Overview
The Human Resources Data Analytics concentration is an option within the Master of Human Resources and Industrial Relations (MHRIR) degree at Illinois. The purpose of this concentration is to equip students with knowledge and hands-on skills to conduct, understand, and communicate advanced analyses with HR data. This expertise will better equip MHRIR graduates for the information economy and data revolution in HR practice, leveraging people-focused analytics to make better HR decisions for hiring, training, compensation, retention, and labor relations.
Available courses cover topics such as advanced predictive and regression models, applied measurement, spreadsheets, data visualization and presentation, machine learning/AI, workforce planning, and firm performance/finance for HR.
All courses in the concentration must be taken for a letter grade, and the student must earn a cumulative 3.0 grade in the courses to have the concentration appear on their transcript.
Upon completion of this concentration, students will be able to:
- Demonstrate a practical understanding of key concepts in data analytics and advanced statistics. The concentration builds upon the statistical knowledge gained from the LER core required statistics course.
- Use popular data analysis tools in the field of HR (e.g., Excel, R, Power BI) to visualize, summarize, and analyze organizational data.
- Use findings from data analysis to better understand organizational phenomena, evaluate HR initiatives, and inform HR decision-making.
- Effectively communicate data analysis and statistical findings to various organizational stakeholders.
Required Courses
Choose any three of the four courses below. 12 credit hours total.
LER 510: HR Analytics: Methods & Data-Driven Prediction
Expands your data-analytic toolkit for addressing HR problems. Topics include introductory data handling in the free software R, predictive modeling (multiple regression and logistic regression, for staffing and turnover), factor analysis and reliability (for employee surveys), research design (for training and program evaluation), and mediation and moderation analyses (for understanding training and diversity/test fairness). Assignments include weekly in-class labs and open-note quizzes. This hands-on course develops practical, concrete data analysis skills. 4 grad hours.
LER 526: Machine Learning Applications in HR
The primary goal of this course is to equip human resources management professionals with a comprehensive understanding of how to integrate machine learning and big data analytics across various HR functions. This includes developing a deep appreciation for the opportunities and challenges associated with applying these technologies in areas such as hiring, resume screening, performance appraisal, and worker safety. The course aims to foster critical thinking about the ethical implications and strategic decision-making involved in deploying AI and machine learning solutions in HR contexts. Discussion of potential biases in AI/machine-learning implementation will be integrated throughout the course. 4 grad hours.
LER 527: Applied HR Topics: Spreadsheet & Visualization Analysis
Focusing on the analysis and interpretation of human resource metrics, this course emphasizes the use of spreadsheets and data visualization software to inform evidence-based human resource management decisions. Topics for these data-driven decisions include (1) recruitment and hiring, (2) performance evaluation, (3) executive compensation, (4) training and skill development, and (5) diversity, inclusion, and equity. 4 grad hours.
LER 568: Firm Performance and HR
The purpose of this course is to enable student to understand some basic ideas about and measures of firm performance with heavy emphasis on the role of human resource managers. Students will gain an understanding of how human resource professionals fit into the organization, structure, and function of business firms. Many basic ideas from the field of finance will be studied. The course covers theoretical ideas and has many empirical, policy, and practitioner-relevant applications, all with the goal of providing human resource managers fundamental financial analysis tools to enable them to function effectively in their post-graduate corporate workplaces. 4 grad hours.
Questions?
Please reach out to Becky Barker, Assistant Dean, with questions.