One moment you’re listening to one of your favourite podcasts, the next you’re interviewing one of the guests yourself. That’s basically how Rick and I ended up at Nadieh Bremer’s place. An astronomer turned data scientist, turned freelance data artist, who has created captivating one-of-a-kind data visualisations for clients such as Google News Lab and The Guardian. Not surprisingly, Nadieh has been a much requested interviewee and conference speaker. Nonetheless, we set out a challenge to come up with creative questions and talked about the importance of design, the magic of pen and paper, and why art is priceless.

Design matters

Back in 2012 Nadieh, a graduate student at Leiden University at the time attended Deloitte’s Business Course where she discovered that although strategy consulting may not be for her, the analytics team within the Business Intelligence department could be a great match. As such, she applied for a position and eventually received a job offer. Being a layman in the field turned out to be helpful she explains: “At the start of each case I was far from a subject-matter expert. Therefore, it was quite easy to take a step back and remind myself of how I would have explained it to a younger me.” By doing this she learned about the importance of design: “People trust things that look good. Once our slides were well-designed the client presentations usually went smoother.”

Although aesthetics has always had her utmost attention, Nadieh only later found out there’s actually such a thing as a data visualizer. At a Strata conference in 2013, she was introduced to the principles of the JavaScript library D3 which turned out to be an important turning point in her career. “I fell in love with all the possibilities. For example, I saw these amazing interactive charts on the New York Times website and thought: hopefully I’ll be the one creating them someday.” However, she still needed to acquire the necessary skillset, so she started reading books about design and playing around with D3 in her spare time. Initially, she could barely put her newly acquired knowledge to use at Deloitte, though slowly but surely this changed as she became more proficient in the programming language: “Over time I became more and more efficient so that I could eventually use the tools in my everyday work for clients.”

From then on she increasingly specialized in dataviz and even changed career tracks to Adyen as a full-time data visualisation designer in 2015. After a year of primarily (re)creating dashboards, she eventually decided to continue as a freelancer because of the freedom and variety of projects.

Nadieh Bremer on Twitter

BIG NEWS! From 2017 I'll be a freelancer! 🎉 I'd love to work with you on creating unique #dataviz / data art #d3js https://t.co/duHF1lEtFy

Building community

As you can see above, Nadieh’s decision was received with great enthusiasm by the community which she had built up through her blog: Visual Cinnamon. I asked her about the story behind it: “The reason I started making these advanced data visualisation tutorials was because I couldn’t find anything like it at the time. After I finally managed to put together all pieces from various Stackoverflow threads, I thought: let’s share my learnings with the rest of the world so that others can hopefully benefit from it. Also, I was a bit driven to connect with the community: if I published my work, I may get under the attention of a few people and I’m no longer a complete outsider, I thought.”

Over time she slowly transitioned from blogs to conferences. In 2016 she gave her first talk at OpenVis called “SVG Beyond Mere Shapes”. Nadieh elaborates: “Here I discovered my true passion for presenting about data visualisation. Every single time I truly enjoy the conversations I have with the community members afterwards, especially if you see that people have suddenly become enthusiastic; that they realise there’s more to it than just bar and line charts.”

Always start sketching with pen and paper, don’t limit yourself to just the default charts.

She can’t stress the latter enough: “I would highly recommend anyone — regardless of their technical proficiency — to explore what types of data visualisations exist. Search for it online or pick up a good data visualisation book and look what’s out there.” Her second advice builds upon this idea: “Having this scala of visualisations at the back of your mind, always start sketching with pen and paper; don’t limit yourself to just the default charts. The fundamental question is: how can you best present the data? A sankey diagram, bubble chart or, network diagram? Don’t worry too much about the technical implementation, that will come later. Oftentimes, you can come a long way with your go-to tool even though you might not think so at the start.”

Tech vs Art

At first glance, you may not immediately associate programming with the data visualisation designer’s daily job. Hence, I asked her how much time she dedicates to the technical nitty-gritty as opposed to the actual design process. “The preparatory data cleaning can be long and intensive but most of the time I leave that up to the client. After some preliminary exploratory data analysis, I start brainstorming and sketching on paper which usually doesn’t take longer than 2 to 3 hours. Turning these drawings into pixels doesn’t take too much time either, in fact 90% of my time is spent on design, interactivity, browser compatibility, and responsiveness. Simply scaling back visualisations for mobile devices doesn’t do the trick, more often than not I really need to adjust the whole lay-out to make it work. So you’re basically designing while programming. Although it’s more of a technical job, it’s your personal creative style that makes your work stand out and enables you to earn a living as a freelancer.”

So then the question arises: how does she describe her own style? After a long pause — seeking for patterns in her work — Nadieh continues: “I love working with large datasets that tell not one but multiple stories. In general, I try to present the data in several dimensions so that the audience can come to the same conclusion from various angles. Although my visuals are intuitive, they are not always straightforward. There is usually a deeper level of knowledge and understanding to be revealed. I like to show the underlying data, so for example not only the average but also the variation around it.”

Art is priceless

Now that she’s working as a freelancer it’s no longer a matter of only data and art, but also business plays its part. What challenges does she face in that regard? “Pricing my services remains tough; it’s always difficult to estimate upfront how long I spend on a given project. Moreover, the return on investment for data visualisation is somewhat hard to determine for clients; unlike e-commerce, you can’t speak about higher conversions, it’s about positive brand associations.” One of her pricing tips is, therefore, offering multiple packages ranging from simple to complex: “That way you change the direction of the discussion from yes/no to which one.”

On a final note, she likes to share another word of advice with all aspiring data visualisers: “Just like data cleaning is an integral part of any data pipeline, data visualisation is an indispensable skill: knowing how you can effectively communicate your insights should be part of any data scientist’s toolkit. And don’t forget to experiment; not each and every visualisation you create has to be absolutely beautiful, it’s only the last one that counts.”

Data is beautiful once it’s the best possible version of itself: on the inside and outside. Although you should be careful to mess around with its core truth, it’s your duty to make it shine. It goes beyond communicating insights, it’s about exciting people and reaching an audience that has never heard of matplotlib, seaborn, and ggplot. After all, data science is an art and art is for everybody. Are you the next Picasso?

The Beauty of Data Visualisation was originally published in The Outlier by Pattern on Medium, where people are continuing the conversation by highlighting and responding to this story.

With no less than a four-person team of interviewers and videographers, I recently paid a visit to a highly requested interviewee: Dirk Fahland. An originally Berliner, who moved to Singapore, to eventually end up in Eindhoven where he’s has been an assistant professor in the Analytics for Information System group since 2013. In his work, he now focuses on what some describe as the intersection of data science and process science: process mining. It all started, though, in his youth where he always had an above-average interest in computers which, for example, shone through in his fascination with programming video games as a little child. Only years later, he discovered his true academic ambitions after a professor had recognised his passion for science and offered him a research assistantship. Drawn by the freedom and desire to make a tangible impact by working together with students, he obtained his PhD under supervision of Wil van der Aalst and Wolfgang Reisig and stayed at the TU/e ever since. Not surprisingly, he has always been closely involved in the field of education. As one of the founding fathers of the Data Challenges, we asked him about his vision on 21st century education, his lessons learned teaching, and of course his own research interests.

Educating the Eindhoven Engineer

One of Dirk’s main reasons to settle down in Eindhoven was the TU/e its education set-up that involves regular hands-on work. “I’m really a person who doesn’t want to do things just for the drawer or the books but make it work in practice. That doesn’t mean doing things for myself but also teaching other people to be good data scientists for that matter.” Although this idea is now very appealing to him, he admits he had to get used to it at first: “Here students really like seeing why it matters right from the start. This challenges you as a teacher quite a lot. Even for fundamental things you suddenly have to think: What’s the use of it? How can I motivate students to see applications?”

So how does he do that in practice? “I would rather spend 45 minutes discussing concepts and ideas than presenting definitions. This requires me to bring students to a level where they can actually have a substantive discussion with me. What works well, is asking students to prepare a video lecture, quiz or exercise at home. Then, in class I give them a mini-assignment that is actually too difficult for them. This may be a bit mean to students, but it forces them to start thinking about the problem and eventually get the hang of it.”

As a university that has been experimenting with video lectures and clickers since the very first hour, what’s his vision on the role of technology in modern day education system? “Technology alone won’t do it; I really need to know the subject so well that I know what I can give to students as preparation. As every teacher will tell you: you need a couple of years to get this right. The big challenge is to make it interesting enough for students to invest their time upfront.”

