Education for a Data Scientist in the Age of Fabric? – Ep. 418

Mike, Tommy, and guest Ginger Grant explore what education for a data scientist should look like in the age of Microsoft Fabric. They discuss whether traditional college degrees still hold up, the value of internships, and how the evolving data platform landscape is reshaping what skills matter most.

Main Discussion: Education for Data Scientists

This episode brings guest Ginger Grant into the conversation for a deep dive into a question many in the data community are asking: where should an aspiring data scientist get their education today? With Microsoft Fabric reshaping the data platform landscape, the traditional path of a four-year degree may not be the only — or even the best — route anymore.

The College Debate

Mike, Tommy, and Ginger discuss whether a traditional college degree in data science or computer science is still worth the investment. The conversation covers how university programs often lag behind the rapid pace of technology, meaning graduates may enter the workforce with outdated knowledge. On the other hand, college provides foundational skills in statistics, mathematics, and critical thinking that remain valuable regardless of which tools are trending.

Internships and Hands-On Experience

The panel explores the growing importance of internships and real-world experience. Ginger highlights how hands-on work with actual data problems teaches skills that classrooms simply can’t replicate. The group agrees that employers increasingly value demonstrated ability over credentials, making portfolio projects and internship experience critical differentiators for new data professionals.

How Microsoft Fabric Changes the Game

With Microsoft Fabric consolidating so many data capabilities into a single platform, the skill set required for data scientists is evolving. The discussion covers how Fabric lowers the barrier to entry for some tasks while simultaneously raising the bar for understanding end-to-end data architectures. Knowing how to work across lakehouses, notebooks, pipelines, and Power BI within Fabric is becoming a key differentiator that most academic programs haven’t caught up to yet.

Advice for Aspiring Data Scientists

The episode wraps up with practical advice: stay curious, build projects, engage with the community, and don’t wait for a degree to start learning. The data field rewards people who can demonstrate competence, and the tools available today — including Fabric — make it easier than ever to learn by doing.

Looking Forward

The trio encourages aspiring data scientists to focus on building a portfolio of real-world projects rather than relying solely on formal education. As Microsoft Fabric continues to mature, the most successful data professionals will be those who combine strong fundamentals with hands-on platform experience.

Episode Transcript

Full verbatim transcript — click any timestamp to jump to that moment:

0:33 Good morning and welcome back to the Explicit Measures podcast. It’s Tommy and Mike. Hello. Good morning. Good morning, Michael. How you do? Or does anyone say Michael to you? Is it or is it only Mike?

0:44 It’s typically so at work and online, I try and go by Mike. Michael is usually reserved for my parents, my family. Most everyone else has other names for me at my house that are just not Mike.

0:56 So, we’ll keep it Mike. I have a friend same name same thing with the Michael thing and he had two different personalities and I would tell him I said Mike’s when I see you at school. Michael is when we’re home thing.

1:10 Yeah, exactly. I don’t go by the formal name very often. I usually just go by Mike. You could have had a good name but continue. Let’s do the intro or let’s do an intro.

1:20 So today’s episode is we’re going to be talking about what does education look like for data scientists in the age of fabric. Is this changing? Are we seeing a shift happening here with who should learn how to write code and things?

1:35 Is this adjusting? I think this is also going to be an interesting topic. We’re probably going to bring up vibe coding again because I think that’s a topic that needs to happen.

1:42 And I think also how we learn to educate people about how to use computers. I think we’re on a cusp of something really drastically changing here in the near term with the things that are now possible with AIs and what they can do and train and learn on top of information.

1:57 So anyways, that being said, that’ll be our main topic for today. But before we do that, let’s jump into a bit of news. Tommy, what else do you have here for us? You found some articles today that we’re going to go through.

2:08 Mike, the news just keeps on coming and we had a new PowerBI April 2025 update and we’ll do a quick little draft here. We’ll do a little two rounds of the updates. I don’t know if you want to go first. I’ll give you the floor to pick your favorite feature.

2:24 I hope you pick mine because I got to go. Well, then you go first then if you don’t pick yours, go you go first, Tommy. And then because I still need to check out the links and send them out here on the chat.

2:34 Things going on. Well, if I’m going to go first in the draft and make it like the NFL draft. Well, I’m going to get a good one. I’m going to get a blue chip. I feel here which I’m actually really excited about. My first pick in the draft.

2:48 We got a mobile layout autocreate. And this is like, oh, finally. And really what it is, if you’ve ever used the mobile view, which I don’t hear a lot of, really never seen an article about it, but in terms of from the community, but what we have here is simply going to the mobile view.

3:05 You want to make a mobile version of your report. Well, PowerBI will actually autocreate it now and give you the auto engine understand the desktop layout. What you’ve done on desktop will affect what the autocreate does, which I really really like.

3:21 And honestly, I do think every report should have a mobile version. Like that’s the ideal, but it never comes to top of frame for me. It’s never something like, oh, I have to do this. But I think it’s a good practice to do. This makes it almost no excuse now to just at least get something out there.

3:39 So that’s your feature is the one around the autocreate for mobile. Yep. That’s my pick. So I think and that’s generally available. So it’s been out for a little bit, right? So this is just the general availability of it.

3:52 Yeah. And I think this honestly makes it no excuse just to do something and then what you find out too if people actually using mobile they’ll let you know and that’s something you can implement. But this is too easy now just to get started.

4:04 Love it. Well, Tommy, you probably know what my feature will be here on this one. But I’m going to definitely double down on this feature cuz I did a YouTube video with it this week and I think this is going to change.

4:15 Again, there’s a lot of things that are happening right now that are going to change how we build things and what we build inside our reports. One of them is live edit mode or direct mode with PowerBI desktop currently in preview.

4:28 This is huge. I think this is major because we typically we were only able to make direct lake models in the service. This changes the game for us. Now we can create a direct lake model that connects to multiple lakes all through desktop. So that’s extremely useful there.

4:43 The UI does not exist today to do this inside the service. At this point you can create a direct lake model in the service. But what happens is the direct lake model goes from the SQL analytics endpoint back to the direct lake. So it does use the direct lake experience but it falls back to SQL server when it finds there’s a problem, there’s too much data or there is an issue.

5:03 So there’s some limitations there. This mode goes right from the semantic model directly to the lakehouse which I think is what Microsoft originally intended when they built semantic models.

5:13 Yeah. So I think this is some improvements on the analysis services engine or some completion of features there that are going to allow them to expose this one. I think this one is massive. I think this is really, really important. I think this is incredibly useful.

5:30 And there’s a great video out there on YouTube that describes with Zoe. Zoe goes through and demos the whole feature with us around creating PowerBI Lakehouse elements here. So, or connecting to them basically. I think this is a big deal. I really like this one. Definitely turn this feature on.

