I don’t believe in “data science for all.”

GIF from SNL of a man saying "I'm sorry you said what?"

 

Just as I don’t believe everyone should be a surgeon, or an opera singer, or a pilot. I think that, like these other occupations, data science is a specialized job that requires expertise and high-level skills.

To me, the campaign for “data science for everyone,” is an exaggerated course-correction for the problem we do need to solve: data literacy.

Our internal mission at QuantHub is to be a catalyst for the data fluency of individuals and companies all over the world. I do not believe everyone needs to be a data scientist, but I do believe everyone needs to be data fluent.

For example, there are people who become writers for a living, but elementary students are not taught to be novelists. They start with the ABCs, simple grammar, and reading. If this foundation of skills and their natural talent leads them to a Pulitzer, fantastic! But, what we’re most focused on at this elementary level is helping them navigate a world in which reading and writing is critical to their success.

As digital transformation is changing jobs rapidly, this analogy can be applied to data skills. To be successful in the modern world, individuals need to understand how they create,a graphic depicting the interconnectedness of the digital world consume, and use data. Exposure to these foundational skills might unlock data curiosities and help launch some individuals into the data science or data engineering field, but the exposure helps everyone else navigate a data-rich world with safety and confidence.

In fact, that’s why our first, universal data literacy unit is called, “data citizen.” Of course, data skills will help an individual be more successful in the digital workplace, but this level is also designed to help individuals be more successful in a digital world.

The funny thing is data literacy for all eventually does tangentially mean data science for all. As individuals gain data skills, they are enabling data science projects to be more successful, have language to talk more clearly with advanced analytics teams, and, in general, are more aware of the capabilities and solutions available to them through AI. That sounds awesome, doesn’t it?

Let’s get there one step at a time. Well, make that 10 minutes at a time.