In a nut-shell, open-mindedness is our ability to view things from a wider perspective, with as few assumptions as humanly possible. It’s very much like the “beginner’s mind” concept which I’ve talked about in previous posts. I’ve also written an article about the value of open-mindedness on this blog before, a post that remains somewhat popular to this day. That’s why I decided to go deeper on this topic, which is both evergreen and practical.
The first scenario where open-mindedness becomes practically useful in data science is when you are learning about it. For example, you can learn the craft like some people do, blindly following some course/video/book, or you can be more open-minded about it and learn through a series of case studies, Q&A sessions with a mentor, and your own research into the topic. Having an active role in learning about the field is crucial if you want to have an open-minded approach to it. The same goes for taking initiative in practice projects and such.
Of course, open-mindedness has other advantages in data science work. For example, when finding a solution in a data science project, you may consider different – somewhat unconventional – approaches to it. You may try all the standard methods, but also consider different combinations of models, or variants of them. Such an approach is bound to be beneficial in complex problems that cannot be easily tackled with conventional models.
What’s more, open-mindedness can be applied to data handling too. For example, you can consider different ways of managing your features, alternative ways of combining them, and even different options for creating new features. All this can enable you to use more refined data potentially, providing you with an edge in your data engineering work. Let’s not forget that the latter constitutes the bulk of most data scientists’ workload. As such, this part of the pipeline conceals the largest potential for improvement.
Communicating with data science project stakeholders is another aspect of open-mindedness, perhaps one that deserves the most attention. After all, it’s not always easy to convey one’s insights and methodology to the other stakeholders of a data science project. Sometimes you need to find the right angle and the right justifications, which may not be just technical. That’s why open-mindedness here can shine and help bring about new iterations of the data science pipeline, for a given project. Also, it can bring about spin-off data science projects, related to the original one.
Although this topic is vast, you can learn more about open-mindedness and data science through one of my books. Namely, the Data Science Mindset, Methodologies, and Misconceptions book that I authored a few years ago covers such topics. Although the term open-mindedness is not used per se, the book delves into the way of thinking of a data scientist and how qualities like creativity (which is very closely related to open-mindedness) come into play. So, check it out when you have the chance. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.