5:46 That’s going to be my vote for this month’s update. And Mike, honestly, I think you got the allstar here. I’m actually really interested to hear from the five of you who listen. Submit a mailbag on what you think the impact of this is going to be because we’re going to talk about this more.

6:03 Personally, I would love to do a series on this on what the potential changes are because the feature itself may not be the game changer, but I think when we see the impact, this has the potential to meet our standard of what a game changer is.

6:18 So, if you want, submit us a mailbag at powerbi.tips mailbag on what you think a question around this would be. What do you think the change is going to be? Because I really want to dive into this. I love this feature. I really think this is going to completely change how we build and the standard of what we do.

6:38 Yeah, I think we’re going to start building with this first. And this is another area where I’m with you. So Tommy, this is one of the things that I look at and I go, this is something where I actually need PowerBI desktop for. And so this is one of the areas that I’m having a little bit of friction here.

6:51 This is, we’re seeing something here that evolves. I’ve been a big proponent since day one of the podcast. This has been like 3 years now. Literally day one. It was the first episode we did. I was a very big proponent of let’s just use everything in the service in the web.

7:04 And that’s the data scientist approach. We’ve been talking this series with Ginger about data science and they’ve been pretty much doing like most experiences can be done in the service right whatever those solutions are. Speaking of which I have some updates I need to give about the data science and fabric as well so that’s something I’ll give some updates on.

7:26 But that being said I’m very convinced that the service should do everything desktop does. I don’t really want to use desktop anymore. I have a couple Mac computers and I have a couple Windows computers and of the two I really like the Macs a lot better. It’s a better quality. They last longer.

7:42 Even as a business owner, if I’m giving out computers to people, I’d rather give people MacBook Pros than a Windows computer cuz it’s just less maintenance on me as an owner to make sure everyone’s computers up to date. It has all the features and it just runs better. It works better with the ecosystem.

7:57 So, the more we can get away from desktop, the happier I’ll be. I’m a little bit sad that this feature only came to desktop and it’s not coming to the service. So, I don’t know if that’ll ever get there.

8:09 And this is where you and I differ. I came open arms embracing this like yes because and I still feel like this — applications when you build an application it’s devoted to a single purpose. The browser yeah it can do everything but it can’t do everything great yet. I think we’re getting there with web apps but we’re still not there.

8:26 When I open up PowerBI desktop, PowerBI desktop is not going to browse the web for me. It’s supposed to do one thing. That’s to get my data and work on my data. And so, I know we’re going there. I listen, I see when the train’s leaving the station. I know that’s the point that we’re moving.

8:42 That being said, listen, there’s still ChatGPT, Anthropic, Cursor, VS Code. They are still focused heavily on their desktop applications. To me, for a reason. When you’re knee deep, you want something that’s, hey, this is meant for you for what you’re doing right now. Yeah, agree with that one.

9:02 Awesome. That being said, that is the wrap on some of the news pieces. So, we’ve done a couple of these announcements here for the blog. Is there any other announcements, Tommy, that you want to move on to as well?

9:15 It’s there’s a quick one. It’s enticing, but it’s one of those just out there. It’s like seeing a nice advertising for a restaurant I can’t go to. Build data-driven agents with curated data from OneLake. Pretty freaking awesome.

9:31 So basically the idea here is simply that with Azure AI Foundry which we’ve talked about and access to generative AI but we can build your agents in AI Foundry. AI Foundry if you’re not familiar is like the AI Studio simply renamed rebranded revamped.

9:50 And now what we can do is we can actually use data in AI Foundry so if I’m in the AI Foundry platform there’s a connector in AI Foundry to connect to my Fabric OneLake data. So this is not necessarily a Fabric feature it’s an AI Foundry feature but really this is hugely impactful.

10:08 Mike I’m a beat on the street that goes perfect with this — I’m working on a project right now where they’re really heavily invested in Copilot and they want to do a lot more with Copilot but on their data. And usually the shift is when people say on their data with Copilot it’s on my SharePoint stuff, it’s the soft data.

10:30 And now we’re seeing this a lot more with your actual business data and again this is one of those things that you can say it I’m making the announcement but I’m not sure if everyone understands the impact of this because this is something that just has never existed for us as the business analysts to be able to do.

10:46 The business analysts to be able to do and to provide. This has always been the high level data engineering. So, this is a really big impact, I think, for us working with the rest of the business.

10:57 I’d be remiss here if I didn’t rag on our own podcast here a little bit in the last couple episodes. Remember when we were talking about where does the data scientists live? Where are they having fun? What experiences are they really using right now?

11:12 And one of those experiences I think AI foundry, that is the experience for data scientists. And what you’ll notice Tommy, this blog was announced, when was this announced? April 23rd, yesterday.

11:23 Okay, we were just talking on Tuesday about how we want deeper integration between what AI foundry is doing and fabric. And here we go, not only a day later, there’s a whole section here that says AI foundry is a platform.

11:36 This article it talks about professionals to host, run, and manage AI solutions with ease and confidence. That’s the platform. And it says AI foundry is now built in OneLake, are built on the same shortcut technology.

11:48 So we can land the data engineer things if we want, but we can expose our bronze and silver data right back to AI foundry, which is exactly what we were talking about yesterday. We needed this level of integration.

12:03 So this is, if we didn’t know about it, shame on us. We should have known about this. But on the other hand too, this is closing the gap even further between data scientists and fabric. And so this is an area of interest.

12:16 And I’ll also point out Tommy, I was looking inside Microsoft PowerBI, the workloads for fabric and PowerBI. And I’ll be dog on, if maybe I talked about it and it just showed up now. I don’t know how this works, but now I see SAS decision builder.

12:35 This is one of those, SAS is a solution that builds on Tuesday. Exactly. It’s now in a workload. So now I see a workload that is SAS. And the other one that I see that’s more, I think maybe this is a bit more analytical, and it’s STATSI.

12:55 I’ve heard of the name before. I don’t really know exactly the product very well. But it sounds like they’re also now playing with it and they have their own stat analytics directly as a workload item inside the fabric environment as well.

13:08 So these are the two programs we were literally just talking about these things. We’re saying hey, data science programs, you should be in this, right? You should be building parts of your solution that if someone’s paying for it, should just exist inside this world and it should be there. It should already be in this list of things.

13:28 So I really am feeling though like a lot of this is making more sense as I see other workloads appear. And to your point, Tommy, this is what we should be seeing. We should be seeing those heavy data scientist packages, that stuff that Microsoft’s not going to build, but other companies have this technology and they’re bringing it into the fabric ecosystem.

13:52 So, very pleased to see some other really data scientist centric tools starting to appear as workloads. Who knows? I think a year from now, we’re going to have a lot more options and a lot more things to select from in that whole workload space.

