Our datathon team at work

My Datathon Meetup Experience

One cannot learn from MOOC alone

Just between you and me, I’m revelling in my new student life. After so many years working across multiple teams in large organisations, and most days finding myself rushing from meeting to meeting, switching gears between the different focus areas, it’s a welcome reprieve to spend long stretches of time studying one subject area and to have the luxury of time to learn and build new skills.

While I’m a decided convert to the benefits of online study, I’m still conscious of the limitations of a MOOC (massively open online course) education. The content, pacing and delivery of education by a MOOC is high-quality but the online student community is not building that all important network of peers and potential employers. My motivation for study is to find a rewarding and satisfying job in the near future, and so, I’m putting part of my career transition effort into building and strengthening my professional network through meetup groups. 

Meetup for Networking

Meetup CEO, Scott Heiferman, founded the site after America’s 9/11, to foster and build communities in real life. I’ve found this offline connection tMeetup logoo people provides an ideal complement to the online study experience. The variety of Meetups is exciting and a little daunting at first. In Melbourne CBD alone, there are over 2000 active Meetup groups centred around subjects as diverse as data science, night photography, self-defence and an extensive array of other interest groups. Luckily, I’ve found a couple of active Meetup groups who are connecting me with other Melbournians with common interests.

One of these is the Data Science Melbourne group. They are a well-organised crew with strong business links and a welcoming environment for experienced and novice data scientists. Last week, they held a datathon for members of the group to work on real-life data in a time-boxed exercise. The datathon was a chance to follow a data analysis exercise from exploration and analysis steps through to a final pitch. There was a lot of interest, possibly helped by the incentive for the top five teams to win not just the glory, but also cash prizes donated by the group’s generous sponsors.

The Datathon

What better way to flex my fledgling analytical skills than participating in a datathon organised by Melbourne Data Science Group. The Saturday of the datathon brought together 140 novice and experienced data scientists to work on betting data. All the teams were supported on the day with mentors in statistics, data science and other domain experts. The output of the day’s effort was a pitch submitted to the judging panel, with the top five pitches presenting at the final datathon event the following Thursday.

Datathon team at work

Team Extreme

It was a fantastic learning experience. The datathon provided me with an opportunity to put in some valuable hours of practice as well as to learn from the expert mentors and more experienced team members. Our team had a common goal to learn as much as we could, and managed to have a lot of fun on the day itself, as well as on the days following as we pulled together our pitch. I walked away with a better understanding of how transactional data can be used to understand customer behaviour and as a step further, to build a predictive model using machine learning to drive better business outcomes. The experience has given me renewed impetus to tackle the next lot of subjects, as well as to explore, in more depth, the principles of data visualisation and customer segmentation. More importantly, I met, worked with and learnt from some clever and talented data scientists and analysts.

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