School: Graduate College, Department of Statistics
Description: Combines the mathematical and statistical training of a traditional MS in Statistics with enhanced computational and data analytics training. The program includes fundamentals in mathematical and applied statistics as well as specialized training in data management, analysis and model building with large datasets and databases. Specialized courses emphasize statistical computing, data management and statistical learning, which encompasses topics under the broader title of data mining.
Prerequisites: Undergraduate degree (3.0+ GPA) with a background in mathematics and statistics including calculus through multivariable calculus, linear algebra and an introduction to mathematical statistics and probability. GRE exam required. Students should have experience with computing and data, including prior use of statistical software such as SAS or SPSS as well as an interactive programming environment such as C, R or Matlab.
6 Tools to Protect Big DataMost IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.
Big Data Brings Big Security ProblemsWhy should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.
InformationWeek Must Reads Oct. 21, 2014InformationWeek's new Must Reads is a compendium of our best recent coverage of digital strategy. Learn why you should learn to embrace DevOps, how to avoid roadblocks for digital projects, what the five steps to API management are, and more.