As the Co-founder & CEO of DataCamp, he helped grow DataCamp to upskill over 10M+ learners and 2800+ teams and enterprise clients. He is interested in everything related to data science, education, and entrepreneurship. He holds a Ph.D. in financial econometrics and was the original author of an R package for quantitative finance.
As the COO and co-founder of DataCamp, Martijn helps DataCamp’s enterprise clients with their data and digital transformation strategies, enabling them to make the most of DataCamp for Business’s offering, and helping them transform how their workforce uses data.
Richie helps individuals and organizations get better at using data and AI. He's been a data scientist since before it was called data science, and has written two books and created many DataCamp courses on the subject. He is a host of the DataFramed podcast, and runs DataCamp's webinar program.
When transitioning from learning data science to actually doing data science, there is a lot of friction in getting started, especially for those setting up the Jupyter Notebook environment. But there's several companies now investing in cloud native notebooks on top of the Jupyter ecosystem. Some of them are completely rewriting it and some of them are building on top of it. I think that will be extremely powerful because it takes away the friction for people to get started with notebooks and it enables easier collaboration. I'm very excited about all of the innovation that we're seeing and I think it will completely transform the way data professionals work in their day-to-day life. Some tools seem to be designed for the absolute expert data scientist, and then there are other tools that are more for the intermediate data scientist, whereas at DataCamp, our approach is to make it as easy and intuitive for people to get started with data.
Richie: Welcome to DataFramed. This is Richie. I hope you're all feeling suitably rested after the end of your holidays and looking forward to 2023. It's shaping into an exciting year for data science and data. So today, I'm joined by two of the founders of DataCamp to make some predictions about what's gonna happen.
Jo Cornelissen is DataCamp’s CEO and Martijn Theuwissen is DataCamp, COO. They've spent the last decade worrying about how the data industry is evolving, so they're in a prime position to identify some trends. We're gonna be talking about data skills and jobs and tools, along with some sneak previews of what's been worked on at data. There's a lot to cover. So let's dive right in.
Hi there, Jo and Martijn, welcome to DataFramed. Thanks for joining us today. So to begin with, do you wanna tell us a little bit about what you do at DataCamp Jo? What does being a CEO involve?
Jo: Lots of things, actually. And it's one of the reasons I love this job. It's been changing every year. We started the company nine years ago. Right now, my main tasks are focused on setting the vision, hiring the right team, and then making sure there is enough money in the bank to actually execute on that vision
Richie: And Martijn what does being Chief Operating Officer involve?
Martijn: So also a lot of things actually, but uh, I'm actually mainly active on everything that has to do with our commercial site. Lots of interactions with our B2B customers, those who use DataCamp to get digital transformation going mainly in the data fie... See more
Richie: Always happy to have your support on the media team. Thank you. So with that out of the way, let's get into some predictions cause our core audience at DataCamp is data scientists. I'd like to start by just chatting a bit about the data scientist role, whether you have any predictions for how this role's gonna evolve in 2023.
Jo: So I think my mental model for the data space, in general, is somewhat similar to the software engineering space, but then it kinda delayed by 10, 20 years. So I think what you'll see in 2023 is further specialization, whereas 5-10 years ago, there was kind of one role - the data scientist that became really popular while there is a secondary role- the data analyst. I think what we've seen in the last couple of years, and I would expect that to continue, its further specialization as the space matures. So think about new roles like the analytics engineer that's kind of sitting between the analysts and the data engineer in the engineering department. That's now becoming a role in and of itself. So for those who are unfamiliar with, uh, the concept of an analytics engineer, their role is essentially to create clean, easy-to-use data sets for them, analysts, or other people in the organization to use and kinda self-serve ideally.
And that's a role that I think is fairly new and didn't really exist. Until recently, it became a specialization, but it's just one example of a general trend of specialized roles within the data space. Another example would be an MLOps engineer, somewhat similar to how in software engineering, you have traditional software engineers, full-stack software engineers, and you have DevOps engineers. I think in the machine learning space, there's a similar trend where you have the machine learning teams themselves, but they need kinda an infrastructure and operational team, and that's kinda where the MLOps engineering role comes in. So those are two examples, but the bigger trend is really specialization.
