Ep 42 - Jansen Sullivan and Victor Anjos - Data education deserves better
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Episode Summary
“I you’re largely doing really strong reporting and visualizations that tell a story and deliver business value. You’re going to win and have a really good career. All throughout. Those are positions that are largely needed and hugely available as opposed to the data scientists that everybody believes.”
Victor Anjos
Jansen Sullivan
This was an episode where Jansen Sullivan and Victor Anjos of the All Things Data podcast by 1000ml and I talked about data education and some of the gaps that we hope get resolved. A lot of progress is being made but lots more needs to happen.
Jansen, Victor, and myself all believe that traditional analytics education moves too slowly and is often too focused on checking boxes more than creating good data analysts, data scientists, data engineers, data product owners, etc. There is a lot of great efforts that traditional universities are working to adapt but the system lacks the agility.
Beyond the traditional education approach, the analytics space is a space that requires real projects and experience with end-to-end problems to advance people’s skills. More apprenticeship learning is required and love to see more organizations recognize this and support this.
More about Jansen, Victor and All Things Data
All Things Data Podcast: https://1000ml.io/category/podcasts/
1000ml Website: https://1000ml.io/
Jansen’s LinkedIn: in/jansensullivan
Victor’s LinkedIn: in/victoranjos
Data Able Ep 42 – Data education deserves better
Jansen Sullivan and Victor Anjos of 1000ml
Machine Generated Transcript via Descript
Dave Mathias: [00:00:01] Hey everyone. Welcome to another episode of Data, Able. This is Dave Mathias, and I'm excited to be talking with you today. Now give a little background before getting to the episode today. First of all, okay. This was recorded on a Friday afternoon. So realize maybe didn't have the highest amount of energy as I should have had. So apologize for that from the start at the same time, it's a topic that I am really passionate about and certainly a lot of things that I do are around, coaching and training and trying to do it, in an applied sense. And so one of the things that we talk a lot about our around this type of applied learning cause Jansen and Victor, they have a company up in Toronto and they focus more on the technical side of data teams and they do that applied learning while certainly at, Beyond the Data, we focus more on a more business side persons or when it's technical aside people, it tends to be more on the soft skills or success skills of those individuals. But again, always in that applied instance.
And so really we're both passionate on how education has been done and how it can be done better. So that's certainly one of those topics that we talk about. Uh, but love to get your take too. Cause a lot of people are undergoing different forms of education, whether it's through their kids, whether it's, something where they're experiencing because they're trying to love all up while you're at home. Love to hear what has been going great or what hasn't been going so great. And of course you can email me at dave@gobeyondthedata.com.
And let me know about that. Speaking of training, actually have some upcoming training and you can always check out all of our training at gobeyondthedata.eventbrite.com or just go to our website at gobeyondthedata.com. One of the things really excited about is the upcoming Data Storytelling Boot Camp which starts in October and it's a five week boot camp, part-time, cohort model that's interactive, but of course virtual. Because you know what, we're not going to be in person.
Okay enough about that though, getting into one of the things that, how I had met Jansen through a service called lunch club. And I wanted to let you know about this. Cause I think it's something you might benefit from in COVID times. So lunch club or their website is lunchclub.ai is a service where they match you up with like-minded folks, whether it's, you're looking for other folks in product or data or other, some other interests some community cause whatever it is. They'll try to match you up based on a few of the things that you have identified. So that there's good connection with that other person. You have a good conversation.
And, in that conversation it's a video conference. Every conversation has been fun and interesting, highly recommend it. And if you're looking for a way to get out there, check it out. It's lunchclub.ai. ,There's no paid promotion to do this . I had benefit out of it and I wanted to share with you. And I want to thank Jasmine RuKim, who is a former guest of the podcast, and she's the one who actually introduced me to it. So thank you, Jasmine for doing.
The other thing to mention before getting going today is this is actually a dual podcast recording because Janssen and Victor, they have their own podcast called All Things Data, and there's actually a couple of those out there. obviously this is the Data Able podcast. So we're doing a cross podcast that we both thought was a topic that we were both passionate about. And so we thought it would be an episode, that'd be good for both of our audiences. Do recommend you check out their All Things Data podcast, and subscribe to that. And we'll of course have the link in the show notes. And of course, thank you for listening to Data Able.
So now let's get into today's episode.
Jansen Sullivan: [00:03:53] Welcome to the All Things Data podcast combined edition. We're here actually with Dave Mathias, the data able podcast hosts. So we're combining our podcasts this week.
Dave Mathias: [00:04:02] Awesome. Great to be with you. we're socially distanced between Minneapolis and Toronto. So we're good there.
Jansen Sullivan: [00:04:09] Yeah, that's awesome.
No, thanks. Thanks for joining. And thanks for setting all this up too. I know it was a bit of back and forth, but, and we get to use some new podcast tech, so that's kinda cool.
Dave Mathias: [00:04:17] Yeah. It's always good fun. And talking about leading edge things, so we're going to talk a little data education, analytics, education, but tell us more about what you are about and, and I'll do the same and maybe go back and forth a little bit there and then go into the analytics education.
