Made at Metis: Waste Analysis + Building Data Science Solutions
By Emily Wilson • August 01, 2018
This post features two final projects created by recent graduates of our data science bootcamp. Take a look at what's possible to create in just 12 weeks.
Take a look around and you might notice some waste. It could come in the form of something physical that you can see or feel or smell. Or it might be invisible to the eye, like wasted time or resources due to lapses in efficiency. Two recent Metis graduates took an interest in these different forms of waste and used data science to come up with ways to minimize negative impact and maximize positive solutions.
In this project, Vladimir explores how delivery companies can use the power of machine learning to forecast travel times between two locations and use the genetic algorithm to find the best travel itinerary for each delivery truck. He's interested in the wasted time generated by inefficient route planning. For example, he writes: "Consider this: a UPS driver with 25 packages has 15 trillion possible routes to choose from. And if each driver drives just one more mile each day than necessary, the company would be losing $30 million a year."
Read his blog post to understand how he used the project to tackle the large and small elements of the overarching question, "what is the relationship between machine learning and optimization?"
In San Francisco, human waste is a growing issue, writes Mattie – both for the people who run into it and for those who have no other option than to relieve themselves on public streets. To combat this, he built a model to predict where and when human waste will show up. This sort of model could be used to better inform resource allocation for programs like San Francisco’s Pitstop (a program that brings portable bathrooms to areas that have high homeless populations).
"I believe this model adds to our current understanding by identifying the geographic and temporal underpinnings of the problem. That means when neighborhoods change, as they inevitably do, the model will be able to continue to provide accurate predictions. I hope that this can help advance efforts to keep San Francisco’s streets clean and provide citizens with the services they deserve," he writes on his blog.
Years before attending the bootcamp and switching to a career in data science, Metis graduate Sami Ahmed was focused on music. He studied film scoring and music business in college and worked as a musician, mostly composing for commercial media. Read how (and why) he made the transition to data science.
As a self-described lifelong learner that’s inspired by scientific and technological advances, it’s no surprise that bootcamp grad Sonali Dasgupta made her way to Metis. Read more about how Sonali made her transition to Data Scientist.