Specializing in a field
When the term “data science” was relatively new, and companies started hiring data scientists, the breadth of fields a data scientist might have been expected to know about was a lot more limited. Having strong theoretical and applied knowledge of statistics and supervised and unsupervised machine learning – topics we have covered in this book – may have covered most applied data science projects.
In academia, there was a different story, rapid progress was being made within deep learning, and fields such as natural language processing, computer vision, and reinforcement learning were making huge strides. Part of this progress was due to theoretical breakthroughs, particularly around neural network architectures, and part of this progress was due to the massive increases in compute and data available to researchers.
This academic progress has opened up a much broader range of fields within data science, machine learning, and...
