Ateneo - UPO

The course aims to present the basic methodology of Data and Predictive Analytics for human resource management with the support of ad hoc IT tools.

Alan Agresti, Barbara Finlay (2020). Metodi statistici di base e avanzati per le scienze sociali. Pearson.Fitz-enz, J., & Mattox, J. (2014). Predictive Analytics for Human Resources. Wiley. Martin R. Edwards, Kirsten Edwards (2019). Predictive HR Analytics: Mastering the HR Metric. KoganPage. Further teaching material prepared by the professor will be published on the web page of D.I.R. (https://www.dir.uniupo.it)

The course objectives are divided into three levels: 1. methodological: knowledge of the main tools of HR Analytics, their conscious use, critical reading of results; 2. computational: analysis of some useful software in empirical analysis, understanding of their potential and their limits; 3. team-working: develop the ability to work in teams on specific issues and communicate results effectively.

Contents of basic Statistics and of basic Mathematics in economics (see E0252, E0362 and EA007).

Classroom lectures, laboratory exercises, seminar activities by HR experts, team exercises.

Part of the course will be dedicated t meeting and working with experts from the HR sector.

An assessment is based on a written and oral essays consisting in theoretical questions to test the knowledge of the concepts and the mastery of the language; - numerical exercises to test abilities acquired in the use of calculation algorithms and of the software; structured data-based problems and commentary on the results in order to assess autonomy in statistical analysis.

1. Analytics for Human Resources 2. Conjugate analytic models with business processes 3. Who is the owner of the data? Which business processes / functions are provided? 4. Data formats, computer tools. Introduction to database managemnt. 5. Importance of data quality: data cleaning and data pre-processing. Exploration and data display. 6. KPIs Identification 7. Probability and statistical inference. 8. Survey Methods: statistical sampling and data collection techniques. Layered sampling, multi-stage sampling, cluster sampling. 9. Matrix representation of multidimensional data: data matrix, covariance and correlation matrix, dissimilar matrices. 10. Linear regression models and logistic regression. 11. Foresee the future: Predictive Analytics for Human Capital

1. Acquisition of basic and advanced statistical methods for processing human resources data. 2. Practical knowledge of IT tools and database management. 3. Knowledge of the link between HR function and Analytics in a company.