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Graduate Course Proposal Form Submission Detail - QMB6358
Tracking Number - 5350

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Current Status: Approved by SCNS - 2016-07-01
Campus: Tampa
Submission Type: New
Course Change Information (for course changes only):
Comments: In review; Required for MBA. Approved; To USF Sys 4/21/16; to SCNS after 4/28/16. SCNS approved 6306 as 6358 eff 7/1/16

Detail Information

  1. Date & Time Submitted: 2015-12-22
  2. Department: Information Systems and Decision Sciences
  3. College: BA
  4. Budget Account Number: 140700
  5. Contact Person: Balaji Padmanabhan
  6. Phone: 46763
  7. Email:
  8. Prefix: QMB
  9. Number: 6358
  10. Full Title: Data Analytics for Business
  11. Credit Hours: 2
  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?: N
  17. If repeatable, how many times?: 0
  18. Abbreviated Title (30 characters maximum): Data Analytics for Business
  19. Course Online?: B - Face-to-face and online (separate sections)
  20. Percentage Online: 0
  21. Grading Option: R - Regular
  22. Prerequisites: QMB 6305 or equivalent
  23. Corequisites: None
  24. Course Description: This course will provide an introduction to data analytics for managers. It is targeted for MBA students and provides an overview of data collection, visualization and business dashboards, as well as classification models on customer data.

  25. Please briefly explain why it is necessary and/or desirable to add this course: Needed for program/concentration/certificate change
  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? This course is part of the re-design of the MBA program at the Muma College of Business. The college's emphasis on analytics and creativity will now also reflect strongly in the requirements for the MBA degree.
  27. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? No
  28. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) A terminal degree in statistics, information system or a related discipline with expertise in using tools such as R, Tableau and Excel.
  29. Objectives: In this course, students will learn advanced methods and concepts related to data analytics and data mining. In particular, students will learn

    • methods for collecting data via surveys and web scraping

    • tools and techniques for data-driven decision making via interactive visualizations and dashboards

    • models and methods for predicting consumer choices via segmentation and classification ideas

    The course is very hands-on in that it is built around a combination of interactive lectures, in-class projects and data cases, using modern data mining software, and team-led discussion of common problems and issues encountered in data analytics.

  30. Learning Outcomes: At the end of the course students will be able to demonstrate

    - data collection skills through surveys and the Web

    - the ability to design business dashboards

    - the ability to build predictive models on customer data

    - the ability to use tools such as Excel, R and Tableau to support business data analysis

  31. Major Topics: • Week 1: Introduction and Overview

    • Week 2: Information Analytics 1 - Obtaining Data via Surveys

    • Week 3: Information Analytics 2 - Obtaining Data via Web Scraping

    • Week 4: Decision Analytics 1 - Designing Dashboards in Excel

    • Week 5: Decision Analytics 2 - Designing Dashboards in Tableau and R

    • Week 6: Marketing Analytics 1 - Classification for predicting consumer choices via logistic regression

    • Week 7: Marketing Analytics 2 - Classification for predicting consumer choices via regression trees

    • Week 8: Conclusions and Team Project Presentations

  32. Textbooks: N/A
  33. Course Readings, Online Resources, and Other Purchases: Instructor notes only, which will be provided free on canvas.
  34. Student Expectations/Requirements and Grading Policy: Attendance & Class Participation 20%

    Weekly Team Presentation 20%

    Individual Assignments 30%

    Team Assignment 30 %

  35. Assignments, Exams and Tests: INDVIDUAL ASSIGNMENTS

    Several individual assignments will be due. The primary purpose of these assignments is for students to have the opportunity to practice the concepts learned in class, and to implement them using real data and real software. Careful participation during the in-class projects will allow you to solve the individual assignments more efficiently.


    The team assignment consists of two components: a team paper and a team presentation (to be held during our last in-class meeting).

    For this team project, please identify a complex data-driven problem from your company or organization. In particular, identify a business problem that relates to either targeting or forecasting. Then, obtain relevant customer or company data pertaining to your business problem. In addition, you will also be asked to augment that data via surveys and web scraping. Once all the data is in place, address the business problem using the tools and concepts learned in class. In particular, in addition to your forecasting or targeting model, also develop a business dashboard that can help the manager make efficient decisions based on your data model.

    The deliverable for this assignment is a team paper (15-20 pages) and a team presentation. Presentations will be held during the last class meeting. The paper is due at the same time.

  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: In the case of documented medical or family emergencies students may request an extension of deadlines for the project. Students are expected to comply with all academic integrity rules as specified in the USF policies. Students are expected to review information at
  38. Program This Course Supports: MBA
  39. Course Concurrence Information: N/A

- if you have questions about any of these fields, please contact or