Process Mining vs Data Science

Given his experience in both disciplines, I asked him how they differ from one another: “Process mining deals with time and causality in complex dynamics; we’re not just looking at a time-series of values, but we are looking at how entire organisations move and operate. This leads to discrete events which usually follow several patterns, for example a sequence. However, an organisation is not sequentially structured; not everybody does things in sequence, things are happening concurrently. This is where process science brings in unique concepts and provides models and techniques to describe these dynamics that do not exist in classical data mining.

Modern data mining is dominated by machine learning techniques that allow you to classify or predict. In data science, we are happy to use any model that sufficiently approximates what you’d like to do. Especially with neural networks, we’re fine with these black boxes as they are. That will never work, however, for an organisation that tries to understand how its people are operating. In process mining, we need glass box models to understand where the organisation is not working well and can potentially improve. This allows us to x-ray an organization. We can see deviations or ways of working that are costly like data-entry errors, or highlight bottlenecks that delay a process like waiting for a signature from a business partner.

Further, the dynamics we investigate are extremely complex: there is never one model that will do. Depending on whom you ask within the organisation, they will have a different view on what matters to them. While algorithms often spit out a single answer, you need different answers depending on the question people have. So your solution should be multidimensional and this is what makes process mining so unique.”

Process mining may be a unique field, but it’s still far from data science in terms of media attention. Why is that? Dirk explains: “On the one hand, process mining addresses a problem that usually takes place in the back-offices. It doesn’t happen on the frontpage of a company. Another reason is that the field of process mining is relatively young. The first process mining papers were published in the late 90’s, and the first algorithm was invented in the early 2000’s. So we’re not even 15 years from that point. There are now a couple of start-ups that begin commercialising these concepts. The market is still being developed and not by taking customers from one another but by simply convincing new customers that it’s a useful technology. Recently, a market study found that the total market will grow into a multi-billion market in the coming years, and we’ve only just started.”

The process mining market will grow into a multi-billion market in the coming years and we’ve only just started.

People & Politics

“What makes adoption of process mining so difficult is the moment you start analysing a company’s processes through data, you start providing facts to a discussion that so far has been dominated by opinions, people’s interests, and company politics. In the past, often managers were responsible for internal process optimisation. All of a sudden, some young data scientist comes along and tells them where the problems in their processes are. In that sense, the authority of who can say whether a process is done right or wrong has changed. It’s a very slow transformation that is happening bit by bit all around the world that takes plenty of time.”

The authority of who can say whether a process is done right or wrong has changed.

Networking Effects of Processes

“In the past we didn’t have enough data for process mining. It was very difficult to convince customers to provide their data to develop new process mining techniques. With the market growing, the chance of industry projects is increasing.

You now see commercial process mining being used to analyse and improve local small-scale processes. What I personally expect to happen in the near future is that companies start looking not just at one process in isolation but how processes within a organisation relate to one another: networking effects of processes. In the longterm you want to be able to analyse entire value chains of industries to improve their efficiency.”

Responsible Data Scientist

After we had finished our discussion about process mining, we talked about a topic that’s getting more and more media attention these days: data science ethics. Dirk starts off with the obvious Volkswagen scandal to illustrate how much more complicated matters can be in data science: “If you train a model of job applications over the last 10 years to predict whether a particular application will most likely lead to a hire, and then let this model help you in this hiring process, you’re probably not doing something against the law. But what you have to realise is that there may be bias in how people have been hired in the the last few years. It was in the news yesterday that Amazon had to pull an AI program that was doing this based on training data from the last 10 years. It was biasing against women and favouring men because they were predominantly hired.

What we did wrong in the past is not in the data, it’s outside the data.

I wouldn’t know how to properly regulate this on a legal level because you can’t prescribe the neural network architecture or the validation you need to do because there is always something that doesn’t fit the criteria. It comes down to the responsibility of each individual data scientist to constantly be aware that when we try to learn from the past, we have to understand what we’ve done wrong in the past. What we did wrong in the past is not in the data; it’s outside the data. Knowing this, and embracing this, as you do data science, is what a data scientist must do. You can’t argue your way out there is no law.”

The Flipside of the Machine Learning Revolution

“What happened to Facebook — letting its algorithms decide which content to show and promote in, for example, Myanmar— is a good example of what I’m worried about. The moment you automate anything and leave it running by itself without additional human checks and balances, you leave it open to ways you never intended. If we trust sophisticated algorithms because they seem so smart that they have human like capabilities, then we’re making a mistake.

There are many situations where, after we have solved the problem, we automate it, so that we can focus on more interesting ones. This has always been the promise of the machine learning revolution. However, algorithms don’t always have an extra eye to detect once things go out of line. Everybody makes mistakes, but machine learning models can’t correct them, only humans can.”

After we had shot the article photo and waved goodbye, I walked down the aisle to the elevator to go all the way down from the seventh floor. While cautiously observing the floor indicator counting down, I tell my fellow interviewers: “As you said, that was indeed a very nice guy.” Still, somewhat puzzled why nobody responded, I turn my back once the elevator finally stops and suddenly notice a familiar face in the corner of my eye that whispers in my ear ‘have a nice day!’. Red-faced I left the elevator and concluded: even in process mining perfect timing doesn’t exist…

Process Mining, Data Science & Process Science was originally published in The Outlier by Pattern on Medium, where people are continuing the conversation by highlighting and responding to this story.

From multiple offices spread around the globe, Studyportals is trying to make education transparent globally by listing thousands of international study opportunities in one place. With over 30 million unique visitors from 240 different countries per year this “Google for studies” is not only one of the fastest growing scale-ups of the Netherlands, but also a playground for every data enthusiast. Last April Agis and I visited their Eindhoven HQ to interview Lead Data Engineer Tara Farzami in the Control Room; the same room where I had been job-interviewed four weeks prior to that. Although this time I had the privilege of asking questions, we still talked about work but also about fancy research proposals, the importance of teamwork and whether or not you should mention the significance of your results during presentations.

Data Science is a Team Sport

Originally from Iran, Tara moved to Germany for a masters in Neural Information Processing after she had obtained a bachelor degree in Statistics in her homeland. She explains: “Somewhere in the middle of college I realized that I’d like to apply my scientific toolkit to better understand the brain, not only from a biological perspective but also from a statistical one. The master program in Germany has been specifically designed for people with a Science, Technology, Engineering and Mathematics (STEM) background. It focuses on simulating the brain using neural networks by tuning mathematical models and testing them on data collected from a real brain.”

Thereafter, she joined the new neuro-informatics department at Radboud University as a PhD-candidate. Halfway through her PhD she realized that academia is no longer for her: “They train you to become a successful researcher, someone who can independently investigate and solve a problem. At the same time, I believe teamwork is a very important part of any success. In machine learning most of the computational neuroscience labs in Germany had good publications simply because they collaborated with other labs that mastered their craft. However, you don’t see this in every university. So it’s fair to say that teamwork is a must in business, in academia it’s a choice though.”

Teamwork is a must in business, in academia it’s a choice.

Fancy is not always better

As a researcher you need a leader that creates the right environment to make you shine. In practice, this is not always the case which is why you see a lot of smart and hardworking individuals burn out and leave academia. In the end, it’s all about who writes the fanciest proposal, not necessarily about the best scientific idea. A large network and a number of highly-cited publications will get you far but aren’t always guarantees for a grant. If you don’t manage to win any of them it’s very hard to stay in your current job.

It’s all about who writes the fanciest proposal, not necessarily about the best scientific idea.

Tara mentions another risk of working as an academic: “In most work environments there’s a high chance of getting promoted and securing a permanent employment if you live up to the expectations. In academia, however, many postdocs still have to deal with the uncertainty of a temporary contract.”

Data Science vs Data Engineering

Given her experience in both technical fields, I asked her to share her view on the differences between the two. “By definition a data engineer is in charge of building and maintaining a data system (also known as ETL: Extracting, Transforming and Loading data). So you make sure that whatever data comes in, comes out without losing any information (unless you would like to process it on purpose). A data scientist, on the other hand, cleans the data and then starts extracting patterns. Throughout the process they help data engineers improve the data validity by spotting outliers. For example, a tracker in the front-end may be broken that leads to unusual patterns.

