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Human Resource Analytics

Course developers

Course abstract

The course provides students with in-depth knowledge, skills and competence the fundamentals of human resources (HR) analytics, develops HR analytical skills in synergy with management science. The focus of the course is on maximising the impact of HR analytics in companies, when planning HR development.
The aim of the study course is to provide students with in-depth knowledge, skills and competence in HR analytics, meaningfully applying a variety of HR analytics methods.

Learning outcomes and their assessment

Knownledge:
• In-depth knowledge and understanding of HR analytics and data analytics – test
Professional skills:
• Able to generate and analyse forecasts of HR needs – practical works;
• Able to integrate data from multiple tables and to perform selection and analyse staff needs for the development of the business – practical works;
• The ability to find, critically assess relevant information for practical use and use information creatively to complete your work – practical works
Soft skills:
• Able to collaborate and communicate – practical works;
Competence:
• To critically analyse and independently apply relevant HR analytical methods, planning HR development – practical works, independent work;
• To integrate acquired knowledge and skills into professional work – independent work;
• Able to improve competence and create innovative solutions in professional work – independent work.

Course Content(Calendar)

1. Fundamentals of HR analytics in human management processes (lecture - 2 h)
2. The importance of decision-making in employee attraction, career development, satisfaction, performance, forecasting, retention and company culture (lecture - 1 h)
3. Strategic human resources planning. The importance of employee learning (lecture - 1 h)
First test on understanding HR analytics and strategic HR planning (1 h)
4. Interactive visual analysis of data (lecture - 1 h, practical work - 3 h)
5. Forecasting HR needs (lecture - 1 h, practical work - 4 h)
6. VBA for data modelling (lecture - 1 h, practical work - 3 h)
7. Data analysis optimisation with Power BI and Python (lecture - 1 h, practical work - 4 h)
Second test on the diversity of HR analytical methods (1 h)

Requirements for awarding credit points

The course includes 2 tests, independent work. Practical works to be done during practical work in the classroom. All work must be successfully completed.

Description of the organization and tasks of students’ independent work

Within the study course, for independent works is given 56 hours. Independent works are organized as follows: preparation for the test (15 hours for each work), preparation for independent work (26 hours).

Criteria for Evaluating Learning Outcomes

The final grade in the study course is formed by:
30% tests – on understanding HR analytics and strategic HR planning, on the diversity of HR analytical methods;
50% practical works;
20% independent work – strategic human resources planning and forecasting.

Compulsory reading

1. Diez F., Bussin M., Lee V. Fundamentals of HR Analytics. Emerald Publishing Limited, 2019, p. 281
2. Edwards M. R., Edwards K. Predictive HR analytics: mastering the HR metric. Kogan Page, 2019, p. 536
3. Fitz-enz J. The new HR analytics: predicting the economic value of your company's human capital investments. New York, AMACOM, 2010, p. 368
4. Winston W. Microsoft Excel 2019 Data Analysis and Business Modeling. Microsoft Press, 2019, p. 880
5. Collie R. Power Pivot and Power BI. Holy Macro! Book, 2016, p. 314
6. Kusleika D., Alexander M. Excel 2019 Power Programming with VBA. Wiley, 2019, p. 784

Further reading

1. Alvarez F., Stone D., Castano A., Garcia-Izquierdo A. Human Resources Analytics: A systematic Review from a Sustainable Management Approach. Journal of Work and Organizational Psychology 38, 2022, pp. 129-147.
2. Belizon M., Kieran S. Human resources analytics: A legitimacy process. Human Resource Management Journal. 32, 2022.
3. Margherita A. Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 32, 2021.
4. Alexander M., Kusleika D., Walkenbach J. Excel 2019 Bible. Wiley, 2018, p. 1120
5. Jelen B., Alexander M. Microsoft Excel 2019 Pivot Table Data Crunching. Microsoft Press, 2019, p. 512

Periodicals and other sources

1. Peiseniece, L. (2010). Cilvēkresursu vadīšanas novērtēšanas metodes un to pilnveidošanas virzieni Latvijas lielajos uzņēmumos. Promocijas darbs. Rīga: LU.
https://core.ac.uk/download/71754104.pdf
2. Dombrovska, L.R. (2009). Cilvēkresursu kapitāla vadība. Rīga: Zvaigzne ABC.

Notes

Compulsory study course in the professional master's study programme Human Resource Management,