Richie: So I think a lot of data scientists are gonna be happy about that cause the space has just become so huge that learning absolutely everything is impossible at this point. So having some sort of sense of the specialization and just having a more niche field seems pretty helpful for a lot of people in this area of employment.
Martijn: Another role that I wanted to add, and it's maybe out of the data field, but actually like what I tend to see is that they're more and more within L&D department. They're getting like dedicated L&D data people. So people are responsible solely for getting the digital transformation, and the data transformation going like true educational aspects.
And like when we started DataCamp like nine years ago, or even five or three years ago, like that role didn't really exist, and I hardly encountered it. When talking to some of our B2B customers and now see that more and more companies are incorporating it and dedicating someone just to do that. So not a typical data role, but definitely something that they can describe as a data role.
Richie: Absolutely. Um, talking about the digital transformation of data transformation, this is a recurring theme on the DataFramed podcast, so I think that is an increasingly important role as well. On a related note, we talked a lot about data skills just then and how the roles are changing. Do you see any changes in who needs data skills in 2023?
Jo: Yeah, so may, maybe a little bit of context. Our vision has always been that data skills are not just for the data scientists, for the kind of nerds and Ph.D.s in physics, data skills are essential skills. For everyone in an organization and kinda the level or the types of data skills people need are just different depending on their role. I think it's fairly obvious if you're a data scientist, there are things like R Python, there are statistical models, there's machine learning, but if you're on a marketing team, there's a whole set of skills around A/B testing and things like that are very useful to know as well. Or if you're on a finance team, there's a similar set of skills that are very useful. And so what we've done at DataCamp is we've developed a data literacy curriculum that's suitable. The masses and for a large group in every organization. Cause there are concepts like data security, and data governance, where the more people in an organization are aware of them, the better kinda data transformation will be executed upon.
So it's not just one set of people who can benefit from data skills. I think it's a very broad trend, a little bit similar to how when the internet came about, everyone had to kinda upskill and gain a new set of skills.
Richie: So this goes a little bit back to what you were saying before about specialization and different people are gonna need different sets of data skills.
Richie: Yeah. Nice. Martijn, was there anything else you wanted to say on the subject of broad data skills?
Martijn: No, I think data literacy will go mainstream. I see that more and more and I think that trend will only like continue 2023 and much beyond.
Richie: Nice. So one thing I've noticed personally is that with younger employees, they tend to be very open to the idea of using data. It's often been pushed in school or university, but it tends to be the older employees who've, you know, they've been doing the job for a while, they've got a bit of intuition, and they tend to want to. Use gut feeling more than using data. So I would never suggest this of you two, but it's quite often that the senior executives that tend to want to just stick to gut feeling and they're not, they're a little bit more skeptical about the use of data.
So have you seen any progress in terms of getting senior management to adopt the use of data?
Jo: Totally, and I think we may have a little bit of a biased sample here because the people that contact DataCamp, it's usually when they're already kinda at a certain point of data maturity in their thinking and in their execution potentially. But there is a general trend where a lot of executives at organizations now are very well aware that it's crucial to invest in data science in ai, but also as a starting point to invest in data skills and shifting their culture. To be more data driven. So I do think that that is a general trend in the market, and I don't think that's gonna change anytime soon. If anything, it feels like it's only accelerating.
Richie: So just related to that idea, you talked about investment in data. So do you see any changes in how these data investments are being made?
Jo: That's a great question cuz. I think if you look at the economy right now, things are cooling down. Things are cooling down quite fast. That being said, I don't think that's gonna shift necessarily the fact that there are more investments in data & AI, I think what it will do is shift how people invest in data and AI. So to be specific, whereas I think a year ago a lot of the investments were probably around, how do we gain more insight? How do we deliver better customer experiences that will remain important. But I think the investments will definitely shift towards how we ensure we can understand the ROI of different areas in the business and how we ensure we use data and AI to actually make the business more cost-effective. So the general trends will continue, but how people invest. Will be different, I think, in 2023 compared to the previous decade, where we've seen generally much better macroeconomic circumstances.
