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Regular version of the site

Staffing Analytics Overview

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


Course Syllabus


The course is a part of the people analytics’ track. The main point of this discipline is to understand the basics of the staffing process including creating HR metrics that are necessary for decision-making approaches. It also provides a strong framework to put into practice HRM statistical tools and methods. The course includes online learning over the Coursera platform in which three of Wharton’s top professors, all pioneers in the field of people analytics, will explore the state-of-the-art techniques used to recruit and retain great people, and demonstrate how these techniques are used at cutting-edge companies. In class students will practice in using metrics and analytics to improve efficiency at hiring, placing and retaining the great people (16 academic hours)
Learning Objectives

Learning Objectives

  • Identify what to do for being efficient with HR metrics and analytics.
  • Use analytics to improve efficiency at hiring, placing and retaining the best people.
  • Manage techniques to hire, place and retain the right employees.
  • Recognize the relationship between HRM and organizational performance, business strategy as well as organizational behavior.
  • Analyze the relationship between HR practices and their outcomes for the individual and the organization.
Expected Learning Outcomes

Expected Learning Outcomes

  • Apply received knowledge and understanding of data and tools to real business situations
  • Study and apply people science methods adjusting to working environment
  • Demonstrate the ability to make managerial decisions and evaluate their consequences
  • Demonstrate the ability to catch new knowledge and skills in the fields beyond his/her majoring
  • Create and defend a customized suggestions based on data and analytics given
  • Demonstrate the ability to collect, process, and analyze data necessary for solving tasks
  • Create and defend a customized consulting project based on data and analytics given
Course Contents

Course Contents

  • Topic 1. Introduction to Staffing Analytics. Basics of Finance, Statistics and Data-Analytic Thinking
    1) Introduce to the Staffing analytics course — the evolution of HR analytics. 2) Describe types of analysis. 3) Talk over what analytics are meant, data type and three levels of staffing analytics. 4) Explain what is meant by the eight-step methodology to approach an analytical project. 5) Understand the critical financial terms and concepts for HR professionals. 6) Consider basic HR metrics, demographic characteristics and sample description.
  • Topic 2. Data Collection
    1) Comprehend sources of data. 2) Describe common data challenges and solutions. 3) Know about data cleaning techniques, reliability tests and common method bias. 4) Explain data checking methodology. 5) Talk over gathering data and descriptive statistics for HR purposes.
  • Topic 3. Staffing Analytics in Human Capital Management Systems (MOOC)
    Turning data into information. Force analysis. Metrics and statistical applications.Three value paths. Efficiency measures. Effectiveness measures. Business outcome measures. People analytics trends. Barriers andkey enablers to success.Hiring: Predicting Performance: Fine-tuning Predictors;Using Data Analysis to Predict Performance. Internal Mobility: Analyzing Promotibility& Optimizing Movement within the Organization.
  • Topic 4. Collaboration Environment of Human Capital Management System (MOOC)
    Basics of Collaboration. Describing Collaboration Networks. Mapping Collaboration Networks. Evaluating Collaboration Networks. Measuring Outcomes. Intervening in Collaboration Networks.
  • Topic 5. Talent Analytics and Future Directions (MOOC)
    Self-fulfilling Prophecies. Reverse Causality. Tests and Algorithms. Prescriptions: Navigating the Challenges of Talent Analytics. Organizational Challenges and Future Directions.
  • Topic 6. Employee Turnover
    1) Comprehend three categories of turnover drivers (external, organizational, and individual). 2) Talk over different types of turnover (attrition rate). 3) Discuss the case of Semiconductor Company.
  • Topic 7. Interactive HR dashboards
    1) Create a Dashboard with Excel for HR purposes 2) Outline data analysis expressions (DAX) in Power Pivot 3) Implement HR metrics in dashboards
Assessment Elements

Assessment Elements

  • 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 LMS
    The exam consists of 40 test questions and covers all topics including the Blended course details. Total time: 60 min. No negative marks for wrong answers.
  • non-blocking Individual assignment (interactive HR dashboard)
    Read the file Case Attrition Analysis and identify one HR problem/question. It is possible to use any other dataset for creating HR problem/question. However, in this case, such a dataset should be available for the instructor. Upload an Excel file to LMS in the project called “Interactive HR Dashboard”.
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.35 * Class activities + 0.3 * Exam in the LMS + 0.35 * Individual assignment (interactive HR dashboard)


Recommended Core Bibliography

  • Fermin Diez, Mark Bussin, & Venessa Lee. (2019). Fundamentals of HR Analytics : A Manual on Becoming HR Analytical. Bingley: Emerald Publishing Limited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2204225
  • 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

Recommended Additional Bibliography

  • Pease, G. (2015). Optimize Your Greatest Asset —— Your People : How to Apply Analytics to Big Data to Improve Your Human Capital Investments. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1046506