We recently caught up with Juraj Kapasny, Co – founder at Basecamp.ai .We will be learning about the origins, selection process and outcomes at Basecamp.ai Data Science bootcamp.

Thanks again for taking the time to do the interview. Let’s start with a few introductory questions.

Q : What’s your 1 minute bio / introduction?

A : I have an academic background in applied mathematics. At the university we had one non-compulsory course called “Data Mining” and it quickly became my favorite subject. It opened doors into Data Science for me and looking back at it now, I was really lucky to sign up for that course. During my last year at the university, I got an internship at Teradata as a Data Science consultant and stayed as a full-time employee after my studies. After 2.5 years my friend Lukas Toma and I decided to start something on our own and founded Knoyd, a Data Science consulting business and shortly after, BaseCamp.ai, a Data Science bootcamp.

Q : Can you tell us a little bit about your background and role at Basecamp Data Science Bootcamp?

A : As I mentioned above, I am a co-founder and also one of the mentors. Together with one other person, I am the main mentor – we are there for the participants throughout the whole course. With my background coming from consulting jobs for huge corporate entities like Vodafone, Metro or Saudi Telecom, I have more experience with traditional data analytics like linear regression, logistic regression, market basket analysis and SQL for data preparation. My part of mentoring is therefore dedicated to these more traditional but still very important techniques.

Q : How did you get into Data Science Education / Training?

A : Growing up in Slovakia, we saw that it was not that easy making your way into the field. You can learn all the theory but might have trouble gaining practical experience because companies are searching for people with practical experience (see the problem?). We felt strongly that it should not be so hard and decided to do something about it.

Q : What is your definition of a Data Scientist?

A : I like the one that says a Data Scientist is someone who knows more coding than a statistician and knows more statistics than a developer. Plus I think navigating the business environment is a must for any Data Scientist working in the industry (like deadlines, approaching non-tech people etc.).

That’s great, thanks for giving us that introduction. Now, let’s chat more about Basecamp

Q : What’s the 1 minute bio / introduction of Basecamp Data Science Bootcamp?

A : Today, learning programming and machine learning theory is not enough. We emulate a real business environment – with responsibilities, deadlines and accountability. The only way to know how to be ready for what’s coming is to practice exactly that.

We most of all look for motivation. If you are truly driven to learn, there is nothing stopping you. Everything else is secondary. – Juraj Kapasny

Q : How did the idea for Basecamp Data Science come about and what do you hope to achieve with it?

A : We would like to create multiple locations that are together one internationally connected BaseCamp community. Because we work on real-world problems, the community includes companies, alums working in the industry as well as any new potential fellows.

Q : How do you screen and select fellows for your program?

A : We do a 15-minute Skype interview to check for fit in expectations, motivations and drive. The rest of the selection is based on the application itself like the CV, cover letter and other questions.

Q : Can you describe the typical background (academic / professional) you look for in your fellows?

A : The typical Basecamp student has at least a Bachelor’s degree in some quantitative field (or equivalent experience), some programming background and at least basic exposure to college math (linear algebra and entry level statistics).

Q : What type of skills or traits do you look for in a prospective fellow?

A : We most of all look for motivation. If you are truly driven to learn, there is nothing stopping you. Everything else is secondary. Even with no quantitative background, you can become a great Data Scientist if you are willing to put in the hours.

We know you’ve graduated one cohort. Let chat a bit about that experience and they’re faring in the wild

Q : How many cohorts have you gone through?

A : We just finished our first cohort in Vienna, Austria.

We organized a hiring day at the end of the bootcamp, where students presented their work on their projects. In the afternoon there were one on one interviews with the candidates that companies were interested in. 30% of participants found a job directly on the hiring day. – Juraj Kapasny

Q : Do you run multiple cohorts at the same time?

A : No, for now we are focusing on running just one at a time. We are coming up with spinoffs and different locations for the future though.

Q : What is your typical cohort size?

A : 8-10 people.

Q : Can you share what your placement numbers look like for your most recent cohort?

A : About 70%. The rest were not actively looking for a job. So out of all the people interested, everyone found a position.

Q : What percentage of your fellows eventually get Data Scientist vs Data Analyst vs Other technical jobs?

