Challenge 5
The team focused on establishing strong data quality and analytical foundations for a Project Health and Behaviour Monitor. Using a structured synthetic dataset, they demonstrated how task-level schedule, cost, and resource attributes can be cleaned, validated, and analysed to identify volatility, critical path risk, forecasting accuracy issues, and problematic resourcing behaviours.
Please be aware that this content was generated follwing an automated review so may not be perfectly accurate; refer to the original challenge brief and team files for authoritative information
Improved reliability of planning data through systematic cleansing; clearer identification of behavioural risk indicators such as volatility and ignored dependencies; stronger analytical basis for predictive dashboards; reduced noise and ambiguity in schedule and resource analysis.
Team 5c.ipynb: Notebook demonstrating data quality checks, cleansing, and exploratory analysis of schedule and resource behaviours.Source Data/Fake Dataset 26.05 Data Dictionary.docx: Defines the structure and meaning of the synthetic dataset used for behavioural analysis.Source Data/Supplementary Information Challenge 5.docx: Outlines extended behavioural metrics and future directions for a project health monitor.
team: Project Health and Behaviour Monitor members: tbc topics: solution-centre, hack26, challenge5, python, pandas, jupyter, data-analysis, analytics, resource-management, schedule-forecasting, behavioural-analytics, data-quality, project-controls technologies: python, pandas, jupyter, data-analysis, analytics