Structured datasets
Explore the fundamental terminology and use a structured example to identify the key choices involved in structured datasets.
This subject area brings together essential concepts, decision tools, and workplace applications. The outline below is an illustrative curriculum framework; enquire for confirmed availability, teaching hours, fees, and the precise type of completion document.
01 / 06
Data literacy
This learning component explores the practical connection between structured datasets, data quality, and descriptive statistics. Participants would examine how the concepts interact in realistic organizational situations, where decisions must account for limited resources, competing priorities, and measurable outcomes.
Data quality
Compare alternative approaches, document assumptions, and consider how data quality affects different stakeholders.
Descriptive statistics
Translate the topic into an applied task: develop a concise analysis, explain your recommendation, and identify how success could be evaluated.
In practice: Illustrative workshop: a team receives a business scenario involving structured datasets, then prepares a recommendation that considers data quality and presents an action plan addressing descriptive statistics. The exercise can be assessed through the quality of evidence, feasibility of the recommendation, and clarity of communication.
02 / 06
Business intelligence
This learning component explores the practical connection between dashboard design, key performance indicators, and data storytelling. Participants would examine how the concepts interact in realistic organizational situations, where decisions must account for limited resources, competing priorities, and measurable outcomes.
Dashboard design
Explore the fundamental terminology and use a structured example to identify the key choices involved in dashboard design.
Key performance indicators
Compare alternative approaches, document assumptions, and consider how key performance indicators affects different stakeholders.
Data storytelling
Translate the topic into an applied task: develop a concise analysis, explain your recommendation, and identify how success could be evaluated.
In practice: Illustrative workshop: a team receives a business scenario involving dashboard design, then prepares a recommendation that considers key performance indicators and presents an action plan addressing data storytelling. The exercise can be assessed through the quality of evidence, feasibility of the recommendation, and clarity of communication.
03 / 06
Predictive thinking
This learning component explores the practical connection between forecasting principles, model evaluation, and scenario analysis. Participants would examine how the concepts interact in realistic organizational situations, where decisions must account for limited resources, competing priorities, and measurable outcomes.
Forecasting principles
Explore the fundamental terminology and use a structured example to identify the key choices involved in forecasting principles.
Model evaluation
Compare alternative approaches, document assumptions, and consider how model evaluation affects different stakeholders.
Scenario analysis
Translate the topic into an applied task: develop a concise analysis, explain your recommendation, and identify how success could be evaluated.
In practice: Illustrative workshop: a team receives a business scenario involving forecasting principles, then prepares a recommendation that considers model evaluation and presents an action plan addressing scenario analysis. The exercise can be assessed through the quality of evidence, feasibility of the recommendation, and clarity of communication.
04 / 06
AI in organizations
This learning component explores the practical connection between workflow automation, generative ai use cases, and human oversight. Participants would examine how the concepts interact in realistic organizational situations, where decisions must account for limited resources, competing priorities, and measurable outcomes.
Workflow automation
Explore the fundamental terminology and use a structured example to identify the key choices involved in workflow automation.
Generative ai use cases
Compare alternative approaches, document assumptions, and consider how generative ai use cases affects different stakeholders.
Human oversight
Translate the topic into an applied task: develop a concise analysis, explain your recommendation, and identify how success could be evaluated.
In practice: Illustrative workshop: a team receives a business scenario involving workflow automation, then prepares a recommendation that considers generative AI use cases and presents an action plan addressing human oversight. The exercise can be assessed through the quality of evidence, feasibility of the recommendation, and clarity of communication.
05 / 06
Responsible analytics
This learning component explores the practical connection between data privacy, bias and fairness, and explainability. Participants would examine how the concepts interact in realistic organizational situations, where decisions must account for limited resources, competing priorities, and measurable outcomes.
Data privacy
Explore the fundamental terminology and use a structured example to identify the key choices involved in data privacy.
Bias and fairness
Compare alternative approaches, document assumptions, and consider how bias and fairness affects different stakeholders.
Explainability
Translate the topic into an applied task: develop a concise analysis, explain your recommendation, and identify how success could be evaluated.
In practice: Illustrative workshop: a team receives a business scenario involving data privacy, then prepares a recommendation that considers bias and fairness and presents an action plan addressing explainability. The exercise can be assessed through the quality of evidence, feasibility of the recommendation, and clarity of communication.
06 / 06
Capstone application
This learning component explores the practical connection between business question framing, analysis presentation, and decision recommendation. Participants would examine how the concepts interact in realistic organizational situations, where decisions must account for limited resources, competing priorities, and measurable outcomes.
Business question framing
Explore the fundamental terminology and use a structured example to identify the key choices involved in business question framing.
Analysis presentation
Compare alternative approaches, document assumptions, and consider how analysis presentation affects different stakeholders.
Decision recommendation
Translate the topic into an applied task: develop a concise analysis, explain your recommendation, and identify how success could be evaluated.
In practice: Illustrative workshop: a team receives a business scenario involving business question framing, then prepares a recommendation that considers analysis presentation and presents an action plan addressing decision recommendation. The exercise can be assessed through the quality of evidence, feasibility of the recommendation, and clarity of communication.
Learning outcomes and assessment approach
By working through the six areas above, learners should be able to explain relevant concepts, analyze realistic cases, compare management alternatives, and communicate evidence-based recommendations. A suitable assessment mix could include case analyses, reflective reports, presentations, and a final applied project. Exact assessment weights, teaching hours, prerequisites, and any award remain subject to confirmation.
Suggested final project: select a workplace challenge connected to business analytics & ai; define its context and stakeholders; evaluate evidence and feasible options; propose a practical response; and describe indicators for reviewing the results.
Who this area may suit
This subject may interest emerging managers, established professionals, entrepreneurs, and career changers. The appropriate level depends on the depth of the confirmed syllabus, previous learning, and the participant’s objectives. Contact the team for the current programme specification and completion requirements.