Job tips – how do I become a Data Scientist?

Stefan Handzik could also milk cows every day or take the tractor out to make hay. That’s what the 50-year-old originally learned: to be a farmer. However, there were also these new types of equipment that fascinated him as a youngster. Together with his brother, he often spent hours at the home computer, which was the name of the first devices that moved into more and more households.

In the series “How do I become …?” atechbook talks to people who have very different professions. But how did they get to them in the first place? Here in conversation – Stefan Handzik, Data Scientist.

“The first game we played regularly was called ‘Pong’. Two bars and a square ball were enough for countless hours of fun back then.” Playing alone, however, was soon no longer enough for Stefan Handzik. With the help of books, he learned the Basic programming language, which became the standard in the 1980s, mainly due to the success of the C64. “More by chance, I then got my first programming jobs and supplemented my pocket money by doing so.”

Nevertheless, programming initially remained nothing more than a hobby for Stefan Handzik. Instead, he decided to study for a degree in agricultural economics. “At that time, the topics of regenerative energies and renewable raw materials came up, which interested me a lot.” Through this interest, he ended up in the field of market research and also dealt with data and data processing for the first time professionally.

“In the 10s, the topic of Deep Learning spilled over from the USA to us, i.e. how can data be processed with the help of Artificial Intelligence.” Stefan Handzik recognized the potential inherent in this new method and became intensively involved with the topic of AI alongside his actual job.

Data scientists must think practically

“AI is essentially based on statistics. Alexa, Siri, Google Assistant and Co. in other words, all speech recognition works on an AI basis.” Filter systems in search engines or social media also use AI technology. The name ‘Paris Hilton’ posed problems for developers of intelligent search filters for a long time. “On the one hand, the search filters spit out information about the city of Paris; on the other hand, the filters returned results about a large hotel chain. Only on the public figure were there few hits.” AI experts have since solved this problem.

“Anyone working in AI should be able to think around corners. In the real world, things regularly happen that are not foreseen in theory.” For this reason, Stefan Handzik also criticizes the education at universities. Students are only taught theoretical thinking. Practical thinking often plays a subordinate role, he says.

Stefan Handzik now works independently as a Data Scientist for various clients, both nationally and internationally. He offers his services as a freelancer. At the customer’s site, however, he works with the company’s own IT teams to find a suitable solution to existing problems.

“Often, I just provide the framework to solve the problem. The programming is then done by others.” Since there aren’t as many people as Stefan Handzik – experts who have a broad knowledge of data and AI – he usually gets inquiries through headhunters. “However, we are currently experiencing a new development. Companies are now even hiring Data Scientist on a permanent basis.”

Read also: How do I become … an AR expert?

A broad field of activity

What excites Stefan Handzik about his work? “I can work flexibly and independently. And I love puzzling, so how do I get a problem solved simply but in the best possible way.” If you want to make a career as a Data Scientist, you should enjoy playing with data and not fall back on standard solutions. “I only exist because there is still no solution for many IT problems.”

For young people, Stefan Handzik sees good career opportunities. A degree in computer science is not even mandatory, he says. “People who bring the thinking with them always find their way into IT.” Development is advancing rapidly. Especially in the field of blockchain, Stefan Handzik sees a broad field of activity in the future.

“The English language is also very helpful in the field of IT, because most of the freely available documentation on the web is in English.” A good introduction to the field of artificial intelligence is offered by the AI Campus, a learning platform for artificial intelligence. Those specifically interested in the field of Deep Learning should look for videos by Geoffrey Hinton, a pioneer in the field.