Master of Science in Applied Data Analytics
Detroit Mercy's Master of Science in Applied Data Analytics Program is a new 30-credit-hour-program that is designed to uniquely position graduates as employment ready in a growing and versatile field. From e-commerce, to business, to government, to health care, organizations are turning to data science to understand their customers, improve efficiency and deliver targeted results. Detroit Mercy's program is unique in its emphasis on the application of knowledge and skills in the area of data analytics.
Data Analytics is the science of analyzing data in order to make meaningful and useful inferences. Its techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize the overall efficiency of a process, business or system. The ability to act on data lags far behind the ability to collect and store data, which is why data analytics professionals are in high demand.
Detroit Mercy's curriculum is interdisciplinary and includes courses from several departments and colleges including Cybersecurity & Information Systems, Computer Science, Economics, Architecture, Business Administration, Mathematics and Health Professions. The capstone project, completed under the supervision of program faculty, provides a formative and interactive learning experience. Students will explore a dynamic range of courses. Upon completion of the program, students will be able to sift through mountains of data to extract simple relationships and use these data-informed decisions to identify new opportunities for an organization and approach information collection and analysis from an ethical viewpoint.
Whether you want to advance your knowledge or enhance your career with graduate degree, Detroit Mercy's Master of Science in Applied Data Analytics is a flexible program with online or on campus options backed by faculty expertise. It is ideal for students around the world who are looking for a competitive advantage in an in-demand field.
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