- 1 week ago
Come ask me anything in my Weekly Q/A!
In this weekly series you can come and ask me questions about all things Data, Analytics, Tech, or anything else you could want.
Never Tried Analyst Before? Get 1 Month of Free Courses on Analyst Builder with code: 1MONTHFREE
Select Monthly Subscription here: https://www.analystbuilder.com/pricing
Already a Member? Get 35% off any purchase with code: 35OFF
____________________________________________
SUBSCRIBE!
Do you want to become a Data Analyst? That's what this channel is all about! My goal is to help you learn everything you need in order to start your career or even switch your career into Data Analytics. Be sure to subscribe to not miss out on any content!
____________________________________________
RESOURCES:
Coursera Courses:
📖Google Data Analyst Certification: https://coursera.pxf.io/5bBd62
📖Data Analysis with Python - https://coursera.pxf.io/BXY3Wy
📖IBM Data Analysis Specialization - https://coursera.pxf.io/AoYOdR
📖Tableau Data Visualization - https://coursera.pxf.io/MXYqaN
Udemy Courses:
📖Python for Data Analysis and Visualization- https://bit.ly/3hhX4LX
📖Statistics for Data Science - https://bit.ly/37jqDbq
📖SQL fo
In this weekly series you can come and ask me questions about all things Data, Analytics, Tech, or anything else you could want.
Never Tried Analyst Before? Get 1 Month of Free Courses on Analyst Builder with code: 1MONTHFREE
Select Monthly Subscription here: https://www.analystbuilder.com/pricing
Already a Member? Get 35% off any purchase with code: 35OFF
____________________________________________
SUBSCRIBE!
Do you want to become a Data Analyst? That's what this channel is all about! My goal is to help you learn everything you need in order to start your career or even switch your career into Data Analytics. Be sure to subscribe to not miss out on any content!
____________________________________________
RESOURCES:
Coursera Courses:
📖Google Data Analyst Certification: https://coursera.pxf.io/5bBd62
📖Data Analysis with Python - https://coursera.pxf.io/BXY3Wy
📖IBM Data Analysis Specialization - https://coursera.pxf.io/AoYOdR
📖Tableau Data Visualization - https://coursera.pxf.io/MXYqaN
Udemy Courses:
📖Python for Data Analysis and Visualization- https://bit.ly/3hhX4LX
📖Statistics for Data Science - https://bit.ly/37jqDbq
📖SQL fo
Category
📚
LearningTranscript
00:00:01Hello, hello, hello! How's it going, everybody? Let me just make sure this is
00:00:06all working, and then once it's all working, we'll get going. But thank you,
00:00:11this is our last live stream for July. Let me see if this is working.
00:00:20Oh, I see what's happening. I see what's happening. Let me do this real quick.
00:00:27All right, it's working. We're good to go. All right, so if you haven't joined the live
00:00:33streams, here's how they typically go. Let me mute this real quick so I can actually see things.
00:00:43Give me one sec. There we go. I need to be able to see the chat. But thank you guys
00:00:48for joining.
00:00:49Here's how these live streams usually go. I am going to do this for a whole hour,
00:00:53so this is from 9 a.m. to 10 a.m. my time. Besides just Q&A, if you have,
00:01:01you know,
00:01:02other things you would like to ask or you'd like me to do, let me know. If it's reasonable,
00:01:07I might do it. That's all I have to say. It's pretty simple though. You can just ask questions
00:01:12in the chat. I already see people coming in. Thank you guys. I got Nitin from India. DLS Fury.
00:01:18How's it going? Pinky Thanu with a question. And I'll answer questions in a second. I'm just
00:01:25saying hi to everybody. We got Taki. He's one of my moderators. Thank you for joining Taki.
00:01:31His name's not actually Taki, but that's his name in chat. So if you see him, watch out. All right?
00:01:37We don't do spam in this chat. It gets a little bit too crazy. With that being said,
00:01:45we are going. We are going live. So here's how it's going to go. I'm just going to start reading
00:01:50through the chat and we'll get answering questions at the very end. I'm going to do a giveaway and
00:01:57that's that. The giveaway is my courses for free. I just give them away because I appreciate you guys
00:02:03being here. And I'm just trying to give back and support you guys. All right. The first question
00:02:07says, hi, Alex. How did you get into data analysts with nine years experience in mainframe? I don't
00:02:12know what that... I don't know what mainframe is. I don't know what... I've never worked with someone
00:02:18in that position or that industry. So I don't know. If you could give me some more information,
00:02:22I might be able to answer it. Elvis F.A. It's good to be here finally. I'm glad you're here.
00:02:28Thank you for joining. If you have a question, just go ahead and ask it. KDPOC, is data analyst
00:02:35roles dead now in the coming four months? I'm guessing that's what that says. As of right now,
00:02:42I genuinely haven't seen a huge slowdown with data analyst roles in the past, I don't know,
00:02:51six months, a year, stay pretty steady. We're seeing a slowdown just because companies are
00:02:57slowing down hiring. That's for all positions. It's not just data analyst positions. In my opinion,
00:03:03it's not... Excuse me. There's like this thing on my glasses. But it's not necessarily even due to AI.
00:03:13It's more... It's still there. I'm just going to ignore it. But it's still maybe partially AI,
00:03:22but honestly, AI does not have a huge part to play as much as the economics of
00:03:26keeping people hired. I think if we see lower interest rates, especially in the US,
00:03:33if we see lower interest rates, we're going to see a lot more companies hiring again.
00:03:37And that's just... That's the honest part of it. And there are kind of ebbs and flows in hiring,
00:03:42and we're just in one of the slumps right now, in my opinion. I don't think this is going to
00:03:45be a
00:03:45permanent thing. We'll see how things go. How can I make projects to be put into my CV?
00:03:52This is THCOA. All right. So projects are great. You want to add them to your resume for a few
00:03:56different reasons. One, so that you can have it on your resume. People can see it. The automatic
00:04:04systems kind of look for those things sometimes. And so it's nice to have. But then when you get into
00:04:08an
00:04:08interview, you can also point back to those projects and say, hey, I built this project.
00:04:12They're going to ask you, how do you know SQL, Excel, Tableau, Power BI, whatever tool they're
00:04:16using in the job. And you can point to those projects and say, here's something I've built.
00:04:20And here's how I built it. And here's why it was really useful. And I can do a really good
00:04:23job
00:04:24at your company. But building projects can be really intimidating. I recommend doing guided projects
00:04:30first, which is what a lot of my channel is based on. I have tons of guided projects on Python,
00:04:35Excel,
00:04:36SQL, R, Tableau, Power BI, AWS, Azure. I have tutorials on all these things to build projects.
00:04:43I recommend doing the guided first. Get a feel for it. And then if you think that, you know,
00:04:49you kind of got it, then use your own data. Try building your own pipeline. Try doing your own
00:04:54analysis. Creating your own visualizations. That's how you grow. And that's how you build your own
00:04:59projects. All right. I skipped down again. Sorry, guys. Vince, Vince Andre got completely
00:05:07blocked by Taki. Listen, I trust the guy more than I trust myself. So if he's blocking you,
00:05:12it means you're doing too much. Let's see this question. Bimbola said, hello, good sirs. My first
00:05:20time here on this live. Thanks for joining. I really appreciate it. I'm just reading through
00:05:24questions. If you've got a question, let me know. GhostPlays said, Alex, sir, I watched your
00:05:30SQL videos and I got placed in MNC on campus placement. Thank you a lot. Awesome. I don't
00:05:36know what that really means, but it sounds like a good thing. So I'm really happy for you. That's
00:05:41awesome. Joyce. It's good to be here. It's going to be here too. Michael from Nigeria. How's it going?
00:05:48Elvis asked, is it okay to start with Excel? I think Excel is like a great place to start. Either
00:05:54Excel or SQL are kind of the two places that I would start. The reason being is because it's a
00:05:59simple enough tool or a simple enough language to get started with the basics pretty easily for
00:06:04anybody. And it's really accessible and open source. And, you know, even if you can't get
00:06:08Microsoft's Excel, there's online tools that are basically the same thing as Excel that are free.
