Category Archives: Student Work
Dear Students, There is a new Kaggle Visualization Competition in our honor! I encourage you all to enter it! I received this email from Will Cukierski from Kaggle. This email was sent to me and Chris Mulligan. (See the p.s. for the Legend of Chris Mulligan.) Yours, Rachel Chris and Rachel, Thanks to your blog […]
Congratulations to Maura Fitzgerald for taking first place in our in-class Kaggle competition! First a couple comments, and then the final results are below. Were these Kaggle-competitive scores? The top scores were in the ballpark of the winning scores in the external version of this competition. The students in the class were given slightly different […]
Each week Cathy O’Neil blogs about the class. Cross-posted from mathbabe.org. Thank you Cathy for doing such a wonderful job this semester capturing the course in this way, and also for being a respected voice in the classroom, a question-asker and role model for the students. Here’s our class photo, and Cathy’s blog post follows. Cathy’s post captures the presentation done by a subset of students, which represented a collaboration of many/most students in this course, as part of their work for a think piece. More on this to come at a later date. It also captures my synthesis of the semester.
In the final week of Rachel Schutt’s Columbia Data Science course, we heard from two groups of students as well as from Rachel herself. [...]
This is another part of the students’ final project. A small group designed a survey to assess their classmates on different dimensions that capture the skills of a data scientist, and administered the survey to their classmates. The questions were of the form “Do you know what ___ means?”, or “Have you ever implemented ____?”. The students were well aware of potential biases in their questions, the limitations of self-reporting, etc. The survey was a great first pass.
This is an innovative way of describing and visualizing Data Scientists — it captures the variablity among data scientists, and allows for the potential for effective Data Science teams to be constructed by creating “constellations” of these stars, or overlaying the stars on top of each other to create “complete” data science teams. The visualization and survey represented an improvement over the data science profiles I gave them at the beginning of the semester. This was a collaborative effort among many students including Adam Obeng, Eurry Kim, Christina Gutierrez, Kaz Sakamoto, and Vaibhav Bhandari. Full report of last lecture still to come.
Last night the students gave their guest lecture. It was awesome! We’ll have a more detailed report tomorrow, but this image was already posted on twitter, so I thought I’d get it up here as a sneak preview for the rest of the lecture. Part of the students’ design concept was constellations and stars, so they have another nice visualization of “data science profiles” as stars. It will make more sense when you see it. Kaz Sakamoto, Eurry Kim and Vaibhav Bhandari created this as part of a larger class collaboration.
Dear Students, I want to let you know about the following: (1) Institute for Data Sciences and Engineering: Columbia University’s new Institute for Data Sciences and Engineering has launched a website: http://idse.columbia.edu. The word “Data” also modifies “Engineering”, in case there was confusion. Data Sciences and Data Engineering. (2) Kaggle Competition: As you know, our class […]
Each Tuesday, Eurry Kim, a student in our class, picks one example of data visualization to share with us. This week is a little different. Eurry didn’t pick it– she created it! I asked if we could feature it. Eurry and Kaz Sakamoto, also a student in the class, submitted a visualization to the Hubway Challenge. Here is their submission. You can view the public leaderboard here, and vote for Kaz and Eurry’s submission! I’m excited to see students in our class collaborating in this way. Below I asked them to describe their collaborative process:
Kaz Sakamoto is a student in our class and he created the following infographic to explain the process our class will use to create the Think Piece. Kaz is a Master of Science in Urban Planning candidate at Columbia’s Graduate School of Architecture, Planning, and Preservation (GSAPP) one of his interests is where public participation and technology intersect. He currently works for the New York City Economic Development Corporation (NYC EDC) which is the City’s official economic development corporation; some of their successful projects include the Highline, Coney Island, East River Ferry service, and the upcoming Cornell+Technion campus on Roosevelt Island. He works in the asset management and GIS departments where he has been working on analyzing the economic impact of College Point Corporate Park in Queens and digitizing project data for the EDC. In his free time he likes to ride trains, explore the city, and makes maps… usually in that order.
Last week we talked about the Art of Data Science. Let’s turn that on its head this week, and think about the Data Science of Art. I turned to the experts I know over at AEA Consulting. AEA Consulting is a New York-based cultural consulting firm that works with arts organizations and funders all over the world. Established in 1991, AEA’s founder Adrian Ellis was most recently Executive Director of Jazz at Lincoln Center from 2007 – 2011. The AEA team, including AEA principal Elizabeth Ellis, Brent Reidy (not to be confused with our TA, Ben Reddy) and Becky Schutt (my sister!) put together the following on museums and data.[...]
The sudden sexiness of museo-success metrics by Tyler Green
Dear Students, Lest you think (yes, I know I used that turn of phrase in posts before. I like it.) that I am bragging about my character traits (in which case you don’t know me well enough yet– I never brag, and who isn’t human?), wipe that thought from your mind, and read on. On […]