Module manager: Mia Zhong
Email: M.Zhong1@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
LUBS5346M Data Analytics for Human Resources
This module is not approved as an Elective
This module introduces students to the core statistical principles that underpin data analysis and demonstrates how these principles are applied in real organisational contexts. Students will work with datasets about employees and organisations using statistical tools and data visualisation to better understand human behaviour at work. Teaching is delivered through computer-based lectures and interactive practical tutorials.
This module aims to introduce students to basic statistical concepts and tools for data analysis applications in human resources management and organisational contexts. The objective of the module is to empower students with statistical literacy and skill sets to confront, manage, explore, and analyse data to tackle real-life organisational challenges. Students will obtain data manipulation and management skills, data visualisation skills, and statistical analysis skills, including regression models for exploring patterns and associations underlying real-life data.
On successful completion of the module, students will be able to:
1. To develop a conceptual understanding of statistical models and critically evaluate the strengths and limitations of commonly used statistical methods in the context of human resource analytics.
2. To develop a conceptual understanding of how domain knowledge of people and organisations can be used to draw causal inference from the outcomes of statistical modelling.
3. To be able to synthesise theoretical knowledge to make critical judgements about how statistics can be used to support decision-making in the field of human resource management.
On successful completion of the module, students will be able to:
Academic Skills
A1. Critical Thinking: Apply cognitive skills of critical thinking, analysis and synthesis
Work Ready Skills
W1. Cultural/ diversity awareness: Display an ethical awareness and a sensitivity to diversity in terms of people, culture, business and management issues, reflect on diversity issues and identify appropriate solutions.
W2. Information technology: The ability to select and use new information and technology to enhance efficiency and productivity. Willingness and openness to learn and adapt to new technologies, including data analysis with programming language and coding skills.
Technical Skills
T1. Statistical Acumen: Develop a good understanding and skills of how to use statistical programming tools and perform statistical analysis in the context of human resource management
Enterprise Skills
E1. Working and communicating with others: learn to present stories and analytical recommendations with insights from data analysis.
Indicative list:
1. Populations, sampling and ecological validity
2. Descriptive statistics and data visualisation - what are they? What are they useful for?
3. Distributions and their statistical properties
3. Correlation
5. Multiple regression (different types for different distributions).
6. Confounders, endogeneity, omitted variable bias, etc.
7. Regressions using limited dependent variables
8. Moderation and mediation analysis
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Workshop | 10 | 2 | 20 |
| Tutorial | 4 | 1 | 4 |
| Private study hours | 126 | ||
| Total Contact hours | 24 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Students will receive formative feedback as part of practice tutorial tasks conducted on the basis of the material covered in lectures and computer workshops. The feedback will be written, helping students to improve their analytical and report writing skills.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | 3,000 word report that critically evaluates the implications for people management practice of statistical analysis from a case study | 100 |
| Total percentage (Assessment Coursework) | 100 | |
The resit for this module will be 100% by 3,000 word report.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team