Introduction
The programme “Recruitment through Data and Predictive Analytics” develops professional practice in Human Resources. It connects the subject with workplace challenges by examining existing evidence and practices and choosing methods appropriate to the organization and its available resources.
The learning progresses from reliability, predictive maintenance, and failure analysis to performance, competency, and individual development management, with practical work on recruitment, selection, and candidate experience, data analysis and decision support, human resource planning and job analysis. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Analyze asset failure and condition data to define a justified warning and maintenance intervention in an application related to “Recruitment through Data and Predictive Analytics”.
- Design a selection process based on job requirements and consistent assessment evidence.
- Turn available data into documented analysis that supports a specific professional decision.
- Connect workforce and job requirements to objectives and required capabilities.
- Build a performance agreement, feedback process, and development plan tied to specific gaps.
Learning areas
1. Reliability, Predictive Maintenance, and Failure Analysis
- Define the asset's function, functional failure, and usage context, and assess the effects of failure modes before selecting a maintenance task type.
- Organize fault, downtime, repair, and condition reading histories, checking the quality of timestamps, classifications, and record completeness.
- Analyze trends, vibration, temperature, or other appropriate condition indicators, linking alerts to documented thresholds and specialist technical review.
- Distinguish correlation and prediction from proof of a failure's cause, and assess false and missed alarms and warning lead time before intervention.
- Design a condition monitoring pilot for a training asset, with escalation criteria and documentation of the maintenance decision, and verify the intervention's effect on reliability. The application examines a case related to “Recruitment through Data and Predictive Analytics”.
2. Recruitment, Selection, and Candidate Experience
- Translate the job description into observable selection criteria, separating essential requirements from attributes that can be developed.
- Prepare a clear job advertisement and recruitment plan targeting appropriate sources, avoiding requirements unrelated to the work.
- Design a structured interview, behavioral and situational questions, and a standard template for recording evidence and evaluating answers.
- Select appropriate assessment tools and work samples, and review fairness, privacy, and permissions for accessing candidate data.
- Document the selection decision, its rationale, communication steps, and onboarding, and measure quality of hire and candidate experience after implementation.
3. Data Analysis and Decision Support
- Frame an analytical question linked to a specific decision, and define the unit of measurement, time period, and data needed to answer it.
- Check data quality for completeness, accuracy, consistency, and duplication, and document cleaning steps and limitations on reliance.
- Select appropriate comparisons, indicators, and charts, and distinguish statistical correlation from causal explanations of results.
- Interpret results in the work context, test assumptions and unusual values, and present alternatives and their associated uncertainty.
- Prepare an analytical report containing the finding, recommendation, data source and date, review owner, and next follow-up step.
4. Human Resource Planning and Job Analysis
- Analyze workforce demand according to workload, services, and anticipated changes, and document estimation assumptions.
- Inventory current skills, jobs, and vacancy risks, and compare the current position with target requirements.
- Prepare a job description covering purpose, responsibilities, relationships, competencies, and performance indicators without confusing the job with its incumbent.
- Select options for closing gaps through recruitment, development, redeployment, and work design, estimating resources and impact.
- Build indicators to monitor vacancies, workforce movements, retention, and skill coverage, and review the plan when organizational needs change.
5. Performance, Competency, and Individual Development Management
- Translate unit objectives into agreed individual results, specifying weights, success criteria, and sources of performance evidence.
- Define competencies using observable levels and behaviors, and connect assessment to role requirements without broad personal judgments.
- Conduct a follow-up discussion covering achievements, obstacles, and required support, and record facts and commitments professionally.
- Analyze performance gaps to distinguish causes related to skills, procedures, resources, and motivation, and select an intervention suited to the cause.
- Prepare a development plan with practical tasks, support, and review dates, and monitor the transfer of learning into actual work results.
Practical outputs
- A failure mode analysis, condition monitoring plan, and alert and review rules.
- A selection matrix, interview guide, and candidate scorecard.
- A dashboard and decision memo explaining the data, assumptions, and alternatives.
- A workforce plan, job description, and capability gap map.
- A performance agreement card, individual development plan, and review session guide.
Who should attend
- Practitioners, specialists and supervisors whose work relates to the programme subject.
- Managers and teams working in human resources, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “Recruitment through Data and Predictive Analytics” within their organizations.
Prerequisites
- Working familiarity with the programme field and the ability to discuss a professional case or challenge.
- Confirm the suitability of your experience and any specialist prerequisites with the Union before participation.
Conclusion
The programme “Recruitment through Data and Predictive Analytics” brings together concepts, case review and a practical application. Participants prepare steps that can be adapted to their responsibilities and organizational resources, with an indicator to follow application and review results after returning to work.
Participation arrangements and any special requests are specified in the confirmation issued by the Union.