Richie: Yeah, cost saving does seem to be on everyone's mind at the moment for sure. So I'd just like to move a little bit outside talking about data at work, because you mentioned before both of you about how data skills are becoming very widespread for everyone really. So can we just talk a little bit about maybe data outside work and how do you see data sort of further permeating our society?
Martijn: I think a good example in this area is like all the stuff that we do with classrooms and, and universities and the programs that we have there, like where we give university professors like six-month access to DataCamp for free to use it for their students. Things that we see is that more and more schools and universities as well, it's like their nonprofits now that are focused on this as well, started to do programs for data skills, around data skills. Like, I wouldn't say like there's this major shift going on where like, there's a very structured approach behind it, but you see there are these grassroots projects, and they're coming up more and more where university's professors take initiative to rethink about like, okay, data is this really important thing. It's not gonna go away. It doesn't matter if you're studying marketing, finance, or hr or like data science itself, we need to do something with it. We need to develop the skills for it, and we see lots of pilot projects going on. We see in our data that they're taking lots of programming courses, like in areas where you wouldn't even expect it. Like I gave the marketing example. They're upskilling themselves in biting. There's this shift going on there, and I think the innovation is happening at the base. It's not being pushed down. That's great to see.
Richie: That's really cool that people are actually kind of wanting to learn data skills in these, a lot of like kind of a small scale grassroots projects as well as sort of more sort of high level strategic big projects.
Jo: I wanted to add to that. I think there's definitely a positive shift. Like Martijn said, I do think it's important to realize the traditional educational system. Tends to shift slower than what Martijn was describing. What we've seen compared to five, 10 years ago when we started a company is that there's much more focus in a lot of university programs on data skills.
And so that's really encouraging to see, and we've been very supportive of that, where I think there's still a huge shift. Is starting to happen, but is definitely not there yet is at the high school level. There's definitely still a big focus in, in a lot of high schools on things like calculus, and I think in today's world, it would be amazing if we can shift that to data skills, or at least in addition to the skills that are currently taught.
I think it would be very valuable for people to gain a basic level of data literacy while they're in high school. Is something we would be excited to support even more in the future, but we've seen mostly traction at the university level and much less at the high school level right now.
Richie: That is pretty interesting because you don't necessarily need to be 18 years old in order to start getting some data skills. Certainly, a lot of the stuff around descriptive statistics, and data visualization. This is something that almost any moderately smart teenager can understand.
Jo: Absolutely. And the market needs these people. Right. There's still a supply-demand imbalance. There are more jobs for data professionals than there are people who are qualified for those jobs. So the market really needs those people, and the earlier we can upskill folks, the better. Even at a country level, it can become a competitive advantage. There are some countries that are definitely ahead of the curve in Integrating Data skills in their curriculum, both at the university level and the high school level. And then there are some countries that are definitely behind in investing in data skills of their younger population.
Richie: Yeah. Let's hope that 2023 uh, brings some changes to the, uh, educational curriculum for school kids. Alright. We've talked a lot about data skills, so let's change it up a bit and talk about tools. So it kind of baffles me that even now, Excel is the world's most popular data analysis tool. There are hundreds of different tools around for analysts, for data scientists and other data professionals. Can you maybe talk to us a bit about how the tooling landscape is changing?
Martijn: Maybe a little bit of a disappointment to you, Richie, but spreadsheets are still very popular. I don't think that will change in the next couple of years, but I think it's definitely true that market share is moving away from that. And I think we see like two types of directions. Uh, on the one end, there is the low-code, no-code movement. If you think about platforms like Power BI, Like Tableau. So the business intelligence platforms, like we see that more and more, that they're taking more and more share and more and more companies find it important that their employees know how to work with these tools.