14:01 Listen, what it’s telling me? Since we’ve been talking about data science the last two weeks and these three things come out, Microsoft, I have three more requests. Please bring back the quick ribbon since I know you’re listening. We really want the quick ribbon back.

14:18 We want a little more keyboard shortcuts and anything else since we know they’re listening. Yeah, I know. Literally coming up the day after. Yeah. Why can’t Clippy be an AI that’s inside? It’s got to come back.

14:30 Yeah. So, I expect that for Tuesday for our next podcast when we’re talking about, hey, did you see that Clippy announcement? Yeah. Yeah. I want to see it.

14:39 So, let’s start. We have to say what we wanted it to do on the podcast and then it just happens. So, anyways, what sounds great?

14:49 Yeah. So, I will say this, a side note about Clippy real quick here before we get into the main session today. I believe Clippy has still some major brand equity. Yeah, Tommy’s got one.

15:07 Clippy still has some major brand equity that’s going on here. Tommy, for those of you who listen to the podcast, Tommy has just picked out his 3D printed Clippy that we have to give mad credit to Betsy Weber, who’s one of the MVP connection points for us to Microsoft.

15:19 She printed them on her 3D printer. It’s in black, white, and gray and it’s a Clippy. Tommy, I showed this Clippy thing to my daughter Mia. And she loved Clippy and she went to school and she printed a Clippy and now everyone at school is printing Clippy on 3D printers.

15:37 Not really. Yes, they are. They’re printing multiple. They’re like, “Oh, it’s so cute. It’s that little Clippy.” And like, “Yeah, that’s his name, Clippy.”

15:44 And I think if we do this right, Tommy, if we connect to middle school age students going through their tech classes and get them all picked up with Clippy, right? We just need to give them the 3D print files and then they can build it themselves.

16:00 I think we could reinvigorate a whole new generation having large language model conversations with Clippy. How amazing would that be? I’ll do you one better. And man, it’s going to be a segue.

16:13 I think we need to start printing out Fabric and PowerBI icons so they’re aware of what this is before they go into the real world. Oh man, printing out. Well, my kids definitely know what fabric and PowerBI are because I talk about it way too much.

16:28 Okay, so we have, you have three kids. We have six kids in the world right now who know what PowerBI is. Okay. Because here’s the thing. There’s not a lot of education in what we do. So, I think we need to start printing this out, getting it aware.

16:40 Exactly. I do agree. Ginger, you’re spot on. Maybe Clippy could get its own movie just like Minecraft got its own movie as well. That’s hilarious. Clippy saves the world, I guess, is the title of the movie or something like that.

16:53 Anyways, that being said, let’s jump into our main topic today. Oh, hear that again, Tom. We’ve got a guest today. We’re going to bring back Ginger Grant. Ginger, welcome back to the podcast.

17:04 We appreciate you being here for another deep conversation around random data science technical things, the water cooler of conversations. Hello and welcome back. Thank you very much.

17:17 Really appreciate being back on. So, and to come in on Clippy. So I’m jealous because I didn’t get one. And on that note, they’re amazing. Awesome. I believe Betsy would share the 3D printed file with anyone if you ask nicely.

17:30 So, if you have a 3D printer or have a school in your area that is teaching 3D printing, you can definitely get those things made there as well.

17:40 All right, that being said, let’s jump into our main topic today. Tommy, give us the story of what is our main topic going to start off with and maybe kick us off with the first direction here. Where should we take this?

17:50 So, yeah, this is literally we’ve talked about this a ton, Mike, in spades in the past. Data education and the space that we do or the lack thereof in colleges, high school, really other learning that most people go through the standard learning course that people go through.

18:07 There’s not a lot of it and we’ll talk about this too also. We’ll change the angle rather than just repeating ourselves and we’ll talk about this a bit with data science. We know there’s academia and quite a bit of education on data science.

18:23 But with the change with fabric, with Microsoft data platforms, what does education look like? Is it the same standard course, same theoretical learning that I do? Or are we going to dive a lot more to your point Mike with the different platforms and rely more on the platforms such as fabric, AI foundry?

18:45 Than we are the normal statistical-based knowledge. When we actually get to the real world, what are organizations looking for? But more importantly, what should a data scientist look for? How are they going to be successful in the future and where we’re at right now?

19:00 Now just, you mean data scientist, right? You said look at data science. Yeah, it sounds like you were saying like where is the data science? Where did you find, I cannot find my data science.

19:11 Yeah. Are you doing the data science? It would be good for the radio. But no, for the data scientist or someone who’s interested, wants to dive into data science, am I going to school or can I rely on the online education platforms or focus on those platforms if I’m going to be successful?

19:32 Yeah, I’m going to punt this over to Ginger. Let’s get your initial thoughts, Ginger. I think you’ve done a lot of time, I didn’t realize this as we were talking before the podcast. You’ve done a lot of time in academia and learning education pieces of this.

19:44 I’d love to hear your initial thoughts. Where do you think this is going, Ginger? And do we see this staying online, more online? Do we even go to a school anymore? How’s this going to change?

19:53 I think education in general needs a big overhaul in my opinion. But what do you think? Oh, yeah. This couldn’t be more timely. I’ve done quite a bit with education.

20:07 One of the things that I have done is I worked, shout out to Western Governor’s Association. They had this program where they basically took people who had a degree and then they taught them what they needed to know in a 10-week program.

20:21 Because it, yeah. A friend of mine’s son’s going to school right now and I advised him against it. And he’s learning art and I’m like, that’s great. You’ll never use it. He’s like, “I won’t.” I’m like, “Nope.”

20:32 He’s like, “Why do I have to take it? I’m paying money for this.” I’m like, “Yeah, it’s the pay-to-play game a little bit there.” It’s at some level.

20:43 So, I’ll go through my career just a little bit and what education, where I got stuck. So, I was mechanical engineering by trade. I did that for a number of years. I was not very good at mechanical engineering.

20:53 My joke in college was if I ever build a bridge, please go over it thinking about, a C-level engineer built this bridge for you. So, tread lightly. If I build a bridge, you may not want, if it’s really over a very big chasm, I would recommend taking a different bridge. Don’t use mine.

21:10 And then, so I didn’t do very great in the engineering world grade-wise, but I got out in the world and I actually was pretty well prepared. My school, I think, was just overly tough at some degree. And I was like, man, I’m just dumb. I can’t figure this stuff out.

21:25 I think it was just difficult professors and challenging for me. I didn’t understand how I learned or what was the best way for me to understand.

21:33 The best way for me to understand. And I liked learning things that resonated with my brain. If my brain liked it, oh, I was all over it. I would get really deep and really understand it.

