What DataCamp's Learner Data Says About Learning with Pieter Moors, Lead Data Scientist at DataCamp

time on how much time people take for completing courses, but also when they learn. There's actually a very striking pattern to that when people learn on DataCamp because it's not the same for every type of learner. So we have many enterprise learners, people who do learning at DataCamp because their company gives them the option to do or their company wants them to learn on new stuff about AI or something like that.

And for these people, we very typically see a pattern where they learn through their working day. And we... I just looked at the data again, and we see people carve out most of the time just before lunch or just after lunch, more or less like that, spread out across the week, so there's not a real pattern of closing out your week with a, a two-hour learning session or something like that.

Richie Cotton: But they almost never- Yeah if you're learning at work, then you're gonna do that during the workday rather than at weekends. That makes a lot of sense. But before lunch or after lunch are the big times. How about for individuals then, if you're not learning as part of a, a work program? 

Pieter Moors: So for individuals, like it's not that they don't learn during the day but we more frequently see them learning in the evenings during the week, and then also during the, like during the weekends.

Like about half of the time spent learning by individuals happens in the weekend or in the evenings. Whereas only like maybe 10% of learning time is in like devoted in the weekends by our enterprise learners. 

Richie Cotton: Okay. Yeah, that certainly makes more sense. If you're an individual, then maybe you get some time to learn at work, but maybe you have to work or you have to learn in the evenings or maybe you have to work learn over the weekends.

So I can see how that would be a bit more spread out. Are there any kind of extreme cases where you see differing or changes from the normal pattern? 

Pieter Moors: Yes. And one that I found pretty cool myself was also one of the observations that was the... One of the first things we did for this insights program was during the World Cup, we were interested to see whether, like sometimes you have the feeling that a a, a country pauses like as a whole for when their national team is playing.

In this case, I'm sorry, like I'm from Belgium, I know World Cup is equivalent to football or soccer as some people call it. We were interested in looking at that past summer And so we we thought, okay, let's look at, like zoom in on the specific game times of learners in countries where the, the national teams are playing.

And overall, like we saw like a 30% drop in study time compared to the same time a week before, two weeks before when there was nothing like special happening. And so this varied a lot like a lot between countries. So for some it didn't budge a lot, but for most it was between 30, 50 to even up to 70% of study time that was dropped.

So it was pretty cool to see that, like as we saw earlier when people learn at DataCamp, it's in the context of their own lives and when something big happens in the world you see it also in the dy- the learning dynamics at at DataCamp as well. 

Richie Cotton: I can certainly see how DataCamp or as compelling as it is maybe not as compelling as as, as the Soccer World Cup.

So yeah. Was there a difference between countries then? Were there any countries where you saw a big effect? 

Pieter Moors: Yeah, so like the biggest one, if I remember well, was Morocco. Like they dropped around 70% of their study time. There was a f- one funny observation, which was Belgium actually. So in our like first analysis we saw that they were the outlier in terms of the fact that they actually added a bit of study time during the game.

And the reason why we thought it was funny was that sometimes there's criticism about the national team and like maybe people didn't wanna watch anymore, blah, blah, blah, blah. But actually there was a confounding factor in the sense that we also have academic groups at DataCamp, so like people s- doing courses for their study programs.

And there was a specific university group from a, a Belgian university doing some crunch time learning on DataCamp during that game. And that kind of- confounded the results a bit. So as soon as we again, looked at it more generally, then we saw that trough appear as well. 

Richie Cotton: Okay. Yeah. I suppose that's like a standard thing with students is that you will do everything at the last possible minute before the deadline.

Maybe that takes priority over even a World Cup game. But the Morocco thing's interesting 'cause Morocco had a really good World Cup, so yeah, I can certainly see how like the whole country was just very excited and maybe it takes priority over DataCamp. Okay. Before we start talking about differences between learning patterns from our enterprise users and our individual users I'm curious as to how much time people spent.

So how many times per week people learn, how long do they spend per session? 

