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HRM40930

Academic Year 2024/2025

Fundamentals of Ppl Analytics (HRM40930)

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.

Over the past several years, the Human Resource Management (HRM) department has undergone a significant digital transformation and is increasingly relying on technology and analytics platforms to impact decision-making. This foundational module will provide students with a comprehensive understanding of People Analytics, an evolving and dynamic HRM practice. Specifically, students will gain critical insight into People Analytics as both an evolving academic discipline and a growing function within HRM departments. In this way, students will explore the theories, concepts, prominent analytics models and frameworks, and will critically evaluate the ongoing debates in people analytics to better understand how people analytics can impact organizational performance. Specifically, students will be introduced to how modern organizations utilize technology and various data analysis techniques, including HR metrics, regression, and network analysis, to make data-driven decisions linked to business outcomes.

About this Module

Learning Outcomes:

On successful completion of the module, students should be able to:
• Explain the core concepts of people analytics and its maturity, from basic metrics and KPIs, to predictive analytics and generative artificial intelligence.
• Understand the importance of people analytics and its alignment to developing HRM strategies linked to organizational goals.
• Identify various data sources within HRM and evaluate the quality, reliability, and relevance of the data for analysis.
• Critically analyze the ethical considerations and privacy concerns related to the collection and use of employee data in people analytics.
• Apply people analytics theoretical concepts and frameworks to make evidence-based decisions to effectively address a broad range of HRM challenges.
• Understand the various methodologies and techniques available to translate workforce data into actionable insights.

Indicative Module Content:

1. Introduction to People Analytics and Evidence Based Management
2. Designing and Implementing People Analytics Projects
3. Applying People Analytics for HRM Strategy
4. Building Effective People Analytics Capabilities and Future of 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: Students will develop a comprehensive People Analytics strategy for a fictional mid-sized company experiencing HR challenges. Week 6 Standard conversion grade scale 40% No
40
No
Individual Project: Students will design a people analytics project that addresses a specific HRM challenge within an organization. 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) - 40, 42 Fri 09:00 - 16:00
Summer Lecture Offering 51 Week(s) - 42 Mon 09:00 - 16:00
Summer Lecture Offering 51 Week(s) - 40 Wed 09:00 - 16:00