In general data scientists also have some gut feeling: ‘hmmm… I think this data got something in it!’. That is something that engineers are sometimes lacking a bit. Therefore, data scientists moving to data engineering are a really valuable asset because they bring these insights with them. I wouldn’t say the one is better than the other, but I do think mutual understanding of both fields will be really helpful. Knowing the data engineering principles helps you become a better — and less naive — data scientist. For example, you can give suggestions on how to load and prepare the data optimally or what data is good to be collected.

So, in short, you can’t have a data science department without a data engineering one, because otherwise you don’t have any data for your analysis. But you also need a data science department to gather proper feedback about the quality of your data.”

Leadership in Data

Initially Tara started out as a Data Science Researcher, now she’s leading a team of data engineers. What’s her view on leadership in data? “A manager’s highest priority should be protecting the people from all the noise and distractions. At the same time, you need to inspire the team so that individual dreams become team dreams.”

She continues how this affects her recruitment policy: “I tend to look a lot at potential in people: I don’t necessarily hire someone with twenty years of working experience. I prefer to work with people whose eyes start shining once they start talking about data. As a leader it’s your job to provide them with opportunities to become a better version of themselves.”

Same story, different versions and all are true

Every few weeks Studyportals organizes a company wide showcase where all engineering teams give an update about their progress. I asked Tara how she deals with the variety of backgrounds in the audience while presenting there: “The first thing that comes to my mind is: stay away from the details. It’s very important to put yourself in someone else’s shoes. In practice this means I usually present differently to the engineering department than to colleagues in sales and marketing. For a non-technical audience I typically focus on high-level results. For example, this recommendation engine is winning because all metrics are going up. You don’t necessarily need to explain the details like why, how much, whether it is significant or not. Of course, behind the scenes you should check for that, but you don’t need to present it on stage. On the other hand, most data scientists and data engineers really do want to know such details, it makes them excited and they are really good at picking them up.”

Job Hunting Advice

Finally, I asked for her advice for students entering the job market. “Data Science is a bit of a buzzword these days: some companies need simple bar charts but are looking for data scientists. I don’t see that as data science, it’s just basic statistics. Nowadays anyone who brings value with data is a data scientist. But as a candidate I think it’s good to choose your job title wisely, that it suits your interests best.

Zvi on Twitter

Data Scientist" is a Data Analyst who lives in California.

Also realize that job interviews are the opportunity for you to ask questions, not just the interviewer. So keep questioning your potential employer until you really understand what job you are going to do; that you don’t end up in the wrong place at the wrong time.”

Data Science vs Data Engineering — so can we conclude the one is better than the other? Nope, we can’t. What we can say, however, is that they are different but inextricably connected at the same time. The one cannot live without the other, because it takes both sides to build a bridge...

Which side are you on? Let us know in the comments down below!

Data Science vs Data Engineering was originally published in The Outlier by Pattern on Medium, where people are continuing the conversation by highlighting and responding to this story.

Teaching others is one of the most rewarding things you can do and you learn a lot from it. In the last few months I have had the privilege of teaching to a variety of backgrounds about the art of data. Here you find my five biggest lessons learned.

1. Put yourself in their shoes

One of the advantages of still being a student is that you know how they think. Students highly appreciate it if you speak their language.

Don’t get me wrong here: you should always keep it professional. That doesn’t mean, however, you can’t create a positive and stimulating classroom environment.

2. Giving feedback is powerful

Feedback is not only important but also very powerful. A poor grade without any explanation is like getting a speed ticket without a photo.

Mistakes are oftentimes highlighted while understanding remains underexposed. I think we should challenge this idea and celebrate the triumphs in just the same way.

3. Peer reviews are great (but be careful!)

While feedback is essential for learning, it is also very time consuming to give. That’s why peer reviews can be a great additional source of feedback. One format that works well is tops & tips.

It’s a different question, however, whether you should allow students to grade each other’s work..

4. If you are excited, they will be too

In the eyes of students you are a role model. They think you know the answer to each and every single question (although you obviously do not).

However, teaching is more than sharing information, it’s about inspiring people. That happens both inside and outside the classroom and the little things really do matter.

5. Create a 21st century classroom

Admittedly, there is tremendous opportunity to better integrate technology in our current education system. Not only do students love it if you do, it saves you time too.

Some of the tools and channels I played around with lately: Github, AllAnswered, YouTube and TypeForm. Give it a try yourself below!

Learning never ends

After the summer break I’ll be back in the classroom. As a student again. But that doesn’t mean my teaching ends.

Teaching has been a fantastic learning experience and it will always be.

5 Lessons Learned Teaching Data was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

Last month I got the opportunity to pick the brain of some of the T500 nominees during The Next Web Conference 2018. In this article, I’ll share their views on a frequently recurring presentation topic at the event: Artificial Intelligence (AI). Not only did I ask them about the opportunities they saw, but also about the — at least as important — challenges and risks they identified.

Daniëlle Tump (25)

What are your opinions on Elon Musk’s view that AI could become more intelligent than the humans that created it?

I’m in two minds. On the one hand, I do believe that AI can teach itself to become smarter than humans. But on the other hand, I think that if a human creates AI it can therefore control the limits in which it functions. For Brain Computer Interfaces (BCI) I don’t see any problems because it requires humans to read brainwaves. BCI still has a long way to go, before being able to function in a more complex manner. At present, BCI can be used in the healthcare system, for example. It can help people move, eat or steer their wheelchair. But it can also be used for entertainment purposes: changing the song you’re listening to just by thinking about it, instead of typing it on your phone or computer.

Do you think there is sufficient knowledge about the brain to make optimum use out of BCI?

Understanding the brain is a huge challenge on itself, let alone understanding each individual brain. Each brain is different, depending on the person and their surroundings. But I don’t believe we should delay further development of BCI, just because we don’t understand everything yet. There are already several useful applications, despite not having a complete understanding of the brain and its capacities. That said, it will become necessary to develop policies when BCI is used in daily life. If used for a polygraph, for example.

Coen van Hees (23)

What do you think about the possible risks in the AI industry?

As an entrepreneur, you tend to think in opportunities. But I think this is just the tip of the iceberg: there are lots of positive sides, but we shouldn’t forget to keep an eye on our own safety. There should be a lot of transparency within the AI industry and we need to be aware of the scenario in which computers become smarter than humans. For that reason, we should already develop laws and regulations based on that possible scenario to make sure AI has a positive impact on the world.

In which fields do you see any business opportunities for AI?

Last Christmas tens of millions Alexa devices were sold in the US. It doesn’t take long before we see the same numbers here in Europe. Given the rapidly increasing percentage of searches through voice, I think in 3 to 5 years time personalized AI assistants will be a big thing. Also, conversational AI will reach new heights: currently it is mainly being used in the customer service industry but in the short term we will also see applications in other verticals.

Lyuba Polyanskaya (22)

How do you see AI develop in the next few years?

At the first day of the conference, there was a talk about emotional artificial intelligence: how to make AI more empathetic. In my opinion, this is a direction we are definitely going into — I found out that 37% of all people want to have their AI more resembling a human in a psychological sense. They want to have the experience of talking to a person when they talk to a chatbot. I think AI will become ever more advanced and it will become virtually impossible for a human to distinguish between talking to a real person or a robot. This could lead to personal psychology assistance. Machines will thus be able to help with empathy and emotions in general.

What challenges do you see in the field of AI?

Personally, I think the biggest problem is that personal consumer data are being used by corporations while the least minority of people understands to what extent our data is being exploited and how it exposes our private life. That’s why I think we should start educating people to have computer literacy. For example, people just unconditionally accept cookies. They don’t read the terms of agreements at all. There should be courses on educating people on the privacy aspect of digital work and of course there should be new regulations.

Yassine Gherbi (21)

How do you think technology affects our ability to follow our dreams?

I was helping my little brother in choosing a study. When I checked this website where you could see your chances of being able to find a job after your study, I was shocked: everything in the technology branch had a 90–100% chance of finding a job, but the rest only around the 60% or 70% mark. Technology is developing so fast, it makes me wonder if people still get the chance to follow their dreams if their job guarantee is so low. Machines are taking over a lot of jobs and this is not a bad thing per se, but I do think lots of people will become very unhappy if they start doing jobs they don’t actually enjoy.

How do you see technology having an effect on society?

