The Myth of Data Science Unicorns: Does One-Stop Data Science Exist?
It's time to debunk the myth of the so-called 'unicorns' in the world of data science. These so-called unicorns supposedly possess an extraordinary ability to excel in almost every aspect of data science projects. However, is this portrayal accurate, or is it just a romanticized notion?
True Unicorns: Anyone Who Can Learn Anything Quickly
In the dynamic realm of data science, tools and skills are constantly evolving. According to many industry experts, there is no such thing as a true data science unicorn. While there are exceptional data scientists who possess a broad range of knowledge, they still fall short when compared to specialists who have dedicated years to mastering specific areas of expertise.
Do They Exist?
A data science unicorn is someone who can deliver on all aspects of data science projects, including data engineering, statistical analysis, business analysis, machine learning, and data visualization. However, these individuals are not as mythical as the term suggests. They are simply people with a wide array of skills, but they do not necessarily excel in any one area to the level of a specialist.
Myth vs. Reality
Let's take the example of a machine learning researcher who builds a Recurrent Neural Network (RNN) to solve a Natural Language Processing (NLP) problem. While a data science unicorn might be able to build this RNN, it is unlikely that they could do so as effectively as someone who has dedicated years to studying NLP and deep learning.
Acquiring Unique Skills
The key to becoming a highly effective data scientist is to focus on two main areas: the fundamentals and the ability to learn new skills quickly. By mastering the core principles and understanding how to continuously acquire new knowledge, one can become a formidable data scientist.
Learning the Fundamentals
The first step is to thoroughly understand the fundamental concepts in data science. This includes statistics, machine learning, programming, and data visualization. While it is challenging to become an expert in every area, grasping these core principles provides a solid foundation.
Leverage Learning Strategies
The ability to learn new skills quickly is also crucial. This requires a strategic approach to learning. By understanding how to efficiently absorb new information, one can adapt to the changing landscape of data science effectively.
Polymaths and Specialists
In Norway, there is a different expression for someone with a broad skillset: the potato. This expression comes from the traditional Norwegian dish, where potatoes can be used in various ways. Similarly, a person with a wide range of skills is often called a potato, making the term sound less glamorous. The polymath, or someone who can do many things, is not uncommon in many fields, but they are not always the most impactful.
What Makes a True Polymath?
A true polymath in the field of data science would be someone who has a deep understanding of a specific subfield and continues to contribute to its development through novel research and discoveries. For instance, the development of XGBoost by a single individual who managed to outperform entire teams at Kaggle competitions is a perfect example of a data science unicorn. This individual was not a generalist but a specialist who saw an opportunity to innovate and deliver outstanding results.
Conclusion
While the concept of a data science unicorn sounds impressive, it is more accurate to view these individuals as polymaths or specialists who have a deep understanding of specific areas. The most impactful contributions come from those who continue to push the boundaries of their expertise.