Data scientists have a deceptively simple job: to unravel the flow of data that enters an organization as an unstructured crowd. Because somewhere in this confusion there are (hopefully) important insights.
But is the skillful handling of algorithms and data sets sufficient to be successful as a data scientist? What else do you need to know and be able to do to advance your career path?
Although many tech professionals think it is enough to evaluate data from the query to completion, you should also know how the entire process works and how your data work ultimately affects strategies and revenue.
The current high demand for big data analytics services means that companies are demanding more and more from their data scientists.
You need hard & soft skills
There is a shortage in data science – a competence gap. This gap is enormous and is constantly growing.
Modern data science developed from three areas: applied mathematics, statistics and computer science. In recent years, however, the term “data scientist” has expanded and also includes people with a background in the quantitative field.
Other areas – including physics and linguistics – are increasingly developing a symbiotic relationship with data science, especially through the development of artificial intelligence, machine learning and natural language processing.
In addition to skills in mathematics and algorithms, successful data scientists must also master the so-called soft skills – social skills. In other words, in order to move forward, data scientists must work with people who understand the larger relationships in the company.
You must interact with managers who influence the company’s far-reaching strategy, as well as with colleagues who turn data results into real actions. With the input of these stakeholders, data scientists can better formulate the right questions to advance their analyses.
Soft skills usually mean healthy curiosity. Ideally, the applicant loves to understand data and wants to understand what is happening in the world.
This can become a problem for those data scientists who hide behind the data and do not interact with other business units.
Bias vs. Objectivity
Getting something correct right at the beginning is not a sign of victory. So always be skeptical. Do you have all the data? Is the data too good to be true?
The trick is to remove the human factor from the equation. Just let mathematics speak for itself. The data skeptic can then take the next step and show how much of a conclusion is not based purely on chance.
Don’t try to be perfect. The solution you create just has to be enough to get the user from A to B. Better build a good, reliable Volkswagen than a Cadillac. Sometimes you just have to be able to settle for a Volkswagen.
The prejudices of the team are often incorporated into algorithms. Take, for example, a credit algorithm that evaluates the applicants for a loan. While you may think that the underlying mathematics is neutral, the programmer may have incorporated his prejudices into the code.
Bias is not a new problem. Engineers often have to make “subjective decisions” when trying to achieve goals. You have to create individual solution steps that meet immediate needs. But it is not the case that the underlying algorithms are black boxes: data scientists have to decide for themselves whether the software gives a good result.
For data scientists, both technical and social skills are necessary to be successful in the job – coupled with a healthy dose of skepticism. In order to get up the data science career ladder, you should not simply blindly trust the collected data.
