M.S. in Data Science
The Data Science MS degree is a cooperative program in of the Department of Computer Science and Engineering, the Department of Electrical and Computer Engineering, the School of Statistics and the Division of Biostatistics. Together, these departments house a uniquely large variety of faculty in data mining and data management. The University of Minnesota is ranked 4th in the world among academic institutions in data mining.
Why Data Science?
There is a growing demand for talented professionals that can harvest, process, analyze data and extract insight. This is a new and growing academic discipline that has attracted significant attention in business, physical sciences, biological and health sciences, and research. Broadly trained data scientists are needed by a wide variety of organizations such as large internet companies like Google and Microsoft, retail firms like Walmart and Target or smaller regional retailers, financial firms like banks or credit card companies, many different Government departments in the military, intelligence, law enforcement, human services, health, as well as in scientific and medical research. In particular, a recent McKinsey study suggests a demand just for business applications for 440,000to 490,000 people with deep analytical skills by the year 2018, while the supply will be only around 300,000.
Is a Data Science M.S. Degree Right For Me?
The Data Science MS degree is a 2 year, 31 credit hour, program that combines substantial components in statistics, computing systems, and data-driven applications. It is designed for a pool of students who want to learn the fundamentals of statistical & algorithmic tools while also gaining hands-on experience with methods and algorithms appropriate for managing and processing big data.
The program is designed to help the next-generation data scientist develop critical thinking abilities, and the skills and tools necessary to help organizations compete in today's hyper-connected and data-rich landscape. By developing the ability to ask the right questions, draw on methods for extracting relevant structured and unstructured data from the multitudes of sources, and the ability to advance the state of the art scalable data mining, econometrics, machine learning, experimental design, and network data analysis, the Data Science program fills a much needed gap in today's environment.
For more information visit: datascience.umn.edu