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HRM40940

Academic Year 2024/2025

Data Analytics for HRM (HRM40940)

Subject:
Human Resources Management
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Dr Steven McCartney
Trimester:
Summer
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

As organizations increasingly rely on data to inform their human resource management (HRM) strategies, understanding how to leverage data analytics has become an essential skill for HR professionals used to inform decision-making and drive organizational success. This module will provide students with a practical understanding of data analytics in the context of HRM, focusing on the application of analytical techniques to solve real-world HRM challenges. Students will explore fundamental methods such as descriptive statistics, linear regression, and correlation analysis, learning how to apply these techniques to real-world HRM challenges such as talent management, employee engagement, and diversity and inclusion. The module emphasizes hands-on experience, with students using Excel and ChatGPT to work through HR data sets and case studies, developing the skills needed to interpret data, generate insights, and make informed decisions that align HR practices with broader business objectives. Overall, this module is designed to equip students with the analytical tools and knowledge necessary to effectively leverage data analytics in HRM, to support data-driven decision-making in their organizations.

About this Module

Learning Outcomes:

On successful completion of the module, students should be able to:
• Apply advanced functions and formulas using Excel and ChatGPT to organize, clean, and analyze HR data.
• Apply a range of data analysis techniques to address specific HRM challenges, including but not limited to recruitment and selection, employee performance, turnover prediction, employee well-being, and diversity and inclusion.
• Design data visualizations to effectively communicate HR metrics and advanced insights to aid HRM decision-making.
• Demonstrate an ability to tell a compelling story around HRM data and effectively present findings and recommendations to technical and non-technical audiences.
• Critically evaluate the core concepts and theories related to data analytics in people analytics, including the people analytics data process, data analytics techniques, and methodologies.

Indicative Module Content:

1. Introduction to People Analytics and Data in HRM
2. Analysis Techniques and Data Visualization
3. Applying People Analytics
4. Advanced Topics and Data Analysis Techniques in People Analytics

Student Effort Hours:
Student Effort Type Hours
Lectures

24

Autonomous Student Learning

226

Total

250


Approaches to Teaching and Learning:
This module is structured around a series of lectures, reading, and viewing materials for discussion. All sessions are planned for in-person classroom delivery, and it is a requirement of the module that students attend every session in person. Unexcused absence is not permitted. You must provide written support explaining any absence. Contact Dr. Steven McCartney, preferably in advance, if you know you will be absent.

Materials for each class will be uploaded to Brightspace beforehand. It is vital that you use these materials as these will form the basis for class activity. Lecture slides/notes will be available on Brightspace each week. However, these notes/slides are available to clarify the main points made in each class, and to indicate where students may find further material for additional study. Lecture notes/slides do not provide a complete set of materials which will suffice for reference material in assessments. In all cases, I encourage the practice of actively taking notes during class to increase your engagement with the material discussed in lectures.

It is each student’s individual responsibility to ensure they keep up to date with the reading, viewing, and listening requirements for the module. Students are expected to read, view, and listen to all materials in advance of lectures. From time-to-time additional reading may be recommended. Students are strongly encouraged to read outside the essential and recommended material, especially while competing assignments. Likewise, all students are encouraged to engage in class discussion to facilitate the formation of their critical judgements.

The teaching and learning philosophy underpinning this module is constructivist: learning by doing. The expectation is that the lecturer will ‘scaffold’ learning to enable and encourage students to construct their learning under guidance, from grasping core foundational knowledge to a deeper understanding and engagement with the topic. In line with this, students will apply their knowledge through various methods, including but not limited to lecture discussions, presentations, practical examples, and case studies.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Work Assignment: In groups students will clean and analyze a dataset to extract meaningful insights for an individualized HRM challenge and develop an interactive dashboard. Present (“pitch”) their findings. Week 9 Standard conversion grade scale 40% No
40
No
Individual Project: Students will be provided with four datasets and a corresponding workbook containing specific questions related to the data. Students must select two of four datasets to analyze using ChatGPT. Week 12 Standard conversion grade scale 40% No
60
No

Carry forward of passed components
No
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Students will receive written feedback on their group presentation and report via email to each group member. This feedback should be read in conjunction with the UCD grade descriptors. Feedback will be provided within 20 days of the completion of the assessment task, with the exception of work submitted late as per Section 4.35 of the University’s academic regulations.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Summer Lecture Offering 51 Week(s) - 43 Mon 10:00 - 15:00
Summer Lecture Offering 51 Week(s) - 43, 44, 45 Thurs 10:00 - 15:00
Summer Lecture Offering 51 Week(s) - 44, 45 Tues 10:00 - 15:00