And they're doing quite a lot of investments in making sure that people know how to use these tools. So it's definitely a change. Compared to a couple of years ago, there's really a transformation going on in the number of people that get access to it, the type of data they get access to it all thanks to these low-code and no-code platforms. They can do a lot more in those than they can do in Excel and a lot easier.
So I think that's one move away from spreadsheets. I think the second thing is there are notebooks. Notebooks are already. Popular with data scientists, and data analysts. I think a really good example there is Jupiter Notebook. The way that they're used today is still very much within that space of data scientists and data analysts. Now, as I said, they go in there, write some stuff, and they do their analysis now. I think when we're moving towards a situation where also this notebook is getting more and more accessible by the non-data scientists and the non-data analysts and the non-machine learning engineer in the company. I think it's gonna democratize the workflow that, that you see today. For example, we're building a core product like the Workspace and what it does is, it doesn't not only allow the data scientist analyst to very quickly do their own analysis. It allows it to share very easily. It allows for people to comment on it. To make their own edits and that lowers the barrier to go in and start doing your own stuff using a notebook.
Maybe just to summarize, I think that what we'll see is that next year notebooks are gonna go more and more away from just a tool for data analysts and scientists to like a tool that's gonna be used more and more by data practitioners. And we're gonna get more and more data practitioners in the world given how important everyone starts to see it.
Jo: I wanna second that last point. I think it's kind of crazy. If you look at the design space, you have tools like Figma that really level the playing field and basically brought product managers, engineers, and everyone kind of in a central place to collaborate around design tools like Notion who've done that for general documents. For data professionals, there are no standard tools yet that really have spread wildly that kinda do this, and I think it could be super, super impactful and it's almost certainly gonna happen in the next few years. For some reason, the data space has just been behind other areas here.
Richie: It does seem like there are many tools, but there's no one sort of standard tool.
Jo: Yeah, and that, it's an excellent point, Richie. That's one of the things that makes collaboration so hard in the data space. It's like there's a huge fragmentation of tools, and depending on the data literacy level of the individual, they tend to use different tools. So people on the BI team, use certain set of tools. People on the data science team use a certain set of tools. People on the machine learning team use a different set of tools, and that fragmentation makes collaboration really, really tricky. And I think there's gonna be a trend towards kinda standardizing easier-to-use tools that really build collaboration at the center rather than specific use cases.
Richie: That's interesting. So maybe we can talk about this sort of market consolidation a little bit. I really like Martijn's point about how there are sort of two different ways this is going with the business intelligence tools and also the notebooks. Maybe we'll take business intelligence first.
So of course with DataCamp we've been focusing on Power BI and Tableau in the BI space, just cause they're the market leaders. Jo, do you want to talk a bit about how you see this?
Jo: I'm not an expert in the design space, first of all, but I like it as a comparison where Adobe has this whole suite of kinda creative tools for designers and other creative professionals.
And you can think of that similar in the data space you have Power BI, and Tableau, those tools are not gonna go away. If anything, I would expect them to gain strength, especially Power BI. We've seen an incredible surge in training on Power BI. It speaks to the fact that once Microsoft starts pushing something, they have an incredible power to kinda set a new standard.
All of that being said, I do think in parallel there's gonna be another shift towards collaboration tools that enable not just the analyst and the BI team but enable a broader set of people. At least, that's our expectation. That both things will be true. So, on the one hand, you're gonna see continued dominance of some of the BI players and they'll continue to grow.
On the other hand, there's gonna be a shift to where it's kinda cloud native tools that focus on collaboration, that enable a broader group of people.
Richie: Interesting. Matan, is there anything you want to add to that?
