Support Vector Machines (SVMs) may not be as popular as Neural Networks within data science, but they act as powerful, useful algorithms. One of the difficulties of SVMs has been the computational effort required to train them. However LIBSVM, which has been used for over a decade, can fairly easily handle the 30,000 training points in the National Data Science Bowl competition’s data set. That makes SVMs a viable tool for you to use both in general, and for the purpose of competing in the Data Science Bowl. Read More

We were incredibly excited to finally meet our data science partners from Booz Allen and Kaggle face-to-face during their site visit on January 15-16, 2015. We had communicated for months prior to their visit, discussing data, analytics, the number of plankton classes for the data science competition, and the general ins and outs of the Data Science Bowl.

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Building on Paul Yacci’s earlier post on the importance of feature selection in data science and data analysis, the creation of new features from your existing data set can play a large role in the performance of your model in data science. There are multiple methods of feature creation and data transformation. Often, finding the right transformation of your data can reveal relationships that would be difficult see otherwise, and may also make it easier for your model to separate classes. Read More

Data Science was named Forbes Magazine’s sexiest profession of 2014 as well as being the most trending STEM career in Ebony Magazine’s July issue. This has led to many wondering how they, too, can enter the data science profession. So, what is a typical day in the life of a data scientist? Read More