Victor Anjos: [00:04:32] 1000 ML really started as a data consultancy way back in the day. and as we kept going, we noticed very quickly that, All the people that we'd hire or work with and whatnot. they, it's not that they necessarily lack data literacy altogether, but they didn't have the calluses of a data professional, who's been in the world for various time.
And we're purporting to be, excellent data people, whatever that data people means. it could be data scientist or analyst or AI practitioner, whatever. And we kept creating, programs such that we could, it would take them from entry level emerging or what have you, and make them into fully fledged, fully fledged and fully rounded, uh, data practitioners that were super useful to us.
And very quickly, we found that a lot of our contemporaries in the community really wanted, some of our staff and wanted what we were doing. And it was a bit of a, of a moat that we were building around the business that allowed us to, scale and have a, I guess, Lower operational costs across the board.
So that led us to a world of let's create this for other people as well, and let's make the products, the people that we're training up. So that was the crux of a thousand of mouth. And we keep doing, consultancy work and project work and product work. Cause we have a, I guess, a dearth of data practitioners that come through and a lot of them are great that we want to keep them.
So we keep the ones that we really want around at times. when we have projects for them specifically, and if not, we help get them placed into jobs somewhere in the community. And now it's actually been worldwide. Really
Dave Mathias: [00:06:13] cool. That's a, almost like a sort of a apprenticeship like model, whereas the hand in hand, learning together, little old school, that's coming back more in popularity in some different cases, right.
Jansen Sullivan: [00:06:25] That's right. Well, and I think we're big.
Yeah, no, we're just saying we're big proponents of, hands on and, learning from textbooks is great. I think theoretically, but, uh, at the end of the day, employers and ourselves, we want people that can do things and actually operate and execute.
So we think that the fastest way to learn, especially in the data world, there's a lot it's trial and error. It's a lot of like experimentation and tinkering around. So we encourage people to do that and that's the way we deliver curriculum also.
Dave Mathias: [00:06:53] Cool. That's very cool. And somewhat, even though we both saw problems in the spaces we're in, we're sorta both saw some, problems in different areas, I think.
And so for us, we certainly, we focus on more the business side of data and the challenges that are there. So less the data scientists or folks like that, and more the product managers, the finance person, the marketing person, the HR person, and getting that data literacy up across the board, along with some of those, more success skills for the technical people, but we don't really get into the more rigorous training of more technical people, other than some of those success skills.
So I think we have some overlap that we also have, some differences where we're playing in
Victor Anjos: [00:07:38] that's for sure. And we, we keep noticing all across the board data literacy is very top of mind for a huge number of organizations. I don't know if it was a catalyzed really because of, the COVID and the pandemic and people working from home quite a bit and having to do more, maybe stress themselves a little more than they had in the past.
Uh, but yeah, like we, we keep running into conversations over and over and over again, with all kinds of organizations who are, uh, you know, basically telling us we are not literate enough and we really need to understand what kind of insights and what kind of power data really has.
Dave Mathias: [00:08:12] Yeah, I was in fact actually recording another data able episode just this morning, with these, chief data officer of a company out East that sort of like a medium sized software B2B type company and him and I were chatting.
And on this day of literacy of front is as one of the components and really the data translator role and data storytelling role. And he looks at it as there's going to be those folks that are uber technical and that are really good at it.
Yeah. It does sort of diving in and, being the rule really strong data scientists. And then there's this huge Number of people that are going to be, data storytellers, data, visualization, data literacy, and just with really strong domain expertise. how do you get more of that.
I just got off the phone with somebody that's looking to make a career change and she's done a lot of things. In fact, she was actually working for the Canadian Consulate at one point, here in Minneapolis and we're chatting on the first thing that she picked up, cause she was like, I want to leverage my creativity, but I also like the field of analytics and I was thinking about data storytelling and data visualization.
So the first thing she picked up was a Coursera Python course. And I was like, okay, I don't know if that's the first thing that you, why don't I dive into? I mean, that's great. Like if you're curious, but how do you, especially because you get a lot of folks that are career changers, right. And how do you.
Help educate and transition somebody that's maybe a career changing person where they weren't, let's say they want to jump into the very technical or maybe even more of an analyst or a data scientist versus like somebody that's coming out of school or they're pretty young in their career.
Jansen Sullivan: [00:09:44] Well, I think there's a couple of things, like first off, I think it's the hype of marketing. so the idea, everyone's saying like data scientists, Python, these are all the sexy things you need to be doing. And, sometimes they're just not, that's not the thing they need.
You know, and I think it's, you're, you're right by just kind of asking the questions and it's not necessarily coaching, but just kind of informing people that there are other roles outside of data scientists that are in the data world. But, you know, once you kind of get past that, I think a person who's been in industry for awhile career change, but it doesn't necessarily mean industry change.
Right? So these folks who've been in the game for a while and seen, the problems that, occur in their industry or at their business are extremely valuable. Right. I mean, it's like taking someone who's a great operator and then adding the data layer onto them. Right. So they know a lot of things that, you know, People like the general population don't know, so they can apply that kind of specialist lens.
And usually what I tell those folks are just like, what, or I'll ask them questions around the idea of like, what kind of problems do you see, or what kind of questions would you like to answer if you had all the data in the world type of thing, and then go try and solve that, you know?
