174 : He was unemployed for 1,000 days. Now he’s a data analyst. (Josh Gledhill)

[00:00:00] Avery Smith: After 1026 days of unemployment, you officially have two data job offers that you got in the last month or so. Yeah. That's Josh Gladhill and he's just like you, a listener of this very podcast. And he landed his first data job using the SPN method. In this episode, he'll show you exactly how he did it.

[00:00:21] Avery Smith: You'll learn the key to landing a data job despite battling things like dyslexia.

[00:00:25] Josh : I make notes that are good enough to be a textbook,

[00:00:28] Avery Smith: what not to do. In an

[00:00:29] Josh : interview I set down and literally my brain just left. I couldn't even do basic things. I couldn't even do a V lookup and how a portfolio got him the job.

[00:00:38] Avery Smith: You're officially the biggest data nerd I know. If you're carrying around 40 pages. Uh, data portfolio,

[00:00:44] Josh : the job, and that's the most important thing. I think

[00:00:46] Avery Smith: he went from a normal listener just like you. I

[00:00:49] Josh : never thought in a, in a million years that listening to your podcast a year ago, that I would've been sitting here

[00:00:55] Avery Smith: on the podcast a year later to breaking into data, and he's here to tell the full story.

[00:00:59] Avery Smith: Well, [00:01:00] that's what happens when you post on threads. You guys start documenting your learning. You guys might be the next guest on the Data Career podcast. By the way, this interview is brought to you by Julius. Your AI data analyst connect your databases and business tools. Pull insights in minutes, no coding required.

[00:01:15] Avery Smith: Thanks Julius for sponsoring this episode. Let's get into it. Alright, Josh, after 1026 days of unemployment, you officially have two data job offers that you got in the last month or so, and I'm so excited to have you on the podcast to walk through. You know, everything that you learned in that almost three year journey.

[00:01:34] Avery Smith: Of learning data analytics. So let's first start with your background. So you were in the music industry, right? Can you give us a little idea of what you were doing and how long you did it for?

[00:01:44] Josh : Yeah. So, um, I was in the music industry for two years once I finished my master's degree and which I did in music.

[00:01:52] Josh : Um, and I was yeah, there for two years and I kind of did some great stuff. I worked with some Grammy award-winning artists, um, which I think is still, [00:02:00] although I'm not working in music, I really like it and it makes me smile every time I say that. Um, so yeah, I did that and then I transitioned outta that into teaching.

[00:02:08] Josh : Um, while I was, while I was actually in the industry, I was working on some like really cool cutting edge tech. Um, which involved a lot of, um, programming and a lot of kind of working with data, but not data as we know in the spreadsheet. More like data in terms of electronics and stuff. So that was, yeah, there was a lot of really cool stuff.

[00:02:25] Josh : I did. Got to travel the world and, and do some really cool stuff. Worked with the, an orchestra out in Japan. Um, and I did some other stuff elsewhere in the world as well, which is really cool.

[00:02:35] Avery Smith: That's awesome. So obviously not, not a ton to do with data like music. Data. There is some overlap, but, but not a ton.

[00:02:42] Avery Smith: So what made you get interested in data?

[00:02:44] Josh : So I, um, once I'd finished teaching, I had decided to transition into data because I had done a Python course on Udemy and I'd done the Python course. I was like, yeah, I know how to do Python. Which has always been an ambition with what I had done [00:03:00] because I was doing a little bit of programming, not with some of the work that I was doing in the music industry, but it wasn't line code.

[00:03:05] Josh : It was like dragging boxes around and kind of go, yeah, that's programming. I can now call myself a program update, you know, Instagram with program. And then it got to the point where I finished this course and I was like, mm, okay. I know how to do stuff in Python. I can do, you know, hello work, um, print, hello world and, and all that fun stuff.

[00:03:24] Josh : What do I do now? I just don't understand what I can do with it. So I started to, I think I typed into YouTube, Python, probably just typed in Python and a video of. Of Luke Bar popped up, bear Wicked, what's going on? So I kinda watched his video on how we use it. I think it was his, how do I use Python in my analytics word?

[00:03:42] Josh : And I got really interested in that and I was like, yeah, this is cool. But I just didn't know where to start. I just didn't have, I'd done the course, but I just didn't know kind of the next step to take. And I think that's always been, that's a challenge with most people. I mean, you'll know this from doing your, your accelerator.

[00:03:58] Josh : It's kind of a where do I even [00:04:00] start on this journey of, of doing data. So once I'd, I'd done the, watched that video, I then started to scratch my head and realized that while I might know the how on how to clean data in Python and how to make a, a bar chart or whatever in, in math plot label. Whatever, seaboard, I didn't know why I was doing it.

[00:04:17] Josh : I didn't know why I was cleaning data and that's, that's where I then decided to kind of do my, I wanna do a data analytics bootcamp, we can talk about in a, in a minute, I guess. But yeah, that's kind of the background. I, I watched a video when, this is amazing. I'm interested in data, always kind of have.

[00:04:33] Josh : But never really had any real exposure to it other than kind of writing reports at university. How do I learn more about how to use my new skill at Python and, and, you know, become a real data nerd, I guess is, is the, you know. Story that,

[00:04:47] Avery Smith: yeah, that's super neat. It's, it's kind of funny because I love Python.

[00:04:51] Avery Smith: Python is my favorite data analytics tool, but I encourage people to not really learn it before landing their first data job. And here you are learning it before you even like, [00:05:00] want to be a data analyst. So, I mean, there, as you probably, you know, experienced, there is a little bit of a, a steep learning curve to Python.

[00:05:05] Avery Smith: Maybe you'd done some programming previously, uh, that made it a little bit, a little bit harder. But the cool thing about Python is. This is gonna sound really dumb. And I, this is, I don't know if this is like countrys against, against England, but in, in America, when I hear, uh, in a British accent, I'm like, first off, very cool.

