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Building new worlds with data science

 By the time I graduated university four years ago, I  sensed that maybe I had missed out on something by not fitting any computer science or coding courses into my schedule. 

Computer Science: two words that still held every ounce of mystery when I received my diploma as they had when I first met a few of the guys who lived down the hall that were majoring in it. Wasn’t computer science just something you studied if you wanted to work in IT and ask befuddled office workers, “Have you tried turning it off and back on again?” 

Soon after entering  the professional working world, I understood that computer science was, in fact, a whole new way to work with information …

My math skills in high school were strong enough to be accepted into three top engineering schools—I had plans to pursue a career as a civil engineer. However, after my first year, I decided that pursuing a course of study that was so rooted in numbers and theory was cutting out the part of my personality that excels in approaching conflict with open eyes, gathering information and facts from multiple involved parties, and developing  solutions with optimal outcomes for all stakeholders. Thus, in my second year at university, I pivoted out of engineering into Political Science, hitting a stride with a balanced curriculum of political theory and economics.

Needing a new outlet for my future work, I realized I didn’t need to look further than my own backyard –Harlem. There was (and still is!) a national need for quality public education to combat the systemic under-education of traditionally underserved minority communities, especially in urban areas. Charter schools looked like the solution to me, so I jumped aboard the train.

Over the last 4 years, I have worked at two district-sized charter systems in New York City–two systems that couldn’t be more different from each other in so many ways: Their metrics of success, rates of growth, ability to be self-reflective as an organization, office culture—you name it. I’ve been able to work in positions focused on performance of the organization and in positions that focused on the performance of the people that make the organization. No matter the approach, the goal is the same: offering kids the opportunity to become the first generation of college graduates in their families. 

One thing I’ve learned is that numbers talk. If you can prove there is a problem area that has negative impact on the organization, or can show that a small positive occurrence could drive positive results if multiplied, leadership is often willing to devote the necessary resources. This understanding is what awakened and then sustained my interest to learn to paint pictures with information. I want to  dig into the nitty gritty and uncover the drivers of the deepest impact. Unfortunately, my toolbox has been somewhat limited. Spreadsheets don’t make great brushes–but I have had the opportunity to play with some of the tools of a true artist. 

For most people, finishing a graduate degree means you are  done being tested forever. Not the teachers at our schools. My current role has me assessing the performance of teachers; and let me tell you, we’re tough graders. What helps me get comfortable with the rigor of our instructional staff assessments is the thought that, eventually, I will want the best-of-the-best teaching my kids. That, and we have some seriously sweet perks for teachers who knock it out of the park.

So, to capture all the great work these often-under-appreciated professionals are doing, I’ve learned a little SQL and figured out how to build a report in Tableau. It’s exciting to wrangle information together and present it, and the self-teaching that has accompanied learning these programs helps me relate to all of the students in our schools. Personally and professionally, I have always gained satisfaction when I can give someone a helpful answer; so imagine my appreciation for these newfound tools and the answers they could help me deliver.

These are the exciting days for me in the office, because it is when I am doing this work that I get back to learning and challenging my mind. I’ve been exposed to new concepts that have started to build off one another–basic data structures, data types, joins, etc. When I am given an assignment for a report to build, I open my small toolbox and get to work. And when I hit a groove in these projects, or when I hit a block that I eventually solve, I’m transported back in time. Once again, I am 10 years old, sitting on my living room floor, with my 12-gallon tub of LEGO pieces spread out in front of me, and the world is a blank slate for me to build upon. Just like sorting the parts out to build the next great structure, I can sort information into helpful piles and look for the piece that will bring everything together to complete my masterpiece. 

There is a sense of freedom knowing that I have access to instruction manuals written by fellow builders, some master, some junior, all creators. I wanted to just glance at a picture to get an idea for a project, and when I needed to really follow some instructions step-by-step to get what was on the box—and believe me, I'll be reading a lot of instructions for the time being! The first few steps along the path of learning Data Science—dabbling in SQL and Python, differentiating data by types, building math equations with pandas functions—have solidified this vision in my mind. It is incredibly exciting for me because I know that while today, I’m stacking big blocks to build rudimentary houses, soon I’ll be constructing spaceships.

P.S. Yes, I still have that 12-gallon tub of LEGOs.

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