21:41 But if it was just stuff that like, why do I need to know about this stuff? This is just boring to me. I had a hard time focusing on that. Then I moved into, I got into the business world. Did that for a number of years.

21:53 Then I got my MBA, aced it, 4.0, no problem. I’m like, okay, but maybe I actually can learn. But this was after I’d been in the business world for like 5 years or so. I’m like, okay, not bad.

22:03 And then we started talking about data analytics. And then I decided, oh, let’s go more towards data science. That’s what I want to become. I want to become a data scientist.

22:11 And my manager at the time said, look, you’re doing a good job in analytics. I don’t think you’re a data scientist. You need more education. Like, well, screw you. I’m going to go figure it out.

22:20 So, I went out and got a degree in data science. But I got halfway through and didn’t finish it because I felt like I could make more money on it by just doing it as opposed to finishing the degree.

22:33 And at the time I was leaving large corporate America and at that time most of the education programs in corporate America they would pay for your education. If you put in the time they’ll pay for the education. I thought that was great.

22:44 So I do firmly believe if you’re going to get higher education I think it does make a lot of sense for you to pair up with a company and work in a company or find a company that will pay for your higher education for things.

22:54 I do think that should be an added value to them by investing in you in that way. I think that’s another added bonus there. But I never finished my data science.

23:03 I basically got a cert in data science. So I’ll say I would say I have a half a masters in data science. I skipped all the boring classes like ethics and advanced algorithms and the stuff we really don’t care about.

23:18 But the everything else all the fundamentals was there, like that’s how I learned about Hadoop and data science and I spun up all these things. But I felt like when I was in the Microsoft stack and maybe Jinger you feel the same way as well.

23:28 I feel like academia is about 5 to 10 years behind where the industry is in technology at this point. I don’t know. Do you feel the same way?

23:37 Only 5 to 10. The issue is that’s why I was in data science. Yeah. The issue is that they’ve got vested interest in keeping things the same because it takes a while to write textbooks which is what professors write, make money on their students about.

23:55 Oh, now they can just vibe code a textbook, right? Just vibe book, just tell the AI to build me a book now and it should just, you should be able to be current now. Hey, take my existing book and update the new chapters for this, this, and this that happened this year. That should be easy.

24:11 You made a really good point. In my educational career, I didn’t think that I learned the most important thing that I needed to learn, which was how to learn.

24:21 Think about it. We’re talking about Power BI and Fabric, tools that weren’t even around. Power BI is 10 years old. Fabric is, it depends on how you count it, three years old, two years old.

24:35 You have to learn how to think, how to adapt. AI, all new stuff. So, I think the most important thing, which I think that schools are falling down on, is teaching you how to learn.

24:46 And I know that I learn different than I found lots of other people think. So it’s a real personal journey and that’s what’s required to be successful because things are changing all the time.

25:00 And I think that would be the most important thing for schools to do and they don’t do it, and colleges. Yeah. I didn’t ever get a masters. I’m the only person in my family who didn’t get one because I don’t want to take things that I don’t want to take. I like learning.

25:13 I don’t want to take things that I think are dumb. This conversation’s firing me up. I am charged. So, first off, you ruined my joke now because I’ve been saying for a while that the only people I want with a college degree are people who are going to fly a plane, check for diseases, or build a bridge.

25:33 And now you tell me the person with the bridge, should they, shouldn’t you shouldn’t trust the guy building bridges. Actually, honestly, yeah.

25:40 So now but to your point Tommy, there is the need for, like if we look at society as a whole, there are barriers to entry that you need to make sure there’s a certain level of professionalism that goes with that experience.

25:54 Like you do want some level of, we need to understand you’re not just going to throw a random lawyer there who’s just studied a bunch of YouTube courses for a year or two to go out and do something.

26:07 Maybe someone could learn but the information age is so interesting right now and this is one thing that I find very fascinating. The tech space is a lot more loosey goosey, I guess, with what you need to know and what’s relevant to work.

26:21 And if you’re going to be in the tech space, it’s less about where you get the education. And I feel like it’s more about what project work have you done? Can you point to things that you’ve built? Can you show me how to do certain things?

26:33 I think if I’m hiring people, I’m looking for individuals who are able to step in and show me examples of the products or solutions or things that they’ve built and then be able to speak to why it was hard, where the challenge came from.

26:48 To me, that really speaks a lot more to the knowledge that I want people to build. And I don’t, again, to your point, Jinger, earlier, academia is not catching up to this.

26:57 I don’t see, I see maybe some classes like MIT they’ve got some like data scientist 101 introduction data science but I don’t see them going out there and teaching about how to make a vibe coding experience in a large language model that’s coming out of San Fran and other tech spots across the US.

27:16 This doesn’t exist so how are we going to teach this in schools? The fastest way to get education now is someone talk about what they built, go out to YouTube, watch their video that can produce literally hours after they’ve figured something out, and learn that way.

27:31 That’s when does YouTube University get kicked off and we get certifications from YouTube University for different skills that we need?

27:38 Like, no, I want to push back a little on that, Mike. There’s a few factors here that I’m not going to say I’m on the other side of this, but here’s the thing.

27:46 First off, from college point of view or from higher education, they’re not intended to go through the latest and greatest. Obviously, there’s a standard rigor that it has to go to.

27:58 And colleges, even if you gave higher education all the resources in the world, they’re still not going to have a Fabric or Power BI course because it’s not the structure, right? Because it goes through the iterations and the changes that really no education does.

28:15 The YouTube thing, I get worried with, and I love it too and I learn a ton online. But the problem is compared to data science especially, like our worlds, the worlds that we’ve been in around the data analysts, well anyone can put a YouTube video together, heck we do.

28:31 But is that the best practice? And when I say best practice I mean, who do you trust, who are the actual voices out there that are actually saying what to be the most successful, to have the biggest impact for the widest audience, here’s the way to do it.

28:46 Man, you all know there in our space there are multiple ways to do a lot of things. There are a lot of ways to skin a cat. Some of them are good. A lot of them are good. Not all of them are great.

28:58 And that’s what we have to deal with. And I think part of what our job is, just navigating the waters. To your point, doing experiments, projects, etc.

29:09 Yeah. But data science is a little different. Data science has the academia behind it. It has theoretical knowledge. It has statistical, lots of statistics, right? It has that foundation.

29:23 Whereas our space, even data engineering too, yeah there are courses but there is no rigorous past since 1965 so to speak around this space which makes it very different.

29:35 So data science, go yeah that’s your problem and that’s always going to be your problem just by the nature of the beast. But me, well I have all these books that have been written in all this background knowledge.

29:48 So, I don’t think the argument initially doesn’t resonate to a data scientist. Well, there’s a lot you can go with that. Yeah, I got thoughts.