00:06:14Once you start breaking into other things like AWS and Azure, and it can be harder to get resources for
00:06:20free. Um, and so Excel is perfect, but it gives you the building blocks of how data sits, how it
00:06:26is
00:06:26stored, uh, how to clean data. You can learn a lot of those things in Excel. So it is a
00:06:30great place
00:06:31to start. Harshit Rawat. Hey Alex, I have just, you just did your data analyst videos. Thank you so
00:06:41much for doing that. I've learned so much. Uh, love from India. Awesome. Love from the U S
00:06:46Istanbul, Moro, Moro Husseini. That's how you pronounce it. Hi Alex, reading from Istanbul.
00:06:51I love seeing where people are from, by the way. I just always think it's really interesting. Um,
00:06:55all over the world. It's awesome. I love, uh, from Ethiopia, Indusha. Let's see. Okay. This is a
00:07:02question. It's, uh, this is from, I'm going to try to pronounce this. I'm going to butcher it. I can
00:07:05just
00:07:06tell, but I'm going to try, uh, Arjur Uchukwu Anya Lebechi. Hey, I think I, I think I did all
00:07:14right
00:07:14on that one. Uh, you're a medical laboratory scientist. How do I get a job as a healthcare
00:07:18analyst? Um, that's been like my bread and butter for people in the past several years is people
00:07:22switching from healthcare roles into analyst roles in healthcare companies. I've helped a lot of people
00:07:29do that. Here's typically how it works is there are companies that are going to value that laboratory
00:07:35scientists experience a lot. Like that is like gold to them, but it's hard to find someone good who
00:07:40has the technical skills plus the previous experience of your role, your job. And this is
00:07:45the hard part, but your job is to do a lot of research and identify companies or even departments
00:07:53within a company that are going to value that. And that is the toughest part. Then you have to reach
00:07:57out to them. You have to get in contact with them. I actually think that's easier than sometimes doing
00:08:01the research. But honestly, with the advent of, you know, AI search and Gemini and ChachiBT,
00:08:07you may be able to do some of that research easier. Um, but target companies that value it
00:08:12because you can't just get with your experience. You can't just go to, you know, any company that
00:08:17is related to healthcare and get a job. It's going to need to be something that will, will really, uh,
00:08:22like that. Um, ghost plays gen AI versus data analysis, which is best. Um, I'm guessing you
00:08:31just mean like the general AI of using Gemini and ChachiBT. There's even a data analyst, uh, uh,
00:08:37ChachiBT option. Um, I've used them. I've used them quite extensively. They are good for some basic
00:08:45analysis. Um, once you start getting to, I would consider real data or where there's nuances to the
00:08:52data, it has a lot of trouble. It's not very good at data cleaning either. Um, that's something I
00:08:56wish it was good at because I spent a lot of time with that. And I try to identify the
00:09:03ways to do it.
00:09:04It's not amazing. Um, so if you look at like what a data analyst actually does,
00:09:10the data analyst works with the clients, they understand what they're trying to do,
00:09:13and then they get their data in. So they have some type of data ingestion process,
00:09:18either with the data engineer or the data analyst is going to somehow work with their team to do that.
00:09:22Then they have to clean the data. They have to automate that process. Uh,
00:09:25so you can get those new files in or the new database backup file, or, you know,
00:09:29a stream of data, whatever it is. Then you start, you create that process. Then you have reports and
00:09:35dashboards that you build. Uh, AI doesn't really do a lot of that consistently well, or even at all,
00:09:42they don't really work with the clients directly. They don't understand the nuances of the data.
00:09:46Um, what's really, so, and I'm just gonna, I'm just gonna tiny, tiny little offshoot of this.
00:09:55But data can be cleaned in a hundred different ways. And AI is great at maybe identifying ways
00:10:00that it can be cleaned, but it doesn't know what the right way to do it is. That's where you
00:10:03come in.
00:10:04So a data analyst can say, okay, we could clean the data one, two, three, or four different ways,
00:10:09but we have to do it this first way. And the reason is, is because downstream, they're gonna be
00:10:13reporting these numbers in a specific way to submit that for claims data, or they're gonna be
00:10:19submitting that to their clients, or they're gonna put in a dashboard. It has to sit this very specific
00:10:23way. Otherwise it'll look wrong and clients will get mad. They'll lose money. And so AI doesn't know
00:10:29that. And so you have to kind of be that interpreter. You have to understand the nuances and the
00:10:33complexities of the data. So there's a little bit of both, but honestly, for most data analysts,
00:10:38you're not gonna use AI super extensively as of right now. But you're gonna use it, right?
00:10:45If your company has it, you should use it in some ways. Brainstorming, simple code generation,
00:10:50things like that. Which platforms are less crowded for freelancing and data analysis? This is from
00:10:56Dev Kapadia. I don't know. I don't know what best platforms are best for freelancing. I've heard
00:11:02people like Upwork and people like Fiverr. I don't know any of the other ones. I really don't.
00:11:08I don't know which ones are best. Sorry. Let me get a drink of water. I got my cup back.
00:11:17All right. Lightsaber. Cool name. Please enlighten us about what the AI is gonna do in data science
00:11:23machine learning job prospects. All right. It can go in a few different ways, and I'll tell you what I
00:11:28think is the most likely to happen. One is that this is like worst case scenario for like all industries,
00:11:38all jobs, not just data analysts. This is like anybody who works on a computer. AI goes way beyond
00:11:47what they can do now. It can take over and do all the work pretty seamlessly. And that happens in
00:11:53the
00:11:53next five years. That'd be bad because every job that does a computer could be AI. It doesn't seem
00:12:01like it's going that way, in my opinion. From what I've seen, what I've heard, people I've talked to
00:12:09who are even more knowledgeable, a lot more knowledgeable than I am, what it seems like is
00:12:14going to happen is we're going through this huge wave of AI is everything. And if you don't use AI,
00:12:19you are like the worst company in the world. That's like the vibe out there.
00:12:25What's happening is that so many companies are adopting AI that not everyone's going to use it
00:12:30right. Let's say everyone used it perfect. They would need a lot less people, right? But nobody uses
00:12:36AI perfect. So let's say AI is implemented at almost every company, but most people don't know how
00:12:41to use AI well, or they don't really understand the basics of using data and they don't really
00:12:46understand how to use it. They're going to need people. And if that's every single company out
00:12:52there not using AI perfectly, or AI is making mistakes in some ways, or the data quality isn't
00:12:57perfect, which no company is, there's going to be a demand. So I think what's going to happen is over
00:13:02the next couple of years is we're going to see a lot of companies adopt AI. That's great. I love
00:13:07that.
00:13:07But they're not going to use it perfect. And then what's going to happen is, is they're going to
00:13:12run into all these issues. And they're gonna be like, we just need to hire somebody to do this.
00:13:15Because I as the manager, or I as a single person cannot do all this work. It's impossible. So I
00:13:21need to hire people on. I actually think the next five to 10 years, we're going to see a vastly
00:13:27larger
00:13:28need for data people. That is my prediction. That's been my prediction for the past,
00:13:36I don't know, two years. And it hasn't changed. I haven't seen anything that has changed that.
00:13:43I think even the current job market has more to do with economic factors than it is AI.
00:13:49From everything I've seen. And I try to stay pretty up to date. All right. DLS Fury.
00:13:55DLS Fury. Got multiple rejection emails in the past two weeks. I'm sorry to hear that.
00:13:59I'm trying to focus on finance. Can't I just use SQL and Python only? I'm on a SQL course already,
00:14:05though. So finance is, I never worked in finance. So I'm going to put this disclaimer out there.
00:14:13But I know many people who've worked at bank, you know, Bank of America as data scientists,
00:14:18data analysts who work at Chase. I know a lot of people in that banking side of finance.
