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Regular version of the site


Academic Year
Instruction in English
ECTS credits
Course type:
Elective course
3 year, 3, 4 module


Course Syllabus


Building on traditional HR skillsets, the course includes understanding traditional HR processes in the context of data analysis and making decisions based on data-driven approaches. In addition, the course discussions are based on the idea of how analytics can be incorporated in various HR processes such as workforce planning, recruitment, performance management, etc. Upon completion of this course, students should be able to:  Differentiate the main analytics involved in HRM.  Apply HR analytics for organizational performance, business strategy as well as organizational behavior.  Analyze the relationship between HR practices and their outcomes for both the individual and the organization.
Learning Objectives

Learning Objectives

  • The aim of this course is to provide practical, hands-on approaches to connect data to HR policies and practices in order to improve overall business performance.
Expected Learning Outcomes

Expected Learning Outcomes

  • Synthesize and analyze information from relevant sources to solve professional problems
  • Estimate the required resources and plan their use to solve practice-related problems.
  • Collect data necessary for solving research tasks
  • Collect, process, and analyze company data to decrease turnover rate
  • Process and analyze data necessary for solving tasks
  • Use modern technical tools and information technology to solve analytic and research tasks
  • Communicate efficiently based on communication goals and situations
  • Collaborate as a team member
  • Develop the ability to participate in the development of strategy, in human-resources management within an organization, and in the planning, designing, and conducting of different events.
  • Design analyses of the operational activities of firms for preparing managerial decisions
Course Contents

Course Contents

  • Topic 1. Introduction to HR Analytics. Basics of Finance, Statistics and Data-Analytic Thinking
    1) Introduce to the HR analytics course. The evolution of HR analytics. 2) Describe types of analysis. 3) Explain what is meant by the eight-step methodology to approach an analytical project. 4) Understand the critical financial terms and concepts for HR professionals. 5) Consider basic statistics and various methods for data analysis. Workshop: Demographic Characteristics. Sample Description.
  • Topic 2. Tools for HR Analytics
    1) Comprehend what is meant by tools for HR analytics. 2) Consider the components of analytics technology. 3) Understand the various technology options available. 4) Discuss the costs of implementing on-premise vs cloud. Workshop: Descriptive Statistics.
  • Topic 3. Data Collection
    1) Comprehend sources of data. 2) Describe common data challenges and solutions. 3) Know about data cleaning techniques. 4) Explain data checking methodology. 5) Talk over the overview of exploratory data analysis. Workshop: Gathering Data and Descriptive Statistics for HR purposes.
  • Topic 4. HR Analytics Modelling
    1) Be able to define details of the analytics design framework. Sources of problem and opportunity. Scoping the project. 2) Establish links HR variables to business measures. 3) Understand the differences between Hard and Soft Data. 4) Comprehend question types of data analysis. 5) Talk over how to build models. 6) Define supervised vs unsupervised methods. Workshop: Correlations for Diversity Analytics.
  • Topic 5. 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. Workshop: Analysis of Variance, Post-hoc Analysis and Independent-samples t-test for Turnover rate.
  • Topic 6. Training and Development
    1) Comprehend the meaning of Return on Investment (ROI). 2) Understand the ROI of learning and development. 3) Identify Kirkpatrick’s Four Levels. 4) Evaluate the effectiveness of training. 5) Delve into optimization, as the search for the best and most effective solution to an analytics problem. Workshop: Nonparametric Statistics for Evaluation of Training.
  • Topic 7. Strategic Resourcing.
    1) Comprehend workforce supply and demand and their gaps. 2) Understand forecasting techniques. 3) Describe and critically evaluate Explanatory/Causal Models. Workshop: Creating an Excel Dashboard for Onboarding Program Measures.
  • Topic 8. Recruitment.
    1) Define employee profiling and segmentation. 2) Determine the combination of critical values, competencies and skills. 3) Discuss employee loyalty analysis. 4) Ascertain which sources of candidates yield the best cost-benefit. 5) Analyze labor market skill availability vs. internal needs. 6) Consider using the gamification in recruiting. 7) Consider tests for the selection process. Workshop: Factor Analysis for Selection Predictive Analytics
  • Topic 9. Compensation and Benefits.
    1) Consider HR analytics and benefits. 2) Discuss conjoint analysis and MANOVA as analytic tools to look into compensation and benefits plan design. 3) Calculate ROI of various pay schemes. 4) Identify patterns of compensation vs. performance to forecast turnover rates. Compiling Engagement Surveys and Testing Their Reliability.
  • Topic 10. Career Planning.
    1) Consider what types of behaviors, skills and attributes best fit the current and future leadership needs of the organization. 2) Determine how to use analytics to determine mobility possibilities. 3) Identify types of decision trees. Consider career paths by using decision trees. 4) Discuss how to identify High Potentials via combinations of competencies, skills, values and performance. 5) Talk over potential promotions via network analysis. Workshop: Linear Regression to Predict Employee Performance.
  • Topic 11. HR Policies vs. Profits.
    1) Determine the general concept of multiple regression. 2) Develop meaningful metrics to track talent strategy success. 3) Discuss linkages between HR Metrics and Business Metrics. 4) Discuss the case study “Creating an employee value proposition”. Workshop: Nonparametric Statistics for Employee Referral Program.
  • Topic 12. Thoughts on the Future of HR Analytics.
    1) Discuss productivity, revenue, and profit impact of HR programs. 2) Determine people analytics, talent analytics and workforce analytics. 3) Talk over advantages of the data-driven approach to people's decisions. Workshop: Display Group Presentations.
Assessment Elements

Assessment Elements

  • non-blocking Class activities
    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. Participation in class discussion.
  • non-blocking Kahoot game
    At the beginning or at the end of some classes a 15-minute Kahoot game is conducted. If the student missed the class, there is no option to participate in this activity.
  • non-blocking Team-based project
    The team-based project includes a presentation in the class and the paper. Both files must be uploaded to the LMS not later than the deadline agreed at the beginning of the course and set in the LMS. No grades will be given if files are not uploaded to the LMS. The group should be no more than 4 members.
  • non-blocking Written Exam
    The examination is conducted in writing using synchronous proctoring. The exam is conducted on the online HSE Moodle platform https://hse.student.examus.net. You must connect to the exam 15 minutes before the start. On the Examus platform, system testing is available. Student's computer must meet the requirements: To participate in the exam, the student must: go to the proctoring platform in advance, conduct a system test, turn on the camera and microphone, and verify identity. During the exam, students are prohibited from: communicating (on social networks, with people in the room), writing off. During the exam, students are allowed to: use A4 sheets. A short-term communication disruption during the exam is considered interruption of communication up to 10 minutes. A long-term communication disorder during an exam is considered to be a communication interruption of 10 minutes or more. In case of a long-term communication disruption, the student cannot continue to participate in the exam. The transfer procedure is similar to the surrender procedure. Written Exam includes 25 test questions (up to 2 marks for each positive answer), one practical exercise with five calculations (up to 4 marks). Total questions: 30. No negative marks for wrong answers.
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.25 * Class activities + 0.25 * Kahoot game + 0.25 * Team-based project + 0.25 * Written Exam


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

Recommended Additional Bibliography

  • 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
  • Nelson, G. S. (2018). The Analytics Lifecycle Toolkit : A Practical Guide for an Effective Analytics Capability. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1727899