Course Descriptions

OPRE Course Descriptions

• OPRE 202 STATISTICAL DATA ANALYSIS (3)

  A second course in the statistical analysis of data related to business activities with emphasis on applications in various functional areas including accounting, finance, management, marketing and operations management, among others. Topics include estimation, hypothesis testing, contingency tables and chi-square test, analysis of variance and covariance, simple and multiple regression analysis and correlation analysis. Computer implementation using Excel-based statistical data analysis or other relevant software and interpretation of results for business applications are emphasized. prerequisites: OPRE 201 or MATH 115.

• OPRE 315 BUSINESS ANALYTICS FOR DECISION MAKING (3)

  An introductory course in business analytics and its application to support business decision-making and improve business performance. The focus is on descriptive, and predictive analytics, including, optimization, decision analysis, data mining, data visualization, forecasting and simulation and their application to various functional areas in business such as finance, marketing, operations, and human resources. Special emphasis is placed on analytical concepts, computer implementation and communication of recommendations based on findings. Prerequisites: MATH 115 and OPRE 202

• OPRE 493 HONORS SEMINAR (3)

  An advanced interdisciplinary seminar that focuses on important books and issues and encourages independent thinking, clear presentation and an understanding of the concerns and methods of various disciplines. The course may be team taught; topic and instructor(s) may change from semester to semester. Course may be repeated for credit when topic changes. prerequisites: 3.3 GPA and permission of the Denit Honors Program director

• OPRE 494 HONORS PROJ/THESIS (3)

  Directed individual instruction in an advanced project of the student's choice; the project must be academically related to this discipline. Each student works closely with a faculty director who guides his/her progress. The project must be of honors quality and must be finally approved by both the faculty director and a second faculty member. Course is eligible for a continuing studies grade. prerequisites: 3.3 GPA and permission of both the Denit Honors Program director and the faculty director

• OPRE 497 SPECIAL TOPICS IN OPERATIONS RESEARCH (3)

  An intensive exploration of topics in the area of operations research. Refer to the semester class schedule for exact title of topic offered. This course may be repeated for credit when topic changes. prerequisites: determined by the instructor, Merrick School of Business students.

• OPRE 499 INDEPENDENT STUDY: OPERATIONS RESEARCH (1 - 3)

  An ­independent study under the direction of a faculty member. For eligibility and procedures, refer to the Merrick School of Business Independent Study Policy. Department consent required.

• OPRE 505 FUNDAMENTALS OF STATISTICS (1.50)

  Emphasizes applications of descriptive statistics in business. Topics include basic probability concepts, summary measures of location and dispersion, discrete and continuous probability distributions, sampling distribution of mean, and introductions to confidence interval estimation and hypothesis testing. Excel-based software is used for computer implementation. prerequisite: graduate standing

• OPRE 506 MANAGERIAL STATISTICS (1.50)

  Emphasizes applications of inferential statistics in business. Topics include confidence interval estimation, hypothesis testing, analysis of variance, simple linear regression and an introduction to multiple regression. Excel-based software is used for computer implementation. prerequisite: OPRE 505

• OPRE 605 BUSINESS ANALYTICS (1.50)

  Explores business analytics and its applications to management decision-making for a range of business situations. Covers problem structuring; big data; data mining; optimization; computer simulation; decision analysis; and predictive modeling. prerequisite: OPRE 504 or OPRE 505 and OPRE 506 or equivalent or permission of the M.B.A. program director.

• OPRE 606 DATA MINING FOR BUSINESS (1.50)

  This course provides an exploration of data mining techniques to discover meaningful insights within vast and complex datasets for business problems. Students will understand the role of data mining in today’s data-driven world and gain practical skills for exploring data to extract patterns and associations, making predictions, segmenting data, and evaluating data mining models. Data mining algorithms covered in this course include single linkage cluster analysis, Kmeans, K-nearest neighbor, discriminant analysis, decision trees, market basket analysis, etc. Python is used as the main software in this course to implement data mining techniques. Students will apply data mining models to real-world case studies using Python to extract actionable knowledge from data.

• OPRE 607 BUSINESS ANALYTICS (3)

  This course explores business analytics techniques to discover meaningful insights and support decision-making in business. Students will understand the role of business analytics in today’s data-driven world and gain practical skills for exploring data to extract patterns and associations, segmenting data, making predictions, modeling uncertainty using simulation, developing and solving optimization models, and performing risk analysis.

• OPRE 792 SPECIAL TOPICS IN OPERATIONS RESEARCH (1.50)

  Explores advanced topics in operations research of interest to faculty and students. May be repeated for credit. Prerequisites and topics are selected and printed in the schedule of classes. prerequisite: department consent

• OPRE 797 SPECIAL TOPICS IN OPERATIONS RESEARCH (3)

  Explores advanced topics in operations research of interest to faculty and students. May be repeated for credit. Prerequisites and topics are selected and printed in the schedule of classes. prerequisite: department consent

• OPRE 799 INDIVIDUAL RESEARCH: OPERATIONS RESEARCH (1 - 6)

  Individual research in an area of interest to the student. The expectation is that work equivalent to a regular graduate course will be completed. Formal paper(s) will be written under the supervision of a full-time faculty member. prerequisites: approval of information systems instructor, department chair and academic adviser