00:14:25From what I've heard, you definitely need SQL and Excel. But a lot of, you also need cloud
00:14:30platform knowledge because almost every single department in a bank is using some type of cloud
00:14:35system. And it's really good to know tools like Power BI. Most are Microsoft shop of some sort,
00:14:43or at least departments within them. So Power BI would be a nice thing to have on. Honestly, out of
00:14:49those two, I would say SQL is the only have to have. Python's, in my opinion, isn't a need to
00:14:55have for most finance positions. You need to have a good statistics background for a lot of finance
00:15:00jobs. So heavy math background is really nice. Again, I don't know what your resume looks like,
00:15:06but, you know, make sure you have SQL and SQL projects. Make sure you have some type of statistics.
00:15:13You know, maybe take a statistics course, or you take some type of, you have a statistics degree,
00:15:17or you just put some, some evidence of knowing statistics on there would be nice.
00:15:23But then I wouldn't just be applying. I'd also be, you know, targeting finance companies that you
00:15:29really like and directly reaching out to them and reach out to the recruiter. I have a course on,
00:15:35uh, uh, I also talk about this in my data analyst bootcamp on YouTube, but I have a full course
00:15:39called landing a data job, um, on analyst builder. I will put a code. I'm just going to make that
00:15:44free
00:15:44for everybody in here real quick. Give me a sec. I'm just going to give you guys a code. It's
00:15:50only
00:15:50going to be active and for the next, like, uh, you know what? I'll, I'll leave it active to the
00:15:55end of the day. Just give me one sec. I'm going to make that course free. I want you to
00:15:58go take it.
00:15:59Um, give me a sec. Here it is. Um, go take that course because in there, I show you how
00:16:06to reach
00:16:07out to recruiters. Um, and that's how I've landed almost all my jobs in the past. Um, and that's how
00:16:12people who, who reach out to me, by the way, um, give me a second. I'm going to go find
00:16:17it.
00:16:20I just got to go find the place. Uh, here it is.
00:16:25I'm going to make this code real quick. Um, and I'm going to put the expiration at the end of
00:16:29today.
00:16:30So it's going to be tonight at midnight. It's going to go, um, the code is going to be job,
00:16:36uh, get job. That's all caps, uh, get job. Um, there we go. All right. Just real quick. I'm going
00:16:45to put this in the chat. Um, use this code at checkout for the landing a data job on analyst
00:16:55builder.com to get it free. Okay. Um, that's just for you. Anybody in the chat who wants that,
00:17:02you guys can have that for free. It just tells you how to, it helps you create a resume,
00:17:05create a portfolio website. Um, that's what I recommend doing. I just recommend following
00:17:10that and then reach out to recruiters in the right way. Um, and it's really good.
00:17:16All right. I'm going to keep going. Is Power BI enough for visualizations or should I learn Tableau
00:17:20as well? Honestly, it is enough. Um, Power BI is an amazing tool. I used it for many years. I
00:17:28was a
00:17:29data analyst manager when I implemented Power BI for, uh, an entire department. Uh, Power BI is an
00:17:34amazing tool and it can be great for so many things, but not every company uses Power BI. So if
00:17:40you only
00:17:40know Power BI, you're limiting yourself, at least for a data visualization to only that
00:17:45tool, if you add Looker, if you add Tableau, or if you add any of these other, uh, uh, visualization
00:17:51softwares, then, you know, you get picked up in more systems for having that skill. Uh, when you
00:17:58apply, you might check off more boxes, right? So, you know, learn them both. If you already know
00:18:02Power BI, you can learn Tableau very quickly, uh, very quickly. And I have free tutorials on how to do
00:18:08that. So I would do that. Okay. So Pinky, this is the mainframe guy. He said, I've worked in
00:18:14mainframe program analysts for nine years. So I moved to data analysts. What I consider a fresher
00:18:19experience in the data frame mainframe program. I'm going to Google this mainframe program analyst.
00:18:26What is it? Just give me a second. Um, responsible for maintaining troubleshooting or optimizing mainframe
00:18:33systems that support critical business operations. I honestly don't, I don't know much about that.
00:18:40I'm, I wish I knew more about that. Um, it seems like a really interesting job. It seems like maybe
00:18:45you're like working on like, uh, the backend systems. So you're looking at log data. Uh, you're looking at
00:18:51errors and caching and, um, you know, access to things. Am I close? Tell me if I'm close. That sounds
00:18:59like what you're talking about. I've just never heard of that title. Um, so cybersecurity said,
00:19:05hope you had a good week. That pantry that your dad built was pretty fire. I must, I must say
00:19:10so
00:19:10myself. Thank you. I really appreciate it. My wife helped a ton. Uh, she did all the painting,
00:19:15all the decoration. She picked out the kind of the design of it. Me and my dad just built it.
00:19:20You
00:19:20know, that's the easy part. Um, but yeah, my wife is a big part of that too.
00:19:28All right. Let's see. I'm, I'm looking for questions now.
00:19:34Okay. Here's a good one. Uh, Kalyan Kotla. I'm working as a data analyst using SQL, Excel,
00:19:41Power BI, and started learning Python recently. Love it. That's great. But it says planning to
00:19:47transition into data engineering. What's your suggestion about things where I should focus
00:19:50on. All right. So back in the day, I was a data analyst from, you know, five, six years at
00:19:56this
00:19:56point, I was working on a, uh, uh, not a data ingestion, a data collection team under a data
00:20:02science umbrella. So we would create, and I would analyze a lot of the data for our clients, uh,
00:20:07for healthcare, but I would help with a data collection process. That's creating, uh, automated
00:20:11processes for data pipelines, um, creating data cleaning processes, automating reports, things like
00:20:17that. That was a big part of my job. Um, and I just started picking things up and, uh, the
00:20:23data
00:20:24engineers on our, on my team were like, Hey, you know, if you wanted to get into data engineering,
00:20:27you probably could, um, because you know, X, Y, Z. And so I actually thought about that for a while,
00:20:32but then I got a promotion to a manager of analytics, which was what I wanted to do anyways,
00:20:37but I thought about it because I liked that stuff. Here's what I would focus on.
00:20:41And this is kind of the basics of any tool or any data engineering job.
00:20:47Create data pipelines and get really good at it and then try automating it in a different
00:20:54way. So you're going to start with something really simple, something like, uh, uh, just
00:21:01pulling in one file each night. That's it. Then you're going to escalate that. Maybe you'll
00:21:07do batch processing. So you're going to, you're going to take multiple files and process them
00:21:12overnight. And then you're going to want to get really big files and you're going to have
00:21:17to see how you can segment those files. Or even you, you do a database backup and you get
00:21:21those files or that file. And then you want to hit off of some type of database online.
00:21:28And then you create a pipeline through AWS, AWS and Azure, which I do in my, uh, AWS and Azure
00:21:35course on analyst builder. I think I, maybe I touch on it a little bit in the YouTube stuff.
00:21:40I can't remember. I think I do with AWS glue, but creating data pipelines, super, super important.
00:21:47And then just go from there. Data engineering does get really complex, but you have to understand
00:21:53those basic building blocks. And that's what I would do. Start with tools like SS, um, SSIS,
00:21:59um, SSRS for reports and stuff like that for like, that's like through like SQL. Then you can go and
00:22:05you can try to build it with Python, or you can try to build it, um, with something like Scala,
00:22:10or you can go to Databricks, or you can go to AWS or Azure Google Cloud platform. There's lots of
00:22:15places.
00:22:15I love data engineering. I think data engineering is fantastic.
00:22:20I would just need to study it a lot more to be really good at it.
00:22:30Oh, geez. It skipped down. Oh, I just got a donation. You don't have to do that. Tyrone
00:22:34Williams. I don't need any, um, don't need any, uh, donations, but I appreciate it. I'll read your
00:22:40question. Cause you did. It said, what would you suggest for criminal court sector?
00:22:47I, I didn't even know that I genuinely don't, didn't even know there was a criminal court sector.
00:22:51Maybe you're talking, maybe I can interpret that as saying something in the law realm.
00:22:55Um, I have some law friends who they have a lot of data, um, from different things that they work
00:23:04on and they do hire people, um, to work on that. Usually like consultants that come in. Um, but that
00:23:12is a specialty. I don't know enough about, if I'm being honest to answer that, uh, just refund your,
00:23:17your thing. I, I wish I, if I had a better answer, uh, you know, I would, I would, uh,
00:23:23uh, give it to you. I'm sorry. I don't, I got to go back up. There was a question up
00:23:28here.
