What Does It Take to be a Successful Data Scientist

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