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Regular version of the site

People Analytics: Prediction of Performance & Prescription of Policy

Academic Year
Instruction in English
ECTS credits
Course type:
Elective course
2 year, 1, 2 module


Course Syllabus


This practice-oriented course focuses on effective People Analytics as a source of business value creation in the context of Big data. The core objective is to explore how to efficiently integrate data analytical approaches to each stage of the talent management process, from talent acquisition to talent retention. Throughout the course, examples for each People Analytics Success stage will be provided to help illustrate the key concepts to effective HRM.
Learning Objectives

Learning Objectives

  • Develop high-impact People Analytics in order to generate business value from the Big Data and little data available to the organization.
  • Identify types of people analytics used in a company.
  • Obtain valuable people analytics to improve the efficiency of workforce planning, hiring, placing and retaining the best employees.
  • Choose relevant data science methods for specific problems in human resource (HR) management.
  • Analyze outcomes of HR practices and identify their effects on both the staff members’ attitudes toward the company and the organizational performance.
  • Explain how data-driven decision-making affects organizational performance and HR practices.
Expected Learning Outcomes

Expected Learning Outcomes

  • Able to reflect (evaluate and process) obtained scientific and work methods
  • Able to identify the data required for the solution of research tasks in management; to gather data from both the field research and desk research as well as from the social and economic sources
  • Able to choose tools, modern technical means and information technologies to process information for the assigned scientific task in management
  • Able to present the results of the study in various forms, such as a report, an article or a presentation.
  • Able to work out the organizational development programs and put them into practice.
  • Able to develop his/her intellectual and cultural level, build a trajectory of career development
  • Able to prepare and manage the consultancy project.
  • Able to self-study of new research methods, change the scientific and professional profile in his/her practical activities
Course Contents

Course Contents

  • Introduction to People Analytics
    What is analytics? The skills gap in the labor market. Data types. Four levels of people analytics. Analytic capabilities. The seven stages of people analytics success. Typical applications of people analytics in human resource field. Ethics and other considerations.Learning objectives for the seminar:  Discuss four major challenges in people analytics.  Talk about preparing for an analytics unit of a particular company.  Identify steps taken to reorganize the analytics unit within an HR department to make its output more useful to business leaders.
  • Workforce Planning Analytics
    What is workforce planning? Workforce diversity and inclusion. Key components of workforce planning analytics. Turning data into information. Workforce optimization. Hard-to-fill position analysis. Financial benefits of workforce analytics.Learning objectives for the seminar:  Discuss data sources for HR reports.  Practice to include measures of efficiency, effectiveness, and outcomes for the chosen company.
  • Sourcing Analytics
    What is talent sourcing? Digital evolution of the workforce. Metrics and statistical applications. Job seekers decision stages. Big Data analytics on job postings. Social Media sourcing. Mobile sourcing matters. Optimizing sourcing channels. Learning objectives for the seminar:  Consider various recruitment channels under the digital evolution of the workforce.  Discuss the advantages and disadvantages of recruitment channels and identify the most relevant sources for the chosen company.  Case study – Diversity Analytics.
  • Talen Acquisition Analytics
    What is talent acquisition analytics? The cost of a bad hire. Preinterview assessment analytics. Recruitment and selection analytics. Reliability and validity of selection methods. Human bias in recruitment selection. Hiring through predicting performance.
  • Analytics for Onboarding and Organizational Culture Fit
    Peculiarities of organizational culture. Stages of onboarding. The connection between onboarding and employee performance.Onboarding predictive model. Learning objectives for the seminar:  Practice predicting the effectiveness of the chosen company investments based on the analytical reports for one of the HR practices.  Shape the analytical framework for effective onboarding.
  • Employee Engagement Analytics
    What is talent engagement? Employee engagement surveys and measures. Factors that contribute to positive employee experience. Learning objectives for the seminar:  Making employee engagement surveys predictive.  Articles’ discussions.
  • Employee Life Time Value and Cost Modeling
    What is Employee Lifetime Value? Three vital metrics for every employment role. The employee performance curve. The employee cost curve. Human resource accounting. Replacement cost. Employee tenure in a survival analytics framework. Big Data approaches to employee development. Learning objectives for the seminar:  Practice predicting the effectiveness of the chosen company investments based on measuring the cost of attrition.  Articles’ discussions.
  • Retention Analytics
    What is retention? Talent retention framework. Components of talent attrition predictive model. Sociodemographic and geodemographic data. Monitoring the impact of interventions.
  • Employee Wellness, Health, and Safety Analytics.
    What is employee wellness? The importance of understanding the workforce profile. Big data and people analytics for employee wellness. Learning objectives for the seminar:  Display group presentations.
Assessment Elements

Assessment Elements

  • non-blocking Individual-based project “Examining the role of talent management practices using fsQCA"
    The individual-based project includes two parts: 10-page paper (50% in grading) and presentation in the class (50% in grading). Both files must be uploaded at Ms Teams not later than the deadline agreed at the beginning of the course and set at Teams. No grades will be given if files are not uploaded. The paper should include the following parts: 1. Introduction with a problem statement (research motivation and research question) 2. Brief review and analysis of academic literature, proposition development. 3. Methodology: Sample description, data collection approach, short description of the statistical analysis method(s). 4. Findings and theoretical/practical implications. 5. Conclusions References. The paper should be single-spaced throughout; Times New Roman 12-point font (except for the title page); A4 size page formatting; 2.5 cm margins on all sides.
  • non-blocking Class activities
    (1) Kahoot game. At the beginning or at the end of some lectures a 15-minute Kahoot game is conducted. If the student missed the class, there is no option to participate in this activity. (2) Individual assignment during seminars. At the beginning or at the end of some seminars students will get a written task for 10-15 min based on their home reading or materials discussed during the previous classes. If the student missed the class, there is no option to rewrite this task, except for sickness absence. In this case, the student should notify the instructor about his/her sick leave.
  • non-blocking Exam in the Ms Teams
    The exam consists of 22 test questions (up to 2 marks for each positive answer), 2 open questions with the explanation of your ideas (up to 3 marks for each positive answer), and one practical exercise (up to 4 marks). No negative marks for wrong answers. Total time: 60 min. Total questions: 25
  • non-blocking Coursera course "Qualitative Comparative Analysis (QCA)"
    All students are required to take the Coursera course "Qualitative Comparative Analysis (QCA" , https://www.coursera.org/learn/qualitative-comparative-analysis?
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.3 * Class activities + 0.3 * Exam in the Ms Teams + 0.4 * Individual-based project “Examining the role of talent management practices using fsQCA"


Recommended Core Bibliography

  • Isson, J. P., & Harriott, J. (2016). People Analytics in the Era of Big Data : Changing the Way You Attract, Acquire, Develop, and Retain Talent (Vol. 1). Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1226538

Recommended Additional Bibliography

  • Edwards, M. R., & Edwards, K. (2019). Predictive HR Analytics : Mastering the HR Metric (Vol. 2nd Edition). New York: Kogan Page. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2037089
  • Fitz-enz, J., & Mattox, J. (2014). Predictive Analytics for Human Resources. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=812792