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HR Analytics Applications and Issues
Spring 2018
Tuesday, 4:30-7:10pm; 103 Janice Levin Building
Professor: Helen Liu
Email: [email protected] (preferred)
Office: 208 Levin Building
Office Hour: 2-4pm on Tuesdays. Other times by appointment.
Course Description: This course addresses the growing need for data-driven, analytical
approaches to managing talent. In addition to understanding basic statistics and data management,
being able to design, coordinate and communicate the results, undertaking and executing an
analytics project requires:
(1) Understanding of the “big picture” (i.e., how an analytics project relates to the
organization’s business and strategy)
(2) Identifying and framing the problem, the desired deliverables/outcomes of the project, and
the process/timeframe for getting to the outcome (i.e., project management skills)
(3) Understanding different types of data (big data, small data, messy data) and their
implications, plus understanding different types of analytics, such as differences among
descriptive, predictive, prescriptive and automated/machine learning analytics
(4) Working with quantitative people (“quants”, data scientists) and with the owners/managers
of the data you need (i.e., communication, teamwork, politics, persuasion/negotiation skills)
(5) Being aware of legal, regulatory, ethical, and other considerations when it comes to data and
to analytics projects
(6) Communicating the findings of the project in professional written and oral communications
in ways that are understandable and actionable by decision-makers
Required Text: There is no textbook for this course. Readings and resources are posted on Sakai.
SMLR Learning Goals: This course is designed to meet three SMLR Learning Goals:
II) Quantitative Skills – Apply appropriate quantitative and qualitative methods for research on
workplace issues.
o Formulate, evaluate, and communicate conclusions and inferences from quantitative
information
o Apply quantitative methods to analyze data for HR decision making including cost-benefit
analyses, ROI, etc. (HRM)
VI) Application – Demonstrate an understanding of how to apply knowledge necessary for
effective work performance
o Apply concepts and substantive institutional knowledge, to understanding contemporary
developments related to work
o Understand the internal and external alignment and measurement of human resource
practices (HRM)
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VII) Professional Development – Demonstrate an ability to interact with and influence others in
a professional manner, and to effectively present ideas and recommendations
o Develop effective presentation skills appropriate for different settings and audiences
o Work productively in teams, in social networks, and on an individual basis
Course-Specific Learning objectives: Upon completion of this course students should:
1. Be able to understand specific business challenges and how the available data/HR analytics
in an organization can (and sometimes cannot) be used to address those challenges.
2. Be able to effectively plan, manage, and communicate the results of HR analytics projects.
3. Develop enhanced knowledge and skills in interacting/being the liaison with different
organizational actors (e.g., data scientists, data owners, decision-makers) in executing HR
analytics projects.
4. Have an understanding of major existing (and evolving) legal, regulatory, ethical and other
considerations in HR data/analytics projects.
Student Accountabilities
1. Complete all assigned readings before class. These reading assignments provide a basis for
both lectures and discussions and must be completed prior to each class session.
2. Your attendance at every class is required. Absences for illness, religious holidays and other
events recognized by Rutgers University will be excused. If you know you are going to miss
a class because of a religious holiday I would appreciate an email prior to the holiday.
3. Contribute to class activities. Your responsibilities for class participation include but are not
limited to: active listening, making thoughtful contributions, answering questions raised to
you, and participating in discussions of case materials with your team.
4. Complete assignments as assigned and as scheduled. Unless you have written documentation
of a University approved excuse, assignments and projects are due on the assigned date. Late
submission will be penalized at 10% per day.
Evaluation of Student Performance
Individual paper and presentations = 200
Case study (team-based slides and presentation) = 200
Case write-ups = 100
Class participation = 100
Total points = 600 points
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1. Individual term paper / presentation. Select an organization you are interested in and write a
paper addressing how the organization tackle HR analytics challenges. The term paper is
expected to be 8-10 pages, double spaced.
2. Case study / presentation. Each student will be responsible for leading the discussion of an
assigned case. The discussion leaders should prepare 2-3 questions for discussion relevant to
the lecture topic. These questions must be sent to the instructor and other students by noon
the Wednesday prior to discussion. Late submissions are subject to a grade penalty.
3. Case write-up. All students must read the case articles and be prepared to answer the
discussion questions. To enhance preparation, I require that you submit a short write-up (no
more than one page, single spaced) of two discussion questions before class. Students are
required to turn in five write-ups during this semester. Write-ups are due by 10am on the day
of class. The write-ups will not be graded, but will be checked for satisfactory completion.
4. Class participation. Students are expected to participate actively and regularly in class
discussions. For example, students should read assigned material before class and be
prepared to discuss questions or issues that they encountered. They are also expected to
participate in in-class exercises and activities.
Academic Integrity
As an academic community dedicated to the creation, dissemination, and application of
knowledge, Rutgers University is committed to fostering an intellectual and ethical environment
based on the principles of academic integrity. Academic integrity is essential to the success of
the University’s educational and research missions, and violations of academic integrity
constitute serious offenses against the entire academic community.
Dishonesty of any kind will not be tolerated in this course. Dishonesty includes, but is not
limited to, cheating, plagiarizing, fabricating information or citations, facilitating acts of
academic dishonesty by others, having unauthorized possession of examinations, submitting
work of another person or work previously used without informing the instructor, or tampering
with the academic work of other students. For more comprehensive information on academic
integrity, please refer to the academic integrity website at http://academicintegrity.rutgers.edu.
Access and Accommodations
Your experience in this class is important to me. If you have already established
accommodations with Office of Disability Services (ODS), please communicate your approved
accommodations to me at your earliest convenience so we can discuss your needs in this course.
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If you have not yet established services through ODS, but have a temporary health condition or
permanent disability that requires accommodations (conditions include but not limited to;
mental health, attention-related, learning, vision, hearing, physical or health impacts), you are
welcome to contact ODS at 848-445-6800 or [email protected].
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Course Schedule
Time Topic
Week 1
Jan 16
Overview of Analytics Applications in Real Organizations
Case: 2017 Deloitte Global Human Capital Trends
Week 2
Jan 23
Preliminary considerations in any analytics project
• Ethical, legal and regulatory considerations
• Case: Weapons of Math Destruction
Weeks 3&4
Jan 30 & Feb 6
Framing the problem
• Understanding the business question(s) behind an analytics project.
Cases: Apples and Oranges – Inappropriate comparisons
Unlocking engagement – the Federal Workforce
•
Week 5
Feb 13
Planning the project
• Managing the financial budget and timeline
• Case: Federal Employee Viewpoint Survey (1)
Weeks 6&7
Feb 20 & 27
Relationship management
• Developing successful relationships with “quants” and data owners
• Cases: Google’s scientific approach to work-life balance
• The Dublin Goes Dark Project
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Week 8 / Mar 6
Mid-term exam / presentations
Week 9 / Mar 13
No class / Spring Recess
Weeks 10 &11
Mar 20 & 27
Summarizing and packaging the results
• Case: Federal Employee Viewpoint Survey (2)
Telsa and its employee referral program
Weeks 12 &13
Apr 3 &10
Communicating results to decision-makers
Case: The Project Oxygen
•
Week 14
Apr 17
Turning knowledge into action
Case: Talent Strategy at Chevron
Week 15
Apr 24
Reflections
Final presentations
* The instructor reserves the right to change this syllabus and course schedule during the
semester as needed.