[00:05:21] Avery Smith: Second off I think of is Harry Potter. Especially when people say like, wicked and stuff. And that's what, that's what Python feels like to me. Is it, is, it is like Harry Potter wizardry. It is very magical because it can, once you, once you start getting the hang of it, it can basically do. Anything you want it to, um, in data analytics, in cybersecurity, in web design, like, it's, it's an amazing tool.

[00:05:40] Avery Smith: So I just think it's funny that that's, that's where you started, but okay, you, you, you get hooked on Python, you're like, okay, I want to get more into data analytics. You end up doing this bootcamp that I think was pretty intense. It was like a 10 week bootcamp. You're just like studying basically all day, every day.

[00:05:53] Josh : Yeah. Essentially. Yeah. I, it was 10 weeks, um, five days a week with some days of self study within that week. And yeah, it was full [00:06:00] on. It was kind of. Learning the very basics of Python. Then we moved into introduction into Pandas, moved into an introduction into kind of doing loops and stuff within, within Python.

[00:06:10] Josh : I don't we'd ventured any further in Python in terms of looking at like OOP and stuff. I think we kind of missed that out. Figured it. Important for data analytics in a technic boot count. And then we kind of moved on to visualization, using map, map, pot lib, and um, and some other things bouquet, uh, as well and go maybe.

[00:06:28] Josh : And then from that we then, um, to, yeah, data visualization. Then we did like statistics and took the kind of the, the maths behind it. Which by the way guys, you don't need to be great at math in order for a data channel list. You just need to know how to craft reference There a couple of basic, actually on the very basic level anyway, uh, and then we look to kind of storytelling and how to present that to, you know, stakeholders, which again is, is really important.

[00:06:52] Josh : 'cause if you can't actually tell a story with your data. To the people that it's important to your, your really, your data in your work is [00:07:00] really pointless 'cause it's, there's nothing behind what you are kind of doing if it can't be translated to those people. So, yeah. Yeah, it was pretty intense.

[00:07:09] Avery Smith: It, it does sound intense.

[00:07:10] Avery Smith: Um, you're, you're obviously coming from the, the music background. And like you said, like you probably haven't done all that much, you know, higher level math in the last couple years and then all of a sudden you're trying to do programming and, and statistics. So that might be a little bit of an an intimidation there.

[00:07:26] Avery Smith: What are some tricks that you did to kind of. Learn, even though you actually, and I hope I'm okay talking about this, you talk about it mm-hmm. On your social medias, but you also experience dyslexia as well. So I'm imagining learning is difficult for you just because reading is a big part of learning sometimes.

[00:07:41] Avery Smith: Right. And you hadn't gone and like really gone back to math school in a long time. So how, what were some tips that you kind of gleaned from this learning process?

[00:07:49] Josh : Yeah, so I think my kind of, my big thing that I've always made sure all the way through my kind of my academic career is to make notes that are good enough to be a textbook.

[00:07:59] Josh : And I, I don't think [00:08:00] people, like, I was always was shocked even on bootcamp sitting there and people were hardly putting pen to paper and I was like scribbling away. In fact, if you go onto my, um, Instagram, I, I did some like time es of me studying in, in, in like class, and you can see I'm scribbling constantly.

[00:08:16] Josh : That's one of the things, make sure your notes are complete. I don't think people make notes that are good enough to go back to reference. It means that you're always having to kind of Google stuff as well, and, and it just feels, it's complete if you've got it in a, you know, if you've got it in an actual notebook and you can, you know, use it and, and be able to just go, oh, I wonder what lesson matters was, and you go back three weeks to look at how to, you know, use eye lock or whatever it is.

[00:08:37] Josh : So that's one of them. But also make sure it's complete. I mean, the problem is there's so many, there's so many courses now. I mean, I, I'm somebody who buys so many new me courses and I've recently bought a lot of producers courses, which again, guys, they're great. Go check 'em out if I'm allowed to promote somebody that weren't available on the YouTube.

[00:08:55] Josh : But you can also get the, the kind of the, um, the notes and stuff that help. Learning. Yeah. [00:09:00] So make sure you complete your learning as well. And also don't skip the fundamentals. I think we've got this thing where we'll like want instant gratification and learning something and we'll just skip like the basics.

[00:09:09] Josh : But the basics are really important because if you don't understand how to do something on a basic level, when you get into the more advanced stuff, if you haven't got the underpinning of what the fundamentals are, it's very hard to kind of make the connection. And I think that's really important that you do make sure that you complete everything from start to finish and don't.

[00:09:27] Josh : Skip bits and also color code. I use a lot of color coding just to make my notes kind of. Stand out. So anything that's important, like true, you don't do this is always in red. And then I kind of use a, a color code system. So, um, orange is tips and then green I use for titles or so Patty, just to, you know, make my notes look a little bit more exciting.

[00:09:47] Josh : And also more recently, just use any orange, check your GP or something to just help be like a study buddy or be the teacher. So if there's something that I don't quite understand, I'll just bang it into chat, GTP and say, explain to the. Explain this [00:10:00] concept to me as if I'm, you know, fine. And then I'll explain it in a basic level as if I'm a 5-year-old.

[00:10:05] Josh : And then you step it up to explain it to me as if I'm a junior data analyst. And they kind of give you some words in your like, uh, I don't understand what, you know, um, GVA stands for, for example. But then you can kind of go back and it'll tell you, and I think they're kind of the, some of the tips are, yeah.

[00:10:19] Josh : And I think also just not rushing it. Make sure you know it well. Before you kind of go onto the next thing as well. I think that's really important. So make sure you have a very clear understanding of it before you move on and do more technical thing or more complicated things, because again, having that kind of fundamental underpinning is, is, is really important to a complete rounded learning.

[00:10:39] Avery Smith: Those are some great tips, uh, and I think, uh, the audience will really learn from those quite a bit. Another thing I think, uh, that you do very well and the reason you're actually on the podcast. Um, is because you started posting on threads, which if you've never heard of threads before, it's Instagram's version of Twitter, basically.