Dave Mathias: [00:10:57] That makes perfect sense. I think people that often times. Want to do these career change. There is this article I'm still waiting to publish it. It's talking about the whole unicorn view of data scientist and product management.
I think both of those roles have this misconception of this is just like the roles, the two types of roles that are so sought after nowadays, other than being like the billionaire entrepreneur. Right. Uh, so I think that the question is really what's motivating. Why do you want to be that role?
What do you see your skillsets that line there? And like, what's your passion? Um, I think too many people are driven by the hype nowadays. And we think , oh, I can learn anything on a weekend or in a bootcamp. And certainly some boot camps can learn a lot, but that's only going to be like the very cursory level of your career as to be good at anything. It takes lots of time to be that polished, whatever it is.
Victor Anjos: [00:11:50] People are often drawn, just by the salary numbers that they see or have read in some magazines and publication, the economist, whatever it is. Right. And they're like, Oh wow. Data scientists make so much money that's exactly what I need to do. And it's also future-proofing me. So they jump in and think, well, I'm going to be a great data scientist, especially at the entry level where it's really tough to get hired as a data scientist.
Right out of school and it's really tough to get hired without the real experience. So oftentimes these people need to, think through, well, how do I get my foot in the door and do something that is very close to, or adjacent to data science and work my way into that data science workflow. So, as soon as I possibly can, so I demonstrate value and to give me more responsibility.
Jansen Sullivan: [00:12:36] I think it's also understanding the roles like data science is one of those things that, you know, it's one of those terms where you could just say like IT or developer, there's so many things. You could be in research. So you're just looking in models. You could be in the engineering side where you're productionizing, you could be in the analysis side.
Right. So I mean, it all depends what you want to know. And I think data science is this like catch all term, but. You know, we're seeing this now where you're getting new role titles or new specialization titles of like machine learning, engineers and data science, researcher, data, science analysts, things like that.
So I think, people don't know what they're getting into, so they're just like immediate, I'm taking Python. So they don't know the type of problems they want to be solving or like what they want to actually be doing. They just know that it's data is the new goal. Then I need to go mine for it.
Dave Mathias: [00:13:26] Yeah, that's a great point.
So for you guys, when you're talking with somebody initially that first time, what are the key questions that you're asking them to understand, okay, what is this person's motivations and how much do I want to invest in? Cause you're with an apprenticeship type model, that's a fairly significant effort from your end.
And also from there end. Their end and certainly if they're not doing great work, it's reflects on you, especially when you're doing like the consulting type work. Tell me a little bit of how that process goes.
Victor Anjos: [00:13:57] Well, generally to look at quality and ensure that we keep quality high, we generally work them through the model is we work them through, a ton of, projects all the way through this experiential learning, over a longer period than a bootcamp would, all the, while they're also being mentored.
So we have staff mentors who, sit by the wayside. So it's not a selfless program, but that would be pretty crazy. To try and get people to really do that. Um, that's more akin to a fellowship and that usually works or can work for, postgraduate degrees. But if you're, and I don't know, belittle it, but if you're a lowly, batch their degree, or even an honors bachelor, you are possibly not of the same rigor and critical thinking that, somebody with a postgraduate degree might have. So you need a little bit more hand holding and a little bit more showing you the way. So you were completely right at the start where you sort of juxtaposed it to an apprenticeship because that's really how we work.
It's the mentor is the one who is paid well and Israel charge of delivery along with an account manager for our projects. And we give it the rigor and professionalism that we do on the consulting side. It's just that much like, if you were talking about the Deloitte or Accenture or any of these big consulting houses, Um, you generally don't get the most expensive people doing all the work for you.
They're usually the network and they're the ones selling you the work rather than actually producing all the deliverables. They often will package it up and give it to you at the end. It does have a professional sheen to it, but. The, the majority of the work is often in source to, the, I guess the junior staff.
Jansen Sullivan: [00:15:42] Yeah. It's all about billability at that point, right. Where you're billing out at higher rates, but you're having juniors take on that, take on those roles. But I think in terms of like questions, like where the types of folks that we want. Was that the original question?
Dave Mathias: [00:15:56] Yeah. It was just when you're trying to identify is this a person that I want to take on at the beginning, because I think that's a big commitment on your end to go.
Right.
Jansen Sullivan: [00:16:04] And I think, the big thing is that the questions I am most, I guess concerned about like, you can pass a quick math test. You can pass those. We can test you technically, but it's the curious folks, it's the people who are wanting to dig, the people who aren't afraid to make mistakes.
The people that are, a little more, more, a little less risk adverse, I guess. Uh, but the people who are willing to try things out, like you can have a lot of practitioners, like, I mean, we're not looking for unicorns and we're not looking for. People who are necessarily like, great, great operators, great storytellers plus great technicians plus you can't always find that stuff.
So, we do look for people more about more around the idea of like curiosity, and people who are willing to learn. So, I mean, there's a lot of questions around that where you can just ask people how they would figure something out. No, just, I want to see how you think through problems. Um, and people who, people who want to, well, you can admit and just say like, things like, you know what I don't know, but I'll go find out versus trying to BS your way through something to make yourself like an authority.
