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”.

Note I recently had this article published in Synapse Magazine and wanted a broader audience to access it. So here it is. As Data Science and AI practitioners, and organisations looking to adopt AI, we need to be aware of how systemic oppression and discrimination is represented within our data. It is important for us to identify problematic biases and tackle them head-on to ensure the products we put in people’s hands make their 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.

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”.