[00:10:55] Avery Smith: And, you know, you're just about to start your first data job. You've had kind of a, an [00:11:00] internship or a work placement that we can talk about here in a bit. But basically you've been talking on threads about data analytics. Uh, you're making posts and some people might be like, oh, in order to, you know, talk about data analytics on a pla, a social media platform, you have to be an expert.

[00:11:14] Avery Smith: But you've been doing this for, you know, months. I mean, I've seen it for months, maybe for years. Maybe you can talk about how long you've been doing it for, but you've been making content, uh, about data analytics. Can you just talk through about like why you started doing that in the first place?

[00:11:27] Josh : To start with, it was the fact that the bootcamp had finished.

[00:11:30] Josh : So I finished that, um, April last year, so April, 2024. And kind of when that had finished, I kind of realized that I was by myself in a massive world of, of kind of unknown and, and kind of unemployment and trying to find a job and I trying to look for a job and I kind of need. Somehow reach out to people somehow.

[00:11:48] Josh : And I kind of was always aware of kind of this personal brand idea that you've gotta kind of have a personal brand to kind of sell yourself and kind of make yourself heard in a very noisy world. So posted on on Threads [00:12:00] to start with. I don't even know what I started posting, to be honest. It's so long ago.

[00:12:03] Josh : It must have been awful. I've not even gone back through my feed to check what I, what I was writing. I started thinking, oh, if I get 10 people following me and I can just share my very vague knowledge on data analytics to them, I'll be happy. And then eventually it started to pick up where I was kind of just posting.

[00:12:21] Josh : I don't even know what, again, I don't really remember the journey of me doing threads has been such a weird kind of. It just feels like it's been like, boom, there it is. There's your four and half, 5,000 followers for you. I don't even really remember what I was posting or what I was doing, but whatever was happening, I was picking up attention and eventually you started following me and, and so on and so forth.

[00:12:39] Josh : And then, yeah, and I realized that actually if I started to post what I was learning, I would then start to kind of understand it better for myself. 'cause the one thing that I didn't want to do was post stuff that was wrong. And you know, and that's always something that you can always get called out. So you're always making sure that even what you're posting is pretty accurate and, and as good as it can be.

[00:12:58] Josh : And yeah, so I was, I was just, [00:13:00] I'm always a bit, I'm a bit shocked talk about threads 'cause I don't really know where the success, I set that the other day actually on threads. Don't really know where the success came from on my following. I must have just been, uh, people were saying you're very authentic.

[00:13:11] Josh : Um, which I guess I am, but also I don't really know that much still, like, I still haven't had my first data role yet. I've done a work, yes, but I've not worked the pay to work with data yet, so yeah, I don't really know what works out really. But yeah, that's kind of a spiel about my threats

[00:13:28] Avery Smith: for me and why you resonate with me and why I enjoy following you is one, I think people love a journey.

[00:13:33] Avery Smith: I think that's really interesting. We wanna, in a movie, like imagine like, I don't know, just take like any sort of superhero movie. If they cut out the whole part of like. You know, the superheroes low point of their life and how they changed it around and then became the champion. What if, what if the movie was just like the last 30 minutes, which is just them being a champion.

[00:13:52] Avery Smith: I don't think that's as interesting. And so I think we love watching you, you know, go from, I teach music at a university and then I'm gonna be [00:14:00] employed for 1026 days and then I'm gonna come out the other side, a data champion. You guys get to literally watch every single part of the process. I think that's a super fun movie.

[00:14:09] Avery Smith: The the other thing is that I think really suits you well. You just share what you have learned. You know, I try to get all of my Accelerator students to post on LinkedIn. In fact, I just had one accelerator student who's probably been with me about six months or so. He just said, Hey, I'm out of LinkedIn ideas, and I'm like, oh my gosh.

[00:14:27] Avery Smith: There's so many LinkedIn post ideas that you can do. Literally just share what you've learned every single day. And I think your Threads account is a really good example of just sharing, Hey, I learned this recently. I learned this recently. If you wanna do this in data, this is how you do it. And you're not like having to sit down and being like, what content do I create?

[00:14:43] Avery Smith: I don't know how you think about it, but to me it's almost as like, Hey, what did I learn today? Let me put it in my learning journal. Yeah. It just happens to be public to the world. And I think one, like you said, that helps solidify what you've learned. So it makes you better at what you're doing because you're like, okay, you know, if someone asks you [00:15:00] what's a P value?

[00:15:00] Avery Smith: It's like, oh, I've made a thread post about this before I remember it. But also it, it does build your personal brand and opens up more doors. Like in this case, you actually have, you know. Gotten job offers, so that's great. Uh, I don't know if they know about your threads or not, but when I originally reached out to you to this interview, you had not gotten those job offers yet?

[00:15:18] Avery Smith: Not really. So like there's an opportunity where you come on this platform, maybe you get seen people like your story, we share about you on LinkedIn. Maybe that leads to a job and that's all coming from threads. So really the idea of threads can really open up doors that you didn't even know existed because it's just like a world of opportunity where you have thousands of people looking at Josh Gledhill posting.

[00:15:39] Avery Smith: Interesting things, data, things. It's just, it's like you said, I like, I like how you said I was just, uh, swimming in a sea of people in the exact same journey who are also unemployed and like, how do you stand out? And I think creating content was a great way to do so.

[00:15:52] Josh : Yeah. And I, and I, I would also love to share, and I'd probably go back to being someone who's always wanted, had a kind of.

[00:15:59] Josh : A thirst for [00:16:00] knowledge and always wanting to kind of develop my, my skills more and more. And I think, yeah, being able to share that knowledge, um, and also just kind of, yeah, network with people. I think that's, you know, so important in the job market and, you know, just in life generally and where our lives are so insular, I figure it's really important.

[00:16:16] Josh : So, yeah.