But, it's, it's around the idea of just, uh, being an Explorer and like, Okay with okay. With, I'm not coming up with the right answer right away, because I mean, at the end of the day, data science and data analysis and technology and stuff, it's a lot of exploration. It's a lot of trial and error.
You don't know the answer up front a lot of the times. So you kind of go in half knowing and then you'll figure, you'll figure it out along the way. But I mean, you do have methodologies and frameworks that you work through, but it's not like a typical engineering problem where you know what you're going to do, you're just like, here's the problem, you know, I need to make code or I need to make a bridge or I need to, whatever. I just, these are the things, these are the constraints I work in. And then I just kind of build it up, you know?
Dave Mathias: [00:17:58] It makes lot of sense. And so one of the things I've seen a lot, I mean, certainly there's tons of Universities and we had chatted on this a little bit before where there's so many universities doing so many programs in this space, whether it's data science or analysts or engineering or whatever you want to call it, different programs and a lot at the master's level or, or boot camps, even things like that.
If you had the power to go into in that type of program, how would you change maybe one of your local universities and adapt their program? Would you try to do the whole program as an apprenticeship model? Or how would you sort of restructure, uh, universities, uh, education in the space?
Jansen Sullivan: [00:18:38] Well, I guess one thing is that, universities aren't really open to the idea of traditionally anyways, are open to the idea of like apprenticeship, it's very, everyone's going to say experiential learning, but I find a lot of like, some universities are doing this decently where they're having more of like applied or co-op programs and stuff, but actually to teach it, um, I think it's, they need to be taking more like case based learning, uh, Bringing in real problems and real data sets, not just like here's a public data set of, 5,000 records, perfectly formatted. So yeah, you have to do, you can do this piece analysis.
It's like, you have to understand the rigors of, doing these things and going through the pain of data. Um, What's the word, like, uh, the data engineering and then yeah, like the, the wrangling and stuff. So, and, but the thing is that should be a course because right now, the way university teaches is in discrete chunks, right.
You would take a data engineering course. Then you would take a data science course. Then you might take like a model course. And then, but at, in the real world, all of those are combined to solve a problem. Right. It's like taking a course in cooking, but instead of cooking, you just take a chopping course.
Then the next course you take is just a boiling course. And then the next course you take is a frying course. Like that's not how you cook. Right. You cook all, you have to take all the skills at once. So, that's my thought, like if you were to do something, it's more of like you might spend a semester solving problem and going in and working with, industry to do it.
How about yourself? I'm going to flip that back to you.
Dave Mathias: [00:20:16] Yeah. I would like to have a lot more experiential and I would, I think all learning well, so there's the master's level versus the bachelor's level. I would separate that out a little bit.
I think. A lot of the master's level learning that I've seen is so focused on checking boxes that are perceived to be important in organizations and checking as many boxes. It's sort of that quantity over the quality that I, get good at a couple of things and identify. What those people are passionate about what they're good at.
Um, like you said, we're not trying to create unicorns here. So I think for me, it would be spending a lot of time at the beginning to really know the student and then designing an education. That's going to be good for that person, him or her, that is going to have a significant experiential component.
I'm not against some of those courses that are gonna help get you along. But I think it's also the same thing as like, I'm not against doing, the hackathons or those types of things and like those data hackathons are great. They're good energy things, but it's not on point to a normal project that you're going to do, just like, if you want to do Kaggle competitions hey great, go do it. Um, but you don't have that interaction with the business and that asking questions and those types of things, if you're a data scientist or data analyst are such core features. So, bringing in real projects, the tough thing is, and in fact, one of the universities just was reach out to me saying, Hey, we're, we're looking for companies to do a part of this capstone. We had a couple of companies drop out and we need some companies to fill in and asked if I knew anyone. But it's the capstone project, right? It's the last thing that the students get to do. And it's like, Hey, if like, actually really apply it. Well, I think maybe that first semester there should be understanding where those gaps are for those individuals and what that person's good at, and then trying to accelerate them as they're doing multiple of those types of capstones throughout the the effort. And I also think the other question is on domain knowledge, if students are coming in with a significant amount of domain knowledge they're going into a master's program and they have 10 years of domain experience and in healthcare or an oil and gas or somewhere like that. That's a very different perspective and how you can lean in with that student and how education should be versus okay this person's just sorta very much on the path finding. I would question whether masters make sense for those students though too.
Jansen Sullivan: [00:22:30] yeah. And I think one thing is that they really understand, like when you've been in the workforce for a while, you're very, you're. You're more sensitive to your learning needs, right?
When you're coming out of school, you're just learning to learn. You're still in that kind of like head space where you're like, I'll learn anything. You know, it's not, it's not as targeted or focused. Um, but yeah, as an adult, like a more mature learner, you know, why you're there, you know what you're, you know, you're kind of taking stuff for the next steps for the most part.
Dave Mathias: [00:22:59] Yeah, that's a great point. So have you, you seen any one else doing so the apprenticeship model obviously has a long history in many different forms, and certainly there has been more of the emphasis on boot camps and things like that. But have you seen anyone else sort of taking on the charge, something similar to what you're doing in other areas of the world that you're feeling happy that things are progressing because the tough thing is scale, right? Like the model you're doing from a scale perspective, it's tough. A, a lot of people sort of buy into this type of approach and make things better.
