Description: Established in 2007, this 10-month program is designed to give students a thorough understanding of the tools, methods, applications and practice of advanced analytics. The goal is to provide an education that is directly applicable to a career in industry rather than to provide a prelude to a PhD.
Topics include data mining, text mining, forecasting, optimization, databases, data visualization, data privacy and security, financial analytics, and customer analytics, as well as communication and teamwork skills. Team projects are based on analytical problems using real data from sponsoring organizations.
Prerequisites: Past academic studies in mathematics, statistics, engineering, science, computer programming, business and economics are relevant prerequisites. About half of applicants have prior graduate education, including MS, MBA and PhD degrees. Applicants must hold a bachelor's degree and have a proven track record of strong academic performance. Prospective students who have not majored (or minored) in mathematics or statistics will need to have successfully completed prerequisites in these subjects to be admitted.
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.
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