A : Half of the people got Data Science positions, the other half more technically data oriented positions.

Q : Can you share with us where some of your graduates work or will work?

A : For example Gleebees and Knoyd.

Q : Can you share with us what industries your graduates work in?

A : IT sector, Data Science consulting

Q : How do you prepare your fellows to be very competitive for Data Science jobs?

A : We are handing over our experience from Data Science jobs, giving them insights into what they need to know if they want to have such a job. Plus, we let them work on projects which they can use as references for practical experiences once they are done with the bootcamp.

Q : Do you have a hiring day and what percent of students are typically placed from a company they meet at hiring day?

A : Yes, we organized a hiring day at the end of the bootcamp, where students presented their work on their projects. In the afternoon there were one on one interviews with the candidates that companies were interested in. 30% of participants found a job directly on the hiring day.

Q : For organizations looking to hire Data Scientists what should they look for..Ivy Degrees, PhDs, Extensive experience, Quantitative Background, Technical chops, grit or determination?

A : They should definitely look for experience if they are hiring for senior positions. If they are looking to fill a junior role, they should look for technical and coding skills, problem-solving and out-of-the-box thinking. Academic backgrounds from mathematics or computer science can be an advantage.

Thanks again for sharing that. I think it’ll be interesting to talk about how you manage your operations

Q : Can you give a short summary of a typical day in the life / week in the life for your fellows?

A : Our day is structured into 2 blocks, 3 hours in the morning and 3 hours after lunch. Usually, we have a lecture in the morning and an exercise about the same topic right after that. Sometimes the exercise can take longer than 3 hours (participants create a lot of functions and algorithms from scratch to improve their understanding and coding skills). In this case, we continue with the exercise the next day. At the end of each week, we have a short session where participants show their approaches and compare them with others.

We believe that our way is unique because our participants work on real projects with real data during the course. During the bootcamp, approximately 30% of time is allocated to the independent project work with a mentor available at all times for support – Juraj Kapasny

Q : How do you improve your process from cohort to cohort at Basecamp Data Science ?

A : Mainly we improve by using feedback from participants. We don’t just wait until of the end of the cohort for feedback, but we are constantly seeking feedback. For example, in the 1st week of the 1st cohort we got the feedback that the course was more theoretical than it should be (because they can always look for some stuff online in case they need to) and we immediately started to focus more on parts like why do we need this and where can we use it. And we actually got feedback at the end of the cohort that it was really nice to see the improvements made during the course.

Q : What skills and tools do you think should be emphasized more in Data Science education?

A : I believe math and statistical skills are very important. Nowadays, there are a lot of tools which can be used without knowing the theory behind the machine learning algorithms but I believe that it is exactly what differentiates strong Data Scientists from the rest.

Q : Given the very fast progression in the field, what skills do you think will be most important for Data Scientists in the next few years?

A : I would say deep learning, NLP and distributed computing. And of course, all the necessary background knowledge that are needed to be a pro in these areas.

Q : What makes Basecamp Data Science unique and how do you differentiate Basecamp from the other offerings out there?

A : We believe that our way is unique because our participants work on real projects with real data during the course. During the bootcamp, approximately 30% of time is allocated to the independent project work with a mentor available at all times for support. At the end of each cohort, we organize a hiring day, where headhunters and other people from big and small companies are invited. On this day, the participants present the work they have accomplished during the course. We also provide one on one feedback interviews after the presentations. To prepare the students for real world situations we have them communicate with the company that provided us with the data project – regularly, during the whole working process. The student faces the stakeholders and needs to meet all the partial and final deadlines which are there throughout the duration of the project. We think that this is the best way to learn Data Science: Working on real projects in a real business environment.

Q : How do you help your fellows deal with burn-out?

A : We plan team activities every now and then. We have dinners and lunches together, go to the movies. Last time we went curling at a winter market in Vienna.

Q : Are fellows ever asked to leave or are kicked out mid-way through the program or at anytime during the program?

A : We haven’t kicked out anyone yet, but one of our participant left in the second week. He said there was a lot of interesting and new content for him, but he wouldn’t be able to continue at that pace.

Let’s also discuss parts of your curriculum

Q : Can you give us a sample of the tools, languages and techniques your fellows are exposed to during the program ?

