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Data Science and Analytics

Draft Version
This is a DRAFT catalog for review and advising purposes. Items in this catalog draft are subject to change until the catalog for 2024-2025 academic year will be officially published on August 19th, 2024. The statements set forth in this catalog are for informational purposes only and should not be construed as the basis of a contract between a student and this institution. Should changes in a program of study become necessary, those changes will be applied liberally by the institution while the catalog is in draft mode.

MS in Data Science and Analytics

Bradley University offers an interdisciplinary graduate program leading to the degree of master of science in Data Science and Analytics. This course of study is designed to prepare students for professional careers in the field or for further study and research.

The Data Science and Analytics graduate program provides students with the necessary skills to effectively use large data sets to solve problems and potentially find new insights.

Students can concentrate their study in various application areas including: 1) business analytics, 2) computational data science, 3) engineering analytics and 4) logistics analytics.

Admission requirements to the Data Science and Analytics program are given below:

  • completed at least one semester of calculus
  • must submit GRE General Test or GMAT scores taken within the last five years. The applicant may request a GRE or GMAT waiver under certain circumstances.

Note that prospective students who do not meet the conditions for admission may be admitted conditionally, in which case the department will prescribe a program for the removal of such admission conditions. Conditional status must be removed prior to graduation.

In addition to satisfying all the Graduate Education requirements for the degree, all candidates for the master’s degree must satisfy the following departmental requirements:

  • At least 30 hours of graduate-level coursework.
  • No "D" grades can be counted in the completion of requirements for the degree.
  • Every student must take a comprehensive exam as defined and administered by the concentration department that the student is in.
  • Students may register for only three courses per semester. Any exceptions must be approved by the appropriate department chair.
  • To satisfy the core (breadth) requirement, five courses or 15 credit hours must be taken:
    1. IME 511 Probability & Statistics for Analytics (3 credit hours)
    2. CS 541 Python Programming for Data Science (3 credit hours) or CS 560 Fundamentals of Data Science (3 credit hours)
    3. CS 571 Database Management Systems (3 credit hours) or IME 568 Engineering Analytics (3 credit hours)
    4. MIS 573 Data Visualization for Business Analytics (3 credit hours)
    5. MIS 590 Business Analytics Consulting Project (3 credit hours) OR (CS 594 Capstone Project for Data Science (3 credit hours) OR  C S 699 Thesis) OR (IME 690 Capstone Project for Engineering Analytics OR IME 691 Research/Practicum (3 credit hours) OR IME 699 Thesis)
    6. To satisfy depth requirements, the student must take the 15 credit hours from one of the concentrations listed below. No course taken to satisfy the core requirement (item 2 above) may be counted as one of the courses in this requirement.

    Concentrations

    Business Analytics Concentration - 15 credit hours (ch)

    The Business Analytics concentration provides students with the necessary skills to analyze organizational data to aid in business decision-making. The concentration is comprised of 15 semester hours of study.

    Required courses (5 courses):

    1. MIS 571 Business Analytics Software and Applications I - 3 ch
    2. Q M 526 Business Forecasting - 3 ch
    3. Q M 564 Decision Support Systems - 3 ch
    4. Choose (2) Electives (See List Below) - 6 ch

    Computational Data Science Concentration - 15 credit hours (ch)

    The Computational Data Science concentration provides students with the necessary skills to understand the theory and algorithms utilized in data science and to be able to implement and apply them. The concentration is comprised of 15 semester hours of study.

    Required courses (3 courses):

    Take three out of the following four courses, that you have not yet taken to fulfill the common core.

    1. CS 560 Fundamentals of Data Science - 3 ch
    2. CS 562 Machine Learning - 3 ch
    3. CS 563 Knowledge Discovery and Data Mining - 3 ch
    4. CS 572 Distributed Databases and Big Data - 3 ch

    Note: If you fulfilled the common core by taking CS 541, then one of the 3 courses taken from the list above must be CS 560.

    Electives (2 courses or 1 course for thesis students):

    Two electives (6 ch) or, for those who choose the thesis option instead of capstone, one elective (3 ch). Electives must be approved by the student’s graduate advisor.