30:02 Yeah. So, one of the things too is that this couldn’t be more timely because yesterday there was an executive order that came out that wants to start teaching AI in K through 12 education.

30:17 And talk about people, we’re talking about colleges that are 10 years back. Yeah. High schools, they’re even further back than that. But I think the important thing I’m getting back to is how do you learn?

30:29 And there’s a really, there’s a company that came out that was helping schools do boot camps called 2U and they stopped. And the reason why is because they want something shorter.

30:43 So I figured out how I learn by studying for certification exams. And interesting, I’m not all in on certification exams, but anything that makes you, at least if you study, and I’m going to be doing some Microsoft Reactor stuff.

30:59 What are you saying? DP-700. Yeah. It’s like okay, I could learn anything. What can I learn? And also it does demonstrate, hey, I have learned something now even if you don’t pass.

31:12 So, because again, I don’t want to learn anything that I find boring. So even if you don’t pass your certification exam that you’re studying for, the fact that you have taken the time to learn is the most valuable thing in the process.

31:26 And I think that for data science instead of like, oh, you have a degree, I think it’s more important to have demonstrations, like maybe a cert for this or a cert for that.

31:37 And one thing I tell people when they’re looking for a job, what they need to do, and oh by the way, before you go look for that job, you better put yourself together a GitHub repo and say, I have done this.

31:47 And so that you can demonstrate what you’ve done and show people, hey, I can do this cool thing in Power BI or here’s an example of the kind of code that I have written.

31:59 And do it while you do it in time with what you’re doing it because if you’re like, I hate this job and need to leave, it’s too late. You forgot that thing you did three years ago that was cool.

32:12 Yeah. So, I do agree with that one as well. I would agree with you a lot on that one as well. There’s a lot of the…

32:20 I never learned how to learn. I never learned how to figure out what makes me tick, what makes me learn. And I find I learned a lot very well with doing things.

32:48 This is why I think code is so rewarding to me. I can do a little bit of work, I can get some help, I can build some things and get out of it and then I’ve learned something. And teaching things has also been a really big win for me.

33:18 Telling other people what I’ve learned has also really affected how I portray or figure out how to learn. So that’s also another area that I’m thinking, that’s another why I do what I do, right? This is why we have training.tips, right?

33:51 It’s this idea of where do the experts live, right? Let’s try and be a domain around some of these expert areas. And I think to your point Jinger, where does the education live? Certifications is a great place to start.

34:27 You can look for a goal, you can get things that you’re trying to understand and learn. And maybe the future of education isn’t about a full-blown degree in all areas, maybe it’s a lot about getting multiples in different areas and going to known areas of expertise.

35:03 I think Google did a program where they were pushing people through, not necessarily school, but they were just pushing a bunch of people through how to write code, how to build things, how to work with computers. I think they did a great job of investing in that space even though it’s not a formal degree, but giving a lot of people knowledge of what they need to do in the real workspace.

36:02 That’s a really great point because Google when they first started out, as did Microsoft, they required that you had a four-year degree. And Google wouldn’t hire anybody who didn’t have a 3.5. Wow. Obviously, all that’s gone by the wayside.

36:39 I have some shout outs to some of my buddies who work for Microsoft. I don’t know if it’ll embarrass him that they never got a degree, but to the guy that I know who lives in Florida, went to community college for a year and a half, and is now a thing at Microsoft because he’s a smart guy, more power to him.

37:16 Never incurred any debt. And oh, by the way, his biggest problem when he started working was the fact that he couldn’t get a rental car because he wasn’t 25. Oh my goodness. Because he taught himself what he needed to know to be very successful. And he’s very successful today.

37:53 You don’t need a degree to do what we’re doing. And Microsoft and Google and Facebook, people who used to say, “Thou shalt have a degree,” got rid of it. And they’re doing that in government now. They’re saying, “Do you really need a masters for this?” No.

38:27 I think that it’s changing to some degree. But you talk to a data scientist or you talk to those departments, they still require it. I don’t want to say backwards because there is a place obviously for the education system or some structure. Structure is good.

39:10 But data scientists, compared to what we do, I think we’re in an area — and I think data science covers it now — and we have to be aware of this. We’re in an area by nature of the beast that is ever-changing. And part of our skill set, which I love, is learning how to learn.

39:47 If you cannot do that, I don’t care how good you are in Power BI. I literally could not care less how good you are in Fabric or Power BI. If you have no ability to learn how to learn and keep up, you’re not going to do well. You’re not.

40:22 That’s just part of the program. That’s what you sign up for. Data science, if it keeps going, can the argument be made? Ginger, I’ll pass it to you here. Can an argument be made, where we’re going with AI Foundry, where we’re going with AI, where we’re going with Fabric, that data science is going to shift ever so slightly even to maybe not being so academic?

41:08 Well, there’s a really big thing out there about their history of it. I don’t know if you knew anybody who was a professional statistician, but they still have to take 12 certification exams.

41:47 Jeez. And a friend of mine was doing that. I worked at one point in time for an insurance company and he came over from the stats department because he said, “I took five exams and it really annoyed me because I know that everything that we’re doing, we’re doing it in Excel and we have all the formulas.”

42:22 So what’s the point of taking all these exams and memorizing these formulas that I don’t care about? So if you come from a background where you have to take 12 exams to be a certified statistician, then you’re like, well, black belt online thing? That’s not good enough because of what I had to do.

43:04 But what’s really important is knowledge. And I’m all for that. I think there probably should be recognized certifications in things that are important like data science. And I know that because it’s academic centered, it started in academia, they’re not letting it go.

43:45 Especially if you are being hired by data scientists, if you don’t have a degree in data science, they won’t hire you. Is that true though? If you have examples of projects that you’ve done or if you are able to speak to the things that they’ve seen?

44:22 I agree with you, Jer. I think to some degree there’s still a level of like, I still think you need a computer science degree, right? I think that one is very relevant. There’s a lot of basic things. As a mechanical engineer, I touched many different topics of mechanical engineering, but it was all very surface level.

45:03 It was all just a little bit deep in a lot of different areas. But when I got into the real world, that’s when I started building, working at a company. And that first job of mechanical engineering, once you got that first job, worked there for a couple years and moved on. The rest of the jobs I got, I don’t need my mechanical engineering degree now to do what I do. It’s totally irrelevant.

45:49 I’ve now so far removed from what I originally had been working in. I’ve gained skills and the work that I’ve produced is now visible to people. It’s very easy for me to show them, hey look, we run a podcast. We’ve got hundreds of hours of content talking about things that I know in this area because I’m spending time day-to-day building it.

46:44 I think school or academia is trying to condense that knowledge, give it to students so that way when everyone leaves we can all have the same level of understanding and knowledge to be effective as quickly as possible inside a business.