Jansen Sullivan: [00:23:33] Um, we haven't seen anyone else doing this model, uh, in our. Well, not necessarily just in our backyard, we don't, we don't know anyone in Canada that's doing this, but we also don't know anyone really globally doing this. Uh, but there are folks who are doing other, um, doing this in other verticals, right? So, you know, there's sales and operations, um, people that are doing this for startups.
Uh there's I believe there's like a marketing one. That's similar. So, I think this model just lends itself to , the times right now people are looking, you know, if you're looking at it, job experience data science is not a first job. So if you see this as data scientists, there's always like the two to five year experience type of gap there.
So, or request even for junior. So how do you have a junior that has two to five years? That's not a junior anymore. I feel that data science like is being viewed as this like transitionary or transition type of career, where you started out as an analyst, then you might someone at your company gave you a shot and you move up into, data science, um, typically, but, scale is another issue, I guess like for us, we're looking to validate the model.
I think it's been validated, scale will be kind of the next problem that we tackle, but I don't think this model is, is not scalable. I mean, it's like boot camps, boot camps. I think we're at the same. Scale problems where it's you need instructors and you need, you know, mentors, like we call them mentors because we look at this as more as a mentorship program.
Um, and you still need people to deliver it. You know, this isn't a Coursera, like where you're just watching videos. I think that is a, that's a model for certain people, right? I mean, I think different learning methodologies are for different folks. I mean, some people need the in class, some people need a person, some people just want to do it on their own.
Right. Some people need books, some people need videos, some people need problems. So we're going after the people that want the experiential learning, not just like the classroom stuff.
Dave Mathias: [00:25:44] Is there anythings that you've seen out of COVID or other things recently that make you, excited or think that there might be finally a push to have better disruption in the education space?
Victor Anjos: [00:26:00] For me, I think it's largely been, uh, just sort of the, the backlash against universities generally. And I'm sure you've seen it as well. Like students are all up in arms as to why am I paying you this ridiculous tuition at this point? Uh, for nearly the same value. That I would get from some online learning platform, whatever that is.
Right. So I think there's a bit of disruption coming. I think the model has largely been flawed, not quite broken for quite a long time. Um, and in a world of capitalism, like it really has, it's a business. Let's go make money rather than, in a very altruistic way. Let's do the best for the student. Right.
So, I think it's very plausible that, we see a shakeup in it, but I don't know that the entrenched, colleges and universities are going to largely change unless they're losing money. So it's all a money game for them. And we'll have to see how this all shakes out in the next, I dunno, three to six or nine months, or however long there not allowing students into the halls of these higher learning institutions.
Dave Mathias: [00:27:06] so what are you most hopeful about with education that the data science or the analytics space?
Jansen Sullivan: [00:27:14] I think a big thing that we're seeing is just digital literacy. People are starting to really kind of put money into it. I, and people, companies are starting to put money into it. They're seeing it as not a competitive advantage anymore, but they're seeing that as table stakes, it's, it, I don't want to say, you know, alarm sound alarmist and saying like, this is do or die, but for companies to move forward and kind of be modern or modernized, uh, you need data, you need data to drive that way.
And that doesn't mean that, every one of your company needs to be a data scientist, but it's that ability to use data, to make decisions, ability to interpret data, the ability to form arguments with data, you know, the ability to discuss data. And it's not, I think it's inherent to everybody. I mean, a lot of people are like, Oh, I'm so bad with data.
I don't know this, that and the other. Um, but at the end of the day, I'm like, if you can look at your grocery or a seat and tell me, uh, how much did you spend on vegetables? You can do that. And you've just done data analysis, and it's one of those things where I think it just needs to be reframed.
And I think a lot of people are doing it every day. I think everyone's doing it every day. You just don't know that you're doing it and taking that in and applying it to the business context. Right. And just like how you're making decisions, how decisions are being formed. Right. Not just making the decision, but like, why am I making this decision?
And then getting into that idea of like evidence and using, any of that information to help you drive your business a little further. So I'm really excited that companies are investing into that or looking into it at least like it's. It's conversation now, at least. Um, and universities are pushing for this like machine, like the academic side of it, which is like machine learning. Um, you're seeing a lot more of these like master's degrees and analytics. Well, not, I mean, I don't know if they're good or not, but universities are jumping on the trend in that bandwagon and that it's pushing the profile and people are aware of it and people are talking about it.
So I think that's important, even just being able to have the conversation. Yeah. I'm sorry. I think it's a good thing. I think, if you're digitally native, we've talked about this on our own podcast, but if you're digitally native, this is an important, um, this is an important thing to look out when you're forming companies. And companies that are doing it right off the bat are growing at exponential rates. Like those are the companies that are killing it because they're able to use data right from the get go.
Dave Mathias: [00:29:50] That's a great point. And certainly. I think the sort of, "yes, and"ing that as I'm a big fan of improv is more leaders are even willing to start asking those questions and get a little bit better with data literacy.
And I'm surprised at how many leaders are, although getting to, I had, uh, I've had a couple of executives recently say, Hey, do you know a good Python class I could take? Which I'm always like. Okay. Like, tell me more, why is it just total mental curiosity, but yeah. Uh, but I think there's, there's at least just a broader recognition of the importance of this.
