Learning objectives
By the end of this course, participants will be able to:
- 01Explain the key principles, terminology and standards of Predictive Analytics
- 02Apply proven tools and methods of Predictive Analytics to real workplace situations
- 03Identify risks, common failures and improvement opportunities
- 04Measure performance using practical KPIs and benchmarks
- 05Build an action plan to implement the learning in their organisation
Who should attend
Managers, engineers, analysts and professionals adopting AI and data.
Course outline
- 1
DAY 1
FoundationsConcepts, terminology, international standards and the role of Predictive Analytics in Artificial Intelligence & Data.
- 2
DAY 2
Core principles & methodsKey processes, techniques and tools, with guided exercises.
- 3
DAY 3
Practical applicationsHands-on workshops, calculations and real industry examples.
- 4
DAY 4
Risk, compliance & performanceRisk control, quality, KPIs, audits and lessons learned.
- 5
DAY 5
Case studies & action planningIndustry case studies, group project, assessment and personal action plan.
+ 10-day edition — additional modules
- 6
DAY 6
Advanced topics & emerging practiceDeeper technical content and the newest developments.
- 7
DAY 7
Digital tools & dataSoftware, data analysis and digital methods applied to Predictive Analytics.
- 8
DAY 8
Extended workshopSimulation, site-based scenarios and team problem-solving.
- 9
DAY 9
Leadership, cost & improvementBudgeting, people, contractors and continuous improvement.
- 10
DAY 10
Capstone project & final assessmentGroup capstone presentation and final knowledge assessment.
Training methodology
Interactive lectures, practical workshops, real case studies, group exercises, daily quizzes and a pre/post assessment. Participants receive a Certificate of Completion stating the CPD hours (35 hours for 5 days, 70 hours for 10 days).
Upcoming classroom sessions
Training plans 2027–2030: 2027 2028 2029 2030