Abstract
Given recent claims that data science can be fully automated or made accessible to nondata scientists through easy-to-use tools, I describe different types of data science roles within an organization. I then provide a view on the required skill sets of successful data scientists and how they can be obtained, concluding that data science requires both a profound understanding of the underlying methods as well as exhaustive experience gained from real-world data science projects. Despite some easy wins in specific areas using automation or easy-to-use tools, successful data science projects still require education and training.Keywords: data science, analytics, practitioner, education, insights, discovery
Highlights
Given recent claims that data science can be fully automated or made accessible to nondata scientists through easy-to-use tools, I describe different types of data science roles within an organization
I provide a view on the required skill sets of successful data scientists and how they can be obtained, concluding that data science requires both a profound understanding of the underlying methods as well as exhaustive experience gained from real-world data science projects
Data science is as much about knowing the tool as it is about having experience applying it to real-world problems, about having that ‘gut feeling’ that raises your eyebrows when the results are suspiciously positive
Summary
At some point in the past years, there was hope that a single, simple solution could enable everybody to become a data scientist—if we just gave them the right tools. Data science is as much about knowing the tool as it is about having experience applying it to real-world problems, about having that ‘gut feeling’ that raises your eyebrows when the results are suspiciously positive (or just weird). Once asked ‘Are you sure this makes sense?’ they realize and begin to question their results, but this is learned behavior. These are often things as simple as questioning a 98% accuracy on a credit churn benchmark. Becoming a successful data scientist requires both knowing the theory and having the experience to know how to get to, and when to trust, your results. The big question is can we teach ‘real-world experience’ during our courses as well
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