Like you say, across the board and taking a step back. Although, one thing I have wondered with COVID is. Cause you would think that, Hey, we're looking at more visualization and data and things like that just on a normal basis. I think from the general public and at least more journalists and things, or at least appear to be talking about it. But I do wonder, do you think COVID is a good representation of where that's, how pushed data literacy forward or do you think this is maybe some of the evidences of some of the shortcomings of data literacy in our society?
Jansen Sullivan: [00:31:03] Um, that's a really good question. I think, COVID it's interesting because there's data coming out about COVID all the time and it's being pushed to the public, but I don't think you can convince people who don't want to be convinced. So I mean, there's people who are looking at the numbers and interpreting them and trying to do things from them.
And then there's certain folks that know that numbers exist, but they don't care about them. You know? So I think that, um, The conversations around COVID for the folks that care about the numbers are starting to become more interesting. Um, I think that the, uh, I think that you can use that information to help educate the public and actually boost the profile of data literacy and inspire others.
And I think that inspiration comes. Especially at like the youth. I don't know if you can change adults' minds, you know what I mean? But the young and impressionable ones, um, can still do it and the ones that want to do something about it. But, um, I think it's not, I don't think it's necessarily boosting the profile, but it's giving you data to work with that's relevant and usable, and to have conversations about, does that make sense?
Dave Mathias: [00:32:23] Yeah, it certainly makes sense. But I do think most people are not in the camp that don't want to be convinced. I think oftentimes we think people don't want to be convinced, but really it's because we're maybe coming at them with a message presented in a form that seems to either be talking down to people or not really seeking their understanding, but basically dictating what the answer is.
So one of the things I think though, when we look at COVID, that really comes out is that there's just a lot of data and a lot of uncertainty. And even with the experts, you hear a lot of disagreement, uh, take place. And so understanding data and where that data is being, gotten, what that data really speaks to and what it doesn't speak to are challenging for experts, let alone the general public and certainly journalists and others. And so I think one of the things that this teaches us is:
a) we're certainly needing more data literacy than we have currently, not only by us, but also by our experts and
b) we really need to be thinking about. What type of data do we collect and how do we collect it? And what is that process and how is that communicated out to the public?
So if there's one lesson we can learn from this is to be more prepared ahead of time. And be used data ahead of time to be proactive instead of reactive, because we know from system one and system two thinking or thinking fast and slow, the research that Kahneman and Traversky did. That we aren't so great in the moment and making decisions and when our emotions get involved . And anytime you're having to clean up messes, instead of getting prepared for potential messes we know that you're not simply, it's just going to be a lot less expensive. And so there's a lot of stuff we could have done and we could do in the future, to minimize such pandemics and that from actually, basically shutting down our society for as long as it has or cause dramatic impacts even in areas of the world where it hasn't been as impactful. And so hopefully we learn from those lessons and we make changes and that's the best we can do uh certainly we can't change the past, but hopefully we'll learn from our mistakes and capitalize them in the future. Along with of course being better day literate professionals ourselves whether it's journals whether it's politicians or whether it's the general public
Jansen Sullivan: [00:35:11] I think it's a big thing with your leadership too. I mean, not your leadership being like the American leadership, but maybe that is a thing.
But, I think the important thing is that people showing people that, you know, To trust data, and showing people how to use data to come up to their own, come up to their own conclusions, but for people to be completely dismissive of it and say like, that's not true. It's like, well, what can you know like what are you going to say?
Like, you can't really say anything to folks like that, but I think, uh, you know, I'm a, I'm a big fan of American news and I watch, I watch it every day, but, uh, the interesting thing is, is that the current administration seems like they pick and choose the data that they want to use.
but then completely ignore everything else. So it doesn't, it's weird, right? Like they're saying don't listen to the data except the data I give you. So I think it's just having that ability to cut through what anyone is saying, look at the evidence and then ingest it and do it, come to your own conclusion about it.
But at least you can have a conversation with that.
Dave Mathias: [00:36:14] I think you make a good point when you're mentioning about really trying to find data to come to the conclusions that you want. And certainly this is a problem that we face in organizations all the time. And I don't think this is unique now or was in the past or will be in the future. I think this is just going to be something you're going to face and the more data literacy that people have, the better they'll be able to identify and be better at minimizing this occurring, but I don't think it's going to stop this from happening.
What are your thoughts Victor?
Victor Anjos: [00:36:51] I believe that a lot of the OG visualizers and I guess data scientists, they're not really scientists, but I guess, you know, largely visualizers, um, our data journalists, uh, whether or not they have the rigor of a scientist, that's to be determined.
But the news, the news media, um, for a very long time has always put this stuff together, uh, rightly or wrongly. And again, again, with a bias or a lens towards whatever they believe in a little bit more, uh, or whatever the, their bosses believe in, I guess, a little bit more. But that world, has existed and permeated within society for a very long time.
And, I feel like we've all missed an opportunity all throughout to really shine a light on them a bit and be like, this is some of the coolest stuff that can happen, uh, from the data world. And it's not necessarily going to be all AI all the time, all neural networks, all deep learning. You're not going to get into, AGI with everything you do, very often describing things really well.