[00:16:17] Avery Smith: Uh, speaking of networking, um, good segue there. One of the things that you had told me that I didn't even know that you actually, and correct me if I'm wrong, but you were actually a, a listener to the Data Career podcast before I followed you on threads. So you've listened to some episodes.

[00:16:32] Avery Smith: Historically, and, um, you were actually a fan of the SPN method, so tell me like why you were a fan of the SPN method and how you felt like it worked in your job search.

[00:16:42] Josh : So that skill portfolio network, right? Yes. I guess the kind of the skill sort of stuff that I was doing was. Think I was thinking about this earlier actually.

[00:16:50] Josh : I couldn't really pinpoint a, a time where I kind of was, I was obviously working on skills, but it wasn't like it was a, a definitive line where I went, right. I've, I've learned all the skills that I need. [00:17:00] Obviously your, your kind of, your philosophy is all about kind of learning the, the basics and then getting into a job and then learning the more advanced stuff as your, you're working, isn't it?

[00:17:09] Josh : That's kind of your strategy behind. So for me, start with Python. Obviously I kind of did it backwards and then kind of got to excel at the very end, which is funny 'cause my, my actual kind of trajectory was Python, sgl, power bi, and then did Excel at end of Learn Excel really well, kind of within the last six months from being on work placement.

[00:17:27] Josh : And so that was that. But the kind of, the, the big thing was. The networking was key, and I think the reason why I managed to get my second job offer, which I have now accepted and be starting in the next month or so is because I'd already done my work placement there. So although it's in a different team and a different department, they were already aware of the fact that I had already been in place and learned about their culture and that how they work as a, as an organization, essentially my local authority.

[00:17:54] Josh : Then I think that's probably what made my resume and cover letter stand out. The fact that [00:18:00] I'd already got that kind of. Understanding. So I haven't networked directly with those people, but I'd networked with the people within the council and obviously word must have traveled that, dunno how it worked out.

[00:18:10] Josh : I obviously will never find out, but whatever happened, something must have been on my, on my resume that was. Good enough for them to want to interview me. And then secondly, yeah, portfolio. Although my portfolio isn't actually published at the moment, I took it off because I was doing a lot of projects and not really finishing them.

[00:18:26] Josh : You kind of get to a point where you're like, well, no power bi enough. I don't need to do anymore. So they weren't really at the point where they were finished. I, I actually took, I will get onto my interview, I guess at, at some point. Uh, but kind of the thing that I made sure that I did was that I took my portfolio with me into the interview because I knew that I would only be in the top.

[00:18:42] Josh : Maybe 1% of people interviewed, although there wasn't that many people interviewed for the job. I thought I would, it would stand me in good stead to come out on top if I took in my portfolio and that's what I did. But yeah, massive kind of key things to kind of take away there that yes, you need to know the skills, but you do also need to be networking [00:19:00] and things like threads.

[00:19:01] Josh : Being on a podcast, it's the number one podcast on for data on Spotify. And Apple Music. It is, you know, they're massive things, but also having something to show people that you can, you can take and say, this is what I've done. I can actually do this, and I've got proof of doing this by showing you this, you know, work that I've done.

[00:19:19] Avery Smith: I, I really wanna make sure that our listeners understand what you did. When you say you took in your portfolio, you printed out like charts and dashboards and code snippets. That's right. Yeah. And you like had it in like a physical binder and you brought it to the in-person interview and you're like, Hey, that's right.

[00:19:36] Avery Smith: Here's my portfolio.

[00:19:37] Josh : I've made sure that I pitched it to the right people as well. So in my first interview, I didn't know who was gonna be there. I knew from LinkedIn that it was gonna be somebody who would work with in finance. I knew that I wasn't gonna take anything that involved Python or anything that was involving more complex code because essentially you present code to somebody, like a hiring manager, and they look at it and think you're, you're [00:20:00] talking to them in Greek, which, you know, is something to always bear in mind.

[00:20:03] Josh : Don't make it complicated for the person that's hiring you. Make sure you show them the best work that's most accessible. So even if they're somebody who's technical, I mean, I'm saying this as a bum, a hiring manager, but if you present me a, I actually presented two PowerPoints. One of them was of the capstone project from my bootcamp, which was done in Python, looking at Uber pickups in New York City.

[00:20:23] Josh : We've all seen it on kale, and it's a massive data set, and it's quite fun to get into a few user python, 'cause cleaning and a lot of. You know, process is needed. And then the other one was actually a project that I'd done at Work Placement, which was actually published, and I actually printed that off and took that with me being able to show them those two, two PowerPoints essentially, which were like the slides that you would show in kind of the stakeholder meeting.

[00:20:48] Josh : I think solidified my, my skillset to them because again, I was able to actually show them. Yeah. But it was printed. Yeah, it was printed. It was about 40 pages. It was the, it was quite hefty to carry around with me [00:21:00] the job, and that's the most important thing I think that I was, yeah. And it saves me as well, which we'll probably go onto in a minute.

[00:21:05] Avery Smith: Yeah, we're about to, we're about to get to that part. I just wanna say that you're officially the biggest data nerd. I know if you're carrying around 40 pages. Of data portfolio just around around England. I think that's awesome. Uh, good. Put that on bio. Yeah. Threats

[00:21:20] Josh : bio now, but.

[00:21:22] Avery Smith: That's awesome. You're in this interview, you're bringing your portfolio.

[00:21:25] Avery Smith: I like what you said that like you knew it was like a more financial position, so you tried to show the stuff that was more relevant to the hiring manager to this role. So a lot of financial positions they use like stuff like Excel a lot of the time. Um, or they'd use, I don't know, financial stuff. So you're trying to show stuff that more relevant to them versus maybe some crazy automation they did with Python or something.

[00:21:45] Avery Smith: I think that's That's great. You're trying to meet. You're trying to cater your interviewing resume portfolio experience for who you're talking to. I, I think that's, uh, really good. Um, you get into this interview, you, this is, this is actually fun. I wasn't at the interview. Obviously I'm, I'm in Utah, the United States.