Martijn: Nothing in particular, but I think what's worth pointing out is that the BI tools also become more and more powerful, like their ability to connect with data that's stored in the clouds, their ability to make reports available, or the dashboards available to the entire organization. Like not even on just a desktop, but on your mobile phone so that people are maybe in the field working like salespeople can check them. So like it goes way beyond. What you think of, like, if you think of Excel, you think of a spreadsheet and, and that's it, and you open it up on your laptop and you need to understand a bit like the opportunities that BI gives you, like what they're building on top of it and working on it like goes really far.
So I, I'm really bullish on what's happening there.
Richie: Yeah, I definitely agree with what both of you saying that like, well, okay, there are tools for analysis already, but the tricky part is the collaboration between team members and sharing things with people who are maybe less technically literate. So, Sort of a big thing for business intelligence tools is they get more powerful. They, you know, make it easier to collaborate and share and things like that. How about the case with Jupyter Notebooks? So, of course, we've got Data Camp Workspace, which is our own hosted notebook, but there are a few other players as well. How do you see the Notebook space evolving?
Jo: Yeah. So maybe to give a little bit of context, cause this is one of the most exciting changes in the space, in my opinion. So DataCamp had a unique view on the space in the sense that our students kinda come to us and tell us, “Hey, this is where we want more content.” And one, one of the areas was just local setup. A lot of our learners continue to struggle to kind of download all the packages, and set up the Jupyter Notebook environment, and it's a real kinda unnecessary friction to kinda transition from learning to doing data science.
And there are several companies now investing in cloud native notebooks on top of the Jupyter ecosystem. Typically, some of them are completely rewriting it. Some of them are really building on top of Jupyter. And I think that's gonna be extremely powerful because it takes away the friction for people to get started with notebooks.
It enables easier collaboration. And I'm very excited about kind of all the innovation that we're seeing in that space and different people taking different angles. Some of the tools like Hex seem to be really designed for the absolute expert data scientist. And then there are other tools that are kind in the middle. And then our approach is really to focus on making it really easy to get started, really intuitive to get started. Appeal to as broad of an audience as possible, but all of the innovation is, I think, really gonna change the way data professionals work in their day-to-day life. A little bit similar to this has already happened to the software engineering space, right?
If you look at how a lot of engineers do work today, it's very different from 10 years ago and their whole workflow kinda shifted from local environments to a lot of it happening in the cloud. So I think with notebooks, that's the big shift. We're starting to see a lot of individuals are kind of exploring the shift, but a lot of organizations are still kind of in the old world. So that's gonna be exciting to see that shift happen.
Richie: Absolutely. And I do think the software development space is a really good sort of like a leading indicator of what's gonna happen in data and having all these tools move to the cloud. One thing you mentioned, though, was saying that a lot of like new users, really struggle with getting all the software installed and whatever. I have a confession to make cause I'm, I've been working in data science for nearly 20 years now. I still struggle with Python package management. It's horrible. So there are not just the new users that sort of need this.
Jo: It's one of the hardest problems, I think, in the data space. Package management.
Richie: Absolutely. It's just like getting the right version of pandas going. Cool. Do you wanna talk a little bit about what's happening with the workspace over the next year?
Jo: Yeah. Just to give you a sense of where we are, kind of in our journey of developing workspace as a cloud-native collaborative notebook. I think the product today is, Really good in the educational context. So we see a lot of learners use it to kind of do projects to build their portfolio. They show up on their profile and use it in their job search. And I think the next year is really about like perfecting that use case first of all, but then also enabling the team's use case. Right now, I think we have one team actively using the workspace, which is the data team itself. Our data team is very actively using the workspace, similar to how the design team is, is using Figma. So they're, they're pulling in other people in the organization and we're kind of building that use case and perfecting that use case and that we wanna do a big marketing push to shift a lot of our team's users to not just use DataCamp for learning but also start using it to get work done. And that's a really exciting kinda transition we're going through as a company.
Richie: Absolutely. And certainly, there are very different needs from, okay, I'm just gonna write some lyses in a notebook myself to, I'm a company, I've gotta manage like thousands or tens of thousands of different notebooks and have them all findable by the right people and have everyone have the right permissions and know what's what.
