Christian graduated from CUNY Baruch in 2020 and is still finishing his masters in Economic History at Columbia University. His interest in data analytics grew from his study of European economic & monetary policy at Columbia which gave him his first exposure to working with actual data. Discovering Tableau in his Data School application only confirmed his newfound desire to work with data directly rather than just reading about insights in policy papers. If he has learned anything at TIL thus far it's been to enjoy the challenge of being confronted with the impossible and to find the solutions hidden within.
An avid learner, when Christian isn't spending his time improving his technical skills he spends long nights practicing the guitar, improving his Italian and mastering the art of the Chef!
Christian Vincent Curcurato
Certifications
Blog Posts
Thu 01 Jun 2023 | Christian Vincent Curcurato
Dashboard Week Day 2 - Bundesliga
For our second day of dashboard week we had the unique task of web scraping the Bundesliga website for data on different teams performance in the last 20 years. As a result a lot of the time we used today was just using Alteryx to download and scrape the data I needed with RegEx
Tue 30 May 2023 | Christian Vincent Curcurato
Dashboard Week Day 1 - Accessibility
To inaugurate DSNY 3’s dashboard week, our cohort learned about the principles of accessible dashboard design. Accessible design is to ensure that as many people as possible are able to participate in the usage of a dashboard as possible
Fri 21 Apr 2023 | Christian Vincent Curcurato
Spatial Tools in Alteryx: Poly-build and Poly-split
What comes to mind when one thinks of spatial data? Maps? Topography? Coordinates?
To truly understand spatial data one must understand what the building blocks of spatial data are; what they represent and what they are used for
Tue 14 Feb 2023 | Christian Vincent Curcurato
Data Viz Best Practices - Improving a Visualization
After a week of learning the most basic concepts of a data analytics workflow and our first technical lessons in data cleaning and preparation, it was time to start the cycle again this week with the theory of what makes an effective visualization