Graduate Course Proposal Form Submission Detail - MAN6347
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Approved by SCNS
Submission Type: New
Course Change Information (for course changes only):
Comments: Elective for MS in Management. To GC. Approved 5/12/16 To USF Sys 5/18/16; to SCNS after 5/25/16. Nmbr 6380 apprd as 6347 eff 7/1/16
- Department and Contact Information
Tracking Number Date & Time Submitted 5455 2016-04-15 Department College Budget Account Number Information Systems and Decision Sciences BA 1407000 Contact Person Phone Gert Jan deVreede 8139743329 email@example.com
- Course Information
Prefix Number Full Title MAN 6347 People Analytics Is the course title variable? N Is a permit required for registration? Y Are the credit hours variable? N Is this course repeatable? N If repeatable, how many times? 0 Credit Hours Section Type Grading Option 3 C - Class Lecture (Primarily) R - Regular Abbreviated Title (30 characters maximum) People Analytics Course Online? Percentage Online C - Face-to-face (0% online) 0
People drive organization and it is now possible to track performance in great detail. This course provides an overview of people analytics opportunities in todays organizations as well as methods to address in a data-driven manner.
A. Please briefly explain why it is necessary and/or desirable to add this course.
Needed to compete with national trends
B. 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?
The leading organizations today are using data-driven approaches to evaluating and improving the performance of their employees. This course will provide graduates with the tools needed to apply contemporary techniques in the workplace.
C. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times?
D. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.)
PhD in Management or related discipline
- Other Course Information
learn the theoretical foundations of individual and team analytics.
gain a comprehensive understanding of the psychology behind people analytics.
gain an understanding of the role and impact of people analytics in gaining and retaining the organizational competitive edge.
become experienced in setting up and executing a people analytics project.
study how people analytics are conducted in organizations.
be exposed to current people analytics concepts used in business from experts.
B. Learning Outcomes
Upon completion of this course, students will be able to:
Identify opportunities for people analytics in a variety of organizations.
Describe the data and methods needed to address specific people analytics opportunities.
Provide concrete examples of how data on people can be used to drive performance.
Identify gaps and opportunities in people analytics research and applications.
Conduct people analytics analyses from historical data.
C. Major Topics
Data Analytics Overview: Examples & Methods, Descriptive vs Predictive Modeling, Databases & Dashboards; Recruitment: How to bring the best talent into an organizations, conventional methods, the use of analytics in recruitment; Case Study 1: People Analytics in Sports (Guest lecture and class discussions); Retention and Engagement: How to engage and retain your best employees, the use of data to determine employee satisfaction and motivational factors.; Case Study 2: People Analytics in Manufacturing (Guest lecture and class discussions) ; Class Project Presentations: Students will present and get feedback on both the individual and team projects; Performance Analytics: How to understand employee performance based on data and predicting performance in projects; People Analytics in Teams: How does data on groups differ from individual data, understanding group dynamics and team formation from data; Case study 3: People Analytics in Healthcare/Services (Guest lecture and class discussions) ; The Quantified Self Movement: Applying people analytics ideas internally. How individual data tracking is altering how people make life decisions
E. Course Readings, Online Resources, and Other Purchases
Big data analytics:
People Analytics in HR: (engagement, retention, recruiting, talent management) weeks 3, 5, 8, 9:
Quantified Self in People Analytics:
1. Cena, F., & Matassa, A. (2015, September). Adopting a user modeling approach to quantify the city. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (pp. 1027-1032). ACM.
2. Rivera-Pelayo, V., Zacharias, V., Mόller, L., & Braun, S. (2012, April). Applying quantified self approaches to support reflective learning. InProceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 111-114). ACM.
3. Barcena, M. B., Wueest, C., & Lau, H. (2014). How safe is your quantified self. Symantech: Mountain View, CA, USA.
4. Swan, M. (2012). Health 2050: The realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen.Journal of Personalized Medicine, 2(3), 93-118.
5. Pantzar, M., & Ruckenstein, M. (2015). The heart of everyday analytics: emotional, material and practical extensions in self-tracking market.Consumption Markets & Culture, 18(1), 92-109.
2. Pieper, J. H., Grace, J., & Dill, S. (2009, April). Team analytics: understanding teams in the global workplace. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 83-86). ACM.
3. Squadron, B., & Rehman, S. (2013). Bloomberg Sports-Next Generation of Team Analytics. In presentation, Society of American Baseball Research Analytics Conference, Phoenix az, March (Vol. 15, p. 40).
1. http://content.healthaffairs.org/content/33/7/1139.full.html - The Legal And Ethical Concerns That Arise From Using Complex Predictive Analytics In Health Care
2. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4242091/ - Applications of Business Analytics in Healthcare
F. Student Expectations/Requirements and Grading Policy
Weekly Assignments 15%
People Analytics Project 20%
Research Project 20%
Midterm Exam 15%
Final Exam 15%
After Action Review 5%
G. Assignments, Exams and Tests
H. 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: (http://usfweb2.usf.edu/usfgc/gc_pp/acadaf/gc10-045.htm)
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. Its 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.
I. Policy on Make-up Work
Only registered students, assistive personnel, and guests invited by the instructor, may attend the class. Attendance is taken during class and is used to evaluate the participation grade. Any student missing three or more weeks of class meetings is subject to being dropped by the instructor.
J. Program This Course Supports
MS in Management
- Course Concurrence Information
MBA, MS in MIS