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  Degree Requirements - Master of Science in Applied Data Analytics (30 credits)

  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 Database Design (3 credits) / DATA 5550 Database Design (3 credits)
  • CIS 5560 Database Management (3 credits) / DATA 5560 Database Management (3 credits)
  • CSSE 5310 Data Mining (3 credits) / DATA 5310 Introduction to Data Mining (3 credits)
  • DATA 5001 Science and Data (3 credits) / ECN 5001 Science and Data (3 credits)
  • DATA 5060 Advanced Statistics for Applied Data Analytics (3 credits) OR MTH 5270 Applied Probability and Statistics (3 credits)
  • DATA 5130 Capstone Project (3 credits)
  • ECN 5150 Quantitative Foundations for Data Analysis (3 credits) / DATA 5150 Quantitative Foundations of Economic Analysis (3 credits)
  • ECN 5800 Economic Modeling for Data Analysis (3 credits) / DATA 5800 Economic Modeling for Data Analysis (3 credits)

  Electives - Select two courses (6 credits):

  • ECN 5480 Business Forecasting (3 credits) / DATA 5480 Business Forecasting (3 credits)
  • ECN 5810 Advanced Money and Capital Markets (3 credits)
  • CIVE 5910 Geographical Information Systems (3 credits)
  • CSSE 5280 Database Systems (3 credits)
  • CSSE 5480 Artificial Intelligence (3 credits)
  • CSSE 5650 Bioinformatics (3 credits)
  • DATA 5070 Statistical Software Packages for Applied Data Analytics I (3 credits)
  • DATA 5080 Statistical Software Packages for Applied Data Analytics II (3 credits)
  • DATA 5600 Topics in Applied Data Analysis (3 credits)
  • ELEE 5350 Machine Learning (3 credits)
  • ELEE 5740 Pattern Recognition and Neural Networks (3 credits)
  • INT 5200 Data Mining and Reporting in Intelligence (3 credits)
  • HLH 5500 Research Methods in Health Care (3 credits)
  • HSA 5060 Health Economics (3 credits)
  • HSA 5070 Population Health (3 credits)
  • HSA 5500 Information Systems for Health Services Administration (3 credits)
  • MBA 5120 Data Analysis for Decision Making (3 credits)
  • MBA 5200 Modeling, Analytics, and Operations Decisions (3 credits)
  • MBA 5335 Business Intelligence (3 credits)
  • MBA 5340 Business Analytics (3 credits)
  • MBA 5660 Database Management for Business (3 credits)
  • MTH 5270 Applied Probability and Statistics (3 credits)
  • MTH 5590 Mathematical Modeling (3 credits)
  • MTH 5600 Graph Theory (3 credits)
  • NUR 5350 Outcomes Management and Decision Support in Nursing (3 credits)
  • NUR 7450 Analytics for Evidence-Based Practice (3 credits)
  • NUR 7500 Evidence-Based Practice: Theory, Design and Methods (3 credits)
  • PYC 5700 Issues in Industrial and Organizational Psychology (3 credits)
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  Capstone Project Guidelines

  Students pursuing the Master of Science in Applied Data Analytics must complete the 30 credit-hour program with a capstone project (DATA 5130). This is a three credit-hour course with a variable credit-per-semester setup. Students must register a total of three credit hours to complete the project. Students have the flexibility to take the Capstone Project over a few semesters or in a single term. For example, a student could register for one credit hour in a Fall Term, one credit in the Winter Term and one credit in the Summer Term of a given academic year. Alternatively, a student could register for one credit hour in the Fall Term and two credit hours in the Winter Term (or vice versa). Last, a student could simply register for all three credit hours in a single term. The capstone is not graded but rather is evaluated on the basis of successful completion of the three credit hours. Students may register one credit per term (i.e. winter, fall) and will draw upon their areas of interest. Students in their first year are recommended to start exploring potential project topics in consultation with the academic advisor and other faculty members in the program and complete the project over two consecutive semesters. See the Capstone Project Guidelines for more information.
  

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  Admission Requirements

  Prerequisites and Admission Requirements

  In order to be admitted to the Master of Science in Applied Data Analytics program, an applicant must meet Ó£ÌÒÊÓƵ’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.

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  STEM/OPT Program - International Students

  The Master of Science in Applied Data Analytics program is classified as a STEM program by the U.S. Department of Homeland Security. International students who attend the on-campus program have the opportunity to apply for an extended Optional Practical Training (OPT) using the Classification of Instructional Programs (CIP) code 11.0802.

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  Course Delivery & Tuition

  To provide greater convenience and flexibility, this program is available:

  • Onsite with traditional day and evening in-classroom formats on the Ó£ÌÒÊÓƵ McNichols Campus*
  • Online. This program can be taken completely online**

  * Please see the Ó£ÌÒÊÓƵ Graduate Tuition and Fees schedule for domestic and international students.
  ** Please see the Ó£ÌÒÊÓƵ Graduate Tuition and Fees schedule for graduate online programs.