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Graduate Course Proposal Form Submission Detail - ESI6635
Tracking Number - 5101

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Current Status: Approved by SCNS - 2015-04-01
Campus: Tampa
Submission Type: New
Course Change Information (for course changes only):
Comments: For MSIE, PhD in IE, MSEM - Elective. GC appd 2/10/15. To USF Sys 2/27/15. Nmbr 6608 apprd as 6635. Effective 4/1/15

Detail Information

  1. Date & Time Submitted: 2014-10-15
  2. Department: Industrial and Management Systems Engineering
  3. College: EN
  4. Budget Account Number: 210300
  5. Contact Person: Hui Yang
  6. Phone: 45579
  7. Email:
  8. Prefix: ESI
  9. Number: 6635
  10. Full Title: Advanced Analytics I
  11. Credit Hours: 3
  12. Section Type: C - Class Lecture (Primarily)
  13. Is the course title variable?: N
  14. Is a permit required for registration?: N
  15. Are the credit hours variable?: N
  16. Is this course repeatable?: Y
  17. If repeatable, how many times?: 1
  18. Abbreviated Title (30 characters maximum): Advanced Analytics I
  19. Course Online?: C - Face-to-face (0% online)
  20. Percentage Online: 0
  21. Grading Option: R - Regular
  22. Prerequisites: EIN 4606 Engineering Analytics I; ESI 6247 Advanced Statistical Design Models, or equivalent
  23. Corequisites: none
  24. Course Description: Data are motivating a profound transformation in the operation management in all fields of engineering and business. Navigate the overload to optimally prepare and enrich data to use as a key ingredient for powerful analytical insights.

  25. Please briefly explain why it is necessary and/or desirable to add this course: Replacing Selected Topics with Permanent number; already listed in program
  26. What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service? Anticipated annual enrollment is between 6-15 students once per year.
  27. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? Yes, 3 or more times
  28. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) PhD in IE (or related mathematics/statistics focused discipline)
  29. Objectives: 1) Enhance analytic effectiveness with dynamic data visualization.

    2) Discover useful trends and patterns from the data.

    3) Use statistical data analysis to drive fact-based decisions.

  30. Learning Outcomes: After successful completion fo this course, students will be able to (1) handle rich data sets from complex engineering systems; (2) extract useful and meaningful knowledge from the data; (3) exploit the extracted knowledge to enhance the prediction, design and optimization of systems.
  31. Major Topics: Introduction to data analytics

    - Overview of analytics

    - Data management (Correctly consolidated data is the first step for analytics)

    - Data challenges (Errors, outliers, missings, size), Missing data imputation

    - Software used in this course (Matlab and SAS Enterprise Miner)

    Predictive modeling (A concise representation of the input and target association)

    - Logistic regression to model the relationship between a categorical response variable and a set of predictor variables

    - Maximum likelihood estimation and Newton-Raphson algorithms


    - Time series characteristics and components

    - Autoregressive (AR) model, moving-average (MA) model

    - Autoregressive–moving-average (ARMA) models

    Neural network

    - Neuron model and network architectures

    - Feedforward neural network and Radial basis neural network

    - Self-organized neural network (SOM)

    - Response surfaces and performance optimization (steepest descent, conjugate gradient

  32. Textbooks: Lecture notes, research articles, and other Materials or links to them on the world wide web will be provided as the semester proceeds. MATLAB® will be used for some homework assignments and projects in this class. It is available in College of Engineering computer laboratories, or obtain the student version for use at home. Tutorials for MATLAB can be found in the following link:
  33. Course Readings, Online Resources, and Other Purchases: None required for purchase
  34. Student Expectations/Requirements and Grading Policy: 2 Exams – 35 pts each (approx 25.9 % each)

    Homework/Quiz – 30 pts (approx 22.2%)

    1 Project – 35 pts (approx 25.9 %)

  35. Assignments, Exams and Tests: There will be two exams, numerous homework/quiz sets and one project. Exam dates will be announced as the course progresses. The top score from two exams will be added to the project and the homework/quiz scores to obtain the final grade for the course (out of a total of 100 pts). No make-up exams unless previous arrangements have been made. Students will be expected to attend class and prepare assignments. Habitual failure to do so will result in a reduced grade. An incomplete grade will only be given when a student misses a portion of the semester because of illness or accident. Cheating on examinations, plagiarism and other forms of academic dishonesty are serious offenses and may subject the student to penalties ranging from failing grades to dismissal.

    Grading scale will be used: A: 90+; B: 80+; C: 70+; D: 60+, F:

  36. Attendance Policy: Course Attendance at First Class Meeting – Policy for Graduate Students: For structured courses, 6000 and above, the College/Campus Dean will set the first-day class attendance requirement. Check with the College for specific information. This policy is not applicable to courses in the following categories: Educational Outreach, Open University (TV), FEEDS Program, Community Experiential Learning (CEL), Cooperative Education Training, and courses that do not have regularly scheduled meeting days/times (such as, directed reading/research or study, individual research, thesis, dissertation, internship, practica, etc.). Students are responsible for dropping undesired courses in these categories by the 5th day of classes to avoid fee liability and academic penalty. (See USF Regulation – Registration - 4.0101,

    Attendance Policy for the Observance of Religious Days by Students: In accordance with Sections 1006.53 and 1001.74(10)(g) Florida Statutes and Board of Governors Regulation 6C-6.0115, the University of South Florida (University/USF) has established the following policy regarding religious observances: (

    In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Blackboard, Elluminate, Skype, and email messaging and/or an alternate schedule. It’s the responsibility of the student to monitor Blackboard site for each class for course specific communication, and the main USF, College, and department websites, emails, and MoBull messages for important general information.

  37. Policy on Make-up Work:  Exams must be taken on the scheduled exam dates. Students are required to arrange with the instructor in advance for a make-up exam in the event of extenuating circumstances that prevent them from taking the exam as scheduled. In the event of an unforeseen emergency that prevents the student from taking the exam as scheduled, the student must provide documentation to the instructor before a make-up exam can be arranged.
  38. Program This Course Supports: IMSE: MSIE and PhD programs
  39. Course Concurrence Information: Master of Science in Engineering Management (MSEM) can take this class as an elective if prerequisites are met, and potentially other graduate engineering degrees from other departments

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