At the moment, we’re trying to solve everything with technology. Sometimes I think: shouldn’t we be more careful in the adoption of state-of-the-art technologies? For instance, take the example of the self-driving cars from Tesla: people started trusting it right away and then this awful accident happened leaving a woman dead. On the other hand, given the developments of medical automation systems, I would not be surprised if in 20 years from now you can order your medication online without the intervention of any GP.

Whether that will actually happen is hard to say, but one thing is for sure: the future of work is going to look radically different and AI will play a big role in that respect. Computers will be able to write articles undistinguishable from ones written by humans. How long will that still take? Only time will tell, but it might be sooner than you think. Talking about that, did I actually write this piece of content, or was it already created by AI? Now think again and take action before you are the next one being irrelevant.

P.S. Special thanks to Antoine and Georgia for helping me bring this republished article to life! Read the original article over here to learn more about inequalities of opportunities and the future of education.

The Future of Artificial Intelligence was originally published in The Outlier by Pattern on Medium, where people are continuing the conversation by highlighting and responding to this story.

For the second year in a row HEX took place in the breeding ground for innovation as well as the smartest city of the world: Eindhoven. 300 people with over 60 different nationalities gathered together for this 32-hour long hackathon hosted at the 8th and 9th floor in the ‘Veemgebouw’ at Strijp-S to build state-of-the-art technology that will be part of our everyday life in a world 32 years from now. It’s no surprise that such an exciting happening attracts a lot of press and attention. As such, plenty of others have already written about the participants’ experiences and the atmosphere during the hackathon. The reason I still decided to dedicate another article to this event is to focus on the problems, ideas and implementation of the 3 winning teams. After all, building the future does not stop after just 32 hours.

🥇Kitten Run VR

Laura Donadoni (Italy), Davey Hurkmans (Netherlands), Nikolas Skartsilas (Greece), Sergio Soto (Mexico) & Philipp Durnay (Germany).

In hindsight, this internationally diverse team must have been positively surprised that they were eventually awarded the first prize of €1500. After all, on stage their presenter, Philipp, openly acknowledged that the team never expected to be selected for the top 10 teams shortly before that and therefore had not even prepared a pitch (or perhaps it was just a clever tactic to come across as humble..) Anyway, despite the fact that you may have improvised your final pitch, you guys somehow managed to convince the jury and I must admit that your proof-of-concept really hits the nail on the head in terms of the solution for the identified problem. With a bit of luck you may again unexpectedly take home any of the grand prizes at Europe’s biggest hackathon, Junction 2018, later this year!

Problem: VR equipment is still relatively expensive and therefore its potential is not yet accessible to the masses: at current you easily pay over $200 for a VR-headset alone, not even including accessories.

Solution: Google Cardboard VR Sports App (Android & iOS) that can be used in conjunction with any IoT-devices you already have at hand. For example, motion sensors, a pressure mat, voice input devices (e.g. Alexa) or even a smart tooth brush!

Proof of Concept: Easy-to-play Temple Run style mobile game in which the player needs to avoid running into obstacles such as kittens (you guessed it..) As you can see in the picture above Laura wore a harness to which custom hardware has been attached. This allowed for motion tracking throughout the Kitten Run VR game (e.g. jumping as she elegantly demonstrated).

Tagline: A virtual workout for body and soul… and there’s kittens
(check it out yourself in the video below! — made by Sergio 👏)

🥈CherryTea

Maxim Markelow (Russia), Andrei Vinogradov (Russia), Yulia Mishina (Russia) & Lucenko Kirill.

In university alone I already get overwhelmed by the number of requests for commitee work and board years. As such, it’s challenging to keep track of all the opportunities for voluntary work that are out there, let alone all the opportunities in, for example, Eindhoven. As Bloomberg highlights in the Future Society challenge especially smaller charities suffer from this lack of visibility. The app that you have developed, CherryTea, ticks most of the boxes regarding the assessment criteria (idea, creativity, demo, design, business model) and you even integrated the Twilio API to inform volunteers about their application status and possible changes for the event. Although I really like the problem you are trying to solve I’m curious to see how such a two-sided marketplace will work out in practice. In a way it’s a chicken and egg problem: not only charities need to become aware of the existence of the app but also the potential volunteers living in close proximity to the event. On the other hand, the most successful unicorn companies of today also had to overcome this challenge some day..

Problem: People miss out on opportunities to volunteer for local small-scale charities.

Solution: Peer to peer philantrophy Android app that matches supply and demand of volunteers. Recommendations within the app are based on the volunteers’ current location and preference for type of work (e.g. first aid).

Proof-of-Concept: Voluntary work categorized by type (Medicine, Ecology, Children, Animals, etc.). Users can view details about the event (description, date, location, number of volunteers asked vs signed up), get personal recommendations, sign up for an event (love the cherry shower after sign-up..) and stay up to date about last-minute changes through SMS. Moreover, the profile page showcases the volunteer’s past and upcoming activities in order to showcase trust.

Tagline: Want to contribute to your city? It’s time for CherryTea!
(check out their screencast below — if you miss some nice background music it may help to daydream about a bunch of delicious cherries..🍒)

Note, the fact that the requests do not match up with the categories is probably due to the usage of self-generated data.

🥉FleetVisio

Jérôme Fink (Belgium), Andrian To (UK), Jian Hong Lim (UK), Soulaiman Assikiou (Spain), Isabella Nubari (UK).

Again, a multidisciplinairy and multicultural team that wins a prize which reinforces the idea that having multiple points of view can be really beneficial for your overall team performance. Although their official team name, Table 18, might have not been so creative the combination of their highly technical solution to the vehicle optimisation problem for VDL-buses — combined with the most visually pleasing dashboard I have seen — turned out to be successful. As one of the few teams you managed to finish your well-structured pitch within 2 minutes and convince the jury, despite the fact that your Github readme says that you had no idea what you were doing (fortunately, the jury probably did not look into that..)

Problem: Opportunity to save costs on the purchase, operating and charging of electric VDL-buses.

Solution: Linear programming algorithm to determine the optimal number of vehicles, vehicle type and battery type as well as a realtime overview of the battery condition of operating buses as an indication of future costs.

Proof-of-Concept: The visual dashboard gives a good impression of how the final product could look like. Transportation managers have an overview of all the operating and charging busses for each line as well as the real-time battery condition and the total number of travelers. The fleet optimizer calculates the optimal distribution of bus types (LFA/LF), battery types (MP/HP) given the constraints entered by the operator in order to match total demand per city under minimal costs. Additionally, technical feasability has been demonstrated using existing Python optimization libraries (e.g. PuLP).

Tagline: An interface allowing administrators of public transportation fleet to visualize metrics about their network (have a look at their clickable InVision dashboard below!)

Click here to view the project `FleetVisioApp`

Did you spot the next Facebook among these 3 ideas? One that may be an integral part of our daily life in the year 2050? If not, then have a look at the other 54 teams in order to spot what-might-be a hidden gem or of course build the future yourself during #HEX 2019!

And yeah be prepared for the most intensive 32 hours of your life..

P.S. If you know (or just want to gossip) who👆this person is, feel free to do so to make sure he or she is there again in 2019!

P.P.S. On Sunday morning you might have seen me walking around to capture all your sleepy faces 😴. Have a look below to compare your team’s energy level after 29–31 hours with your fellow participants :-)

As a JADS-student you have likely heard through the grapevine of the Data Science Study Association Pattern which had already spread across two cities and now it has also formally been established in the capital of the province. The new ‘Master Committee’, including its five-member body, will be officially presented during Pattern’s General Member Meeting next month. Although they are currently still in the formalisation phase in this article I can already lift the veil on their future plans and ambitions as well as a personal introduction to the people behind the committee.

A Local and National Network

As the Master Committee is a brand new initiative you may wonder how it actually came about; at least I did so I asked Robbert (Secretary & Treasurer). “Well, honestly it’s kind of a boring story. During the Thursday afternoon drinks Nigel (program coordinator JADS, Ed.) told us about the activities Pattern was organising after which we got in touch with them.”