    On the thesis option:

    Interested and qualified students pursuing the Computational Data Science concentration have the option to write a master’s thesis. Students selecting this option are encouraged to choose a thesis advisor and topic as early as possible to plan the thesis development and any needed supporting coursework.

    The following policies apply to theses:

    • A minimum grade point average of 3.5 in graduate courses taken at Bradley is required for students enrolling in a thesis course, i.e., CS 699.
    • No student may register for a thesis until 9 hours of graduate courses have been completed in the program.
    • Six credit hours of a thesis course are required and, upon completion, the thesis must be defended in an oral examination. The six hours must be in consecutive semester or terms (3+3).
    • No grade will be given for a thesis course until after the oral defense. The thesis oral defense substitutes the comprehensive exam that the non-thesis students have to take. 
    • A written outline of the thesis project and a tentative schedule must be submitted to and approved by the graduate coordinator and the chair prior to the registration for a thesis course.

    Engineering Analytics Concentration - 15 credit hours (ch)

    The Engineering Analytics concentration provides students with the skills to analyze and process large-size and complex data, to utilize proper methodology in identifying problems, formulating mathematical or algorithmic models, and to solve problems arising from engineering applications, including product design, process design, manufacturing execution, inventory management, production planning, quality control, economic analysis of engineering decision.

    Required Courses (3 courses):

    Take three out of the following four courses, that you have not taken to fulfill the common core.

    1. IME 514 Introduction to Operations Research - 3 ch
    2. IME 561 Simulation of Manufacturing & Service Systems - 3 ch
    3. IME 586 Logistics and Supply Chain Systems - 3 ch
    4. ECE 565 Engineering Applications of Machine Learning - 3 ch

    Two electives (6 ch) or, for those who choose the thesis option instead of capstone, one elective (3 ch). Electives must be approved by the student’s graduate advisor.

    Logistics Analytics Concentration - 15 credit hours (ch)

    The Logistics Analytics concentration provides students with the necessary skills to analyze organizational data to aid in business decision-making. The concentration is comprised of 15 semester hours of study.

    Required courses (5 courses):

    1. IB 502 Global Trade Management and Analysis - 3 ch
    2. MTG 502 Logistics Tools and Techniques - 3 ch
    3. MTG 506 Marketing Analytics or MTG 507 Customer Analtyics – 3 ch
    4. Choose (2) Electives (See List Below) – 6 ch

    Possible electives for the Data Science and Analytics Program include courses required by the other concentrations, or additional courses listed below, or courses approved by the department chair. It is the responsibility of the student to ensure they have met the prerequisites for their elective courses.

    • CIS 576 Data Management
    • CIS 580 Digital Society and Computer Law
    • CS 541 Python for Data Science
    • CS 560 Fundamentals of Data Science
    • CS 561 Artificial Intelligence
    • CS 562 Machine Learning
    • CS 563 Knowledge Discovery and Data Mining
    • CS 571 Database Management Systems
    • CS 572 Distributed Databases and Big Data
    • ECE 565 Engineering Applications of Machine Learning
    • ECO 519 Econometrics
    • IME 501 Engineering Cost Analysis
    • IME 514 Introduction to Operations Research
    • IME 526 Reliability Engineering
    • IME 561 Simulation of Manufacturing & Service Systems
    • IME 568 Engineering Analytics I
    • IME 578 Engineering Analytics II
    • IME 583 Production Planning and Control
    • MIS 613 Advanced Algorithms for Business
    • IB 502 Global Trade Management and Analysis
    • MTG 502 Logistics Tools and Techniques
    • MTG 506 Marketing Analytics
    • MTG 507 Customer Analytics
    • MTG 624 Marketing Decision Making
    • MTG 640 Obtaining, Analyzing, and Applying Marketing Information
    • MTH 510 Numerical Methods I
    • MTH 511 Numerical Methods II
    • Q M 526 Business Forecasting
    • Q M 564 Decision Support Systems