00:23:31Um, and I, and I, I wanted to answer it and then it skipped down. It does that every so
00:23:37often.
00:23:42Give me one sec. It was a good question. That's why I wanted to read it and then just get
00:23:47down.
00:23:47And I'm geez, there's been a lot of questions. I'm far behind.
00:23:55Oh boy. Give me a second. This is way up here.
00:24:00Okay. I'm close now. Okay. Faux Fidei. Hello. I'm trying to change my career as a data analyst
00:24:07and currently studying with your video data analyst bootcamp. Awesome. All right. Here's
00:24:11the question that I saw, but I just want to read that. Um,
00:24:20cybersecurity usually is asking some good questions. Just don't ask, don't, don't, uh,
00:24:24spam here, but, um, thoughts on logistics and supply chain analytics. Uh, I think it's a pretty
00:24:30slept on field. I'm two module away from completing the Google data analytics course. Then I'll be
00:24:34focused on just learning the fundamental supply chain logistics. That's a huge industry,
00:24:40large companies, most large companies that deal with any product or good have supply chain and
00:24:46logistics divisions within their company. There are some consulting and, uh, uh, companies that
00:24:54just do analytics for logistics and supply chains. I've seen those, but there's within a lot of,
00:25:00so think Amazon think target Walmart, they produce or not produce, they move so many goods. There's
00:25:08a lot of supply chains that go on. And so they hire a lot of people to analyze that data.
00:25:12I think it
00:25:13is, uh, Oh, give me a second. I got to change the battery pack. It, uh, it died on me
00:25:21guys.
00:25:22We'll see if we can get back.
00:25:35All right, let's see. Let's see if we're back. Just, it just died on me. Whoops. I should have
00:25:44checked the battery first. Oh boy. Oh boy. Now we're, now we're not doing good. Uh, let me,
00:26:00see. So this is, uh, let me see. Is it not working anymore? Oh, I'm back. I'm back. Sorry about
00:26:08that
00:26:08guys. Um, my battery died. Whoops. Um, all right. Daniel Wright asks, hi, I'm new to analytics
00:26:17coming over from medical coding. What courses do you recommend I start with? Um, from the medical
00:26:23field. I want to, I'm going to create, by the way, my own healthcare course. I love healthcare.
00:26:30I was in it for many years. I just love it. I think it's fantastic. Um, I'm going to create
00:26:34my own. I want to say I saw one, maybe it was on Coursera. That was good. I can't, it's
00:26:42a, it's been a long time since I've looked at it. But anyways, um, I don't have one off
00:26:48the top of my head. I did do a small healthcare introductory healthcare series on YouTube.
00:26:54It's already on there. So you can go check out my, uh, healthcare for data analysts series,
00:27:00uh, on, on my channel.
00:27:14Um, yes. Sorry about that guys. Sorry about that. People are pointing it out. I get it.
00:27:25Major data's here. Hey, Albert, how's it going? Thanks for joining. I always appreciate it when,
00:27:32um, people show up, even though they probably know most of the stuff already or they themselves
00:27:37help other people. So if you don't know, uh, Albert Bellamy, he's on LinkedIn a lot.
00:27:43He makes some good, really good content. So go check him out, but he's in there.
00:27:48Eastern North Carolina. One of my best friends lives out in, uh, Wilmington, North Carolina.
00:27:52And I got a lot of good friends in, uh, Charlotte, North Carolina. I got a lot of friends in,
00:27:58um, Raleigh, North Carolina, and I'm out in Charleston, South Carolina.
00:28:01Uh, Joseph Menza. What do you, what take do you have on AI replacing the need for data analysts
00:28:15going forward? Is this something that will happen or it's just a fuss? It's not just a fuss. I mean,
00:28:21AI definitely has a lot of use. I actually think what's amazing. This has been one of the most
00:28:27interesting takeaways that I found is that more people are getting interested in data.
00:28:33People who couldn't care less about data are using AI to get into data. And I've seen a lot of,
00:28:41a lot of people have reached out to me, especially on Twitter or X specifically, not as much LinkedIn,
00:28:46but on X specifically, um, they'll say, Hey, I, when using AI to analyze data, but I ran into this,
00:28:55this issue. And the issue is usually something very common in the data analyst world, but people
00:29:00outside of data have never encountered it before. And they try to use AI to solve what AI is messing
00:29:06up and it doesn't do a good job. And so they're like, I'm trying to get this to happen with
00:29:10my data
00:29:11so that I can look at it this way because I just launched this new app or yada, yada, yada.
00:29:16And I
00:29:16don't need these basic metrics that are in Google analytics. I need this metric because this is
00:29:21actually what runs my business. And I just can't figure out how to do it. And so I'll give them
00:29:25some
00:29:25basic advice, but I'm like, Hey, listen, I, you know, I have a consulting business. You can hire me to
00:29:31be
00:29:31your consultant for your business if you'd like, but I can't, you know, solve all your problems. Um, so it's
00:29:36been really interesting seeing all the people who've never been in data before now getting into
00:29:41data. Um, and so what's going to happen is, is this is what's going to happen on the large scale,
00:29:45in my opinion, on the large scale, more and more and more and more and more people and companies
00:29:49who've never used data before are going to try to use data and they're going to use AI. I have
00:29:54no
00:29:54doubt about it. They want to, it's, you know, probably going to be the cheaper option to start with,
00:29:59but it's eventually going to hit a spot where AI can't do it, or they can't manage AI doing it
00:30:05all.
00:30:05And so they need someone to hire someone in when they probably never had someone do that before.
00:30:09They're just a small company. Now they see the use of it. They just can't figure it out themselves,
00:30:14or they don't have the time or the energy to spend doing it. And AI isn't just going to magically
00:30:18automate everything for you. And it's all going to be perfect and rosy and great.
00:30:23These things are complex. Um, data is not easy. And so I predict in the next five to 10 years,
00:30:30a large influx of data professionals. I think we'll actually see a lot,
00:30:34a lot more people getting hired in data. Um, right now, in my opinion, the slowdown in hiring
00:30:41is mostly due to economic reasons. Interest rates are really high. Um, there's a lot of fear and
00:30:48uncertainty in the markets, which is making companies not want to invest. Um, so if those
00:30:53factors change, we'll see a lot more hiring. I think this is a lot more economic than it is AI
00:30:58related.
00:30:59How do you decide this is DLS theory. This is a great question. How do you decide what technology
00:31:03to use for a project? This, in my opinion, purely comes from experience. You can't really know if
00:31:11you don't have experience. I'll give you an example. I get, I work as a consultant. I have a
00:31:17consulting business, Alex analytics, um, where people reach out to me. I get every single day people reach
00:31:23out to me from companies, large companies, small companies, startup, single person teams.
00:31:28And they asked me very similar questions. They asked me questions just like this. Hey,
00:31:34I just started doing this and I need to be able to, I want to know how to store the
00:31:39data and then also
00:31:40use it for reporting, but we're going to have a stream of data. First, we're going to keep the log
00:31:43data for this. And then we're going to keep our client data for this. And then we're going to track
00:31:48interactions. How do I store this data effectively? This question is not, it's, it's so much broader
00:31:55than just data analysis. It goes all the way down the spectrum. And so this comes with experience
00:32:02because sometimes I'll get in there and they're using something like snowflake, but they only have
00:32:06a certain amount of data. They aren't storing it for certain time periods. Uh, you know, they don't
00:32:12need direct access to it to query off of often, but they're storing it in an incorrect way, but maybe
00:32:17snowflake is good. Or maybe they just need to use something like PostgreSQL and use some cheap
00:32:22CRM that uses PostgreSQL and it'd be a hundredth of the cost. And so it, it, there's so many factors,
00:32:30but what you need to do is you need to test out a lot of these systems that you want
00:32:34to know what to
00:32:35use. For example, um, back when I was at my old job, I had a huge budget given to me.
00:32:43I became a
00:32:43manager. I got a about, it was about a two and a half million dollar budget. A million dollars
00:32:48were for a million and a half was for my team. So for hiring and you know, all that stuff.
