If you’ve been a regular reader of my musings over the years, or heard me bring these thoughts to life in-person at an event or during a webinar, you’ll know that I like to rely upon a few tried and tested phrases. One of my favourites is “’x’ [insert technology
We already know that work and workplaces are changing. But what do these changes mean for the skills that students should be developing to improve their employability? Previous generations had a simple recipe for success in their jobs: They chose a profession, acquired foundational knowledge and slowly became an expert
Employability in general, and in data science in particular, means your skills must adapt to business needs. Thomas Davenport and D.J. Patil described data scientist as “The Sexiest Job of the 21st Century” in 2012. Since then, understanding has grown that businesses across industries need employees who can work with
There is general agreement that employment is changing. There is, however, much less agreement about precisely how it will change, although plenty of crystal-ball gazing has gone on. A study published by the World Economic Forum in 2016 suggests a rather nuanced situation with employment fluctuations likely to vary considerably
The previous post in this series described the first step of the data monitoring process — defining. Therefore, once we have defined the objective of our initiative and, consequently, also data sources and specific requirements towards them, we can move on to studying the data.
Recently I have been thinking about data preparation, but not just any kind of data preparation. I have been thinking about the preparation required for advanced analytics and predictive models. Recently, I had to explain the process of how to create this data and this proved to be somewhat challenging given a