Even though their founding story might not be that spectacular they have big plans for this semester: “We are planning on organizing activities to connect students with companies, but also with other bachelor and master students. After all, the long term idea is that Pattern’s board will consist of a variety of student backgrounds and that it will mainly operate from Den Bosch.” With that said, Jennifer (President) emphasizes that they don’t want to limit themselves to the Brabant region: “In Amsterdam, Rotterdam and Utrecht you currently don’t find any similar data science programs like JADS offers which leaves tremendous opportunity to put our master students in the spotlight and collaborate with the industry. For example, through guest lectures, inhouse days and hackathons.”

Risking Your Life for a Photo

After this brief interview it was time to shoot some photos for the article. In essence that should not be too difficult thanks to all the beautiful spots the Mariënburg has to offer. It turned out, however, given the rebellious nature of the Master Committee that was a misconception. They were in for a challenge: obviously striving to master the perfect image. So against our better judgement, ignoring all the “NO-ENTRY” signs, red white striped barricade tape, ‘risking our lifes’ — after we had convinced a construction worker that it would just be a single picture — we ended up with the header image above. I guess you probably didn’t even notice it was taken in the middle of the courtyard, did you?

Afterwards we continued our way to the backyard for the last series of photos to complement the personal introductions you find below. Note that although the committee members pretend to be smiling they were actually hiding the fact it was freezing. Especially for Rehan — someone who normally enjoys the warmth of a steamy bath — the cold must have been burning. For 19 other facts about the Master Committee keep on reading..

Jennifer Koppens (President)

- Studied: Language & Communication (with media studies) and informatics.
- They call me: Too honest for her own good.
- Weirdest hobby:
Binge-watching my favourite series.
- Favourite country: Malaysia.

“And those who were seen dancing were thought to be insane by those who could not hear the music”

Robbert Manders (Secretary & Treasurer)

- Studied: Business Economics.
- My hero: Elon Musk.
- Best experience: Climbing a 3.7 kilometres high volcano in Indonesia.
- Dream: Visit a TED Conference and live in San Francisco.
- Favourite food: Salmon with potatoes and spinach.

“I guess it comes down to a simple choice: get busy living or get busy dying.”

Rehan Saif (External Relations)

- Studied: Technology, Policy and Management (TU Delft).
- Weirdest hobby: Bathing for hours more than advised.
- Favourite sports moment:
Louis van Gaal bringing in Tim Krul just for the penalty shootout during 2014 World Cup quarterfinal.

“When all else fails, take a bath.”

Martijn Keizer (Promotion)

- Studied: International Business Administration (UvT).
- Alter ego: Phoxy.
- Guilty pleasure: Singing while riding my bicycle.
- Greatest achievement in past 6 months: Running the marathon of Eindhoven to raise money for charity.

‘Give that man a cigar! Bullseye!’ (First Blood part I)

Lieke van Uden (Activities)

- Studied: Econometrics (UvT).
- Enjoys: Photographing during travels.
- My super hero: Roger Federer.
- As a data science student do you believe in coincidence: Yes.
- Favourite country: Japan.
- If I could change the world then: I would make people more empathetic.

“Better regret the things you did do, than the things you didn’t do”

Hopefully that all sounds “masterlijk” (i.e. Dutch slang for “awesome”) to you! Perhaps you even have your own master plan in mind or want to organise the next master class for the master students? Let them know in the comments down below or simply join this new initiative. After all, the Master Committee is always looking for new members to expand their team. Also for you pre-master students out there: don’t shy away from the big boys, so feel free to drop them a line too. And don’t forget to follow D.S.A. Pattern on Facebook and claim your membership to stay up to date about all the upcoming activities.

Meet the Master Committee! was originally published in The Outlier by Pattern on Medium, where people are continuing the conversation by highlighting and responding to this story.

A warm welcome to the Mariënburg, the second (and now: first!) home of many students, academics and start-ups. The place where both data science and entrepreneurship is pursued, where classics revived and where the JADS-flag always flies (no not literally, we are not at Zweinstein..) Like your freshmen welcome week it’s a new beginning and another milestone in your life. By choosing for JADS you showed that you are an early adopter, an innovator, a born entrepreneur or perhaps a ‘wantepreneur’. But you’re also a guinea pig, because not only the monastery but also the courses are constantly under development. While there may be many unknowns, remember — no matter what — you are at the forefront of technology and innovation and in pursue of the sexiest job of the 21st century. In no time you can formulate advanced SQL-queries, program in Python and predict the future with Machine Learning. But at this stage, while you still have no clue what you’re getting yourself into, you might desperately look out for a glimpse of what is coming up. With that idea in mind, this ‘handbook’ gathers all the bits and pieces of everything you want — and perhaps don’t want — to know about JADS (A) and the premaster courses (B).

Table of Contents

PRACTICAL
- A.1 — Study Books
- A.2 — Email Correspondence
- A.3 — Notebook
- A.4 — Student Housing
- A.5 — Academic Calendar
- A.6 — Course Exemptions
- A.7 —Resit Registration
- A.8 — Seats During Exams
- A.9 — Being Late During Exams
- A.10 — Rounding Grades
- A.11— Pass-Fail Criteria
- A.12 — Thursday Afternoon Drinks
- A.13— The Outlier
- A.14 — Change Password of JADS Hub
- A.15 — iStudent App
- A.16 — JADS PowerPoint Template

COURSES
- B.1 — Introduction to Data Science (JBP010)
- B.2 — Programming (JBP020)
- B.3 — Data Structures and Algorithms (JBP030)
- B.4 — Introduction to Machine Learning (JBP040)
- B.5 — Foundation of Databases (JBP050)

A. Practical

A.1 — Study Books 📚

That is where it all begins, right? Dabble in piles of literature to become the next level data science genius. No, not really to be honest. In fact, in the premaster program the focus is more on gaining practical skills. All the necessary materials will be uploaded on Blackboard and the JADS hub. In other words it won’t cost you a dime, lucky you!

A.2 — TU/e or UvT? 📩

You probably know that JADS is a joint-initative of Eindhoven University of Technology (TU/e) and Tilburg University (UvT), don’t you? This basically means you have the best of both worlds: you can make use of the student sport centers in Eindhoven and Tilburg, you can follow additional language courses at the UvT while you can buy an affordable notebook through the TU/e, you receive graduate diplomas from both institutions, and the list goes on and on.. So kind of two for the price of one. That sounds great, right? Honestly, it can sometimes be a bit confusing too. Am I supposed to write down my TU/e student number or my UvT one? Which of the two email accounts should I use? More importantly, which email address is read by my professor? Generally speaking, teachers use the email address from their mother university: so if they’re originally part of a research group in Eindhoven they most likely read their @tue.nl email. But be careful to double-check it. I can tell you I’ve got some bad experiences sending assignment files to the wrong one. So it’s up to you: 50% chance. Do you want to take a chance? Is luck on your side? If not, then send your urgent emails to both.

A.3 — Notebook 💻

Due to the continual exponential growth of data and the rise of big data you would almost get the impression that having a supercomputer at your fingertips is a must for anyone in the field of data science. Actually, truth be told, all datasets I have worked with so far were all very small in size (<100 MB) and few computation intensive tasks were needed. But keep in mind that might change throughout the remainder of the program. That’s why it is probably more meaningful to consider current master students’ point of view. For that matter check out this recent Facebook post (update April 23: post has been removed) in which a prospective DS&E student asked whether there is a required notebook configuration in terms of CPU, RAM or Windows version. In summary, a couple of master students mention that any notebook with ≥ 8GB RAM and at least an i5 processor will be more than sufficient for your coursework.

Tip: the TU/e offers the Notebook Reduction Program to (pre)master students which basically means that you get a substantial discount on a relatively high-performance notebook bought in bulk by the university.

A.4 — Student Housing 🏚

As many of the premaster students did their bachelor in Eindhoven, Tilburg or Utrecht, the majority still lives in one of those three cities (20–30 minutes away by train). However, if you moved from abroad it makes sense to find a place in Den Bosch. One option is the Mariënburg itself: Gapph rents apartments on a short-stay basis (<6 months). If you are looking for a permanent residence instead: Camelot Europe offers 1- and 2-person student rooms for a minimal renting period of 6 months. For more details about the rooms, price and location have a look over here.

A.5 — Academic Calendar 🏝

It’s still winter but due to our lovely Dutch weather I would not be surprised if you are already looking out for summer. So in case you’re tempted to score those early bird fly deals to your favorite tropical holiday destination, you may want to have a look at the exam schedule first.

Tip: In the electronic study guide you can find the exact examination dates for each course (Course Code > Examination schedule).

