Skip to main content


HR Analytics

Module description

This module investigates opportunities for HR functions to improve strategic decision making by transforming data into information and insights, bringing analytical rigour to HRM and ultimately enabling organisations to make more informed decisions and gain competitive advantage.  The emergence of cloud-based data storage, data warehouses, AI and machine learning, alongside reduced data storage costs, means organisations have access to large pools of data, including employee specific data, plus technology capable of interpreting and analysing large data sets.  Students will briefly consider historical HRM metrics, then consider implementation of HR analytics using a variety of contemporary HR analytics models and academic frameworks, gaining understanding of key data-related terminology and processes, using example data sets to develop understanding of the HR analytics process using statistical models, design thinking and root-cause analysis.  Students will make connections between core HRM activities, HR analytics insights and strategic goals. 

Drawing from a variety of disciplines including psychology, management, information technology, economics, law and education, the module calls upon examples from a wide range of business scenarios, including consideration of ethics, governance and Corporate Social Responsibility (CSR).  This module will provide you with an opportunity to learn the theoretical potential of HR analytics, and appreciate the practical hurdles required to successfully implement theory to practice, based upon organisational and HRM context, using case study examples from a variety of organisational scenarios.  This module reflects the increasingly inter-disciplinary nature of research and employment and is suitable for students from interdisciplinary pathways.

Full module specification

Module title:HR Analytics
Module code:BEMM395
Module level:M
Academic year:2023/4
Module lecturers:
  • Mrs Fiona Smith - Convenor
Module credit:15
ECTS value:



This module is restricted to HRM Masters students only in the first year of delivery. To be reviewed after the first year. 



Duration of module: Duration (weeks) - term 2:


Module aims

Understanding how HR analytics is a core element of organisational strategy is at the heart of the module, and this module aims to equip students with the foundation knowledge and skills to critically interpret, evaluate and select HR analytics methods and models to introduce HR analytics to the HRM function, By taking an interdisciplinary approach, the module will expose students to a variety of models, core principles and ways of working to enable students to demonstrate contextual awareness of both organisational and wider external factors.  As a result, the module provides students with core HR analytics employability skills and knowledge.

ILO: Module-specific skills

  • 1. Identify and apply appropriate HR analytics theories, methods and tools to demonstrate a strong understanding of HR analytics
  • 2. Explain and interpret contemporary issues regarding HR analytics and use of data – including ethics, governance and change management,

ILO: Discipline-specific skills

  • 3. Critically evaluate the impact of HR analytics on businesses and individuals
  • 4. Discuss how to develop HR Analytics capability within organisations

ILO: Personal and key skills

  • 5. Develop HR analytics skills including ability to apply a variety of statistical models

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled learning and teaching activities1010 x 1 hour whole cohort session
Scheduled learning and teaching activities84 x 2 hour seminar session
Guided Independent Study66Core and supplementary reading
Guided Independent Study66Assignment preparation

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Seminar exercisesVia 4 x 2 hour seminars1-5Group verbal/written feedback

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Individual essay301000 words1,2,3,4Written feedback
Individual report702500 words1,2,3,4,5Written feedback

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Individual essay, 1000 words, 30%Individual essay, 1000 words, 30%1,2,3,4Relevant re-assessment period
Individual report, 2500 words, 70%Individual report, 2500 words, 70%1,2,3,4,5Relevant re-assessment period

Syllabus plan

Core themes considered in the module will include:

  • Introduction to HR analytics
  • HR analytics as a strategic enabler
  • Organisational context for successful HR analytics:  AI, machine learning, data mining, psychometric testing
  • The language of HR analytics, models, frameworks, statistics, data & insights
  • Problem identification, predictive analytics, prescriptive analytics
  • The future of HR analytics

Indicative learning resources - Basic reading

No single textbook is used throughout this module.  Example data sets and case studies are drawn from the following two books, both are available online via the library, and further guided reading will be provided in advance of lecture via the ELE pages.  Indicative recommended reading will include:

For case studies

  • Edwards, M., & Edwards, K. (2016). Predictive HR Analytics Mastering the HR Metric. London: KoganPage. Retrieved from
  • Provost, F., & Fawcett, T. (2013). Data Science for business. Beijing: O'Reilly. Retrieved from

Module has an active ELE page?


Indicative learning resources - Web based and electronic resources

Online learning -

References include:

Gaur, B. (2020). HR4.0: An Analytics Framework to redefine Employee Engagement in the Fourth Industrial Revolution. 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). Kharagpur: IEEE. Retrieved from

Rasmussen, T., & Ulrich, D. (2015). Learning from practice: how HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236-242. Retrieved from

Rosett, C., & Hagerty, A. (2021). Introducing HR Analytics with Machine Learning Empowering Practitioners, Psychologists, and Organizations. Champ: Springer International Publishing. Retrieved from

Sharma, G. (2021). A literature review on application of Artificial Intelligence in Human Resource Management and its practices in current organizational scenario. 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (pp. 594-600). Palladam: IEEE. Retrieved from

Origin date


Last revision date