Pieter Moors: Like for enterprise learners, we typically see or like the half of the users around that point spends around one like day a week, which is in line with their like focused learning moment that they carve out.

Individuals like average more around two days a week because, yeah, they might be a bit more engaged, they might be a bit more focused to achieve their new goal. But it's not the case that they do a very big chunk of learning typically when they learn. So it's very often like average study times is like between 20 and 30 minutes or so.

So it's not that people typically crunch full courses. Like we see them typically do a lesson, maybe a chapter or something like that. And that's the kind of pace at which people learn. 

Richie Cotton: Okay. That's interesting that 20 minutes is the standard sort of study length period. Yeah, after 20 minutes of learning maybe your brain's full you don't necessarily want to do a whole course in one sitting and then not be able to remember it because you've because you've gone through too much.

But yeah, learning once or twice a week, that seems like enough to just keep your brain ticking over and your skills gradually staying up to date. I guess related to that, if that's the average learner, whether they're doing one or two sessions a week, they're doing 20 minutes is there like a ramp up from that to if you are, I- if you want to become a top learner rather than an average learner, are there any sort of techniques to help you learn more or learn better?

Pieter Moors: Like what's very typical of any learning platform, I think it's that cadence is most important, and it often beats like big like bouts of volume that are irregular. So we very often see that if you distribute your study across multiple days and you do this like 20, 30-minute sessions or you go maybe like up to an hour or something, that makes you come back much more often to the platform rather than you doing it once a week, but then trying to devote two hours of learning time two hours of learning time once a week.

So yeah, I would say make it like something habitual that is cadenced very specifically and not too long so that you don't get too tired because in the end, yeah, learning something new is also a bit tiring, like I would say. 

Richie Cotton: Okay, that sounds a bit like if you wanna get fit, like you could go to the gym once a week and just wreck yourself, but it's probably better to do a little bit of exercise every day and that's gonna get you fitter and you don't- Exactly

have to make the effort as much. Okay, so it sounds like learning every day is a good idea. How do you go about building that daily habit of learning? 

Pieter Moors: We try to be an enga- engaging learning platform, and so we have things like like gamification of features, like streaks that try to encourage you to come back to the platform and keep your habit going.

So the nice thing about streaks is that you com- can combine them with practice sessions on a platform. So next to courses which are a bit bigger and learn, use something new, you can also do practice on the topic of a specific course, and that just takes you about five minutes if five minutes.

And so the combination of like kind of keeping your streak while doing practice is like a very big lever to make you return make you come back to the platform. And specifically, like for keeping streaks, we see this, like that habit formation is difficult, but as soon as you get there you're likely to stick there.

So the, like the going from day one to day two is very difficult. If you then go up to day, like day seven and even day 14, then it's almost, I'm not gonna say impossible, but it's no longer like a coin flip to lose your streak. It's very likely people are gonna want to keep their streak going when they have been learning for 14 consecutive days.

For some people, that just makes them come back forever it seems. Like we have people who have streaks for almost eight years. And you see it, they just come back every day. If they've done all the courses they want, they just come back to do practice and keep their habit going. 

Richie Cotton: That's very cool.

And two weeks doesn't sound like a, a really long time. This is a bit like if you're starting a new diet, then you're supposed to do it for two weeks, and then you've built the habit and it's easy to continue. So certainly see how like if you just do it one day and say, "Oh, maybe it's hard work getting back to day two," but yeah, just do two weeks and then you're there.

Okay. Does it make a difference if you're learning on desktop or mobile? 'Cause I know like you can take courses on the app. Does that affect your ability to retain a streak? 

Pieter Moors: Just to come back to practice, like it's very easy to do practice on the app and to keep your streak going. You don't need to sit at your desk, like having your having your desktop with you.

But s- the, like what, what works best, what we see in the data to keep your learning going, to not drop out, to like to keep the habit going, is when you try and combine both fl- both platforms. So sometimes it's a bit s- optimal on the mobile app to do the actual learning, to take the courses because, yeah, like there's videos, there's exercises, that's a bit more difficult.

