Postgraduate Course: HR/People Analytics (CMSE11589)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Technological advances and cloud computing provide an opportunity for HR professionals to guide their organisation's human resource strategies. HR professionals can derive value from data when they develop strong analytical skills and the ability to utilise data science by identifying metrics and data sources. Currently organisations are struggling to capitalise on the data they have and are often unsure of the utility of these data sources. Developing the knowledge and skills within HR/People analytics can improve evidence for decision making and help derive value from data.
This course focuses on identifying pertinent data sources, developing and assessing valid and meaningful metrics, and designing long-term measures to assess the impact of different HR practices to increase both organisation performance and employee wellbeing. This course builds on compulsory HR courses in MSc IHRM and MSc HRM programmes including: Core Competences for HR Professionals, Methods of Research in HR, and Organisation Behaviour. |
| Course description |
The course examines the background and breadth of the application of HR/People analytics in HR practices. Students will learn about the types of challenges that HR departments face when utilising data from the increased digitisation of HR practices such as recruitment selection, performance management, learning and development, and employee retention. We will explore the unique challenges that result from increased data volume, variety, validity, and velocity. These challenges include ethical considerations around inherent bias in algorithms, employee fairness and wellbeing.
A wide range of topics will be covered include including that focus on the implications of the rise of evidence-based HR and increasingly data-driven HR departments. Students will develop and apply a variety of psychological and statistical tools, including specialist software and psychometric instruments, and will learn the purpose and outcomes of advanced analytics in machine learning and artificial intelligence. These analyses will focus on many types of quantitative and qualitative data. Finally, students will gain skills in how to interpret and visualise data, to use these insights for helping decision-makers and making suggestions for improving performance.
Outline Content:
1. Introduction to HR/People analytics.
2. Exploring data-driven Human Resource decisions.
3. The role of HR data strategy in Strategic HRM.
4. Philosophy of science behind HR Analytics.
5. Operational reporting: Descriptive analytics, dashboards, and visualisation of data, which includes interrogating the veracity of data.
6. Predictive models.
7. Prescriptive data analytics examples
9. Working with qualitative data.10. Debating the future of HR/people analytics.
Student Learning Experience:
Students are expected to come to each class session having read the materials and reviewed posted slides to ensure class interaction. Two of the sessions will take place in a computer lab to work through different aspects of data management and analytics.
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Course Delivery Information
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| Academic year 2022/23, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Seminar/Tutorial Hours 15,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
83 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% Coursework (individual) - assesses all course Learning Outcomes |
| Feedback |
Formative: Students will gain feedback on their understanding of the material through class discussion of cases, debates, and other learning activities.
Summative: Feedback based on specialised marking rubric will be given. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Identify the components and implementation process for an HR data strategy within the context of strategic HRM.
- Utilise tools to assess an organisation's readiness for HR/People analytics .
- Identify ethical issues, scientific debates, and potential problems in the application of HR/People analytics.
- Develop working hypotheses and relevant analytics techniques to provide relevant answers from various data sources across all HR practices.
- Apply the tools and techniques develop in the course to analyse cases in which organisation are challenged in implementing data-driven decision making.
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Additional Information
| Graduate Attributes and Skills |
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Cognitive Skills
After completing this course, students should be able to:
Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to quality.
Knowledge and Understanding
After completing this course, students should be able to:
Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact.
Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore and solve them responsibly.
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| Keywords | Not entered |
Contacts
| Course organiser | Prof Susan Murphy
Tel: (01316)51 5548
Email: Susan.Murphy@ed.ac.uk |
Course secretary | Mr Sean Reddie
Tel: (0131 6)50 8074
Email: Sean.Reddie@ed.ac.uk |
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