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Hackathons are good for your team. A well-run hackathon can provide myriad benefits: opportunities for blue-sky thinking, cross-functional engagement, and the most important, moments of fun and play. Yes, play, that same feeling you got when you turned the corner to the park when you were five and ran screaming for the slide!

I remember rock climbing on a team outing, each of us encouraging our teammates to grab that next hand hold and pull!! It was a lot of fun. It took all of us out of our comfort zones, in a really positive way. Most of us were doing things we knew how to do (hold on to things and use our arms and legs to move) but in an entirely different way.

Well-run hackathons are no different. Hackathons aren’t (or at least, they shouldn’t be) made up of tasks we do every day. The work should be largely new and in a different setting with different food/drinks. Done well, they feel like exciting and engaging mental challenges that make time vanish while having a whole lot fun, and dare I say, joy. Yes, at work.

If joy and removal from the day-to-day is the intent, how can we set up a hackathon to best achieve that feeling?

Aside from setting important guidelines for the day’s objective(s), it’s essential to create an open atmosphere with open communication and zero possibility of failure. Hackathon’s aren’t competitions, nor should they have strict requirements. It’s not as much about completing a problem successfully as it is about the journey and fun discoveries and thinking differently that happens along the way. Don’t get me wrong, there has to be an objective, some point for the day, but the emphasis should be on what’s learned along the way, not whether or not a central objective was accomplished.

For example, I first learned about a technology called autoencoders from a colleague at a hackathon. I wonder how else I would have learned about this powerful technology if he hadn’t thought of an innovative, different (and fun!) way of solving something we were working on. Hackathons should provide an anything-goes environment (within reason).

If you’ve never held a hackathon, or if it’s been a while, find some time in the next couple months and set aside a Friday. Go somewhere new. Have food brought in. Come up with an important idea. Invite stakeholders from outside of data science to join. Make it fun. Make a video. Then present it to the entire company (if possible) on Monday afternoon to show off all the great work.

Want a guide that can help you run an effective hackathon? Here’s a link:

Of Interest

Data systems + nonprofits: A new space for innovation:
Data systems in the nonprofit sector are underdeveloped and fragmented, which has long-standing implications for community-based decision making. I have good friends who are involved with nonprofits or run them, and I’ve spent years volunteering in my local neighborhood for a nonprofit. I’ve reached out to lend my expertise and have first-hand experience with how challenging it can be to help nonprofits. This article pinpoints some of the pain that nonprofits face. It’s time for change in this sector, as there’s no doubt that A.I. could provide a myriad of benefits to an industry so thirsty for data driven personalization.

Huge overview of K-means clustering:
While I don’t usually post about data science methods, this is such a comprehensive overview of a staple method of data scientists, that I couldn’t resist. Here’s everything you every wanted to know (and more) about K-means clustering. From when to use it to how to choose the “right” number of clusters by choosing the right inertia. The visualizations and teaching methodology are spot on. Here you go!

Don’t Do Data Science, Solve Business Problems:
The term ‘Data Scientist’ has become colloquialized in modern business speak to signify an individual with almost every data centric skillset there is. Organizations who want to hire Data Scientists look for the ‘Unicorn’ — the Data Scientist professional with such a wide and deep skillset, they practically don’t exist. Organizations are more concerned about the academic and technical complexity of their Data Science teams than the value they bring to the business. How many of us can think of a Data Science project at their company that has significant investment but that has yet to show business value. Why is that? This is a good question and worth more thought. Read more here: