to one of our program advisors they're lovely and I know them all personally and they can also answer any questions that you might have about the dual mentorship model the curriculum, job guarantee things and they're waiting on the calls now Alex thank you so much are we going to see you again this year probably I hope you'll have me back I love doing these webinars I hope so we're going to find a topic we're going to find a really juicy topic so we're going to get you back on the channel but thank you so much for spending your time this evening and that was a great presentation and thanks to everybody who joined us from all over the globe great international crowd and a great evening so thank you everyone and see you next time