What is intellectual curiosity?

According to Wikipedia, intellectual curiosity, also called epistemic curiosity, is a curiosity that leads to the acquisition of general knowledge. It refers to an inclination to acquire knowledge and learn more. Intellectually curious individuals have a genuine interest in learning a wide variety of topics and ideas. In this article, I will share the need for this important soft skill for data scientists.

Technical and Non-technical skills?

Before diving deep into this soft skill, let us consider this: Data scientists have a great task of providing data-driven solutions to organizations. This task requires a wealth of skills and knowledge. One of the challenges to data scientists is being able to have a good balance of both technical and non-technical (soft skills) for them to be productive in their work. An over-emphasis on technical skills at the expense of soft-skills is a dangerous path for any data scientist. There needs to be a good combination of both types of skills.

Why are soft skills important?

Data science involves using data to discover insights that can be used for decision-making. This process needs more than technical skills. It requires a wide range of soft skills that can enable the data scientists to come up with the right insights and also easily communicate the findings. It also involves interacting with other people. In addition to that, the end-users of the data-driven product should be able to understand what is going on in a non-technical manner. This makes soft skills a must-have package for every data scientist.

Is it really important for data scientists?

Intellectual curiosity is important for data scientists in many ways. For instance, data in most cases provides multiple insights that can be interpreted differently. A data scientist must be able to go beyond the surface to discover and understand the hidden insights and patterns in the data. Data scientists must be able to ask the “why” question severally and progressively to get the desired outcome.

Like an investigator with pen and paper, the surface revelations are not sufficient but should act as a lead to the underlying truths in the data. This attribute of intellectual curiosity must be in a data scientist’s tool box.

Furthermore, data scientists may find themselves in the Research and Development (R&D) functions of an organization. This aspect of work requires in-depth analysis and investigation to be able to get the valuable findings for investigative tasks. Intellectually curious data scientist possesses the ability to constantly ask questions and passionately pursue the answers to a reasonable conclusion.

“I am neither clever nor especially gifted. I am only very, very curious” – Albert Einstein.

The ever evolving field

In addition to that, with the evolving field and technologies of data science, one should be able to widely and soberly seek to learn and understand what is going on in the field. Despite the overwhelming content and sometimes information overload, data scientists need to give their best to acquire knowledge on the trends in data science and how they can be applied to solve real-world problems.

The unquenchable thirst for seeking answers by data scientists should be able to help them go beyond the initial assumptions so as to identify the truths. The process of discovering the underlying truths helps the data scientist to answer questions that were never asked hence enriching the insights from the data.

For a data scientist, nothing should be taken at face value. Curiosity should push him/her to go beyond “just enough” to labor for the truth in the data. A data scientist should be able to provide answers to the hidden questions. He should be able to lay bare the revelations identified. Being inquisitive is a very essential skill for a data scientist.

How can you grow your intellectual curiosity?

Now, how can one build intellectual curiosity? There are different ways of developing and maintaining intellectual curiosity. Firstly, develop a habit of constantly asking questions. Always ask yourself whether the solutions you are coming up with are good and true to the project goals. Critical and creative thinking are very important in intellectual curiosity. Also, consider the following;

  • read widely and embrace learning at all times
  • don’t ever take things for granted
  • be open to challenge yourself with new ideas
  • think creatively and critically

In conclusion, intellectual curiosity has rewards and value for data scientists. It leads to high-quality decisions derived from quality insights. Right decisions lead to positive business impact and save the organizations from risks and losses. Depending on the project, this skill has numerous benefits. Develop this skill gradually and take your data science profession to the next level and stay motivated.