And even at the level of counting things really well, uh, is a really good story to tell. And, people often get blinded by the sexiness of whatever the newest image recognition is or whatever the newest NLP model is when those are very hard things to achieve and get to, and you need an army of people, PhDs, infrastructure, all the stuff that goes around it to really do that kind of work.
Whereas if you're largely doing, really strong reporting and visualizations that tell a story and deliver business value. You're going to win and have a really good career. All throughout. Those are positions that are largely needed and hugely available as opposed to the data scientists that everybody believes.
Oh, well, if I go and do this thing with bootcamp X, then Google will come knocking or Facebook, AI will come knocking or somebody. And, people are kind of foolhardy about that, where realistically go and visualize really well. You'll have a great job.
Jansen Sullivan: [00:38:53] Yeah, that's right. Like, one of our friends, you know, to the program, Joseph he's been , working on is data visualization chops, and he's basically doing a viz a day in Tableau and some of his visuals now, like you look at what he started with and what he has now.
I'm like, man, just getting the reps in is really, has been really good, has been really, um, You know, through experience, it's giving him that ability to hone his craft. Right. But at the end of the day, it's a craft. Right. You've got to put, you've got to put the time in. You can't learn anything instantly, if you could, you'd be a professional athlete making billions of dollars.
Right. So, everyone needs more that's right. But , it takes time. Right. So I think a lot of people. Want to get take the shortcut, but at the end of the day, whatever job you do, you're a practitioner. And as a practitioner, you got to hone your craft. Right?
Dave Mathias: [00:39:42] Definitely.
You definitely have to put in the reps. If you're going to read one thing or look at one thing you've come across recently. Let's maybe answer this from the perspective of someone younger in their career. Whether it's a data scientist or data analyst or somebody more senior in their career, any recommendations, for either that junior, that senior person. This could be a book a podcast a course or whatever.
Victor Anjos: [00:40:06] Well definitely your podcasts for sure. I wouldn't get like overly bogged down, with a specific language, but like looking at, the kinds of things that are available, I pick up something like an introduction to statistical learning of some sort, like get your head wrapped around Stotts and maybe a little of Beysian learning.
Uh, and if you can start to understand that world a little more in, you already have the math background. Cause if you don't, it's a little harder to really gain that knowledge. Um, that's a really good crash course into a lot of the things that may. Be worthwhile for you to think of in the future. And then beyond that, like whether somebody chooses to do things in our Python or whatever, I mean, then it's chart your own path and kind of go down it.
Right.
Jansen Sullivan: [00:40:52] Yeah. I agree with that. And like, and I think it's on your background too. Like if you're, if you're, not super technical, maybe you don't want to do that kind of stuff. Like, you know, for the data science world, like I think everyone should learn stats and just. It's super helpful and it's going to help you in your job also, but there's some folks like, you could take the engineering route too, right?
Where you're going in you're less mathy and more like enj. So , along with if math isn't your jam, I think for the three of us, like math is our jam, so it's good. but if Math isn't your jam, maybe you want to go down the engineering road.
If you come from more of a dev background and like looking into it, Dev ops and, you know, maybe just setting up and learning, hunkering down and learning some AWS technology. Right? So like we're some cloud tech and like just getting good at like productionizing. Um, there's a ton of careers out there to in that.
So, I think it's where you come from, right? Like, I was talking to someone the other day and they, they come from a, uh, Risk and anti money laundering background. And they're like, Hey, I'm going to be, I'm taking a data science course. I'm like, cool. Do you like coding? They're like, no, I'm like, I don't what, it doesn't, it doesn't make sense.
You want to do this. And they're like, well, I want to be able to, create teams and talk to teams about what they're doing. And I was like, well, I don't know if you need to. I don't need it. I don't think you need to take a machine learning course to do that. Right. So maybe you need to, maybe you need to just read about it a lot more in terms of, I'm not saying not to take it, but I'm saying like, if you don't like coding and that's something you've always struggled with, maybe that's not, that's not the path, but I think a lot of people see it.
So I was like, maybe you need to learn more about AI strategy or get into more of like, The strats stuff or do some basic analysis first, don't go straight into, ML sometimes I think it's just a big jump because that's what everyone's talking about, but you need to, you need to run, you can't run before you walk.
Right. So I don't know. What do you think about that Victor? Like, is that a, is that crazy? Like, should I be telling people this? I feel like I feel that this is not advice life.
Victor Anjos: [00:42:59] No, it's smart advice. I mean, it's advice that you gain from having been in the field for a long enough time, right? So, people are eager and want to jump in and think that like, it's good that you think that you can do a lot of things and you can do everything.
Uh, but at times you sort of need the foundation before you build the house. Right.
Jansen Sullivan: [00:43:16] So that's right. I mean, do a visualization a day. Right. See if you like that stuff. And then, but I mean, I think, with machine learning and all this stuff, like the predictive side, it's like. You gotta be able to ask the questions, right.
And you can ask lots of really good questions, while doing reports, while doing visualizations and questions, we get questions. And eventually you move into those type of predictive questions, but that doesn't mean you have to be there right now, you know? Um, Dave, what about you? Like what, what do you think what's that what's that starter or that kickoff for you?