47:25 And what I see happening though is yes, that’s useful, but I think a lot of things are missed. And I feel like the world of teaching is getting further and further away from what people are actually doing in business.

47:59 I think there’s some really interesting MBA programs where only the professors are allowed to teach one or two classes and they must have a full-time job in a business as an executive or someone else. And I think those classes are way more relevant because you’re plucking people from the real world and bringing them to academia.

48:41 I teach at UWM. I’m an educator there. So at UWM I teach Power BI 101, the basics, like how to get into it, what do you do with it? I think that is a space where more and more people are wanting to learn. There’s a lot of opportunity there.

49:17 So I do see that you need to bring people who are actually working day-to-day on these projects and real tools, bringing them into education. Mike, let me add some red pepper on your take there and make it a hot take here.

49:48 So all right, a little Sriracha because honestly Ginger, Mike and I are dumb, but sometimes we say very smart things. One of them is to your point, I think we’re going to shift. Rather than we talk about the individual, what’s going to impact this is going to be the organization.

50:22 Mike, you mentioned this in the beginning. If I could do data science things and an organization can realize that and I don’t have the background, but you can pay me less because guess what? I have vibe coding, I have AI, I have the Microsoft data science platform. I may not have the whole background, but you’re not paying me what a data scientist costs.

51:04 Guess what an organization’s going to do, right? They’re going to pay the person that can still achieve 80-90% of what a typical data scientist can do. And this happens in our space now. The path of least resistance applies not just to individuals but to organizations as well.

51:48 It’s a double-edged sword Tommy, I think on what you’re describing there. Go ahead Jar. I’m gonna circle back to what we were talking about earlier about data science departments versus that group of people that works for the executive in itself.

52:23 Okay, this is where I think I was going to go too. I like where you’re going with this one. Keep going. So, in the past year or so I’ve taken a speech class and I’m trying to get to be a better communicator because here’s the thing.

52:58 The reason that Power BI embraced the data science thing is they’re like, “Yep, got results. What are they? Here’s my formula.” If you can’t communicate and show your analysis, your thinking — this is what Power BI is. It’s a visual tool. Look, see how the numbers are.

53:41 If you can’t communicate, it doesn’t really matter that you’ve spent six weeks in a corner and that you’ve got a really low RMSE score. It’s all about: I have solved this and let me show you, let me explain to you how. And that’s communication.

54:23 Which is what the people who work for the CEO do much better than the data scientists. And it’s why Power BI was created to do a visual display. Let me show you. 70% of people learn visually.

54:57 So, I’ve worked for people like, “I don’t understand what you’re saying. Draw me a picture.” Have you worked with that person? I have. And here’s the kicker — I don’t learn that way, which is why school is hard. 70% of the people learn visually. I’m not one of those.

55:33 Interesting. Well, I would say I’m definitely probably more on the visual spectrum of things because whenever someone’s trying to describe an architecture to me or images or a diagram, the first thing I’m doing is I’m running to a whiteboard.

43:04 I’m going to some whiteboard. I’m like, let’s draw it on the screen so I can understand what’s going on. Help me understand the pro. So me, I look at things and pictures and graphics are how I think about things a lot. And that’s the way I like to learn.

43:16 Experience learning method, like hands-on, because that’s I think that’s me. Hands on. I can look at things and I have to fiddle around. So, but no, continue. But that’s important to know.

43:30 Maybe Tommy, you’re more of a kinetic learner that if you don’t do it, it’s out of your brain. And Mike, you need to see it. I need to hear it. Yeah.

43:41 And being part of the 20 odd percent that are like this, education’s not designed for me because that’s like most people. So, everybody needs to know, hey, how do you learn? Figure it out.

43:57 Because more importantly, then you got to communicate, I have these skills. They’re awesome. Yeah. Because if you’re really, really skilled and nobody knows because you never go online, you don’t have a git repo, you can’t show anybody anything, then it’s like, well, that’s nice.

44:13 By the way, Ginger, I’m definitely going to use that I’m a kinetic learner because that sounds just classy. Go Google it. Yes, exactly. You know what I am? I’m a kinetic learner. So, yeah. Yeah, I like connects.

44:29 So I’ll also I like where this is going. And I think if I bring this back to when I think about my children, like my kids, what are they going to learn, right? How are they going to learn? What are they going to need to know how to do?

44:59 I’m already trying to prepare them today, right? I think there’s some missing stories that are inside education or particularly schools and maybe even higher education that some degree one of them I think is a story around just add value.

45:12 Right, if you add value to somebody else, either a business, a person, something, that by itself is enough to facilitate the ability to get paid for that value that you add.

45:25 Right, so I’m trying to introduce my kids to like what are we doing, don’t focus on getting a job but focus on where can we add value, that’s something you like to do, and how can we link that to adding value to other people, because that’s where you can link up the payment of that service or idea or work that you do towards something.

45:46 So one value you were trying to bring in here is think about where you add value and how you can monetize that part. So that’s one piece of this. But I’m also trying to look at my kids thinking like they’re going to interact with computers totally different than we are.

46:02 I look at Star Trek and I used to watch it as a kid growing up and they would always talk to the computer. They would say, “Computer, what’s the distance to the next solar system?” Computer, this, that, and the other thing.

46:13 Why on earth, so total random side note here, they’ve got they’re literally talking to computers and telling it to do these advanced analysis things, yet they still have someone in the driver’s seat of the ship turning the knobs to make it go. Like, the computer couldn’t figure out how to make the ship move forward. Like, how is that not? Anyways, total random side note, right?

46:38 If you can talk, we’re literally talking to the computer and it’s researching its entire search history of all information known to man. Oh, this alien species came from here and all these things and it gives you… Dude, did you just forget to write the AI module to let it drive the ship? I don’t know. Like, this broke the movie for me, man.

47:01 Anyway, sorry, side note, right. They can transport but they can’t… never mind. Yeah, but we’re getting closer to this so I’m looking at this going like this is what our kids will be doing. Our kids are already like, for one, school is banning kids from using AI to do things.

47:15 I’ve also just said AI is making it harder to discern what is truth and what is not truth, what is really real, what is just made up. So the media images you see are not going to be real anymore.

47:29 Your thumbnails on your LinkedIn page. I’m seeing more and more of them just become AI generated. And I’m getting teased. Like, so we were on a quick tips recently and Chris Finlan’s like, “Mike, your AI generated image.” I’m like, “No, no, that’s really me. That was actually me like 5 years ago when I actually have full black beard and there was no gray in it.”

47:45 I didn’t AI generate anything. It’s a real picture and I had a professional photographer take a picture, but he was like, “No, no, it’s totally AI generated.” I’m like, “Well, okay.”