00:32:55And then
00:32:55the other million was like software. So that was all of our software costs. Uh, all of our contractor
00:33:01caught, well, no contractors, the other one, um, contractors in the first part of the budget,
00:33:06but then it was all tooling, training, software, all these things. And so when you get that kind of
00:33:12big budget, you have to be really personal on how you use it. And so what I went through is
00:33:18that I first like couple of months, I was like, okay, what tools are we using? What tools are
00:33:23really useful that people like, and are doing a good job and which are not. And some of the ones
00:33:28that we weren't even using were costing us like 20 grand a month, uh, is horrible. It was not good.
00:33:35And so I was like, well, couldn't we just use this? And they're like, yeah, we probably could.
00:33:38So I canceled that contract and I got a contract with this other vendor. And so those things just
00:33:44come with experience. I got in there, I learned things, you know, you learn different tools.
00:33:48I can't really, that's really the best way I would describe it as to how you'd be able to do
00:33:52it.
00:33:55Yuvraj Sharmi said, hi, Alex. Thanks. You're the reason I was able to find what I know,
00:34:01what I should do. I'm so glad to hear that. That's why I created this entire YouTube channel
00:34:05was to help people kind of have a path. Uh, when I first started out seven, eight years ago,
00:34:11when was this 2017, eight years ago, um, there were almost no channels teaching this stuff or
00:34:17showing you what to do. And so I just kind of figured it out by myself. It was not fun.
00:34:21And so
00:34:22I'm glad that I can help you in that way. That's really great. This is a good question. Let me
00:34:29answer
00:34:29this in just a sec. First Josh, the data analyst said, landed my first, my first role. Woo.
00:34:43Congratulations. I'm super happy for you, Josh. That is one of the most exciting things. I remember
00:34:47when I, my first job was just like, this is like life-changing and it really is. Now you get
00:34:51to grow
00:34:53with the data analyst and data world as it changes over the next five, 10 years. It's gonna be great.
00:34:59Um, this question, nevermore. Never. Yeah. Nevermore. Hello. I want to ask,
00:35:06does data analyst really need to be an expert at public speaking? No, definitely not an expert. Um,
00:35:12I think public speaking is maybe not the right word. Just need to be able to communicate even
00:35:17with small groups, public speaking. I imagine is like, like groups of like 20 people or more.
00:35:25I've spoken in front of a hundred people. I've spoken in front of two people that might be public
00:35:31speaking to some people. Um, but I would just say good communication skills are, can be developed
00:35:36because I was not good at it when I first started. Um, I didn't know how to present things. I
00:35:42didn't
00:35:42know what was important to share, what was important to leave out. I learned that just on the job and
00:35:47people giving me feedback and realizing after somebody asked a question, I was like, oh,
00:35:52maybe I didn't need to include this. They were just looking for this. You learned, right? You know,
00:35:56you don't have to be an expert, um, to be a data analyst, but good communication skills is very helpful.
00:36:07Um, Ashwin Jett, um, real quick. Can some, I don't, maybe give me a sec.
00:36:16I'll, I'll ask the question. I'll answer the question real quick. Hi, Alex from India is from
00:36:20Ashwin Jett Sandhu. I'm a fresher and want to get a remote job internationally. Can you give me some
00:36:26tips I tried applying on LinkedIn? Um, so internationally I have limited experience,
00:36:31but I'll tell you what I do know. So on my previous team, as well as, uh, companies I've
00:36:37consulted with when they're hiring out of country, they're often doing it through consulting companies.
00:36:44So things like Accenture, that's the one that I used to hire through. We hired in Lithuania,
00:36:48Mexico, in, um, India. And the reason for that is purely budgetarily budgetary reasons. Um,
00:36:57if we were to hire on those three other people that we needed in the U S that would have
00:37:02blown our
00:37:02budget for, um, for that quarter, that year, whatever it was. And so what we did was we hired
00:37:10offshore and that saved us a lot of costs. They were consultants. They want full-time, full-time hires.
00:37:14So we just paid them a flat rate. Um, usually it was a good flat rate,
00:37:19sometimes like $65 an hour, whereas in the U S would have been like one 25 an hour.
00:37:23So it was like half. Um, so look at those consulting companies. You usually need to interview
00:37:28for those companies, but you can get in. And those companies usually will help you consult
00:37:34for international or even U S based companies. And we even hired on one person from one of those
00:37:39consulting companies. I was like, Hey, would you be interested in a full-time job? And he's like,
00:37:43yeah, absolutely. Um, and so we paid them more than the consulting, but we have them on full-time
00:37:48and eventually over the longterm that saves us costs. And so that's what I would do.
00:37:53That's what I would do. Um, by the way, I'm going to, um, can someone copy and paste?
00:37:59Well, maybe I need to do it. Let me go down.
00:38:04Yeah. I want everyone to be able to get this. Uh, people were asking about how to
00:38:09get jobs, how to, you know, craft your resume. I have a freak. I have a course on analyst builder.
00:38:14It doesn't, it does cost money just by for today, the end of the day, the code expires just for
00:38:20today.
00:38:20You can use the code get job for that course. Um, you just have to look it up. It's on
00:38:26analyst
00:38:26builder. It's called landing a data job. You go and buy it. You don't have to pay anything,
00:38:30enter the code. You'll get it for free. I hope, I hope that helps people, but I want,
00:38:34I want people who are in here who are investing their time to get something out of this. Um,
00:38:39and so go take that. It's really great. You can build a resume. You can build a portfolio project.
00:38:43You can, um, see how to apply to jobs, how to reach out to recruiters. It's really helpful.
00:38:53Uh, Josh data analyst said huge amount of issues about data privacy and governance. Yeah. For AI and
00:38:59stuff companies are already, um, they're figuring out solutions to this, but there are
00:39:04a lot of issues that need to be resolved. And these are only like companies that are using
00:39:08it extensively. This hasn't even, we haven't even talked about companies that are using it,
00:39:12you know, on a smaller scale that may not have the understanding yet about data governance and
00:39:18privacy, which is if you're using certain tooling, certain AI systems, that is a liability.
00:39:27Um, and so some companies in certain departments aren't allowed to use AI because, um, you know,
00:39:36privacy reasons. Arfuz Zaman, hello, Alex, something love from India, love from the U.S.
00:39:43back man or woman. I don't know, usually international names. Sometimes I can't tell.
00:39:54I'm reading through questions.
00:40:03This just takes a little while. I do apologize.
00:40:05Uh, fit boy, fit boy. I'm guessing he's fit and he's a boy. Data analytics is really maths heavy.
00:40:13In some industries, it is. In some industries, it's not. Finance tends to be more mathematics
00:40:21and statistics heavy. Things like healthcare tend not to be as healthcare, uh, uh, statistics and maths
00:40:29heavy. There's always caveats and rules to this. Um, the one that the, uh, team that I worked in
00:40:37was a data science team within healthcare. We actually tended to be more statistics heavy
00:40:42because we worked with a lot of data science teams and nurses, and it was a lot of medical data,
00:40:47which is pretty complex. And so that was a little bit more statistics heavy, but I've worked at
00:40:53other companies and other places and consulted with companies where it's not super statistics
00:40:57heavy. They just want someone who really knows how to work with data. Well, um, the fundamentals of
00:41:01data. I'm gonna take a drink while I'm looking for more questions. Someone's calling me, but I ain't
00:41:08answering it. It's AI trying to tell me they're coming from my job.
00:41:20What roles in artificial, this is from Zohaib Zishan. What roles in artificial intelligence
00:41:25and data science are most unlikely to be automated with AI. So I have this, uh, I don't remember what
00:41:33video it is. I don't remember, but I did a whole video on the life cycle of data analysis.
00:41:41The life cycle is a cycle, right? It's constant permutations of this cycle. You ingest data,
00:41:51you understand the data, you clean the data, you create automations, you do data quality checks,
00:41:57you create reporting and, and data visualizations. And then, you know, it's a cycle because issues
00:42:03always pop up always. What AI is going to be good at it, at least right now, let's talk right
00:42:11now.