Jo: Exactly. So there's a whole set of features that you'll need as a team that are less relevant for an individual or for a professor who's using it in a classroom setting. Cause that's something that's increasingly started to happen already with.
Richie: Alright, moving away from tools, one of the sort of biggest stories in data in 2022 was in generative ai. So we're talking tools like Chat GPT, we've got stable diffusion, we've got Dali for generating text and for generating images. So how do you see these tools having an impact in 2023?
Martijn: One really interesting one is like because you talk about GP three, like one interesting descendant of it is like the open AI codex. And in essence, what it does is it auto-generates code and the claim is like, thanks to that, as a developer, you can. Focus more on the high-level problem and then focus less on the syntax and maybe your Python package management, hopefully soon. But they're actually like already like real-world use cases of that.
For example, GitHub co-pilot uses it and they released some interesting stats where they said that the software developers that they saw that used GitHub Co-pilot like they saw a 40% increase in productivity and like it's an important number and it's incredible. Because it shows how much productivity gains there can be made.
And it's also counterintuitive because I think we always thought it's gonna be the technical roles, the creative roles, that are gonna be least impacted by AI. And it's showing that it might even be the opposite. If you think that this open AI code makes software engineers 40% more productive and maybe, over time, even fully replace them, don't think that will happen.
But like to a large degree, if you think about DALLE all the creative people being able to all to generate your brand logo or the design of your marketing email. I think all these, like, what's coming at us now like, is gonna extrapolate in 2023, and like, it's gonna be super interesting because I think that the GitHub Co-pilot is just like scratching the surface on like what is possible. And it's interesting to see how it's gonna increase productivity, how it's gonna lead to wider adoption because all of a sudden you don't need to know how the nitty grit detail about the syntax. So, it becomes more accessible is one of those things that I'm really excited about.
Richie: Yeah, certainly just seems to be having an impact both on the creative side and on the sort of more technical side, especially once you have the code-writing capabilities. Jo, is uh, anything you want to add to that?
Jo: Yeah, I just wanna second that I think it's one of the most exciting innovations in this. In probably the last 10, 20 years. I think this is really a game-changer. These large language models liked three. Rumors are GPT4 is gonna be even better, significantly better. And so I think this is gonna be a massive, massive shift where we're only at just the beginning. I think some practical advice for people maybe is this is gonna make it even more important to understand conceptually what's happening with certain statistical models, machine learning models, like Martijn said, that the syntax part is still gonna be important, but it's gonna be a little bit less important in the future. And I'm also excited about how this is gonna enable us to build better educational interfaces, quite frankly. Yeah, I think what data has done is we've innovated on kind of an approach by creating this kinda automated feedback system. As people progress through courses, these models that are being developed now are gonna enable us to get much closer to what our initial vision was, which is we wanna automate the instructor.
We want every student to ultimately have their personal tutor. And I think we're now kind of seeing the technology innovation that will be required that will enable that use case where eventually, every student will have their personal AI tutor. And I think we don't even know how, how much impact that will ultimately have on society because very well established like private tutoring is probably the best way to, and the most effective and the fastest way to learn anything.
A lot of really great private tutoring has historically only been available to a very, very small group of people in the world, and this is gonna open up the best type of education ultimately to the entire world. So I think it's incredibly exciting, and it's really gonna change. Every single sector eventually.
Richie: Absolutely. Yeah. We're just sort of scratching the surface of impacts at the moment. Actually, you mentioned AI Tutors is a really great sci-fi book called The Diamond Age by Neil Stevenson, it’s about a girl with an AI tutor. So definitely worth a read - total side note. But. One thing you both mentioned was the idea that learning concepts is important, and I do agree with this. So even if you've got an AI that can write your psychic learn fit and transform method code for you, it's not gonna tell you what the model means or how to interpret it. So that sort of idea of like understanding what your model's doing does seem incredibly important. Moving on. I'd like to talk a little bit about the jobs market and so at the moment in many countries around the world, the worries about inflation, worries about possibility of recession. So is data science still an attractive field to get into?