[00:21:59] Avery Smith: I know United States, [00:22:00] but you're, you're documenting anything that's happening on threads. So I feel like I was in the interview, uh, like you post on threads. Hey, I have an interview. And we're all cheering for you, and then a couple days later you're like, just got out of the interview. I feel like the behavioral portion, so this is the part where they ask you, you know, questions, getting to know your personality, those types of things went well.

[00:22:16] Avery Smith: But the technical part, I feel like I flopped. You're like, honestly, like it was a lot of financial Excel formulas and I'm not the best at those. I don't have like all of those memorized. So you're like, I think I kind of just screwed it up. Oh, well, it was a good experience. Um, I'm glad I got the interview and I learned a lot, but I think I kind of failed that one.

[00:22:34] Avery Smith: And this is the job that basically this is the interview that ended up giving you a job offer, right? So what the heck happened? You thought you flopped, but you still got the, the offer afterwards.

[00:22:43] Josh : Yeah, so this interview was funny because it, it came the day before the interview that I was intending on having, which is now the job offer that I have and I've accepted.

[00:22:52] Josh : So I kind of originally was gonna back outta the interview and say, I'm okay. I've got one to, you know, as it to say, I would rather focus on the one that was [00:23:00] gonna be more important to me. But I decided I'd take it as a dry run for the next day. So I got into the interview and, um, it was the like chief financial Officer and the direct one of the directors of the company.

[00:23:12] Josh : And I realized at this point that I basically needed to capitalize on everything that I possibly could. I used, you know, the standard star methods, which I'm sure people know what it is if you don't. That's kind of talk about the situation, task, um, action and result. And it's kind of really critical in interviews, um, that you use it when they're talking about competency.

[00:23:34] Josh : Um, questions. Um, I dunno if that's a standard in the us. Talk about the star method. It is a. It is a thing in the us. Okay. Yeah. So using the STAR method to kind of explain a process that a, a task, a situation that you were in. Where you were given a task and then you talk about how you attained or you what as you took in order to get, resolve the task or the problem, and then you talk about the result.

[00:23:58] Josh : Um, so I kind of used that [00:24:00] pretty much. I was sick to it, to the back, you know, I was sick of it by the time I finished. Funny story about the interview, it was scheduled for an hour that was half an hour behavior and half an hour. For the technical and I actually did a hundred minutes, did a around 40 minute interview.

[00:24:13] Josh : 'cause I was basically, they liked me and obviously paid off 'cause I did get the, a job offer of it. So yeah. So then went into the technical, um, and um, it was funny 'cause I remember the, I remember the, the person interviewing me for, um, CCFO saying, um, oh it's okay. He doesn't need to do the technical 'cause he is already used Python and SQL and you know, have to use Power BI in Excel.

[00:24:36] Josh : So it's like. Okay, so this really sets my bad foot in straight away because I'm going into this technical edge fix. I know everything in the world, and I kind of just sat down. He gave, brought me in his laptop and, and Excel was dropped in front. I was given like the sheet to follow along with. And I just froze.

[00:24:53] Josh : I think because it was such a, because I'd been talking for so long, I knew there was some pressure that was riding [00:25:00] on it. It was my first job job. It was my first interview in 1000, 1000, 26 days. I was, I was pretty scared it could all flop and I could be, you know, waiting another thousand days for another, I.

[00:25:12] Josh : So I sat down and literally my brain just left. I couldn't even do basic things. I couldn't even do a vlookup. That's how, like how much it kind of departed me. So I thought, oh no, this is so, kind of did what I could do and kind of left, but, well, you know, got the interview tomorrow for, for the better job.

[00:25:30] Josh : If they're listening, I'm sorry, but you understand the reasons why not. I left there thinking, I've got the job tomorrow. This is good primer. And if anything, it's a learning experience and you know, it's some good content for threads. So, you know, thought nothing of it. And then day later, um, next afternoon, and I, I got a call and said, yeah, we're really happy with everything that you did.

[00:25:49] Josh : You're technical, well, I thinking my technical didn't go away. I answered very few questions, but it must have been the portfolio that I showed. That was the thing to prove that I [00:26:00] could do what I did, managed to get me the job offer. Yeah, I, it's still, I mean, that was two weeks ago, so it's a bit, it's still very kind of fresh in my mind that that kind of, you know, what happened.

[00:26:10] Josh : But yeah. Strange, isn't it?

[00:26:12] Avery Smith: I mean, I'm a yes but no. 'cause the P is important. The SPN method, the P is important, so. Uh, yes. Massive fir First off, technical interviews suck. They are really hard to perform well in. I don't like them. I don't like doing them. I don't like giving them, so I, I'm, I think a lot of us would freeze in that moment, so don't feel bad about that.

[00:26:33] Avery Smith: But yeah, I think having a portfolio kind of saved your butt there as well as maybe just like talking, talking it out. That's the other thing. I've interviewed Tiffany Teasley, not on the podcast, but in my accelerator bootcamp. I had her come talk to our students. Uh, she was interviewing for a data scientist role and they asked her about this Bert model, and they're like, do you know anything about it?

[00:26:51] Avery Smith: And she was like, uh, no, sorry. And so she felt like she kind of flopped it and then she went home and she studied everything she could about Bert, and then [00:27:00] she emailed the hiring manager and said, Hey. I didn't answer that question the way I wanted to. Like, here's what I've learned in the last 24 hours.

[00:27:07] Avery Smith: And that really impressed the hiring manager. Hiring, right? So there's opportunities when you're in an interview and you don't do well to try to save it in, in the end. So I think that's awesome that you did save it. Uh, and, uh, that's the power of a portfolio. Um, I also wanna talk about this, this job, um, description that you, that you applied for because you were, you were re-looking at it recently after you got the offer and you're like.

[00:27:30] Avery Smith: Crap. I don't, I don't meet these requirements that it's asking. It's asking me for all these requirements, and I only meet, I don't know, 70%, 60% of them. Yeah. Talk about that, like how does that make you feel and, and looking on that now that you have the job offer, what did you kind of glean from that experience?