00:42:11And then I'll talk like five, 10 years, but right now, AI is going to be really good at helping
00:42:14with
00:42:14the coding piece. That will be helping with the data cleaning a little bit, right? It's not going
00:42:19to know the nuances of what you need it to look like in the end, unless you really do a
00:42:24great job
00:42:25explaining it. But the data cleaning process is going to be helpful. The exploratory data analysis
00:42:32process, it should be helpful in that. That's really heavy coding. Um, let me see if I got to
00:42:44check it. Uh, apparently that was my, uh, my sister-in-law's mom. I'll have to call her back
00:42:54after this. Whoops. Uh, sorry. I'm not going to say her name, but sorry, sister-in-law's mom.
00:43:03But calling me something data related was funny enough. Anyways, had to check that. Listen,
00:43:10when family messages you, sometimes you got to check it. Um, those pieces are likely to be
00:43:16automated in some way. You're still going to need oversight. I promise you, even when you get these
00:43:22AI agents that say they can do all these things, they just aren't human. And so they may not fully
00:43:27understand it even today, right? They may not fully understand what they're even supposed to
00:43:32be looking for. They're just a system, right? It's like a monitoring system. Sometimes monitoring
00:43:36systems that are, that have been used for like a long time, they mess up and then they cause issues
00:43:43and people are going to need to be there to fix those issues. I, I can almost guarantee it.
00:43:50It's almost like a certainty, especially as of today, the things that won't be automated,
00:43:55because that's the real question. Um, the human interactions with clients should not be automated.
00:44:00I don't see a world where a client just messages your company and asks it, the AI gets it for
00:44:07them,
00:44:07because then why are they using your company? Why aren't they just doing it themselves and hiring
00:44:11a data person themselves to help understand the AI and use the, it just, that's not probably going to
00:44:16work that way. So human interaction, human connection, uh, uh, conveying and helping your
00:44:23clients understand their data, um, is going to be one of the biggest things, the personal part,
00:44:29building out dashboards and not just generic dashboards that AI has been building, not AI,
00:44:34even just automated, uh, report builders. They've been doing that for 10, 15 years,
00:44:40but they're not always super, super helpful. Um, you have to understand the nuances of the data.
00:44:45And so you were there to be the expert of the data, not just writing the code or, or whatnot.
00:44:51I think that is the stuff that you are going to be highly un-automatable, if that's a word.
00:45:06W. Barberena. What books or courses can you recommend for aesthetics versus usability?
00:45:13I've been told my reports and dashboards are data dense, but not attractive. All right,
00:45:17I'm going to give you, uh, if you've taken any of my courses, if you've taken any of my
00:45:21lessons on YouTube, I think you'll know this. I am the same way. I have zero design
00:45:29genes in my body. Um, none. And so I can really understand the data really well. I can convey it.
00:45:38Well, the visualization looks pretty decent, but it's not going to be aesthetically pleasing. Um,
00:45:44what I used to do is we had templates at my job and I use those templates a lot. I
00:45:51relied on those
00:45:52for my aesthetics. I don't know. I don't remember what course would be good for aesthetics. Honestly,
00:45:57I don't, but I bet you could find one on like Udemy, look up like how to design dashboards to
00:46:01look
00:46:02and present well or something like that. I don't know. Hmm.
00:46:11Uh, Moro Husseini. Hi Alex. I'm in the third course on the Google data analytics professional
00:46:16certificate program on Coursera. The, there are some YouTubers playing down the,
00:46:21playing down the importance of that Google course. What's your opinion? All right. When it first came
00:46:25out many years ago, we really didn't know much about it. I took it myself because I was interested.
00:46:32A big company is creating a Google, uh, Google was creating a data analytics certification. Um,
00:46:39and so it was really exciting. And then when I took it, I thought it was good. I'll just say
00:46:45like,
00:46:45I don't want one on a scale of one to 10. It's like a five, it's good. It's five or
00:46:49six. It gives you
00:46:50the bait. Like if you know nothing about data analytics, it gives you the basic concepts of data.
00:46:55It gives you the basic understanding of how to use things like SQL. Um, I think they go into like
00:47:00Excel or something. I can't remember. It was a long time ago. So it gives you the basics. I think
00:47:05it is decent, but that's for what I would say is like an introductory course to data analytics.
00:47:12It does not go in depth at all. It's surface. It's mostly like, you know, a thin, a thin, uh,
00:47:18uh,
00:47:18array of knowledge broadly given. So no, I don't think it's important to know is not a recommendation.
00:47:24I don't really recommend people take it anymore. Um, the reason being is my data analyst bootcamp,
00:47:30which is free on YouTube has more content, more depth, and you will learn 50 times more
00:47:39than the Google data analyst certificate that you'll pay a lot of money for.
00:47:43If you want to pay money, I have a platform called analyst builder.com that goes beyond my data
00:47:49analyst bootcamp and goes more advanced, has more advanced projects, has practice for SQL,
00:47:54has resume templates. It is like the best platform for data analytics in my opinion.
00:48:00So I would go there if you're going to spend money. That's just my personal opinion. Yes,
00:48:04that is a shameless plug for myself, but Coursera, their courses are very good. Some of them are very
00:48:10good. I've taken many of them myself, but all the instructors are different. Um, and sometimes you
00:48:17don't know what you're getting and some of the courses are good. Some of them aren't,
00:48:20all of mine are going to be good, high quality courses.
00:48:29Cyber security said you're too kind, Alex. You're an absolute legend, man. We're lucky to have you,
00:48:34bud. I appreciate that. I needed to hear that. Thank you. Um, what do you do for a living? Are
00:48:41you a
00:48:41senior data analyst? This is from Bujini Taha. All right. So I was a data analyst. Uh, I was
00:48:47actually a data collection specialist and analyst for my very first job back in 2017. Uh, I became
00:48:53a data analyst at a small healthcare and latest company where I became kind of a mid-level data
00:48:57analyst. I was there for over a year. Then I got a job at a fortune 500 company as a
00:49:02data analyst.
00:49:02I worked my way up the ranks. Um, I was like the lead data analyst on my team, but my
00:49:08title didn't
00:49:08reflect it. I had been there for like three years. I had asked my, uh, boss for a promotion. I
00:49:14wanted
00:49:14to be a senior data analyst. And then I also was like, or a lead data analyst. Cause I, that's
00:49:19what
00:49:19I was doing on my team. Um, but then I was like, there were other jobs within our company, my
00:49:25company
00:49:25that I thought were looked really interesting. And so I asked her, Hey, would you mind if I apply
00:49:29to these jobs? She's like, of course, you know, thanks for letting me know. Then I got a job as
00:49:33a
00:49:33manager of data analytics, uh, in our IT department. So we, in our IT, that's like
00:49:39a bunch of different departments, but I was the manager of analytics, um, in that.
00:49:44And so I have a management background in data analytics. Um, and then I quit my job, um, back
00:49:50in 2022 to do consulting and YouTube full-time. So I still do consulting. I have consulting clients
00:49:58on retainer, um, that I consult with. I just did a bunch of work for one, uh, the other day.
00:50:04And, uh, that's what I do. And then of course I have analyst builder.com, which is my like
00:50:09ed tech platform, which is courses and all these things. But I have a whole team
00:50:12that helps me with that, but I create all the contents. All the courses are my courses,
00:50:16except for a few, which, uh, Ben Rogogen or Benjamin Rogogen. Um, you may know, know him as Seattle
00:50:22data guy. He's coming on to make some courses. He already created one called, uh, data basic, uh,
00:50:28fundamentals of data pipelines. So, um, but yeah, that's what I do. That is what I do.
00:50:34And so I consult, I've consulted with, um, big companies that I can name, uh, like Microsoft
00:50:39or small companies that I can't name due to client, uh, NDAs and stuff, whatnot. Um,
00:50:46that are like tech startups in Silicon Valley. I do a lot of Silicon Valley work. Um, it's probably
00:50:53what I do most in consulting or small stuff. Um, bigger companies don't work
00:50:58with them as much. Let's see.
00:51:10I agree. Taki Tyrone. I, I, I answered your question. I didn't know. I didn't know.