It's very easy to do that on desktop. And then when you do practice to make it a daily habit, when you do that on the app, that's super convenient, and those people, we really see them like having a much better retention versus people who do not use the app at all. 

Richie Cotton: Okay. Yeah, certainly I can see how just doing a few minutes of learning on the app i- is very convenient.

If you're, making a cup of coffee or you're commuting or something like that, then where you don't necessarily have access to a computer, then, you can spend five minutes, do a practice session, and that's just keeping your brain ticking over. Okay. Have you seen any other patterns about what helps people learn for longer on DataCamp?

Pieter Moors: It's a bit of a general pattern, I would say, and it's related to the fact how and I can speak from my own learning on DataCamp. It's like how you shape your learning environment or everything we give you to customize the way in which you in which you learn. We have this new AI tutor experience on the platform where rather than having a fixed set of videos and exercises, you actually interact with an AI tutor that kind of shapes the learning towards you as a person.

And we see that as very engaging. But like small tweaks, like you can adjust the voice of the person, for example to, to a voice of your own liking. That boosts your engagement with the course substantially. But also in the classic experience, and this is more anecdotal rather than backed up with solid data, is Whenever you're watching a video, you have a transcript on the side, or you can inspect the slides or something like that.

There is very different ways to consume content in, in also a classic course that might keep you going more easily because sometimes you don't like to listen to a voice all the time. You just wanna go over the slides and then do the exercises, or you just wanna read what's on the slides. So basically shaping the learning environment towards a, a learning experience that suits you very well is a powerful way to actually keep learning, I think.

Richie Cotton: That's cool that just simple tweaks to the user interface just to customize them how you like them. That's gonna have a big difference in terms of whether you keep learning. Actually, just on, on the choosing your voice thing, I remember actually years ago I had this girlfriend who would ignore the GPS whenever we drove anywhere, and it used to drive me crazy.

I was like, "Why are you doing this?" And it turned out she really hated the voice of the GPS, so she would ignore the instructions. We had to change, we had to change the voice mode on this GPS. It was fine. She would follow the instructions and we could, we'd get to places. But just those little things have a huge difference for some people.

I would also like to talk about certifications 'cause that's maybe the other thing that I generally get a lot of questions from our users from. I think also this comes up in the DataCamp subreddit a lot is, like, how do you pass certifications, or what makes it easier to pass? Do you have any data on tips you can share for passing the DataCamp certifications?

Pieter Moors: Yeah. Maybe it's good to first... I don't know how familiar our audience is with our certifications. Should I try to- 

Richie Cotton: Give us some background 

Pieter Moors: So we don't only have a learning experience at DataCamp, but we also have a certification experience. And that certification experience is actually designed for having an independent objective check of your actual learning on DataCamp.

So you can complete courses, you can do as much as you want, but if you want to have a a, a valid test of your skills basically that's what we design certifications for. And we have very big ones that are focused on roles, like going from data scientist, analyst to AI engineer. But we also have very small ones, simple ones, like focusing on your SQL skills or your Python skills.

So there's like tons of these things for everyone. Usually the first part of any certification, or sometimes the only part, is called like a timed exam, and it's very similar to a DataCamp assessment that we also have on the platform. It's like a, it's a, like a series of sometimes multiple choice, sometimes like coding questions that are very often focused more on like broader knowledge and application of your knowledge, but not necessarily in like a real world environment.

That is the second phase of most certifications. And so what we've seen in our data, and I find this very interesting because it's related to the AI tutor that we talked about earlier, is that of course, when you simply look at assessment scores on DataCamp and people who score high on a, an assessment that is relevant for the certification, they're more likely to pass the certification.

But that's people who already decided to test themselves and so on. But so for the AI tutor one of our hypotheses of, of why this, it's so nice to to learn with it is that it it adapts to your preferences, to your desired speed. It will slow down whenever you don't understand something.

