Just a few days ago, Charlotte’s Analytics and Big Data Society hosted the third Queen City Hackathon. The event was an opportunity for students, data professionals, policymakers, educators, and members of the community at large to come together. Attendees joined forces to learn and apply their passion for data and analytics on some of the most pressing social issues impacting Charlotte and the world beyond.
This year, with guidance, expertise, and some extra motivation from Leading on Opportunity, participants sought to identify data-driven strategies for tackling the challenges of economic mobility and economic segregation that plague Charlotte to this day.
As a volunteer, I was lucky to work alongside 200+ talented and tenacious participants, 25+ gracious and knowledgeable mentors, and a host of hardworking and adaptable volunteers. Participants hacked tirelessly through the night on at least one of these distinct competition tasks:
- Using their machine learning chops to predict trends in some of Charlotte’s key economic indicators for a Kaggle-style competition
- Leveraging their analytics and critical thinking skills to create a proposal for socio-economic change and present it to a panel of judges
- Exploring the challenges and opportunities that autonomous vehicles may bring to Charlotte’s future in the AWS DeepRacer competition
By 2 a.m., as I was on my third cup of coffee, my fourth piece of pizza and my hundredth conversation with one of the many equally sleep-deprived yet excited participants. I wanted to understand how they were feeling, identify what parts of the event stood out to them, and find out where they were stuck. Through these conversations, I discovered three critical lessons.
Lesson One: “‘Why’ matters.”
The participants had come for more than just the promise of prize money — they were motivated by the impact their hacks could have on the Charlotte community. They took in the broad vision provided by Rishi Bhatnagar of the Analytics and Big Data Society. Then they heard from Leading on Opportunity‘s Don Thomas, who provided them with expert context and emotional impact. Their encouraging words helped propel participants through the marathon event.
Lesson Two: “If you want to go far, go with others.”
As I met with more teams working through their technical challenges, I became even more impressed by their teamwork and collaboration. Some groups had formed from strangers, yet were still able to leverage their varied expertise. They shared motivations to learn, grow, and tackle the social and technical challenges of the competition.
Throughout the event, I would leave a group struggling with their most recent roadblock only to later discover they had collectively persevered, solved their problem, and were bounding off to face the next challenge together. It was easy to see that the collaborative dynamic of the teams was a clear sign of who would succeed in the competition.
Lesson Three: “Meet people where they are.”
Pictured above: Flora Tran, who won third place in the QC Hackathon AWS DeepRacer League. She built and trained an autonomous 1/18th-scale race car using a Reinforcement Learning model, in just one night — it finished the track in 13.3 seconds!
The hackers that came had broad ranges of experience and comfort with software, data, and technology. Some participants found the task of simply getting started very daunting. A handful of teams drew their ambitions back for fear of the unknown. As mentors, I felt we needed to understand the technological comfort zones of our participants. After establishing this baseline, we could then provide the right guidance and resources needed to push participants to learn new skills and tackle new data challenges.
Many teams created impressive proposals and tools by leveraging their time and expertise. One team created a tool to help non-profits predict economic outcomes of particular geographies, including data such as income level or high-school drop out rate. Not only did their tool provide a prediction, but it also used machine learning to inform those non-profits of which additional data factors the model identified as most predictive of the negative outcome.
Another team created a tool for local policymakers to identify regions where tax incentives would likely have the greatest net-positive economic impact in depressed areas. A third team focused on making data accessible and collected open-source datasets to create a user-friendly website for finding and exploring data.
Despite our best efforts, a simple hack likely won’t solve all of Charlotte’s economic mobility challenges immediately. However, this year’s Hackathon produced tons of incredible ideas that could certainly propel the city’s efforts forward — it was well worth the months of planning and hours of hard work for everyone involved. The Queen City Hackathon gave 200+ thoughtful, talented people the opportunity to collaborate and deepen their technical skills, all while tackling some of Charlotte’s most challenging social issues through data and innovation.