Dave Mathias: [00:43:43] Well, and, and so for that, that more junior person, I actually think getting just your critical thinking skills as much as possible. So there's a, I'm going to recommend a different podcast. Any other ours is Econ Talk and it's been going on since like 2007 and they cover a lot of different spaces. They do a lot of and certainly the word econs in it.
It's actually a former economics professor University of Chicago classically trained type of economist, but he is lots of different guests, whether it's Nassem Taleb or any of those types of guests that are on there covering a whole bunch of areas. And I think, understanding more the problems that different people face.
I think the more and part of it's like you get older, you get wiser. Right. But you see a lot of patterns. Like a lot of things are so similar, like. Industry to industry, problem people like people, problems, people problem. Like these things are just over and over again. Uh, it doesn't matter your role doesn't matter a domain.
so I think, but I do think, the more that you can fast track that and have that strong, critical thinking that uses data as a component of it, the better you're going to be in your career. So for me, Econ Talk has a been a good podcast to do that.
but on a perspective of one thing with people with like very basic programming skills, there's a fast.ai that Jeremy Howard does. And he has stuff around deep learning. He has two deep learning courses and he has a basic machine learning one and fast AI is a framework and it's also he does research around. This
Jansen Sullivan: [00:45:14] is great. It's just, it's so easy to implement.
Dave Mathias: [00:45:18] Yeah. It's so easy to implement, but the question is, is like, even like part of it's like, okay, start doing this stuff. Start seeing if you like it and what the job is. And like, cause I think sometimes people get so dissuaded cause they can't get very far, very quick and it's like, well, if you're a programmer that's wanting to switch into, or at least somebody that's got some basic programming skills that wants to make that switch fast. AI. Is great to give you exposure to say now what our granted, like, you can't just like set these things and like have no context around it. I know you have to go way beyond that to ever get any experience. And that's again, like doing applied learning is the really, the only way you can really do it well, I think I'm like, you guys do it, your folks. So, but I do think it's an easy thing where I put people down to say Hey given that background, this might be a good thing to you. Let's check in once a month. Let's see how you're doing it. Let's find some real projects that you might be able to find a nonprofit you can work with to help solve problem.
Now, oftentimes like the like deep learning, but you're not going to necessarily use, but you may be using some random forest to help a nonprofit in different capacities, for example.
Jansen Sullivan: [00:46:23] Right, right. Cool. No, that, that makes a lot of sense. And I think, just kicking it off, like, it depends on, it depends on the path, but really like, I think critical thinking is really the, a huge component of it.
And just how, um, you need to be answering questions, and critical thinking really helps you really helps you get there. So I think that's a great, that's kind of spot on.
Dave Mathias: [00:46:44] It's been a great discussion and it's Friday afternoon and we have to get somewhere right. But before we go where can the Data Able audience find you Jansen and Victor?
Jansen Sullivan: [00:46:55] you can find us on LinkedIn Janssen Sullivan and Victor's Victor and Joe's, so you can always find us there. Um, you can find us on the whole thing to say to podcast or on spot while we're off anchor, but, you know, a lot of our folks come off on Spotify. And I think, just partying thoughts.
It's okay. Uh, do things, get your hands dirty, find a project, and it doesn't have to be solving a problem a huge problem in the world. If you want to do a visualization a day that's, that's doing something. If you want to go help, help a not-for-profit you can, or if you just want to find, what NBA team is going to, whatever.
Yeah. The team's going to win the finals this year. that's a great project too, but find do something. And find something that you're passionate about and it'll keep you motivated. If you're doing something you don't want. You'll never, you'll never finish. No,
Dave Mathias: [00:47:37] exactly. That's great advice. And I just got off the phone with someone actually was saying, I was giving her advice for a visualization a week.
Now I need to car back and say every day,
Jansen Sullivan: [00:47:47] check it out. There's a challenge. There's a challenge. But, I can link you out to Joseph's, uh, visit day challenge, but I think a bunch of people are doing it, but it's, it's been really, it's been really insightful cause I've been following it on a.
I've been following it on LinkedIn. So sounds good.
Dave Mathias: [00:48:00] Thank you. And Victor, how about you? Uh, where do they find you? What's
Victor Anjos: [00:48:05] your. Uh, I'm on LinkedIn, Victor Anjos. Uh, I'm on Twitter, not super active. Um, but generally, LinkedIn is probably the best place cause it's very professional now.
Dave Mathias: [00:48:16] And same here . It's funny how we're all just referencing LinkedIn now. And I guess it's the LinkedIn's cool professional. So I'm DaveMathias1 with one is a number cause that's what was available for LinkedIn and I'm DaveMathias on Twitter, not Dave Mathias one. I was able to get the, without one although I'm not very active on it. So what does that say? Uh, but of course I gobeyondthedata.com is my company's website and Data Able on the, most of the podcast catchers that are out there. Great talk with you guys today.
Jansen Sullivan: [00:48:48] Yeah, no, it was great. Thanks for asking us lots of questions.
Cause usually it's just Vic and I asking each other questions. So it's good to have being a being on the other end of it.
Dave Mathias: [00:48:58] Inquisitive minds. Right. Well, good chatting with you guys. We'll do it again.
Jansen Sullivan: [00:49:02] Okay. Thank you. So cheers. Alright.