A.6 — Course Exemptions ⚠️

Possibly you are in a home stretch: trying to pass your hardest college course in order to secure your bachelor degree. Or you may even be taking the pre-master on top of all your bachelor coures. In any case, it is good to know that you may be eligible for exemptions for some of the premaster courses. Here is the email I received about it:

Tip: Given that you are only enrolled for the premaster, you may have to pay less tuition (i.e. it’s partially refunded, but you have to be patient..)

Update 30th of January: Important to note that once you decide to follow an optional course it will become part of your exam program. That is to say, you must pass it. Thanks for the tip Nigel!

A.7 — Always Register for the Resit 🔮

Do you know that moment you walk out of the exam room, either feeling relieved and confident or sad and terrified by what just happened in the past 180 minutes? Even though your gut feeling is often right, I would still recommend to register for the resit at all times. After all, it’s not unusual that at the moment of the resit registration deadline you haven’t received your grade for the first attempt yet. So your safest bet is to simply always register for the resit.

Believe me, you will not be the only one doing that. As a result, expect to be bombarded with de-registration emails 1–2 weeks prior to the resits..

A.8 — You Get Assigned a Seat for Each Exam 😳

Yes, it has already been pre-determined where you have to sit in advance. So, you have to be lucky to sit next to your closest and most brilliant classmates..

A.9 — Being Late During Exams 🕥

Having been 28 minutes late myself — due to folks walking on the railway tracks — I would not necessarily recommend it, but it’s good to know:
you are allowed to enter the exam room until 30 minutes after the examination started. Yep, just in time, like always..

A.10 — Grades are Rounded to the Nearest 0.5 🤔

It’s probably best to illustrate this with a numerical example: if you think an 8.7 becomes a 9 then you are sorely mistaken. It will be rounded downwards to an 8.5. On the other hand, an 8.3 becomes an 8.5 as well.

A.11 — Pass-Fail Criteria ✅

In order to pass a course both your average assignment (!) and written exam grade must be at least 5.0. That is to say, an 8 for you exam cannot compensate a 4 for your assignments. Moreover, the total weighted average grade must be 5.5 or higher.

A.12 — Thursday Afternoon Drinks 🍻

Most Thursdays the Borrelcie (i.e. drinks committee) organises drinks from 17:30 onwards (follow the Facebook group for the exact dates). Stampcards can only be bought with cash, because you know… only by evading taxes we can serve you 8 cold beers for just €5.

A.13 — The Outlier ✍️

Bad luck? No not all, thirteen is an outlier and so are the academics and entrepreneurs showcased on The Outlier. You can read about the story behind JADS, the history of Data Science, the AI World Summit, the differences between an entrepreneurial and scientific career, what the “Stanford for Data Science” is, and much more!

As I (yes, I am also a writer for The Outlier!) wrote earlier, there is something for everyone. So regardless of whether you want to be involved as a writer, editor, interviewer, photographer, designer or promoter get in touch with me or one of the other committee members!

A.14 — Change Password of JADS Hub 🔒

Undoubtedly, your new classmates have your best interests at heart. Still, you might want to consider changing the default password (something like “qwerty”) to something even less original. For a step-by-step tutorial on how to do exactly that see section 3.1 in this guide.

A.15 — iStudent App 📱

Although this mobile app was originally developed for students of Tilburg University, you may benefit from it as a JADS-student too (e.g. to have a quick glance at your schedule). You can find the app on Google Play (Android) and the AppStore (iOS).

A.16 — JADS PowerPoint Template 🖥

A good first impression can work wonders and well begun is half done, so better use a fancy PowerPoint to catch your audience’s attention from the very start. You can find one — in line with JADS’ slick brand identity — over here (both PowerPoint and Keynote).

B. Courses

During your bachelor you might have had the luxury of simply googling for your subject’s course code to stumble upon an abundance of course summaries, notes and exams on for example Stuvia or Studeersnel. As the Data Science & Entrepreneurship program is not even 2 years old you won’t find much there, if anything. Besides, you don’t want to face a paywall, do you? For that reason a shared folder has been put together.

What follows below is a list of tips & tricks for each and every course as well as a description of the corresponding documents in aforementioned folder. At this moment of time some remarks might not ring a bell yet, but they probably do once you revisit them just before your exams.

Update 7th of March 2018: all course related files have been removed upon urgent request of one of the lecturers.

B.1 — Introduction to Data Science (JBP010)

Hopefully you enjoy guest lectures because you’re definitely going to experience a lot of them (>10). It can certainly be interesting, but remember that in the end it is your personal quantified project that counts (70%). Two great tracking applications you might consider installing are “RescueTime” and “Moments” which automatically track your desktop and phone usage respectively (without draining your battery!)

Exam
In the final examination you will be asked 1 out of 55 questions as part of the presentation. You can find the list of questions and answers over here (keep in mind that you may get different ones though).

Course grading statistics (μ=7.6, σ=0.6, pass rate: 100%)

B.2 — Programming (JBP020)

After you learned the basic programming syntax, the first assignment will likely put your conditional statements knowledge to the test. In our case we had to program the famous “Blackjack” card-game (the semester before the assignment was about “Hangman”). At first sight that may sound straightforward, however implementing additional game rules — as prescribed by the assignment — may be more work than you initially think. Further realize that a working solution is not a good solution per se. Put differently, code quality > code quantity. In this folder you’ll find the two assignments from the fall semester, a list of common mistakes in the first assignment and an overview of what you need to know for the exam.

Exam
The final exam is structured as follows: first a part on paper (90 minutes) and after a short break another part on a laptop provided by JADS (90 minutes). For the former we were asked to tell what the output of a certain function or line of code (e.g. len(5*’22')) will be and rewrite some functions in a more elegant or efficient way. After the break, we were asked to read a raw “arff”-file format and perform some data manipulations on it (count number of items, filtering, aggregation, in any case: a lot of list and set comprehensions, make sure you master them!).

Some tips for the exam: use your time wisely in the programming part (you probably run out of time) and be prepared to work on a Windows laptop of which the mouse-pad is incredibly sensitive (i.e. while typing the pointer moves around all the time..; so you may bring your own keyboard with you or borrow one from the IT-department ahead of time)

Course grading statistics based on first attempt (μ=5.9, σ=1.7, pass rate: 64%)

B.3 — Data Structures and Algorithms (JBP030)

I must admit that I haven’t taken this course at JADS myself. Still, I have done my best to gather some bits and pieces here and there. In this folder you’ll find last-semester’s assignments, a practice exam and photos taken during the lectures (all credit thereof goes to Sjoerd, Dijon, Axel, Jan and Vincent 👏).

Tip: If you are looking for some additional practice material (incl. students’ solutions) have a look at this Google Drive folder for the TU/e equivalent of this course (2IL50).

B.4— Introduction to Machine Learning (JBP040) 🎁

Due to unforeseen circumstances our edition of this class was a so-called “Machine Learning Bootcamp” (i.e. 6 intensive days of class). For the spring semester this has likely been changed. In that sense, I expect you are going to have an entirely different experience. Nevertheless, in this folder you find a list of representative exam questions.

Course grading statistics (μ=9.2 — seems like a little present to me and that while ‘Sinterklaas’ was already back in Spain! — , σ=0.5, pass rate: 100%)

B.5 — Foundation of Databases (JBP050)

Especially for this course holds, “practice makes perfect”, since the exam is basically a collection of slightly adapted homework exercises (create an relational model for an arts gallery, groupby exercise from the slides, normalize data to 3NF and of course some SQL exercises including window functions). In fact, some of the SQL-questions are related or even identical to the exercises associated with the president.db (trust me, you can dream that database by the time you take the exam). Further, we were asked about the difference between a trigger and procedure and the meaning of the ACID acronym (Atomicity, Consistency, Isolation, Durability). In this folder you can find last semester’s assignment and the solutions to the practice material.

⭐️ BONUS — 268 Data Science Resources ⭐️

So let’s face it: you may like your lecturer or you don’t. The good news is that in today’s world more often that not you can choose from dozens if not hundreds of instructors who teach the equivalent online. This especially holds for hands-on tutorials about Python, pandas, scikitlearn and SQL. The challenge, however, is in separating the wheat from the chaff. You can find a curated list of programming, data visualization, machine learning, statistics and database tutorials I found helpful and would recommend over HERE.

