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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 will be 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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  Degree Requirements

  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.

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  Curriculum

  The program requires 30 credit hours to complete, consisting 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) / DATA 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)

  Electives (6 credits):

  As approved by advisor.

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  Capstone Project Guidelines

  Students pursuing the Master of Science in Applied Data Analytics (MS-ADA) must complete the 30 credit-hour program by a Capstone Project (DATA 5130).  A Capstone Project is a variable credit course with a maximum of three credits and will draw upon areas of interest to the student. 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.  For more details on how to proceed on the project, please refer to the MS-ADA Capstone Project Guidelines on the .

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  Course Delivery & Tuition

  To provide greater convenience and flexibility, programs are available as:

  • Onsite with traditional day and evening in-classroom format on the Ó£ÌÒÊÓƵ McNichols Campus*
  • Fully online**

  Program curriculum adheres to University of Ó£ÌÒÊÓƵ's standards of academic integrity and intellectual merit.

  University of Ó£ÌÒÊÓƵ welcomes online students from around the world. If you are English-fluent, and you have a reliable internet connection, you can enroll in our online programs.

  As a potential student, it is your responsibility to confirm our program meets specific licensure requirements in your state or location of residence. Health professions applicants should contact the applicable licensing board.

  * 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.

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  Faculty

  Due to its interdisciplinary nature, the courses are developed and taught by the faculty with specific expertise and research interests. In addition, a Capstone Project offers students the opportunity to select and work on projects that focus on different discipline-specific content areas. Therefore, students can pursue a specific field of study that relates to their future career plan and a faculty member’s expertise.

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  Career Options

  From software developers to data analysts, the field of big data touches all sectors of the U.S. and world economies. Business, e-commerce, finance, government, healthcare, social networking, telecommunications and science are relying on data science to improve their operations and efficiency. These economic agents are, therefore, increasingly dependent on a new workforce that is skilled in analyzing, interpreting and using data to fit their purposes. In a market study, IBM and Business-Higher Education Forum collaborated with Burning Glass to obtain a better understanding of the current job market. Below are a few of their findings:

  • Average pay of jobs requiring machine learning skills: $114,000
  • Average pay of advertised data scientists: $105,000
  • Average pay of data engineer jobs: $117,000