Click to view past winners at the University of Notre Dame, and presentations if they are available. Note that Notre Dame's first DataFest competition was 2019.
DataFest 2018 - Data source: Indeed Goal: What advice would you give a new high school about what major to choose in college? How does Indeed's data compare to official government data on the labor market? Can it be used to provide good economic indicators?
DataFest 2017 - Data source: Expedia Goal: How do visitors' searches relate to the choices of hotels booked or not booked? What role do external factors play in hotel choice? Expedia provided DataFesters with data from search results from millions of visitors around the world who were interested in traveling to destinations all over the world. The data were in two files, one of which included data collected on search results from visitors' sessions, and another which contained detailed information about the destinations that visitors searched for.
DataFest 2016 - Data source: Ticketmaster Goal: How can site visits be converted to ticket sales, and how can TicketMaster identify "true fans" of an artist or band? Data consisted of three sets. One included events from the last 12 months that tracked customer travel through the website. Another provided information about advertising campaigns on Google, and the third included data on the events themselves.
DataFest 2015 - Data source: Edmunds.com Goal: Detect insights into the process of car shopping that can help make the process easier for customers. Data consist of visitor 'pathways' through a website that helps customers configure car features and shop for cars. Five data files were linked by a customer key, and including data about the customer, about his or her visits to the webpage, and, when applicable, about the car purchased and the dealership where the car was purchased.
DataFest 2014 - Data source: GridPoint Goal: Help understand how customers can best save money and energy. Data consisted of a random sample of customers, with five-minute aggregates over a year of energy consumption that was then aggregated across important features of the commercial properties, as well as supporting climate and location data.
DataFest 2013 - Data source: eHarmony Goal: Help understand what qualities people look for in prospective dates. The DataFest students worked with a large sample of prospective matches. For each customer, data were provided on his or her preferences, as well as four matches, their preferences, and information about whether parties contacted one another.
DataFest 2012 - Data source: Kiva.com Goal: Help understand what motivates people to lend money to developing-nation entrepreneurs and what factors are associated with paying these loans. Several data sets were provided, including characteristics of lenders and borrowers and loan pay-back data.