Applied Data Analytics
Master of Science
Detroit Mercy's Master of Science in Applied Data Analytics Program is a 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 makes meaningful and useful inferences out of large data sets. 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.
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 a 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.
Yu Peng Lin, Ph.D. teaches Microeconomics, Macroeconomics, Financial Economics, Money and Capital Markets, Macroeconomic Policies, and Econometrics. Lin's research and publications have dealt with employee compensation strategies, human resource management, and productivity. He has published three textbooks and many research articles appearing in academic journals such as Industrial Relations and British Journal of Industrial Relations. He is currently doing research in the area of top executive compensation in an international context, the relationship between managerial compensation and firm survival, and information asymmetry. He holds a Bachelor of Arts (International Trade) from Tunghai University, a Master of Science (Finance) from University of Colorado, and a Master of Arts and doctoral degrees (Economics) from the State University of New York at Buffalo. He joined the University in 2011
To obtain the Master of Science in Applied Data Analytics, a candidate must successfully complete a minimum of 30 credit hours of required graduate level courses including a capstone project. The program consists of eight required courses (24 credit hours) and two electives (six credit hours).
Required Courses (24 credits)
| CIS 5550 / DATA 5550 | Database Design | 3 cr. |
| CIS 5560 / DATA 5560 | Database Management | 3 cr. |
| CSSE 5310 / DATA 5310 | Introduction to Data Mining | 3 cr. |
| DATA 5001 | Science and Data | 3 cr. |
| DATA 5060 or MTH 5270 | Advanced Statistics for Applied Data Analytics or Probability and Statistics | 3 cr. |
| DATA 5130 | Capstone Project | 3 cr. |
| ECN 5150 / DATA 5130 | Quantitative Foundations for Data Analysis / Quantitative Foundations for Economic Analysis | 3 cr. |
| ECN 5800 | Economic Modeling for Data Analysis | 3 cr. |
Electives – Select two courses (6 credits)
| ECN 5480 / DATA 5480 | Business Forecasting | 3 cr |
| ECN 5810 | Advanced Money and Capital Markets | 3 cr |
| CIVE 5910 | Geographical Information Systems | 3 cr |
| CSSE 5280 | Database Systems | 3 cr |
| CSSE 5480 | Artificial Intelligence | 3 cr |
| CSSE 5650 | Bioinformatics | 3 cr |
| DATA 5070 | Statistical Software Packages for Applied Data Analytics I | 3 cr |
| DATA 5080 | Statistical Software Packages for Applied Data Analytics II | 3 cr |
| DATA 5600 | Topics in Applied Data Analysis | 3 cr |
| ELEE 5350 | Machine Learning | 3 cr |
| ELEE 5740 | Pattern Recognition and Neural Networks | 3 cr |
| INT 5200 | Data Mining and Reporting in Intelligence | 3 cr |
| HLH 5500 | Research Methods in Health Care | 3 cr |
| HSA 5060 | Health Economics | 3 cr |
| HSA 5070 | Population Health | 3 cr |
| HSA 5500 | Information Systems for Health Services Administration | 3 cr |
| MBA 5120 | Data Analysis for Decision Making | 3 cr |
| MBA 5200 | Modeling, Analytics, and Operations Decisions | 3 cr |
| MBA 5335 | Business Intelligence | 3 cr |
| MBA 5340 | Business Analytics | 3 cr |
| MBA 5660 | Database Management for Business | 3 cr |
| MTH 5270 | Applied Probability and Statistics | 3 cr |
| MTH 5590 | Mathematical Modeling | 3 cr |
| MTH 5600 | Graph Theory | 3 cr |
| NUR 5350 | Outcomes Management and Decision Support in Nursing | 3 cr |
| NUR 7450 | Analytics for Evidence-Based Practice | 3 cr |
| NUR 7500 | Evidence-Based Practice: Theory, Design and Methods | 3 cr |
| PYC 5700 | Issues in Industrial and Organizational Psychology | 3 cr |
Admissions Criteria
In order to be admitted to the Master of Science in Applied Data Analytics program, an applicant must meet Detroit Mercy’s entrance requirements. The applicant must also have completed a baccalaureate or advanced degree from a regionally accredited college or university with a cumulative GPA of 3.0 or better. In certain cases, additional prerequisites may be required.