Hopefully I made your life just a little bit easier. Happy learning!

Any questions, additions or suggestions for improvement?
Please let me know in the comments down below 👇

Data Science & Entrepreneurship 101 for Pre-Master Students was originally published in The Outlier by Pattern on Medium, where people are continuing the conversation by highlighting and responding to this story.

This time the story behind the person who normally shares others’ stories. He was the marketing rockstar behind Science Rockstars, previously worked as a “Travel Scientist” for Booking.com, has built his social media presence under the name “happy bandits”, loves hanging out with extraordinary people, regularly stands in the spotlights while wearing sunglasses, teaches others the art of persuasive technology and who is on a mission to fix the future. I am talking about Arjan Haring 🔮🔨 . Keep on reading to learn more about the underlying ideas and strategies of the Scientific Advisor who sees it as the ultimate challenge to contribute to building the “Stanford for Data Science”.

Distribution is Key

Since May 2017 Arjan has fulfilled the role of Scientific Advisor which he describes as follows: “In the first place I advise on future developments of Data Science, but I also try to connect JADS academics with other prominent researchers in their respective field of expertise.” For the outside world people can see he has been very active on social media (Twitter/LinkedIn) and initiates numerous Data Science meetups at JADS. For colleagues and students at JADS he is often known for his widespread network and creative ideas.

He elaborates on this inner drive to share and connect: “I love initiating meaningful things, but it is as least as important to communicate your message with the rest of world. Producing high-quality work is time-consuming and definitely not for everyone, but it often requires another set of capabilities to get it to the attention of other clever people.”

Getting Your Foot in the Door

One of the things that stood out to me during the interview was his ambitious perspective to aim as high as possible in whatever he is undertaking. An example that illustrates it quite well: “Once I come across an excellent read, I just send the authors an email. In most cases it’s a piece of cake to find out their email addresses online.” My logical follow-up question was whether he always gets a reply to his emails: “I might have a backlog of 1000 people who never responded. But that doesn’t matter, I simply don’t give a crap. It is about the people who do respond.” Then we moved on to some tips and tricks he deployed to get his foot in the door of renowned entrepreneurs and academics. “Ideally you use a funny and energetic voice of tone; big names are all too often taken way too seriously. They prefer to be treated like everybody else.”

Arjan Haring 🔮🔨 on Twitter

1. I tried to be a good father/husband/brother/friend and citizen of this world 2. I wrote some pretty funny emails this year 3. I helped some young people (let's call them students) feel more confident, when this was clearly called for. https://t.co/YfJXY15mj7

Academia 🤝 Industry

In line with my previous post I asked Arjan about his view on the differences between the two worlds: “Most companies are very far away from science. That is a bit of a shame, but experiments can close this gap. Once companies start adopting a data-driven mindset they come closer to the basic principles of science. On the other hand, I expect that scientists will shortly reap the benefits of industry collaborations due to the exponential growth of data collection. This will lead to new research areas which you already see happening at tech giants such as Twitter and Facebook.”

Despite his general concerns about the lack of an experiment-driven culture in business, it turns out Arjan is very excited about the tech giant whose head office is located in the beautiful capital of our very own country: Booking.com. In his former role as — what his mentor Mats Einarsen likes to refer to as — Travel Scientist Arjan was trying to find answers to questions such as: “Are we able to accurately forecast at which holiday destination potential travelers will have the best experience of their life?”

What he relished most in this position was the data-driven culture: “In the morning you drank a cup of coffee, thought of an experiment and delivered it to the ‘Experiment Tool’ (i.e. Booking’s in-house built A/B testing platform). You thus executed the experiment and evaluated its results after which the process started all over again the day after. This approach was so radically different than what was common for most companies at the time: first a meeting to brainstorm ideas, then someone needed to write a project plan, another meeting to discuss the project plan, yet another meeting to create a budget to find out two years along the line that the experiment did not work at all.”

He continues: “In business assumptions are often still the norm: ‘I think that…’ [followed by a list of personal beliefs]. The first thing they asked at Booking.com once you had shared your new idea was: ‘Do you have any data to support that claim?’. I have not yet come across a single company that has integrated this data-driven approach across all departments and processes to such an extent.”

Data Science with a Human Touch

Although Arjan warmly welcomes new flagship, research and educational partnerships, he also expressed himself somewhat critical about certain developments in the industry. “I am not a fan of typical engineering questions such as ‘What is your big data technology stack?’ because it neglects the bigger picture: what is the added value; how does it make the world a better place? In Data Science it is currently one big technology feast. That is the risk of tech fanboys who get down to the nitty-gritty.”

Back in 2013 he wrote an article, Why We Overestimate Technology and Underestimate the Power of Words, which has a similar message: incorporating more, better, faster hot blackbox technologies does not necessarily make a world of difference, it is sometimes as straightforward as copy that does.

“In Data Science it is currently one big technology feast. That is the risk of tech fanboys who get down to the nitty-gritty.”

Doing > Dreaming

Next to his role as Scientific Advisor, he is also a lecturer for the Data Entrepreneurship in Action MSc-course. He elaborates on what characterizes a JADS-student: “We distinguish ourselves by understanding both engineering and business. That is a challenging combination and I must say there is still a big gap to close: in the industry but also in our own thinking. I would, for example, love to see students adopt more of a ‘Maker Mindset’. Regard university as a playground for experimentation and start building and testing. And please don’t limit yourself to the availability of a dataset. You can turn on a video camera, let it record for an hour and you have more than enough data to work with. On a final note, I would encourage all students to openly share their ideas rather than ask for a Non-Disclosure Agreement (NDA) before they have actually put in the work.”

As the first batch of Data Entrepreneurs (picture above) is about to graduate in the summer of 2018 I asked him about their career perspectives and where he currently sees data science opportunities worth exploiting. “If I would start a company tomorrow it would be in the field of energy. That is because the energy transition is every fascinating due to its nature: supply and demand fluctuate heavily throughout the year which makes it an interesting optimisation problem. Aside from that, I believe that plenty of charitable and governmental processes could be significantly streamlined using data science principles. At the same time we should question whether it is ethically sound that Google and Facebook capitalize on, for example, the presidential elections.”

Stanford for Data Science

As a closing question I wondered how he expects the JADS ecosystem will develop over the next few years. “In my vision JADS has a powerful professional network in the same way Stanford’s Computer Science department is well-known for its industry-academia partnerships: the majority of all tech-investments in The States directly go to Stanford alumni. In other words, JADS should be the place to be for investors. Even though our current network (TU/e & Tilburg University) is already strong, there is still room to push through to the next level. On a normal working day I hope to see incredibly smart and entrepreneurial people hanging around in the Mariënburg.”

Some may say ‘your network is your net worth’. Beyond a doubt there is a grain of truth in that statement: your network has a crucial role to play, especially for entrepreneurs in making. But in order to become the worlds’ leading teaching and research institution there is probably more to it than that. Let’s take a step back for a moment: why should world-class companies and the greatest investors be willing to cooperate at all? Most likely because of the people that brought them there in the first place: students, staff and academia. In that regard attracting and retaining the best people is key, because talent speaks for itself. It goes without saying that all unicorns will then find their way to the Mariënburg for sure..

As you have read, Arjan definitely puts the bar extremely high, but at this moment of the year — while you still have good faith in keeping your New Year’s Resolutions — that might actually be a good thing. So on that note, one of the goals for “The Outlier” in 2018 is to increase involvement and engagement among the data science community. Hence my question to you: “How would your ultimate ‘Stanford for Data Science’ look like? Let me know in the comments down below ❤️!

Maker Mindset for Data Entrepreneurs was originally published in The Outlier by Pattern on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s virtually impossible to introduce our next guest, Maurits Kaptein, in just a few words, so why should I? A psychology graduate, self-taught statistician, distinguished visiting scholar at Stanford University, former Chief Scientist and co-founder of Science Rockstars, public speaker, author of “Persuasion Profiling” and you would almost forget it: a professor Data Science & Health at JADS. An exciting personal story follows with a couple of unexpected twists and a vision for the future.