It will give more explanations whenever you want something deeper. So it builds that conceptual base better compared to, I would say, in our our, the, the normal equivalent, the non-AI tutor ones, you-- it's, the onus is more on you to go deeper and to understand everything better, whereas the AI tutor might challenge you a bit more on your own understanding.

And so for the AI tutor courses that have relevance for certifications we see that people who've done those actually have higher chance to pass the timed exams compared to people who didn't have haven't taken, sorry, the, the AI tutor courses, but took the the, the non-AI tutor version of it.

And so one very concrete suggestion on how to pass these things is, like, whenever they're available, try to use the AI tutor version. It definitely will have a positive effect on at least getting past the timed exam in, in our certification process. 

Richie Cotton: That seems like a, a really straightforward learning change there.

Just take AI tutor courses if possible. There's g- no, actually so when I used to run DataCamp's content quality team, like, for any given course the two most common complaints would be this course is too easy and this course is too hard. And it's just getting the difficulty level right for every single user with the traditional format is difficult.

But with the AI tutor courses, yeah, y- it does have that level of personalization to match your own any user's ability. So yeah if it's teaching you at the right speed, at the right difficulty level, then yeah, it gives you a much better chance to pass. 

Pieter Moors: Okay. 

Richie Cotton: I'd also like to talk a bit about careers, and since everything AI related is popular right now, has that changed the types of jobs that people look for on DataCamp?

Pieter Moors: Yeah, very much so- One signal actually two signals. One is, comes from the users themselves very directly as... So we have this like AI assistant feature on our platform. And so you can ask it questions simply like, "Do you have course on X?" Or, "What should I do next?" Or these kind of things.

But very typically, when you just are new to the platform, it will pop up and ask you one of four different things. And for people who are really coming to us to change their careers, which is like there's a bulk of individual learners that really want a career change, or they just come straight out of school, they don't know what to do yet, and they wanna learn something new.

It used to be the case like that data scientist was just almost the one thing mentioned "This is the role I wanna go for. This is what I wanna learn," and "What," "what do I need to do to become a data scientist?" And so over time, in the past two years or so, we've s- we've seen that gradually decline And a term that we, all didn't know about three years ago which is AI engineer all of a sudden started appearing, and it's like it, it kept rising and rising.

And now people who now come onto the platform talk to the assistant about what careers they want. It's one-on-one, like data scientist versus AI engineer. So you can definitely see that what people want to become or what they want to use DataCamp for has changed over time. And we see that, to come back to certifications, we see that in certifications as well.

So where we see most growth in certifications is currently in AI engineering certifications on our platform and our data engineering certifications. So that's the two roles that people are most interested in getting certified for, and that we also see in our AI assistant that they mention most to get learning suggestions learning suggestions for.

So that shift is for individuals, but for enterprise clients the change in the type of courses that enterprise users take has been massive. Like we used to... Like the top courses used to be intro Python, intro SQL, all those things. But now the top courses are all about AI ethics, AI literacy, AI agents understanding ChatGPT or prompt engineering, all those things like enterprise users are really focused on, on, on AI upscaling as well.

Richie Cotton: That's absolutely fascinating that AI engineer as a sort of job search term is reach parity with data scientist. 'Cause that was where DataCamp started was it was training to for, to help people become data scientists, and obviously we've branched out a lot over the last decade or so.

But yeah, the, the fact that AI engineer is now, on track to become the top job search term then that, that's really interesting. Okay I think related to this maybe one of the biggest fears from a lot of people who are coming to DataCamp is like, "Is AI gonna take my job?

Should I still bother to learn to code?" Things like this. Do we see any of this fear manifested in the data? 

Pieter Moors: So that's actually where you see an interesting, I'm not gonna say paradox but an interesting a trend where although people like our individual users frame like their goals more in terms of, "I wanna become an AI engineer.

Is there any room for doing something else left?" All those kinds of things. We don't necessarily see that in the courses they're completing. So they still understand that having a solid basis is still needed even when you interact with AI because I guess whatever is being produced by AI, you still need to be comfortable with it somehow.

