When I first began to work as a data scientist seven years ago, the field was a different beast. It was the early days of Spark, and the term “big data” was still echoing around as people were still focused on how to find insights in large amounts of data in warehouses and silos. Natural language processing at scale was not widely used in business. Deep learning was on the ascent and data scientist was said to be the “sexiest job of the 21st century.”

Things have changed massively since then. As we relaunch the IBM Data Science in Practice blog, let’s go through a few current standout trends in data science which we will cover in posts going forward. We’ll talk about the growing technical areas of data science and AI such as knowledge graphs, NLP, and data governance, while also looking at the human side of data science and practice in trustworthy and ethical AI and DEI in the data science field.