Applied Data Analytics (MS-ADA)

Description

Master of Science in Applied Data Analytics

Ó£ÌÒÊÓƵ'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. Ó£ÌÒÊÓƵ’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.

Ó£ÌÒÊÓƵ’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, Ó£ÌÒÊÓƵ’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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    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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    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.

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    Curriculum

    The program requires 30 credit hours to complete, consisting of eight required courses (24 credit hours) and two electives (6 credit-hours).

    Required Courses (24 credits):

    • CIS 5550 Database Design (3 credits)
    • CIS 5560 Database Management (3 credits)
    • CSSE 5310 Data Mining (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)
    • ECN 5800 Economic Modeling for Data Analysis (3 credits)

    Electives (6 credits):

    As approved by advisor.

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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

Program Contact Information

Professor and Department Chair: Raphael Shen, S.J., Ph.D.
Briggs Building, Room 315
McNichols Campus

Email: shenrs@udmercy.edu
Telephone: 313-993-1738
Fax: 313-993-1166