A couple of years ago, I wrote about rain on different days of the week in Philadelphia. I was annoyed because it felt like every time I took out the trash, it was raining. Our trash day is Wednesdays, so I take it out on Tuesday nights. When I did that analysis, I found that, sure enough, it did rain more on Tuesdays! Maybe not that interesting - it’s gotta rain more on some day, right?

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The 76ers recent Christmas Day game against the Milwaukee Bucks got me thinking about garbage time. The Sixers held a fairly substantial lead for the whole game, but let it get close at the end (they were outscored by 15 points in the fourth quarter). I started to wonder, “When does garbage time start”? According to Wikipedia: Garbage time is a term used to refer to the period toward the end of a timed sports competition that has become a blowout when the outcome of the game has already been decided, and the coaches of one or both teams will decide to replace their best players with substitutes.

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Why use data?

This post is adapted from a talk I gave at the Forum for Justice and Opportunity organized by Episcopal Community Services in Philadelphia. Why look at data? Why do we look at data? In my mind, the response is to make decisions. Research suggests we make thousands of decisions every day, and we want information about those decisions. And in particular, we want to make different decisions. Usually we want to make things better, to grow and to improve, and we recognize that doing so will require us to make different decisions than those we had been making previously.

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Trash day in our neighborhood is on Wednesday, which means we have to put our trash out on Tuesday night. My wife and I always joke that it seems to rain more on Tuesdays than any other day. This may not seem like a thing to even notice, except that in our South Philly row home, we have to lug soaking wet trash cans and recycling bins from our backyard through our kitchen and living to put them out on the sidewalk each week.

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With the NBA season fast-approaching (not fast enough for me), I wanted to play around with some NBA data and explore teams from recent history. My beloved Philadelphia 76ers have made a remarkable rise in the past two years, going from one of the worst teams in history to a contender for the conference championship, so there are some bragging rights invovled in this too. What’s the best way to rate teams?

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A recent report by Monitor Institute at Deloitte attempts to asses the landscape of the use of data in the social sector. They present three ‘characteristics of a better future’: More effectively put decision-making at the center Better empowering constituents and promoting diversity, equity, and inclusion More productively learning at scale In the next couple of posts, I’d like to lay out the case that the tools and techniques associated with data science present the opportunity to help make this future a reality.

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The non-profit organizaiton I work at collects tons of data. But the ways in which many organizations in the sector tends to be fairly limited. I want to talk about why I think that challenge exits and how we are working to change that fact, putting our data to work improving opportunities for young people1. Challenge A lot of infrastructure has been built up in the social service sector around data collection.

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Joe Ciesielski


data analyst

Philadelphia