This year I was fortunate enough to speak at PyCon Ireland 2016 in Dublin. This was a great event with lots of interesting Python based talks and a full PyData track over the two days. The topic of my talk was something I’ve been thinking about a lot over the last few years: how data scientists can work with other disciplines. Recently designers and product managers have begun working more closely with development teams, and in my opinion there are many lessons that data scientists can learn from this experience.

This week I had the opportunity to attend and speak at one of the biggest Big Data conferences of the year. The Strata conferences run by O’Reilly have been running for the last few years and in many ways have driven the awareness and adoption of data science and predictive analytics. My colleagues Alexander Kagoshima and Noelle Sio, and I talked about recent work we’ve been doing on how to use machine learning techniques to understand traffic flows in major cities and predict when travel disruptions will end.

The next few weeks are going to be busy and one of the reasons is that I am fortunate enough to be speaking at this year’s Santa Clara edition of Strata. Alexander Kagoshima, Noelle Sio and I are talking in the Machine Data session on Thursday 13th February about “Driving the Future of Smart Cities - How to Beat the Traffic”. It’s the last parallel talk of the day, so perfect timing for figuring out how to navigate the Bay Area traffic on the way home.

I thought now would be a good time to reflect on 2013 and the changes that have happened in my life. Officially I have now been out of academia for a year. My postdoc contract ended at the end of 2012, and while I stayed on at QMUL as a visiting researcher, I took the opportunity to travel for a few months with my wife, before setting out to find a new job.

Last weekend I took part in the first London DataDive, a charitable event organised by DataKind, who previously organised similar events across the US. The basic premise is that charities have collected large amounts of data, on donors, fund-raising and the actual care, help or interventions they provide. Without costly analysts to sort through and make sense of the data, it goes unused, providing little or no value to the organisation.