data science

The Data Scientist: Explorer of the Data Universe

2 Mins read

There is a big misconception when it comes to data scientists. “People often think it’s magic”, Setareh Sadjadi says. They tend to think data scientist can solve any problem related to data in no time. The truth looks different. It is a new field, that grew massively in the last years and is therefore not yet fully understood.

“In the end it is basically: Getting information from data to be able to tell or to predict something else. That is the very general definition of it”, Setareh says. However, one thing is certain: In the data age, this is a key skill and data scientists are in demand.  Between 2012 and 2017 alone, the number of job openings for Data Scientist increased by 650 percent, according to a LinkedIn analysis. Bloomberg declaring it America’s hottest job in 2018.  

A Hobby as a Job

“Data nowadays is money”, Setareh knows. There is an ocean of data out there, and it is estimated that 1.7 megabyte of data are created every second for every person on earth. And the trend is upward. “You have got this huge amount of data, and you need to be able to use this data to extract information. Otherwise, this data was nothing”, she says. 

A data scientist analyzes often unstructured data sets, recognizes patterns, makes predictions and decisions on this basis. What are the consequences for a company’s finances if I make this or that change in a particular business area? That is a typical kind of question data scientists try to answer. If data is the new oil, then data scientists are the ones who turn it into fuel.

Degree programs for Data Scientist are still in development. Many Data Scientists are therefore career changers. Normally people who become data scientist come from two different backgrounds: software engineering or science. Setareh herself studied chemical engineering. “That’s actually something completely different.” After her PhD, she did not want to work in the chemical industry. “I was interested in Python programming and in machine learning, as a hobby basically.” So, she decided to do it as a job and to become a data scientist. Initially at a Berlin-based startup. In 2019, she started to work for at diconium.

Programming, Mathematics, Statistics

“How are your programming skills?”, is one of the important questions Setareh would ask everybody who wants to become a data scientist. “A data scientist doesn’t have to be a very skilled programmer, but definitely has to know programming.” Python is the language of choice, which most data scientists use. It’s actually a powerful language and it’s very user-friendly”, Setareh says. There is no library of analytical tools a data scientist can simply use on a daily basis. An understanding of mathematics and statistics is also important.

What she finds so exciting about her job at diconium? Seeing how projects are created from scratch. With data scientists as a key player.

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