I recently wrote an article for Forbes that looked at what it takes to be a good data scientist these days. With the huge pace of innovation taking place in AI it’s not only difficult to keep up with a changing industry, but it requires skills today that were not even discussed even five years ago.
The Forbes article looked at courses that are required today, such as fundamentals of Hadoop or Apache Spark, as well as machine learning, data visualization and the standard mathematics, statistics, computer science and engineering classes, but it discusses the critical role of the “softer” skills. As the article states, “Data science is all about human interactions, teaching software to think like humans. In fact, Stanford University offers its computer science students classes in persuasion – how to persuade consumers or customers to buy certain things, buy in to your messages and then build those techniques into the software.” As AI tools take on the role of humans and think like humans in many cases, humans need to learn how to help them take on that role.
What’s key to being an effective data scientist is knowing how software interacts with people –
skills that haven’t always come from math or science-based classes. These skills have been taught in more liberal arts focused studies, such as English, sociology, psychology or even history. It’s wise for today’s rising data scientists to become well-rounded, stepping away from the computer lab and studying the humanities as well.
The article also shares how it’s no longer enough to be a really good programmer – in fact within five years most programming will be done by machines. But what’s key is understanding the business – its challenges, goals and customers, as well as having the ability to communicate and solve problems across departments.
There’s amazing new opportunities for students seeking careers in data science, and by focusing on expanding human skills, along with the scientific ones, they’ll be well-prepared to help software become human-like, instead of the other way around.