00:51:18I'm sorry. I'm just catching up.
00:51:22Let's see. What laptop would you suggest? I've been getting this question so much lately.
00:51:28Uh, I have a whole video, by the way, a whole video on what laptops you should choose in the
00:51:36end. It's actually, uh, not crazy. You don't need anything crazy or expensive.
00:51:42What you need is you need probably 256 gigabytes of memory or storage. You are not memory storage.
00:51:49And then 16 gigabytes of Ram. That's what I recommend. Almost any computer is good.
00:51:54I recommend a P, uh, uh, some type of PC, Microsoft. Um, that's just what I use, what I'm really
00:52:00good
00:52:00with. If you have more money, you could go with something like, um, oh gosh, how am I blanking on
00:52:06this? I have one right here. A MacBook Pro, right? If you have more money, but you don't need it.
00:52:11Um,
00:52:11and so, uh, there, there you go. Let me see if someone's, oh, let's see.
00:52:22Oh my, I hate it when it does this. It just, it doesn't load the questions and it loads them
00:52:26all
00:52:26at once. And then, yeah, it's not good. Yousef Ahmed. Hey Alex, when is the free month going to
00:52:35end? Um, so he's, you're referencing in the, in the description below, you can get a free month
00:52:42of analyst builder. You can take two, three, four courses at a time. If you go really fast
00:52:46for free. Um, I will be doing that. Uh, that is going to stop. You can only redeem that today.
00:52:53It ends tomorrow. I didn't even know. I forgot to mention that you can get a free month of analyst
00:52:58builder completely free. Take courses, do the questions, whatever you want for free. Um, and you
00:53:04can do that down below in the description. Just go and read it. Uh, that is only for new users.
00:53:10The system, our system with Stripe doesn't allow people who already have bought something and created
00:53:15an account. It doesn't allow them to redeem it. You can try it. I just, it doesn't, it hasn't been
00:53:19working. Um, we've tried to try to do it. Um, but it ends today. It was only going through my
00:53:25July
00:53:25live stream. So I'll have to, um, I'll have to announce that somewhere, but I'll do more of those
00:53:31in the future, but I've just been doing it for my live stream. So all my live stream people,
00:53:34you guys know about it, but people off my live stream, they don't know about it.
00:53:41Josh data analyst domain knowledge is the downfall of AI systems in the moment. It's
00:53:45good at working with data, but doesn't understand how it relates to air you work in.
00:53:49Um, that's true. So I had a, I have very in-depth knowledge of healthcare analytics.
00:53:55That is my specialty. That was my background in college. That was my background with experience.
00:54:01That's my specialty. So I'll go in and I'll ask ChatGPT latest model. I'll ask it questions
00:54:07because I was working on something. I'll ask it questions and it's given me things that I know
00:54:11are not true. Like I just know it. I know it's a fact.
00:54:14Cause I worked with it for years. I'm like, tell me about the MIPS program.
00:54:18Tell me how people do their data, how they submit their data, how they collect their data.
00:54:23It got it like 30% right. Maybe 40% on a good day. It was not good. And so
00:54:29if I was a client
00:54:30who was submitting my MIPS data, I would be absolutely ruined. You would lose millions of
00:54:38dollars. Cause the clients that we work with, with MIPS were large, uh, groups of hospitals that would
00:54:43submit their data for millions of dollars in, in data from the MIPS.
00:54:50And if they followed AI, they would be bankrupt a hundred percent.
00:54:55So it's going to get better, but, um, you're right. Understanding your niche really well.
00:55:03And, um,
00:55:07and really being able to like talk to clients about it, talk confidently about it, understand
00:55:14it really in depth is going to be a great thing to know how to do. Great, great skill.
00:55:19Um, um, great skill. Let me see. All right. Everybody's asking about a ton of AI stuff.
00:55:27Here's what I'll say about AI. AI is really good at a lot of stuff. It is, but I have
00:55:35been using AI
00:55:35enough and I've been a data professional enough to know it has a lot of downfalls. And what I've
00:55:40seen time and time and time again in the real world is that people who jump straight to AI and
00:55:44do not understand the basics of working in Excel, right? Understanding file types, understanding
00:55:51data types, understanding how data is stored in SQL databases, even these simple concepts that you need
00:55:58to know, like a hundred percent, you need to know them. People who don't have that foundational
00:56:04knowledge really struggle with using data because the, it's going to spit out some XML file because
00:56:11it's trying to optimize for the data. Then that person doesn't know how to read in the thing.
00:56:15And they try to use AI to help read in that XML file, but it's stored weird.
00:56:19So then they convert it to a JSON. And then the JSON is in this weird nested format that they
00:56:23can't
00:56:23understand. They can't get it into a, a, a flat table to be able to use it for XYZ. And
00:56:30it's a,
00:56:30it's like these basic things that I have a lot of experience in. It's so simple because I'm doing
00:56:38this for so long. People who haven't done it really struggle in it. They really do. Give me a second.
00:56:48My friend, Roger, just, uh, he's from my softball team. He just messaged me saying he doesn't know
00:56:53what I'm saying, but keep it up. Thanks, Roger. Hey, I'm glad you're watching. Thanks.
00:56:59I'll see you next season. Uh, if I don't see you sooner, uh, springs right around the corner.
00:57:04I can't wait. I had a blast. Uh, but yeah, that was Roger. He was our pitcher for our softball
00:57:09team.
00:57:10Good pitcher too. A good pitcher. Don't quote me on that though. I didn't say that. All right,
00:57:16let's keep going. Um, this is a good point. Um, cybersecurity. Now, whenever I go on LinkedIn,
00:57:28every Power BI dashboard is aesthetically pleasing overboard. I agree. I actually made a video on
00:57:33this. Um, some of these dashboards are aesthetically pleasing and are garbage and they serve no purpose
00:57:43and people will make them and put them on their resumes. And when I see it, I'm just like, this
00:57:46is just like, it's overboard. You don't need to do this. It isn't useful. Has like background pictures
00:57:53and you know, all these colors. And I'm like, this is insane. It's not nice. Nobody in the real
00:57:59world in a real job does that. What about the IBM course chess master IBM course? The IBM courses are
00:58:07good. I took those many years ago. I don't know. I don't remember them well enough to know what the
00:58:13content especially was, but I know one of the IBM courses was on Python, which was good. Um, the other
00:58:19one was in like IB, some IBM tool. I can't remember, but it was good. Their courses are actually quite
00:58:24good. All right, let me get a sip of water.
00:58:38I think, so this is a good question. Will, um,
00:58:42Will said in 2025, which skill do you believe is most likely to be replaced by AI? Theoretically,
00:58:48of course, business intelligence like Tableau or SQL. All right. I actually think in my opinion
00:58:57that coding a hundred percent by yourself isn't really needed anymore, right? Uh, uh,
00:59:04AI systems are good at helping with a lot of stuff. So SQL is something that you still need to
00:59:10know. I 100% believe that the basics of SQL, any AI system can do it well. So I don't
00:59:18think
00:59:19SQL at an in at advanced level is going to be replaced because again, the nuances of your data
00:59:25and it, it messes up and you have to be able to debug it. And you create this long story
00:59:30procedure
00:59:30and you know, you don't know what happens, but then your system breaks and you try to get it to
00:59:36debug
00:59:36it. And it's like, I don't see the error. Let's try this. Let's try this. Let's try this. It's not
00:59:40working. None of it's working. You have to be the expert and get in there and know how to do
00:59:44it.
00:59:45So SQL is not being replaced by AI. You still need to know it. I actually do think, and this
00:59:51is my
00:59:51opinion, maybe in like five years is that a lot of the tools for building visualizations
00:59:59will be mostly automated, not all, but mostly in the same way. It's like SQL, right? AI,
01:00:07you're going to say, Hey, I want visualizations that show this data this way. And it's going to
01:00:12be able to do that, but there's so many other nuances to it. Again, it'll get you like 90%
01:00:17of
01:00:17the way there, but it's not going to do it perfectly. I got a donation. I'm going to read it.
