Blog

Justin Rothbart has fond memories of his excited anticipation as the release date for a new video game approached. Video games allow him to connect to his youth, with some of his favorite games and series originating in the late 90’s. For his mid-course project in our data science bootcamp, he wanted to see if his view of video games from the late 90’s being the best matched the views of other video game fans. Read More

The Olympic Games bring excitement to world competition. Athletes from nations around the world gather together to celebrate achievement and to push the human body to do things never thought possible before. While the Olympic Games are so much more than medals, the medals can tell us a lot about the history of the Games. Inspired by the recent Winter Olympics in South Korea, Brandon Sanders created a Shiny app, Exploring Summer Olympic Data, 1896 - 2008, to navigate data collected for the last 112 years of Olympic events. Read More

When you think of NASA, you probably think of the space race, walking on the moon, or a potential mission to Mars. NASA is built on exploration and as a result of that, they have collected lots and lots of data. They currently have over 39,000 data sets! With all of this data and declining funding, NASA is turning to the community to find answers through Datanauts. Read More

This year Kaggle, a predictive modeling, analytics, and data sharing site, conducted its first ‘State of Data Science & Machine Learning’ survey, and received input from more than 16,000 respondents. We share a few of our highlights and how they relate to our Data Science Bootcamp. Read More

When we were talking to working data scientists and data engineers here in Nashville about designing a data science program there were several things that people consistently identified as being important if we were going to familiarize students with what it is like to work in a real data analytics/data science job. One of the things we heard was that Data Science in the real world is messy because the real world is messy. Data in the real world is never clean and/or complete and/or consistent or any of the other things it needs to be for our modeling techniques or predictive techniques to work. Based on the input we received, we have designed into our curriculum and projects the idea that we need to teach the entire data science process, not just the fun/cool machine learning and statistical modeling pieces of the process. Read More