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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.
News media has been through a lot of change during the past decade, especially in terms of its forced and jagged transition to digital production. This shift has come with the struggle to get readers to pay for digital subscriptions when free news online is often available with a click. Metis grad Kai-Ray Wang works to boost digital subscriptions at The New York Times as an Analytics Manager on the Consumer Acquisition team. Read his story here.
Bootcamp graduates Joyce Lee and Matt Maresca covered a lot of ground using geography as a framework to design their final projects. Lee did on a county-by-county analysis to predict mortality rates from overdoses, while Maresca used satellite imagery to map farmland, urban development, and natural resources in Shanghai. Read the post for more!
After 20+ years of working as a senior-level software engineer for companies like Goldman Sachs and Bank of America, Emy Parparita was looking for a change. Read how the bootcamp helped him transition to his current role of Machine Learning Engineer at Quora.