2024/25 Taught Postgraduate Module Catalogue

LUBS5346M Data Analytics for Human Resources

30 creditsClass Size: 70

Module manager: Mia Zhong
Email: M.Zhong1@leeds.ac.uk

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2024/25

Module replaces

LUBS5341M

This module is not approved as an Elective

Module summary

This module will give students grounding in the statistical concepts and machine learning that underpin the practice of people analytics and the application of these concepts in practice using the types of data that organisations routinely collect about their people and operations. Central to the module is the concept of inference; how can we use statistical tools and concepts to understand the causes of human behaviour at work so that statistical analysis can guide management action? Teaching is through workshops that combine lecture material, computer exercises and discussion to interpret the resulting analysis.

Objectives

This module aims to provide a conceptual but non-mathematical introduction to statistical theory and the key elements of algorithmic/machine learning. The emphasis is on the application of data analytics in human resources to empower students with the cutting edge analytics knowledge to solve real-life organizational problems.

Learning outcomes
1. To develop a conceptual understanding of inferential statistics and to be able to critically evaluate the strengths and limitations of classical inferential statistics for making causal inferences in the context of people analytics
2. To develop a conceptual understanding of how domain knowledge of people and organisations, and graphical analysis can be used to plan statistical analysis that provide a basis for causal inference.
3. Develop a conceptual understanding of common methods of causal inference and machine learning methods and be able to critically evaluate the circumstances in which different types of models are appropriate and how results should be interpreted.
4. Develop a conceptual understanding of experimental and quasi-experimental design and their potential for use in people analytics.
5. To be able to synthesise the knowledge from learning outcomes set out above to make critical judgements about how data and statistics can be used to support decision making in the field of people management.

Skills outcomes
- Work effectively as part of a team.
- Display an ethical awareness and a sensitivity to diversity in terms of people, culture, business and management issues, reflect on cross-cultural communication issues and identify appropriate solutions.

Syllabus

Indicative list:
1. Populations, sampling and ecological validity
2. Descriptive statistics – what are they? What are they useful for? How to visualise them
3. Correlation and causality
4. Developing causal understanding through domain knowledge and graphical analysis
5. Distributions and their statistical properties
6. Multiple regression (different types for different distributions).
7. Confounders, endogeneity omitted variable bias etc.
8. Regressions using limited dependent variables and survivals analysis
9. Machine learning random forests, cross-validation, and cluster analysis
10. Tools for causal inferences (Instrumental variables, difference-in-difference, randomised experiments)

Teaching methods

Delivery type	Number	Length hours	Student hours
Workshop	11	3.00	33.00
Tutorial	5	1.00	5.00
Private study hours			262.00
Total Contact hours			38.00
Total hours (100hr per 10 credits)			300.00

Opportunities for Formative Feedback

Students will receive formative feedback as part of personal tutorials and independent analytical 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.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	2,000 word report that critically evaluates the implications for people management practice of statistical analysis from a case study	30.00
Reflective log	A reflective 6-8-minute video log (vlog)	20.00
Total percentage (Assessment Coursework)		50.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc) (S1)	2 hr	50.00
Total percentage (Assessment Exams)		50.00

The resit for this module will be assessed 100% by 3 hour exam.

Reading list

The reading list is available from the Library website

Last updated: 16/08/2024 11:44:41

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