47:56 Okay, but like this is, you’re going to have people question what is the standard? Like this is what we’re talking about. What is standard? Where does real truth? What does real standards come from? Who makes that decision?

48:08 And so I think our kids or my kids are going to have a really hard time discerning what is real and what is made up by AI things. And it’s going to have to be a better way of working with that. And they’re not going to write papers anymore.

48:21 No, they’re going to talk to a computer and there’s going to be an ability to stand back and say, I write the outline and then have the AI run and write part of it. So, what skills?

48:34 Okay, random other side note. We don’t use cursive anymore. Who writes in cursive? My daughter. They still teach it. No, they teach it. Yeah, they’re writing because they need you to write your name and sign it. But she already has better handwriting than I do.

48:50 This gets into how your brain works though, and that hasn’t changed. I put this article up in LinkedIn today and this is how I write. I have a Remarkable. Oh, I love the Remarkable stuff. Not because I wanted to necessarily do that, but I’m all about the data.

49:08 Yeah. And your brain, computers have changed everything. Your brain hasn’t. If you want to remember something, you better write it down. And they’re like, “Typing doesn’t count.” And I’m like, “That’s BS.”

49:20 So I always say, “Put up or shut up.” And so I tried it. I’m like, “All right.” So if you want to remember something, you better write it. And you better use a pen or a stylus to do it because you won’t remember it.

49:34 Now, here’s the kicker. They’ve also studied this with paper. And the difference in reading comprehension with an actual physical medium versus electronic, you lose 30%.

49:48 So your reading drops. Now, there’s so many schools now they’re like, “Oh, we just do ebooks.” Great. So, you are decreasing the effectiveness of learning because again, learning is what we need to do, by 30%.

50:04 And while I think it’s dumb that I have to print things to be able to better remember them, and I was all in on ebooks, and I have switched. Not because I like having, well I do like bookcases, but I mean having books around, but I want to do the best thing for me learning.

50:23 And your brain, that’s physical materials, that’s physically writing, no matter what they’re teaching. It’s funny you say that. I remember I got the hard copy of the Definitive Guide to DAX and halfway through I was like, where’s the search feature? I’m like, oh, this is physical, I can’t do that.

50:41 As we get near the end, I’m going to give you a mad scientist type of scenario here and I want you to tell me whether this would be possible or not. Oh jeez, we’re out near the end almost. I totally lost… We are flying through. I’m having such a great time. I haven’t even looked at the clock the entire time.

51:01 Oh, by the way, just quick note on the idea, Mike, we were talking about with the AI. There’s a feature in all those agent tools with like Cursor and VS Code. Talking to Ginger about this where before it actually does something, you ask it to write a log. So, it writes a log of what its plan is.

51:21 You could not decipher. So I basically one of the things we were doing was write a log of how I asked it to write definitions of everything in the model of every measure but I didn’t want it to write it off the bat. So I said how I want you to tell me your plan to assume what the definitions would be because obviously it only has the formula.

51:41 And I wrote this whole log step by step and then I asked it, what do you need from me? You would think this was a memo from your executive or someone on your team. So that’s hard to decipher, too.

51:52 So let me with all this in mind, with all this AI stuff and all the things we’re talking about education, I’m going to give you guys a mad scientist scenario and I want you to take someone on the Power BI or the Fabric team and I want you to make them a Frankenstein data scientist.

52:08 First off, I want you to think about this and tell me, do you think that’s possible? You can’t give them education there. You can give them online courses, Mike. It’s YouTube University and it’s all the platforms, but they can’t go to college. They can’t go to higher education. They’re not getting any of that. Certifications are on the board.

52:28 Do you think that’s possible? If you add the resources and that scope, could you take someone on the Fabric or even the data engineering team and can you make them a data scientist, a successful one for your company?

52:47 It depends on what space you’re in, right? If I’m in an industry like medical, maybe, maybe not. Right? If I’m doing something a little less pointed, right? A little less narrow focus. If I’m in distribution, sales, marketing things. I think there’s a lot of rich data there that people don’t know how to handle.

53:08 And also I think that market, that area, that market there is actually a little bit more open for self-learned, self-trained individuals figuring out what data science can do in that industry because there’s not a lot of experts in that space. But I feel like the space is a wider space and I’m going to be more accepting of saying okay let’s have our very sharp data engineer, data scientists or senior or principal data engineers, let’s teach them what they need to know and how they’re going to be able to leverage data science inside that space.

53:42 I’m a firm believer you don’t really want to hire a data scientist full-time. I think I’m more of the opinion of you want to borrow them for a period of time, bring them in, develop the tools, the systems, the AI pieces of that and then the data scientists can go off and find another hard project to go build somewhere else.

54:00 But then you can then teach your senior and principal data engineers to like, okay, look, the system’s been formed. Majority of that data scientist work was smooshing around data, cleaning things, getting prepared.

53:50 Data, cleaning things, getting prepared. And I’ve read around data science things. The best way to get better data science out of things is better data. Make it cleaner. Improve the quality of your data.

54:04 Improve the amount of data points you’re providing to the data science learning experiences for the AI. This is why large language models exist, right? They’re training on hundreds of millions of key characters and code like that.

54:20 That’s what this is getting down to. The data sets are getting so large that every book in the whole world is getting trained on these large language models. The ability for us to think that people are going to outperform that I think is minimal.

54:53 Right? I think the new world is going to be how well can we utilize that to our advantages. So if we don’t adapt and change how we think about these things, I think we’re going to get left in the dust.

54:45 Ginger, what are your thoughts? I really think it’s as much as computer — I was at a Microsoft thing. They’re like, it’s a co-pilot, not a pilot.

55:13 I think that you need people to do things like, let’s not ground our AI model using Reddit so that we can have it tell us that we should eat rocks every day. Okay, that’s funny, but it also happened.

55:48 Yes. So you still need people who have brains to go through like that’s a stupid picture. Where’s the guy’s other hand? How come he’s got two? That’s a discernment issue I was talking about earlier.

55:58 There’s no discernment. The AI can’t discern what’s real and what’s not real. So it’s going to have to be people. I’d rather be the one building the AI as opposed to the one using the AI.

56:09 Sorry, Ginger. Go ahead. Keep going. Yeah, but here’s the thing. This is what we need to do as people. We need to have the knowledge because AI just knows what we tell it. We need to figure out what’s important and how are we going to figure out what’s important to our organization?

56:45 And that gets back to communication. Yeah. Because it doesn’t do any good to measure something that nobody cares about. And if you can’t measure that you’ve got it, it’s a thing.

56:57 That was a thing in the 90s that Peter Drucker did where he was basically talking about if you can’t measure it then it doesn’t count. Well, he went too far. Yeah, but we always need to be able to demonstrate that we’re adding value.