Jo: So we're obviously not completely unbiased here, but I think if you look objectively, the growth expected by the US Bureau of Labor Statistics for a data role is 36%. That's way above. Almost anything else. If you look at Glassdoor, like the top 50 jobs in America, it's currently number three. So I think that data scientists and data roles, in general, are still one of the most kinda sought-after or one of the most lucrative and popular jobs to go after. We see the general economy cooling down, and the interest rates going up I do think that we will see a slowdown in terms of salary increases. We’ve seen an incredible job market and fast salary increases in many roles, so that's probably gonna slow down a little bit. But I think within the potential jobs that people can choose from, data roles are definitely still gonna be all the way up there in terms of future potential. They have value for both the individuals, well, for the organizations that end up hiring these folks in the short run, it's maybe worth saying like some sectors are gonna be more resilient.
DataCamp now has a recruiting product where we help organizations hire the right data talent, and we're seeing some, still some, real strong traction in industries like healthcare, for example.
Martijn: Yeah. I think there's another angle of like data science and the data scientist job, but there's also the angle of like data science as a must-have skill to get maybe a job in marketing or in finance or in healthcare. And I think if the economy cools down and it will be more competitive to get certain roles or jobs, I actually think that having that data knowledge is gonna be a differentiating skill and that more and more organizations will ask for it. If you look at the other investments that they made, and we talked about BI and we talked about some of the other technologies and the notebooks and so on, having it as a distinctive skill is going to give you an edge. At least that's, that's my belief given everything else that's going.
Richie: Yeah, that does sound, uh, very plausible. I mean, certainly, at DataCamp, you mentioned the example of marketing, and some of our people in the marketing team are very creatively focused. Some of them are more analytically focused, and there is that kind of divide.
And so I think having those data skills to go, okay, this works is a good competitive advantage there. Are there any other things you think people can do in order to help 'em get a job in a tough climate?
Jo: Absolutely. I think this is gonna be, at least the next one to two years, it's gonna be a job market where it's crucial to stand out. So I totally wanna second what Martijn just said around, Hey, even for non-data roles, if you can show you actually have incredible data skills, that's a huge plus. If you're applying for data roles, I think it will be really important to stand out, for example, by having a portfolio. Just like software engineers often have a GitHub profile where you can kinda see the projects they've worked on. Maybe they've contributed to some open source. I think for data professionals, it's incredibly important to have a portfolio as well. You can for example use DataCamp workspace and add it to your profile. We're increasing number of students who actually take advantage of that. There are certifications, so certifications from specific tools like Power BI or others. And then Data can relaunch our own certification program as well where we're certifying whether people are job ready for the job of data analyst or data scientist. And all these things really help you stand out cuz it is definitely a crowded job market now. In the short run, especially for the more junior roles, we've gone from companies really struggling to hire for those roles to companies getting hundreds of applications for open roles. And so the challenge now is shifted from kinda finding the right talent at the junior level to filtering through resumes, and everything someone can do to really stand out is really essential, I think in the next few years.
Richie: Absolutely. I do like that idea of just creating a portfolio because if you can't show off like your actual work, then is very much a gamble on whether or not you should be hired because, you know, an interview can only tell you so much. How important do you think credentials are in data science?
Jo: It's a tricky question. I think ultimately the market has shifted to really valuing skills, and so I think the most important thing is having credible and fast ways to demonstrate that you're skilled. Credentials are one instrument to do that you can get credentials from DataCamp, Microsoft, and other tech companies, there's definitely value in that.
I think there's still value in kind of credentials from traditional educational institutions as well as boot camps. The challenge here is that, especially in the US and in the UK, That remains really expensive. So, a data science master's or bachelor's will easily set you back tens of, or even hundreds of thousands of dollars. And even if you say, Hey, I'm just gonna go through a Bootcamp, that will easily cost you five to 20, $25,000 as well. There are some companies that have gotten creative with the way they charge that money, but at the end of the day, it's five - 25 thousand dollars. A lot for a lot of people, a lot of money for most people, frankly.