[00:27:45] Josh : Yeah, so the, the, the second interview, which happened exactly a day later. From the first interview, what's the kind of the may or break? That was the one that I really wanted and put all my time and effort and focus on in terms of kind of studying and made sure I understood [00:28:00] kind of the job spec. So I looked at the job spec before the interview, thought, okay, this seemed good.

[00:28:04] Josh : I check. Yeah, tick, tick. Okay, good chance. When I went into the interview, I was aware that I had, there was two days of interviews, of full interviews, and I was the first person on the first deck. So I knew straight away that I had to press off the straight away because there was no kind of, I was the first, I was the first, uh, chance for them to find it, and I would've been the last person that they would've looked at in terms of going through their notes.

[00:28:30] Josh : I've been at bottom a pilot, so I needed to make a massive impact when I actually got into the room. I was given the questions beforehand, which was nice. So they gave me the questions, the receptionist handed me the questions before I kind of went into the interview, so I was able to make some notes.

[00:28:45] Josh : They only asked, I think, six questions in an hour interview. Um, again, I actually went over the time by, I think I did an hour and 10 minutes for my interview. No technical interview this time though, so no technical portion of it, but I just made, needed to make sure that I just [00:29:00] drived home everything that I possibly could about every individual point, and to be honest, but.

[00:29:05] Josh : It was a blur, kind of what the interview actually went on. 'cause I think obviously your adrenaline's rushing and you're kind of, your brain's running with a million miles. But by the end of it, the was just getting, um, I mean hopefully though my future employers don't watch, watch this. Um, but yeah, some of the questions by the end, it's just getting stupid.

[00:29:22] Josh : Right. Some of the stuff they were asking me was just to see if I could come up with some. Kind of idea. I'd never used any of the, I'd never worked in this area. I'd never done anything with any of the software that they were using, but they were throwing me these questions, I think, to see how I'd cope with the kind of show, my critical thinking ability and stuff.

[00:29:40] Josh : And I think by the end of it, I was just calling things out and they're like, yep, that's right. Yep. And I, I knew nothing about it. There was nothing that I knew about that at all. But I think just being able to sit down and go, okay, I've got nothing to lose here, but I need to make some sort of impact. I would just kind of get at my best.

[00:29:59] Josh : [00:30:00] Yeah, that was that. What was, what was the question? I've kind of gone off on a tangent and, and can't remember what you asked me.

[00:30:05] Avery Smith: I was just saying that you looked at the job description and you didn't think you were qualified. Oh, so it sounds like That's right. You were saying some like niche proprietary softwares or, or industry specific softwares you've probably never heard of or touched before.

[00:30:16] Josh : Yeah. Yeah. So yeah, and then I looked at kind of the actual qualifications that they wanted, and I had none of those, um, even at a very basic level, I had. I, I'd got a, I've got a Master's degree, but I haven't got a, I haven't got an a a Levels, which in the UK is what we would do between the ages of, um, 16 to 18.

[00:30:33] Josh : I don't have any of those because I went to college and did a different course, so I didn't even have the basic requirements, like qualification requirements needed for the job. But obviously I was able to show enough of my knowledge and, and what I had. And also I think massively, I mean, talking to people who are, who are, who are friends and, and, and family and and stuff, and people that I trust.

[00:30:54] Josh : I think personality comes down to it a lot as well. I think we focus too much as, as junior as people who [00:31:00] want aspiring data analysts is probably what we should call us out. And then we focus too much on hard skills. And the reality is that hard skills can be taught, but what doesn't get taught is your personality and your soft skills.

[00:31:11] Josh : And I think that's probably what really sh shown looking back in my, in that interview, that I was kind of animated and articulate and was able to kind of present stuff. And I think certainly as a day journalist, and, and I know this from talking to people when I was on workplace, one of the things that people aren't very good at, certainly from.

[00:31:30] Josh : The person who was talking about it to me who was a hiring manager was saying, people these days don't like to stand up and present information to people. They get scared. So if you have an ability to be able to present information to people in a very good way, that's a massive selling point to them because it means you can be thrown into the, into the lions deck, go and present to these non-technical stakeholders, convey what you're trying to get across to them, and know that they're in second hands that you'll be able to cover for it.[00:32:00]

[00:32:00] Josh : And again, that probably goes back to my lecturing time, being able to just kind of go into a lecture and go, okay, dunno everything, but we can definitely give it a go. And I think that's probably one of the things that really happened to me in that room that I was just able to kind of, I almost had them in the palm of my hand.

[00:32:14] Josh : I guess that's kind of how I see it anyway.

[00:32:17] Avery Smith: Well, I, I think I've said this before on the podcast, that the most qualified person does not get the job ever, probably. And it's oftentimes. The person who has the right network. And like you said, you had done a work placement with, with these guys previously.

[00:32:31] Avery Smith: They knew you, um, different team, different organization, but they, they knew, they probably had heard something good, uh, about you, you know, so that's where network can, can really come into play as well as well as portfolio. So it's like if your skills are lacking, you can make up the skills with the portfolio.

[00:32:46] Avery Smith: Yeah. And the networking. So you really need all three. Because if you only have, if you only have the skills, then you don't get that, that added bonus of. Of making up for the skills in, in those situations. We have, we have mentioned like a work placement. [00:33:00] You, you kind of put an emphasis on the word resume 'cause you call it a cv.

[00:33:03] Avery Smith: Um, what are That's right. Yeah. What are some other things that like us, us people in America may not realize, um, that's a little bit different with data jobs in the uk. Can you talk through what a work placement is? Um, maybe like what industries are hiring, um, data professionals in, in England and the UK in general?

[00:33:21] Josh : So work placement is a, um, is like an internship. So I, I went to go and, um, for my local authority, um, which, I dunno, I was trying to think about this earlier. How does local authority boil down to people in America? Because it's a bit complicated with, you know, there's countries, we have counties which are like states to you.