And knowing those basics is still what most individual learners do before they get into the, to the more technical matter or the more technical details. So learning to code in our data hasn't become obsolete yet. 

Richie Cotton: Okay. Yeah. If you wanna be an AI engineer, even if AI can write your code for you, you still need to be able to know how to code yourself.

Yeah I can see how this is gonna be coding is gonna be a skill that's gonna be important for a, a good few years. Yeah. I'm curious if there are any sticking points that a lot of learners have, 'cause quite often you start off easy and you're writing "Hello, world," and then suddenly there's a point where it's like, "Oh, this is very tricky," and a lot of learners get stuck.

What do you see in the data? As we all know, like for the people who have done some programming most of the basics are very simple, but some of the basics are very confusing. And so something that even now in the age of AI still pops up every week in our Intro to Python course is how indexing works in Python.

Pieter Moors: It's super confusing. People always miss by one or something like that, or they, yeah, they don't intuitively understand how it works, and it's one of the, Even when AI will always do it right for you if you need to try and learn it yourself it's still a major frustration. 

Richie Cotton: I can absolutely understand that.

Yeah. That's a thing I've fallen over many times. Python indexing synd- syntax, like for trying to figure out what you're doing with an array. It's really confusing especially yeah, the negative indices are tricky stuff. It's one of those points where 'cause I, I start off in R programming and then moved to Python.

I was like, "Phew, I, I think R does this one better." All right. Do you have any final advice for us on how you can learn better on DataCamp? 

Pieter Moors: Everything is really about habit formation, and it's maybe a bit of a boring advice. But it's true, I think, for if you wanna learn something new you gotta carve out time for it.

You gotta be persistent. I experienced this myself not in a learning environment, but I tried to start running more, for example, as a workout. And as soon as it becomes non-negotiable that you go running every other day or so then it becomes frustrating when you can't do it. So if you can reach the point where learning on DataCamp or actually not learning on DataCamp becomes frustrating that will likely be the point where you want to keep on going, want to keep learning new things want to consolidate your knowledge.

And yeah, I think habit formation, being persistent is key. And sometimes we see in our data, like one example is People want to get certified but they fail the first time, and then you think okay they might be discouraged and drop off." But for some people that's really not the case.

They're very persistent. They keep going, and there's this... Yeah, I noticed there's this one user they tried one of our certifications over the course of three years to obtain it, and then finally after three years of learning they finally achieved it. And I think that's not necessarily something you need to aspire to, but it is a nice indication of persistence that if you keep on learning, if you keep on carving out time for it it's very likely that you'll achieve your goals.

Richie Cotton: In the movies when people have to learn new things, they just get a training montage, and then suddenly they know stuff. And one of the most disappointing things about real life is that it just doesn't work. You just gotta put in the time and keep going at it. 

Pieter Moors: I was gonna say it's like Neo in The Matrix, right?

When they upload or was it Trinity? When they upload new skills to learn and 

Richie Cotton: fight. That would be a really nice feature of Brains if you could just do that for sure. But that was a very ins- inspiring story th- from the user who spent three years trying to pass the certification. I'm curious, how many attempts did they have at that to pass?

Pieter Moors: I think it was over 10 different attempts spread out over time. But if you look into the data, sometimes they wanna try it, but they don't even try the exams, and then they come back, and then they try an exam, and then they come back, and they try it again, or then they pass, and they fail another.

So it's really, It's almost if this, this certification attempts was like a feedback loop for the user. "This is something I still need to do," or, "This is something I still need to learn," or, "This is something I," yeah "still am not good enough." 

Richie Cotton: That's actually quite nice, is you've not failed the exam.

You've gotten data on what you still need to learn. Okay. And the, the persistence of doing that to more than 10 times to, to pass, that's also very inspiring. All right. Yeah, on that note thank you for your time, Pieter. It was some great insights. 

Pieter Moors: It was a pleasure. Thank you.