So I think there's value in that. I think it's just a question of what's the ROI and for how many people those things are available.
Richie: Yeah, I'm certain, you get a very long DataCamp subscription for tens of thousands of dollars. That's a good lifetime subscription there, I think. Alright, so just going back to the idea of recession and potential economic problems, we talked a bit about what it means for people who are trying to get a job.How does it change things for hiring managers?
Martijn: Yeah, I think for hiring managers, it might be easier and might also not be easier. So, on the one hand, if you open up a data science role and you have zero applications like that's not great, and then you need to go out and hunt there. And I think there's been a little bit of a situation in the past couple of years now at the same time, like, okay, if you get like a hundred, 200, 300 resumes in, it's also not that great because then you need to sift through them and, and figure out like, okay, who actually.
Demonstrateable skills here versus those who don't. And so I always think a good ideal world is when you get one applicant in, it's the perfect candidate and you hired a person. That's actually a little bit of what we're trying to do with DataCamp’s recruit product. For us, it's not about giving a hiring manager getting hundreds and hundreds of resumes in their mailbox.
It's about- okay, tell us what you need, and then we gonna try to figure out like, okay, what are the 2-4 people that meet your needs and we're gonna try to connect you. And I think that's like we're moving more and more to a world where that's gonna be valued. Not like finding the situation where you only have, you have zero applications, but like the situation of okay, how do I find the 3-4 right people.
Richie: So it's really a question of searching and filtering out all the candidates who aren't appropriate just to get the right person from a hiring manager's point of view.
Martijn: And knowing the skills of these candidates and, and knowing what they have demonstrated. And that's where I think, for example, our certification is really cool because we know what these people have demonstrated, and so we can link that to what they're searching for.
Richie: It's about a trend towards, I guess, increased transparency, both on the candidate side and the hiring side.
Martijn: Yeah. So instead of just searching, give me everyone from Harvard University, you can also search- “Hey, give me somebody who is in the top 5% of Python skills here.” You can search for demonstrable skills.
Richie: Okay. So are there any other trends you're seeing? Do you have any further predictions for 2023?
Jo: It's partly something I'm very curious about in the context of these large language models like GPT3, stable diffusion. We don't have clarity yet, and where there's definitely gonna be a catch-up is the legal framework around some of these things where the power of these models is ultimately resting on input and a lot of that input might be copyright.
I think this is so new that we just don't have a legal framework. All kinds of lawsuits are starting to happen. I think this is really interesting how that kinda shakes out, and I think that could either be an accelerant or something that slows down that process. So the prediction, I guess, is like, there's gonna be more talk about that in 2023. I dunno where we'll land on it.
Richie: Yeah, certainly interesting times from a sort of the legal and ethical point of view with all this generative AI and new sort of applications of data. Alright, thanks Martijn. Do you have a final prediction for 2023?
Martijn: Yeah, I think one thing that I think will become more and more important is making sure that your data is correct. We got a lot more people now introduced to the power of data analytics and data science. A lot more people get access to it like across all the functions and departments, which is great. But we all know, in this conversation, how hard it is to make sure that like what is presented is actually correct and that the underlying data is correct so that the conclusions are right. And I think there's still quite a lot of work to be done on the data side to make sure that these data observability platforms like Monte Carlo Excel Data, Data Vault like they saw that kind of problem and that's fixed. I think there's gonna be more and more attention. To do that.
Richie: Absolutely. Yeah. Trust in data is such a hard thing. I do like, a quote from Dennis, our financial data scientist, who sort of checks that all of our DataCamp numbers are correct, and he talks about doing paranoid data science where you have to thoroughly check absolutely everything just to make sure things are real. Alright. So thank you both for joining me. I hope you've enjoyed the experience.
Jo: Yeah, thank you, Richie. This was really exciting to do.
Martijn: Thanks, Richie.
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