01:00:24Tech VK. Thank you for donation. You don't have to donate. I try to get to everybody's questions.
01:00:28It says, Hey brother, if I start Python series in your playlist, is it good for becoming a data
01:00:33analyst? Yes, it absolutely is. In fact, I think my Python series on YouTube is one of the better
01:00:39ones for becoming a data analyst. Take my Python for beginners series, build the projects. Give me
01:00:46one sec. When I talk a lot, I burp, build the projects and then take my pandas course or not
01:00:52course, my pandas series on YouTube. There's projects with it. It's free Python. And then
01:00:59pandas take those two, uh, take those two, uh, playlists. Those are fantastic. If you know those,
01:01:09if you, you take those lessons, you can definitely put them on your resume. If you want to dive even
01:01:14deeper, of course I have my full courses, but honestly, you don't have to, uh, if you don't want
01:01:19to. Let me see. Let me see. I'm reading through questions. There's a lot. I'm really far behind.
01:01:38I do apologize. Josh asks, am I still strumming my guitar? So I said I would do this in the
01:01:45last
01:01:45live stream of the month. We have four. Oh geez. Oh gosh. We're at nine 56. I said I was
01:01:54going to
01:01:54stop right at 10. Here's what I'm going to do. Um, I'm going to be doing another live stream starting
01:02:00next week. I'll do another live stream. I got answer more questions. I told people last live stream,
01:02:06I would play something really quick. I got this mic going so you can at least hear it.
01:02:10I'm not going to play a lot. I'm just going to play some stuff.
01:02:13Um, just that I think sounds good. And then, um, I'm going to be doing a giveaway for free courses
01:02:21on analyst builder, analyst builder.com is my learning platform. I create it with my team.
01:02:28Um, all of the courses are mine. You can practice SQL and Python and R. Um, and it's just one
01:02:34of the best
01:02:34platforms for data analysts in the world. I genuinely believe that. So try it out.
01:02:38You can get a free month free down below. Um, and that's just a thank you to you. You guys
01:02:43can take
01:02:44like two or three courses for free in a month. If you really have the time. Um, but I'll give
01:02:50away
01:02:50some, uh, just free courses that you'll get to keep for life in just a little bit.
01:02:54Um, but this is my guitar. This was given to me by my father-in-law. Um, it is
01:03:01a really nice Martin, one of his old guitars. He was a guitar collector. He used to have like
01:03:0630 of them, but gave this to me. Um, I don't even know if you can see this in the,
01:03:10uh,
01:03:11in the screen. You can kind of see it, but it's really nice. Um, really, really nice.
01:03:17And I love it. I play it all the time. It's way better than my last guitar.
01:03:21I don't know if you're going to be able to hear this. So I'm just going to play it. And
01:03:25if you can hear
01:03:25it, great. And if you can't, um, uh, if you can't, that's okay, but I'll play something, um, sad.
01:03:38Let me see. This is kind of like, uh,
01:03:45that's like, you know, like, uh, what's that song? I can't remember what it is.
01:03:49And then there's like, um, that's like the finger picking stuff. I love that sound.
01:04:00That's like my, my kind of sound. Um, but then the, you know, there's like
01:04:12stuff like that. Um, you know,
01:04:22um, I don't know. I, I, that's just me, me messing around, but I do love playing guitar.
01:04:28I do love playing guitar. Um, and I sing a lot. I have a, I have a video. I, I
01:04:35wrote a song for
01:04:36data. It's like, uh, truth about my job. Look that up. Truth about my job, data song, Alex,
01:04:42the analyst, you'll find it. I wrote a song. I made it. Oh, it's pretty good. It's bad. That sounded
01:04:47pretty good. All right, guys, I'm going to be doing a giveaway now. Um, this giveaway is just for you
01:04:55guys. It is all you have to have is a date, uh, an analyst builder account. That's it.
01:05:02So here's what you do. I'm going to pull it. I'm not, I'm, I can't pull it up on my
01:05:05screen
01:05:06because I have sensitive stuff on my screen. Um, I don't want to expose anything for clients or me
01:05:11or whatever. So I'm just not going to show you my screen, but go to analyst builder.com
01:05:17go to pricing, go to courses. These are all my courses. The only one I didn't create is
01:05:28the foundations of data pipelines. That's one's by Ben Rogogen. He's going to be bringing some,
01:05:32uh, SQL for data engineering course, uh, by the end of this year, but click on a course you want
01:05:38to buy. You don't have to buy it. I'm not trying to sell you anything. I just have to give
01:05:42you a code.
01:05:42So let's say you want to take the MySQL for data analytics course. You click buy.
01:05:49Well, it says I already have a bot, but you click buy. It takes you to a checkout screen.
01:05:54There's an ad promotion code. You're going to click on that. What you have to do is add the
01:05:59promotion code. It'll go to zero and you click pay. I don't want you to buy this. I'm not trying
01:06:05to sell you anything. I promise. I'm trying to give things away to you guys. I really am. So
01:06:11then you pay. The only trick here is, is other people are going to be competing for this code.
01:06:16So I'm going to give you a code. Only one person can redeem it. So you just have to be
01:06:20the first one.
01:06:21That is how this works. I don't make the rules. All right. I'm just here. I'm just here.
01:06:28Just here. All right. I got to finish up because I know I got to help my wife with something
01:06:32really
01:06:32quick. All right. Um, I'm creating the code right now. This is for one person to redeem and then I'll
01:06:43do two codes or two people. So five total codes, uh, five total courses that I'll be giving away. So
01:06:51I just created this code. The code is GitHub is life. All one word, all capitalized. GitHub is life.
01:07:04If you redeem that, where am I? Here I am. If you redeem that, then you'll get it for free.
01:07:14And if you
01:07:14did redeem it, let me know what course you got. All right. I'm going to create the next code.
01:07:19Um, I'm going to do two more codes. I'm going to make it something. I'm going to make it something
01:07:24random. Nobody's going to be able to guess this one. All right. In honor of someone who just got
01:07:33their first data job, it's his name. I talked about it earlier in the stream. If you were there and
01:07:40you
01:07:40said, Oh, that person got a job that it's the first part of his name. I just created it. Oh,
01:07:46wait, that was supposed to be two. Uh, that's going to be one. I only, only one person can redeem
01:07:51that
01:07:52one. Somebody got the, um, other one, by the way, someone got it. Uh, it's Josh. You're probably
01:08:00not going to be the first person. Someone already got it, but the, the code was Josh. All right.
01:08:05I'm doing one more code, but three people can redeem this three. Find the course that you want.
01:08:13You can redeem this for free. Jimmy says he got it. If you got it and you had your,
01:08:22you got it checked out and shows under your purchases that you have it in your account.
01:08:26That means you got it. Let me know. Of course you got, um, this next one is something I care
01:08:32deeply
01:08:32about and it's something I feel for each and every one of you. Um, and so I've just added this
01:08:38three
01:08:38people can redeem this. It's friendship. And I care about my friendship with you. I do very much.
01:08:49Friendship is the last code. Friendship is the last code.
01:08:54Um, and, uh, I hope you were able to get it. If you didn't next week, I'll give away more.
01:09:03I'm just,
01:09:04I'm not stopping. I'll give away more next week, next Thursday, 9am. Actually 9am. My time could be
01:09:11nine, not 9am your time, but that is a completely free course just for you. Go try it out. If,
01:09:18uh,
01:09:20let me see. Yep. People got it. People got it. I hope that helps. Uh, glitch master said,
01:09:28just finished a data analyst interview. I hope, I hope it went amazing. I hope you get the job.
01:09:33I hope you nailed it. Awesome. Awesome. Awesome. All right, guys. I have got to get out of here.
01:09:40That's my live stream for July 31st. Next week will be our first August live stream and, uh,
01:09:49we'll be doing the same thing. So, uh, I hope that you guys enjoyed this live stream. If you did,
01:09:56uh, subscribe, I guess could subscribe like this video. I don't know. You don't have to do anything.
01:10:01I just, I would just leave fire you, but if you liked it, let me know. I appreciate it. All
01:10:06right,
01:10:07guys. I will see you guys next week. And, um,
Comments