The “domain knowledge” part of data science doesn’t necessarily need to be business domain knowledge. it could be the problem domain. What do I mean? When you’ve encountered a lot of problems, you start to see that at a fundamental level, these problems look like something you’ve seen before. for example: churn prediction and machine failure at a fundamental level are all a part of the same problem domain, “make a prediction before something bad happens”. ...

As AI practitioners, we must identify and tackle problematic biases in our data and models to ensure equitable technology that makes lives better.

I had an idea. There’s so much to learn, to read, to do and not enough time. I’m having to become a lot better at filtering the content I consume and that why I’ve started this series of posts: A Paper, a Post and a Paragraph. The idea is that I aim to post about a paper I’ve read (Academic or White Paper), a post I liked and a paragraph related to some of my thoughts on current happenings in the topics I’m interested in. While my profession as a data scientist will guide a lot of the content, there may be a few odd new things here and there, like my recent interest in bread baking. ...

On the 17th and 18th of July I attended the Chief Data and Analytics Officer event as a speaker, hosted by Corinium. The event was a great success with the heads of many analytics teams meeting to discuss their successes, failures and other burning data related matters in their respective industries. Scattered through the success stories showcased at the event were some recurring themes that were homogenous across industries. 1 The CDO Role Surprisingly, as I was writing this I came across an article about Usama Fayyad, the first CDO, who mentions that the role “started out as a joke”. However this role isn’t a joke anymore - this role is imperative in moving a company towards data success through data strategy. ...