[00:33:42] Josh : Okay. And then there's even smaller like district level kind of Okay. Authorities. And that's who I was, oh, sorry. I was working for the county, I should say. I was gonna say, county

[00:33:52] Avery Smith: is good for us. We understand that.

[00:33:53] Josh : Okay. So, so I was working for, in terms of, for you, I was working for the, the state of Utah.

[00:33:59] Josh : Okay. [00:34:00] Okay. Yeah. County is a little bit smaller than the state of Utah. I imagine the UK can fit the state of Utah a few times over. In fact, it's huge. Anyway, so, um, so I did my, my place with them and I, that's kind of where I realized while a boot count is great and the knock, knock in the bootcamp, because it gives you the, as I said, it gives you the why.

[00:34:20] Josh : It gives you the reasons why you should be doing something. And it gives you the how. What it doesn't give you is the real life experience. The difference between opening a data set in Kaggle and opening a DA data set in local authority is very different experience, and I think that's really where you learn when your are hands on with it.

[00:34:39] Josh : When four, when your hands on, it's a 40 hours a week, or 37 and a half hours of what runs working, that's where you really realize that you are. That's where you really learn how to use data and it, and kind of get to know it. While kale's a great resource for learning, it's very sterile and very clean compared to trying to learn how.

[00:34:59] Josh : [00:35:00] Data is in a, because it's all rushed, there's not time in these, in these organizations, you use the data for what's needed, and then it shells then pulled out again when it's needed. So it's in such a raw state that you spend probably the best part of three weeks cleaning it in order just to get a basic chart out of it.

[00:35:16] Josh : So that was one of the big things. And also just learning all that kind of stakeholder management stuff and all the skills that, again, you don't get when you're working at home in your bedroom. You don't learn how stakeholders are requiring certain things from you. When you are kind of looking, the Titanic data set that doesn't give you.

[00:35:34] Josh : Any kind of inclination on, is that the right word? Inclination on how to, how certain stakeholders might want it presented. Whether they want it as an, as a, I dunno, a violin plot, which I've never done before for the stakeholder. 'cause again, if they're non technical, they're not, you're gonna spend more time for what a viol plot is.

[00:35:51] Josh : Whatever, you know. Understanding what they want in terms of requirements. Do they want certain questions answered? Do they want to kind of delve deeper into a certain question that you've answered [00:36:00] for them, whatever it may be. Or do they want to be put into a dashboard, for example, which is something we ended up doing.

[00:36:06] Josh : Do they want something to look at? 'cause it's better to be visualized on a map post to in a bar chart. All of that sort of stuff. You don't learn until you are actually in a place being able to kind of work with it. And that helps massively go to an interview. 'cause you then have competencies that you can talk about.

[00:36:20] Josh : When I was, I was given this task where I had to X, Y, Z, this is how I did it, X, Y, Z, and this was the result. Zed. I think that's massive. I don't think people kind of, I can't, I can't drill at home enough that that's kind of, I feel the thing that I was missing. But I didn't have that work experience, which is so key because it actually shows that you're actually able to do it outside of the walls of your, uh, outside the four walls of your house, which again, you know, is so important.

[00:36:47] Avery Smith: It, it is, um, a hundred percent. One thing that we started doing in the accelerator. Is, uh, we're running a month long internship program every other month, so six times a year. Now, inside the accelerator, uh, students have a chance to intern [00:37:00] for my company, data Crew Jumpstart. Um, and we're analyzing a lot of data, job listing data.

[00:37:04] Avery Smith: So one, one of the, the last internships they looked at, like what states. Have the, like, what's the best state to live in, in the United States for a data job? And, uh, anyways, so they just finished their analysis and they sent it to me and they, they probably spent six weeks analyzing the data. I feel like I learned a lot as a stakeholder of like, okay, I actually need to talk to these people probably every other week to make sure that they're, they're on the right page.

[00:37:27] Avery Smith: They did a great job, but I think they learned a lot and I think I learned a lot. And that's on top of, you know, most of those students have already done, you know, nine plus projects that we, we don't get data from, from Kaggle necessarily, but it's just Yeah, yeah. When you're working in with real life, uh, we, we try to make it as real life as we can, even on the projects, but like when there's a real life stakeholder, me, like I'm actually going to be using the graphs that they make and the analysis they do.

[00:37:49] Avery Smith: It's hard to simulate as hard, as hard as I try to design the program to simulate it. It's

[00:37:54] Josh : just no ma massive. And, and I think another thing that isn't talked about an awful lot, and I'm trying to talk about [00:38:00] more on threads. I try and weave it into stuff about, um, the one, the other thing you don't get is domain knowledge.

[00:38:05] Josh : So there's a massive difference between, if you look at numbers, when I first looked at some of the numbers when I was doing my initial analysis. Looking at like minimum max values and stuff. I was alarmed. I was sitting in meet, I was sitting in a meeting and I went, this is crazy. Like this is ridiculous guys.

[00:38:21] Josh : We need to be really concerned about this 'cause we got a crisis on hands in a few years. If this is gonna keep going, they, no, that's pretty normal actually. But because I only had a snapshot of the data for. For three months opposed to 30 years that they had all seen or or whatever. I was like startled by and they were saying, oh, that's fine with that hire.

[00:38:42] Josh : I don't think it have you really. But that's where kind of that, that underpinning of knowing the kind of the sector and understanding the, the domain knowledge is set board. Because again, me walking into a meeting and saying to non-technical stakeholders really scared that this is gonna be like the end of the, like, the end of it for us.[00:39:00]

[00:39:00] Josh : Whereas they will thinking, this guy's just overactive. He doesn't understand that. And that was something that I learned later on where it was a, a process where I know in my, from doing placement and going up when I go into a stakeholder meeting now in my new role, if that's something that I will end up doing, which I assume it will be, I will be there to present the data, but there will be somebody there with more experience to answer the questions that relate to the, like the more domain.

