Syllabus

Title
1609 Global Human Capital Analytics
Instructors
Assoz.Prof Priv.Doz.Dr. Mihaela Dimitrova
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/17/24 to 09/26/24
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Dates
Day Date Time Room
Tuesday 10/01/24 10:00 AM - 12:00 PM LC.2.064 PC Raum
Tuesday 10/15/24 10:00 AM - 01:00 PM LC.2.064 PC Raum
Tuesday 10/29/24 10:00 AM - 01:00 PM LC.2.064 PC Raum
Tuesday 11/26/24 10:00 AM - 01:00 PM LC.2.064 PC Raum
Tuesday 12/03/24 09:00 AM - 12:00 PM LC.2.064 PC Raum
Tuesday 12/17/24 10:00 AM - 11:30 AM LC.2.064 PC Raum
Contents

As more and more organizations continue to expand globally, the need to manage and sustain a global workforce has become vital. Increased digitization and technological advancements have made it possible to have a data driven approach to making strategic decisions regarding multinational organizations’ human resources. This course will introduce you to global HR strategic planning and will explore methods and tools used to make data driven decisions about recruiting, selecting, developing, evaluating, and retaining employees.

This is a highly interactive and hands-on course that gives you the opportunity to learn how to analyze and interpret data for making strategic HR decisions. We will use R Studio and SPSS to analyze data, as well as AI based tools such as ChatGPT Advanced Data Analysis. 

Learning outcomes

Upon successful completion of this course, you will be able to:

  • Identify and describe the central aspects of the strategic approach to HR.
  • Demonstrate an improved understanding of methodologies for analyzing data.
  • Analyze and interpret data to aid various HR functions.
  • Use the skills and tools you learned in this course to make accurate data driven decisions.
  • Demonstrate improved research and critical thinking skills.
Attendance requirements

This course is to be held in a blended format. Students will have to complete a series of short exercises online as well as attend the in-person class sessions. As per WU's guidelines for PI courses, you will fail the course if you miss more than 20% of the course, this includes both in-person sessions and online activities. Please note that any absences will also negatively impact your participation grade. Please also note that the final class meeting date will include an exam. 

Teaching/learning method(s)

The course is designed in a way to maximize your learning by balancing between lecture and your involvement in discussions, cases, and exercises. You will also regularly work hands-on with data, giving you an opportunity to develop your analytic skills and gain experience in using data to make strategic HR decisions. The course will additionally include an exam, which will cover core course concepts and test your ability to analyze data using statistical methods and interpret the results.

 

Assessment

Assessment will be based on both individual and team performance. Breakdown of assignments with percent of total grade:

  1. Individual final exam: 35% of total grade
  2. Team project presentation (final grade partially depends on peer evaluations): 35% of total grade
  3. Participation: 30% of total grade

Please note that successful participation involves thoughtfully contributing to class discussion, engaging in thoughtful analysis of any cases, actively participating in class activities, and synthesizing across readings. You are expected to have read in advance any assigned readings and cases and be prepared to discuss them.

More information on assignments and required course readings will be provided at the start of the course.

Grading Key:

90-100% = 1

80-89% = 2

70-79% = 3

60 - 69% = 4

59% and below = 5

                                                                                            

Readings

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

Recommended previous knowledge and skills

Prior knowledge of human resource management and basic statistics is helpful, but not necessary.

Availability of lecturer(s)

Assoc. Prof. Mihaela Dimitrova, Ph.D.

Department of Global Business and Trade

Office: D1 3.032

I am available for individual meetings by appointment. Please email to set up an appointment (mdimitro@wu.ac.at)

Last edited: 2024-08-28



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