57:29 Not just, hey, look, I can do cartoon pictures with our AI. And so that’s what we needed to do and we need people for that. I’m going to say I’m going to take two things that you said and I think I can make a Frankenstein data scientist.

57:44 So I’m going to start Michael. I’m going to start with you. The idea that I only need a data scientist for a period of time. I don’t need them full-time. That sounds like a perfect candidate from someone from my team.

58:18 Now, I just had a breakthrough as you were talking, Mike, because this whole learning how to learn thing too. Well, oh gosh, guess what? ChatGPT, Anthropic, Claude, and now Google have this research feature.

58:32 So if I want to deep think, deep thinking now. Yeah. And Perplexity too has an agent just like that. And if I want to give my person something to start learning data science or learn the practicals of what I need for this project, get a good prompt, get their curriculum out or what they’re going to learn.

59:14 Almost the learning how to learn thing is almost fugazi now. It’s gone a bit. We have that with our agents. So Mike to your point I can use AI and I can use project based things for them to learn and that’s that first component, that’s the part of the Frankenstein.

59:38 Ginger, you almost are confirming this with the communication. I find someone on my team who is a great communicator and likes data, that’s my candidate. That’s the core I’m going to use for my Frankenstein here because this is a great candidate where they’re going to be able to communicate.

60:14 I’ve given them a curriculum with this project, but for an organization you’re not paying them the same as a data scientist and that’s not all they do, but you’ve given them all the features here.

60:46 Gosh, this is cool, but this is scary. Like how quickly this education and where we can go with this. And again can they build a model? Like what have I tasked them to do in the data science world is some statistical significance, or is the model — if I give them the technology which is readily available now, getting more available with Fabric.

61:29 And I can give them the education and the path to learn and I know they’re a good communicator. You tell me what else we need. Find that person. Good luck.

61:58 This is the same problem I think we have Tommy, also the same problem we deal with when looking at how do we grow our team, where do the skills come from, what skills do we think we really need.

62:18 And so if there are people that we can cater to our business’s needs in certain specific areas, that’s what we should cater them to. Is it a difference of can I get a couple team members or two and get the knowledge that we need to move the business forward, or do we really need a full-blown data scientist to come in and actually do something for us?

62:57 And maybe it’s the idea of you just need an outside perspective for a period of time to understand what skills does our team need in the data science space that we can go pick people out from our internal team to go do this.

63:29 I just feel like there’s going to be an interesting blend here of what this looks like. And again, I definitely know I don’t want to be so hardline about this. There are industries that do require data scientists to be involved in and part of organizations. I think that’s a thing.

64:05 But I also look at smaller and medium-sized businesses and look at the scale of Microsoft. Microsoft should be hiring data scientists for all the things, right? They’re going to do that. They have the money. They have the capital and the resources to do those things.

64:38 What they’re going to do is big companies understand there’s a lot of data science that can be distilled down to a program, an app, a solution that they can build. This is why AI functions exist. That’s AI, but we don’t need to build it. We could just literally call the function now and then the function just works on top of our tables of data.

65:20 So there’s going to be a lot of these low-hanging fruit use cases that solutions will provide answers to and I don’t need a data scientist to come in to help me transcribe a video into text anymore. Done. Known quantity.

65:56 Right? So again right now I see this as the data science space is trending the trend of data analytics in the same way. It’s not a commodity yet, but we’re getting to the point where it’s starting to become more of a commodity. And then everyone will be trying to become a data scientist.

66:31 And now with coding and writing apps, that’s also becoming a commodity. I think Microsoft had the wrong vision here a little bit. They thought Power Apps was going to be that solution for people to build oodles and oodles of apps. And so that was a product they think they built.

67:07 But I think actually in reality, people don’t even care about that now. Now it’s just about building the app from scratch and vibe coding a majority of the app. That is becoming a commodity in my point.

67:37 So great, everyone’s going to build an app now. I can build an app. I did a project where I rebuilt an entire app in like 6 hours. I was trying to add Google Analytics to my project. I couldn’t do it. So I just rebuilt the whole thing and started with Google Analytics as the starting point.

68:13 Like it was just crazy. This is the excitement that I get around this new world that we’re going to be stepping into. I want to make sure that the generation behind us is going to be equipped with the right skills and know what to use.

68:34 So that way they’re the ones either building the next gen of stuff and not being the ones only consuming it. Right? So be the builder, not just purely the consumer. That’s maybe my final thought.

68:39 All right, with that, let’s go ahead and wrap. We are at time. Thank you very much, Ginger, for joining us. This has been a wonderful series on data science. I’ve thoroughly enjoyed it and today went by super fast and super quick. We really really appreciate it.

69:11 Listeners, I hope you’ve enjoyed our series with Ginger here. She’s been a wealth of knowledge from a space of data science. And already just our conversation here alone, we’re seeing features come out in Power BI and Fabric that are already becoming more data science-centric.

69:48 So, Ginger, yeah, maybe we should have topics around Power Query and maybe they could fix that stuff for us at some future point in time. We just need a couple episodes on it and all of a sudden blogs start popping out and things start happening. So we’ll hopefully that’ll come out later on.

70:21 We’ll do some more things. So thank you Ginger. I really do want to say we really appreciate your time. Thank you for joining us, your perspectives and your outlook on life and your learning here. I love learning from you. You’ve been a staple in the Microsoft community here for helping educate and learn, especially around the data science space.

70:58 So we really really appreciate your time here. Thank you so much. I really enjoyed it. Yeah, you can find Ginger online, LinkedIn. My closing thoughts going to be my thank you to Ginger. Just like I said, it’s been awesome having you. These conversations again, good conversations, just enlightened projects.

71:37 They think of, oh, we could do this now. And that’s exactly what the last two weeks have been. Thank you so much. So, thank you. Awesome. With that being said, we will give it a wrap there.

71:47 We do these episodes for free. We don’t charge for them. There’s no advertisements in them. So the only thing we ask you as the listeners, if you liked this and you had a good time listening to these series around data science, we really do appreciate it. Please share or give us your comments, share the video and add your thoughts.

72:21 What do you think about this? Is Mike totally wrong about data science and where this is going to fit? Is the next generation going to do something totally different than I described? Maybe it could totally be that way. So that’s totally okay. We’d love to hear your opinions.

72:53 The community is rich here. Tag us in the video that you post and let us know because we’d love to engage with your comments and across the community around this topic. We think it’s extremely relevant.

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73:54 Do you have a question, an idea, or a topic that you want us to talk about in a future episode? Head over to powerbi.tips/podcast. Leave your name and a great question. And finally, join us live every Tuesday and Thursday, 7:30 a.m. Central, and join the conversation on all of PowerBI.tips social media channels. Thank you all very much, and we’ll see you next time.

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