[00:39:23] Josh : Specific stuff because I won't know that until three years down the line when I've been there and absorbed it and learned it as I go. And again, I think that's a massive thing. It's, you know, it's like given, I guess it's like given somebody who's watched a couple of videos on how to do brain surgery, a scalpel.

[00:39:39] Josh : Gone. Just crack on with it and you're thinking, I don't even know which part of the brain I'm supposed to be looking like going for here. I think that's kind of the difference. It's kind of that kind of, you get that snapshot from the big cap. Yeah. Data's easy kind of. It is, I guess once you kind of get into it, but what you don't understand is you don't understand how the data relates to everything else in terms of the organization [00:40:00] or even.

[00:40:00] Josh : In some cases the world,

[00:40:02] Avery Smith: you're exactly right a hundred percent that your domain knowledge matters a ton, and it's often what separates you from being a good analyst to a great analyst. And the other thing with that is that domain knowledge and how you can tie analytics to. A business, in my opinion, is the reason why we don't have to be scared about AI taking our jobs because ai, you can say whether it's good or not at analyzing data.

[00:40:24] Avery Smith: I've seen it do some things amazingly and I've seen some things Yeah, do very poorly. Um, and I'm like that you did that a hundred percent wrong. And if I just trusted you, I would be in big, big trouble here. But I, I think it's, it, it can analyze data, but it can't really tie it into the business very well all the time.

[00:40:41] Avery Smith: So I think that scale to. To tie domain knowledge and business knowledge to data analytics is why we will continue to not be replaced by AI robots in the next five years or so. Yeah,

[00:40:53] Josh : definitely. And, and that actually talking about that kind of, that AI and learning actually brings us onto the second part us [00:41:00] about, about jobs in the uk.

[00:41:01] Josh : So the uk a lot of jobs are not a lot of jobs actually, there's more jobs that are in the public, in the private sector. But there's a, there's a good proportion of jobs now that are starting in the public sector. And which is kind of government work jobs, so government organizations, so healthcare, there's a load of other departments.

[00:41:20] Josh : I list them all now. We'll be here till 12 o'clock tonight if we've carried on listing them all. But there's a lot of departments and a lot of the investment in jobs is at innovation is coming from the government. So here in the uk we are not so tech driven. We are not, we are not like the US where there's.

[00:41:34] Josh : The big, um, tech companies like Meta Google or the other ones that are driving the innovation, the government are actually funding and helping support the innovation in whether that be in research infrastructure or even in training. So my, my bootcamp is actually funded completely by the UK government in order to give me the skills to get me into work because we wanted to upskill our workforce.

[00:41:57] Josh : So that's one of the big things we're doing at the moment. There aren't [00:42:00] enough people in data. To do the, the work that's needed. So they're people into those jobs through bootcamps. But one of the other big things we are very, very keen on in the UK is, is um, is ethics and legality around using AI things.

[00:42:17] Josh : So one of the things. A lot of government, um, a lot of the government will do at the moment is let data that's sensitive into an AI system that's a third party. Now, there's obviously AI systems that exist, um, that are closed, so work within an organization and design within an organization, obviously by contractors, but it's a closed thing that only doesn't leave the, kind of the, the perimeter of the organization that's happening.

[00:42:43] Josh : But what's not happening is that we're not. Directly put in all 60, I dunno how big the position of the UK is, but all 60 million people's data into chat, GTP, um, healthcare data into chat, GTP to get as a graph on, you know, how many people have got diabetes or whatever. But what is actually happening is there are [00:43:00] things where data is being looked at in very particular ways, say in healthcare.

[00:43:04] Josh : Where, um, hospitals are, are, um, hospital trusted. That's a bit like a university hospital in the us so just a, a connection of hospital work together. Normally the same time or area they're looking at using AI to kinda speed up processes, so looking at x-rays or, or, or diagnosing things that the human eye can't see.

[00:43:22] Josh : So at x-rays and seeing there's certain cancer cells there that human eye wouldn't be able to see from an x-ray, but because it's a, an ai, it's been learned and that sort of stuff is happening. Quite a lot of the moment and something that's being pushed more and more, um, since we had an AI summit, actually I think two years ago, we've can't been pushing this AI thing.

[00:43:42] Josh : But that's a, that is a massive thing here that's different to the us. We are very, they're very, very conscious about the ethics around using people's data because I think it's probably 'cause it's more publicly available than say, in, in the us And also it's different because like healthcare here is a, is a thing that's given to us.[00:44:00]

[00:44:00] Josh : Just because we British citizen, it's taken our taxes that we pay. Whereas obviously in the US it's a, it's a company I guess that you, you pay, do you for your insurance and then they own your data and they can do whatever they want with it. Whereas here, they're a little bit more careful about, I think just because of the nature of, of what it, what it is and how it works with the kind of the service being provided to, to us.

[00:44:19] Josh : Uh,

[00:44:19] Avery Smith: there you go a little bit about how the, uh, the UK is a little bit different than the us. Um, so thanks for that, Josh, and thanks for talking through, you know, your 1026 days of unemployment and how you kind of designed, uh, your study plans and your note taking, and your networking, and your portfolio building and your skill learning to ultimately land a job offer.

[00:44:41] Avery Smith: We're super excited to, uh, hear how your job offer goes. We'll, we'll uh, be watching on threads too. Hear, hear your, your continued journey and we'll have a link to your threads, uh, down below in the show notes. Josh, thanks so much for coming on the show.

[00:44:54] Josh : Uh, bro, thank you. Um, I just wanna say I never thought in a, in a million years that listening to your podcast [00:45:00] a year ago that I would've been sitting here on the podcast a year later.

[00:45:03] Josh : So no, thank you. Your work is great and yeah, really inspirational as well. So yes, thank you for having me on. Yeah,

[00:45:09] Avery Smith: well that's what happens when you post on Threads. You guys start documenting and learning. You guys might be the next guest on the Data Career podcast. Thanks, Josh.