Five types of thinking for a high performing data scientist - KDnuggets

The way you think about a problem and the conceptual process you go through to find a solution may be guided by your personal skills or the type of problem at hand. Many mental models exist representing a variety of thinking patterns -- and as a Data Scientist, appreciating different approaches can help you more effectively model data in the business world and communicate your results to the decision-makers.

Complexity pervades today’s society — whether we look at the economy, businesses that operate within the economy, individuals who play different roles in society, how our physical, social, political, and industrial complex interact with each other, we cannot ignore the complexity of it. There is no single or simple explanation that captures the complexity of it all. As data scientists, we have to understand this complexity and hone in our thinking to isolate what matters, ignore what doesn’t, and push forward with answering key questions posed to us.

In this blog, I expand on some of the key ‘thinking’ paradigms that have helped me in conceptualizing the abstract problems posed to me and how I have been able to address these problems to generate insights. While meta-cognition or ‘thinking about thinking’ is a rich topic of discussion — one I think is critical to the endeavor of AI — I will restrict my attention here to thinking paradigms useful for data scientists.

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