A : 30% of the course work is dedicated to the independent project work. The rest of the time is split between lectures and exercises – approximately 40:60. In lectures, we start with basic background like probability theory and statistics, algebra, data wrangling, data processing and APIs then we proceed with the basics of Machine Learning like regressions, trees and basic optimization techniques. We go through supervised and unsupervised learning, NLP, recommenders, deep learning, reinforcement learning, data at scale (Apache Spark) and so on. We do our best to cover all fields which are nowadays trending in Data Science. After each lecture, there is an exercise on the same topic. Most of the course is done in Python with the exception of Deep Learning and distributed computing.

Let’s also touch on some of the other aspects of the program that may not be as obvious.

We have a slack team with all the mentors and participants. Once invited, mentors and participants will have lifetime access to the team. The idea is to stay in touch and give updates when they find awesome jobs. – Jaraj Kapasny

Q : What do you feel is broken with Data Science education in general and do you have any suggestions on how it can be improved?

A : We think there is still a huge gap between universities and businesses. Some of the universities are catching up but there is still a lack of practical exercises. We try to improve this by project work in our bootcamp and by providing expert mentoring from Senior Data Scientists who have a lot of practical experience from different positions in different industries.

Q : Do you have a structured alumni program ?

A : We have a slack team with all the mentors and participants. Once invited, mentors and participants will have lifetime access to the team. The idea is to stay in touch and give updates when they find awesome jobs. There is one channel dedicated to resources where we post interesting stuff we come across. Once they move up in their careers to senior positions and will be looking for Data Scientists for their own teams, we hope that they will turn to Basecamp once more to help them to find the right talent.

Q : Do you support your fellows after they’re done with the program?

A : Yes, we offer 3 months support with their job search. We help with tuning up CVs and with interview preparation.

Q : Do Basecamp alumni stay involved with the program and help make introductions / referrals for new fellows?

A : As mentioned above, we hope this will be the case once they will be hiring their own teams. It is great for us if they are satisfied with the outcome of the program and recommend it to friends and colleagues who aim to transition into Data Science as well.

Q : What problems in Data Science / Data Science Education keep you up at night?

A : There are no general problems that keep me up at night. However, when we deal with a difficult problem for a client I usually think about it non-stop, even before I go to sleep.

Q : Have you faced any major challenges in running Basecamp Data Science Bootcamp?

A : There were a couple of challenges, but nothing major so far. Typical problems of young companies I guess. The first step was that we had to make ourselves visible so that enough people would apply. Then we needed to find enough companies that were willing to give their data to our participants. And finally, we needed to convince people that we are highly skilled at Data Science even though we had just started with our bootcamp.

Q : What markets / verticals are you currently focused on ?

A : We have applications from all around the world – from the US through Europe to Australia. So we don’t focus on any particular market. However, our preferable target groups are people who want to transition from their jobs into Data Science or students who look for more practical experience to supplement their education.

Q : How do you feel the job market differs between the US and EU?

A : I don’t really have a lot of experience with the US market. But judging from websites like angellist.co etc., it’s obvious that there are more Data Science possibilities in the US. In Europe, there are a lot of jobs open in Berlin but Austria is a little bit behind. That’s one of the things we would like to change.

Q : Who are the Data Scientists that inspire you?

A : I don’t have one favorite Data Scientist. However, I have a couple of influential people whom I follow and admire, like DJ Patil (@dpatil) or Peter Skomoroch (@peteskomoroch).

Q : Any parting words for prospective Data Science students or Data Scientists that are just starting their careers?

A : My advice is that they should never stop learning, even when they finish their education and believe they are ready for their career. In a field like Data Science, which is really evolving fast, it is very important to keep track of new stuff coming out everyday. They don’t need to be experts in everything, but they should know what is out there in case they need it for specific projects later.

Thanks again Juraj for taking the time to interview with us. We do appreciate it.

To find out more about Basecamp.ai you can either reach out to Juraj Kapasny, engage with Basecamp on Twitter @basecamp_ai, take a look at their online offering or reach out to their former students or Instructors.

Also, please stay tuned for the other Data Science Bootcamps Founder Interviews we have in the pipeline at Data Science Bootcamp Founders Interview Series