Transitioning from Psychology to Programming

In 2005 Maurits graduated with a master degree in Economic Psychology from Tilburg University. He has always been very interested in consumer behaviour theories: how do people make decisions and what factors influence this process? What stands out in his educational background is the focus on both qualitative and quantitative research methods. Maurits explains: “Even before I went to university I was fascinated by mathematics. Since I had ample of free time as a psychology major, I had plenty of opportunity to immerse myself in the field of statistics. At some point at the end of my studies, e-commerce was booming and I thought I should really learn programming now. The PDEng program (i.e. post graduate degree) was the perfect opportunity for this: freedom to investigate and learn whatever you want while getting paid. It was absolutely a satisfying 2-year program that taught me a lot.”

“The PDEng program was the perfect opportunity for this: freedom to investigate and learn whatever you want while getting paid.”

To become an academic, it is customary to do a doctorate after your master’s. Did Maurits also take this trajectory into consideration at the time? “I never wanted to do a PhD. In my initial perception researchers literally locked themselves up in a tiny room for 4 consecutive years to undertake all kinds of monotonous activities which were far from practice. In that sense, the PDEng program was more application oriented. At all times I never lost interest in originally theoretical subjects such as mathematics though.”

Books & Beaches

In hindsight it turns out Maurits did choose to pursue an academic career after he had completed his PDEng. He comments on this somewhat remarkable choice given his his former perspective: “During a conference professor Panos Makopoulus asked me if I considered doing a PhD. I said no, except for one case: a research project in close collaboration with industry at a prestigious university.” At first that did not happen: he worked for a market research firm where he developed analysis software and investigated survey results. Nonetheless, 10 months after he had begun working for the firm, the same professor from the TU/e contacted him again for a new research proposal he simply could not refuse: a project funded by Philips where he would spend half of his time at Philips Research and the other half at Stanford University.

Looking back at this period, he concludes that he sincerely enjoyed doing his PhD. “I found out you have lots of freedom and can conduct research in a variety of ways. This is still one of the main drivers for me to stay in academia: you can work on problems you find interesting whenever you want to. That doesn’t mean I don’t work much. In fact, I spent a lot of time on my research. But if the waves were high on, for example, Monday afternoon I went surfing for sure.”

“In academia you can work on problems you find interesting whenever you want to.”

He carries on: “In this respect, a PhD research has few fixed requirements: in the end you must be able to present some deliverable, but its exact specifications are unclear in most cases. The vast majority of researchers find this freedom — especially at the beginning of their doctorate— quite challenging, I loved it however.”

Personalization: a generic problem

In his PhD Maurits paid particular attention to personalised websites: “I tried to select the best version of a website for a visitor given a certain criterium such as profit maximisation or number of clickthroughs. Many people experience this psychological side, especially Cialdini’s persuasion principles (see TED talk below) as very fascinating. However this personalization problem is actually a much broader problem which has broader applications. Most noteworthy I find healthcare where we aim to determine the optimal medicine given the patient’s individual health conditions. In recent years I have worked a lot on this generic problem which I am getting more and more interested in.”

In short, Maurits’ research scope gradually transitioned from marketing to healthcare. It turns out his decision was largely made from a practical standpoint: “The reason I started out in e-commerce is because you can intervene cheaply, easily track website visitors and thus collect data on a large scale. Yet, at the moment I think it’s more fun to apply personalization within a meaningful domain like healthcare. Furthermore, it has become more and more possible over time. For example, during my PhD 7 years ago smartwatches were hardly available.”

Despite the increased availability of health data, he acknowledges that there is still much to be done: “In e-commerce industry practitioners are generally more analytically mature than in the healthcare industry. Hence, now you are seeing that healthcare practitioners show interest in marketers’ best practices.” He elaborates on the status-quo: “At present randomized control is the highest form of knowledge development according to most medical journals. This approach, however, takes a long time and the main problem is that the results will not be representative for an individual. In any case, it will never lead to personalized care. Somehow we need to generalize the trial results to a patient and then adapt it to a group of similar patients.”

“We often make choices based on things we can easily measure, rather than the things we really need to know.”

Another challenge he sees for both marketing and healthcare is choosing the right metrics. “For example, in online marketing the number of clickthroughs is oftentimes chosen as a metric. At the end of the day you hope that this is representative for the Customer Lifetime Value; after all that’s what business is all about. Though, we often make choices based on things we can easily measure, rather than the things we really need to know. The latter is typically much harder to establish and also takes a lot longer. For example, some e-health apps calculate your health status based on the number of daily steps. However, it’s questionable whether that is a good indicator for a healthy and happy life.”

Speaking, Writing, Business and Research

Besides his academic career, Maurits is active in many other areas. Among others he is a professional public speaker for The Next Speaker. I asked him how he ended up there. “My golden tip for becoming a speaker is writing a book (Persuasion Profiling) and also media attention (Wired and FrankWatching) certainly contributed to exposure.”

“My golden tip for becoming a speaker is writing a book.”

In the latter article there is a reference to a start-up (Science Rockstars — later acquired by Webpower) he co-founded in 2012. This company sold a product called the “Persuasion API”. Maurits explains: “The technology is based on an algorithm which determines the optimal follow-up message on a website based on previous clickstreams. In hindsight, I believe it was a rather hacky approach to the problem with a couple of charming ideas which made the algorithm run very fast. But it was definitely a better solution than simply A/B testing, that for sure! You can prove that analytically for elementary examples, or by running simulations.” He continues: “I truly wanted to improve the algorithm. I thought it would be fantastic if we had multiple clients so that we could experiment and establish whether our algorithm actually improved over time. Moreover, I had a long-term vision in mind in which our product would become a platform for website personalisation algorithms. That would be the coolest place to be as a company I think. Also from a commercial point of view it would be a more attractive proposition since we would be a party which offered multiple solutions, rather than just a single one.”

During this entrepreneurial endeavour Maurits encountered many obstacles. “Our clients wanted us to improve what we already had: for example an easier and faster JavaScript implementation process or a nice-looking logo. This is common in business, but I didn’t find these problems that interesting because they have already been solved by thousands of others. As an entrepreneur you should always listen to the voice of the customer and thus deal with requests like these. For me that was the main reason I changed my career path to eventually become a scientist, despite previous concerns about doing a PhD.”

Apparently, Maurits’ opinions about science drastically changed over time. So what are his most recent thoughts about the topic academia vs industry? “To a great extent I believe science allows you to come up with relevant problems yourself. You can twist it any way you want, but in business there is almost always an end customer who determines the matter at issue. Of course you can start out in business first and continue in academia afterwards. After all, as a researcher you can earn a decent living, but if you strive for financial independence I would not recommend it.”

The Future Proof Job

In 2015 Maurits gave a presentation about automation within the accountancy industry (see video below). In this talk he argues that any job which in theory comes down to moving objects from A to B can be fully automated. That is to say, the technology is readily available. However, in many cases we don’t automate these processes yet because it is more expensive than the current way of working. I asked him about the relevance for the data science industry. “In many disciplines technology could essentially replace dozens of job roles but it does not today because the business case is not viable yet and may never be so. The same holds true for the data science industry; data cleaning could theoretically be executed by machines, but in practice it turns out it is financially beneficial to put employees at work. Obviously, this implies the most exciting jobs will remain for data scientists. Still, I think it is even more important that you intrinsically like these non-automated jobs.”

Given these developments, I finally asked him about the most essential skills for future proof data scientists. “I think a lot of data scientists lack statistical comprehension. For example, many people still associate probability theory with calculating p-values and performing significance tests. This knowledge has been mostly irrelevant since the 70’s, it is much more about dealing with uncertainty in mathematical terms. In fact, a common flaw among data scientists is their ability to quantify the uncertainty of predictions. They train a model and assume that it is true; that it represents reality well. Needless to say, the added value of learning more should always be at least considered.”

Hopefully, this article made you critically reflect on the pros and cons of both an academic and entrepreneurial career. Honestly, I could truly empathise with Maurits’ initial thoughts around a post-graduate degree. After all, I sometimes wonder what these brilliant minds are doing all day behind closed doors in a marvellous modern monastery called the Mariënburg. How about you? I am curious to learn more about your opinion about the topic academia vs industry!

In our previous article Rick wrote about his awkward conversation with a rather shy (but stereotype?) developer during the World AI Summit. Fortunately, we had a somewhat more fluent conversation with Maurits resulting in a waterfall of stories and in-depth explanations. Thanks for helping me conduct the interview, Rick 👏!

Academia vs Industry (or both?) was originally published in The Outlier by Pattern on Medium, where people are continuing the conversation by highlighting and responding to this story.