Introduction to Computational Workforce Analytics (with Python)

This 2-day course will familiarise participants, especially those with no prior exposure to workforce analytics, with the context necessary to make data-driven decision making in workforce planning.

Workforce analytics is intended to help HR managers or executives to make data-driven decisions about their employee or the workforce.  This short course will provide you hands-on expertise in using statistics and Python on workforce data which can help you in making better and informed business decisions for your company.

Case studies will focus on different aspects of Workforce Analytics which may include Talent Acquisition and Management, Compensation and Benefits, Performance Management, HR Operations and Learning and Development, Leadership Development. Further, this course will provide foundations for advanced level analytical methods such as optimisation and machine learning to support decision making in workforce analytics.

Duration: 2 Days

Pre-requests: Some familiarity with statistical methods and Python

Required Software: Python (freely available)

Delivery Mode: Live simulcast online course

Presenter

Dr. Hasan H. TURAN

Dr. Hasan H. TURAN is a Lecturer at the University of New South Wales Capability Systems Centre.  He is an expert on the development data-driven solution methods and their application on complex decision-making problems arising in different domains including service and maintenance logistics, defence (fleet management, workforce, and resource planning), energy capacity expansion, and telecommunications networks.

 

Dr. Turan has been involved in several workforce planning projects funded by the Department of Defence. His works on defence workforce analytics have been appeared in the leading academic outlets.

 

Course Information

  • Main Components in workforce analytics and type of workforce analytics
  • Key Metrics used in workforce analytics and dashboard preparation tips
  • Descriptive Analytics (e.g., distribution plots, outlier detection)
  • Diagnostic Analytics (e.g., Hypothesis testing, ANOVA, etc.,)
  • Predictive Analytics (e.g., linear and logistic regression)
  • Hands-on practice with case studies on Recruitment, Employee Attrition, and Employee Satisfaction Analytic

Courses will be held subject to sufficient registrations. UNSW Canberra reserves the right to cancel a course up to five working days prior to commencement of the course. If a course is cancelled, you will have the opportunity to transfer your registration or be issued a full refund. If registrant cancels within 10 days of course commencement, a 50% registration fee will apply. UNSW Canberra is a registered ACT provider under ESOS Act 2000-CRICOS provider Code 00098G.