D.7 Data Science and Analytics

4.1 Identify common structured and unstructured datasets such as CSV �iles, JSON, text, or sensor data relevant to AI-assisted applications.

Assessed by Review of dataset inventory, assessed for correct identification and classification of common structured and unstructured datasets relevant to AI-assisted applications.

4.2 Apply basic data cleaning and preprocessing techniques using standard tools or scripts to prepare data for AI-enabled analysis.

Assessed by Inspection of cleaned dataset, assessed for accurate application of basic cleaning and preprocessing techniques using standard tools or scripts.

4.3 Demonstrate the use of simple descriptive analytics in spreadsheets or basic statistical software to produce insights for AI-supported decision-making.

Assessed by Evaluation of descriptive analytics report, assessed for correct use of simple descriptive analytics and relevance of insights for AI-supported decision-making.

4.4 Recognize ethical and privacy considerations when handling personal, sensitive, or AI- relevant data.

Assessed by Ethics checklist audit, assessed for recognition of ethical and privacy considerations when handling personal, sensitive, or AI-relevant data.

4.5 Create basic visualizations such as charts or dashboards to communicate AI-relevant insights.

Assessed by Assessment of data visualizations, assessed for accuracy, appropriateness, and clarity of basic charts or dashboards communicating AI-relevant insights.

3.1 Identify common structured and unstructured datasets such as CSV files, JSON, text, or sensor data relevant to AI-assisted applications.

Assessed by Review of dataset inventory, assessed for correct identification and classification of common structured and unstructured datasets relevant to AI-assisted applications.

3.2 Apply basic data cleaning and preprocessing techniques using standard tools or scripts to prepare data for AI-enabled analysis.

Assessed by Inspection of cleaned dataset, assessed for appropriate application of basic data cleaning and preprocessing techniques using standard tools or scripts.

3.3 Demonstrate the use of simple descriptive analytics in spreadsheets or basic statistical software to produce insights for AI-supported decision-making.

Assessed by Evaluation of descriptive analytics report, assessed for correct use of simple descriptive analytics in spreadsheets or basic statistical software and relevance of insights for AI- supported decision-making.

3.4 Recognize ethical and privacy considerations when handling personal, sensitive, or AI- relevant data.

Assessed by Ethics checklist audit, assessed for correct recognition of ethical and privacy considerations when handling personal, sensitive, or AI-relevant data.

3.5 Create basic visualizations such as charts or dashboards to communicate AI-relevant insights.

Assessed by Assessment of data visualizations, assessed for accuracy, appropriateness, and clarity of basic charts or dashboards communicating AI-relevant insights.

5.1 Identify common structured and unstructured datasets such as CSV files, JSON, text, or sensor data relevant to AI-assisted applications.

Assessed by Review of dataset inventory, assessed for correct identification and classification of common structured and unstructured datasets relevant to AI-assisted applications.

5.2 Apply basic data cleaning and preprocessing techniques using standard tools or scripts to prepare data for AI-enabled analysis.

Assessed by Inspection of cleaned dataset, assessed for appropriate application of basic data cleaning and preprocessing techniques using standard tools or scripts.

5.3 Demonstrate the use of simple descriptive analytics in spreadsheets or basic statistical software to produce insights for AI-supported decision-making.

Assessed by Evaluation of descriptive analytics report, assessed for correct use of simple descriptive analytics in spreadsheets or basic statistical software and relevance of insights for AI- supported decision-making.

5.4 Recognize ethical and privacy considerations when handling personal, sensitive, or AI- relevant data.

Assessed by Ethics checklist audit, assessed for correct recognition of ethical and privacy considerations when handling personal, sensitive, or AI-relevant data.

5.5 Create basic visualizations such as charts or dashboards to communicate AI-relevant insights.

Assessed by Assessment of data visualizations, assessed for accuracy, appropriateness, and clarity of basic charts or dashboards communicating AI-relevant insights.

3.1 Analyse data from multiple sources such as databases, APIs, or log files to identify patterns and trends relevant for AI-enhanced decision-making.

Assessed by Evaluation of integrated datasets, assessed for correctness of data integration, consistency across sources, handling of missing or conflicting data, and alignment with analytical objectives.

3.2 Integrate structured and unstructured datasets using data integration tools or programming languages to improve AI model performance.

Assessed by Assessment of analytics report, evaluated for clarity of analysis, appropriate use of methods, correctness of results, and logical interpretation of findings in relation to the problem context.

3.3 Apply predictive analytics and basic machine learning methods in Python, R, or equivalent tools to support AI-assisted decision-making.

Assessed by Review of predictive model outputs, assessed for correctness of results, appropriate use of evaluation metrics, and quality of interpretation, including recognition of model limitations.

3.4 Evaluate datasets for quality, bias, and compliance in accordance with ethical, privacy, or regulatory standards.

Assessed by Audit of dataset evaluation report, evaluated for completeness of data quality assessment, identification of issues (e.g., bias, inconsistency), and justification of conclusions.

3.5 Communicate findings effectively through reports, dashboards, or presentations to support organisational AI-driven processes.

Assessed by Assessment of findings presentation, evaluated for clarity, structure, and effectiveness in communicating analytical results to technical and non-technical stakeholders.

3.6 Produce data storytelling artifacts such as visual narratives or annotated dashboards to convey insights for AI-enabled decisions.

Assessed by Evaluation of data storytelling artifacts, assessed for coherence between data, visuals, and narrative, clarity of message, and ability to support data-driven decision-making.

5.1 Identify common structured and unstructured datasets such as CSV files, JSON, text, or sensor data relevant to AI-assisted applications.

Assessed by Review of dataset inventory or reference table; assessed for correct identification, classification, and relevance of structured and unstructured datasets such as CSV files, JSON, text, or sensor data for AI-assisted applications.

5.2 Apply basic data cleaning and preprocessing techniques using standard tools or scripts to prepare data for AI-enabled analysis.

Assessed by Inspection of cleaned and preprocessed dataset; assessed for correct application of basic cleaning and preprocessing techniques using standard tools or scripts, and for readiness of the data for AI-enabled analysis.

5.3 Demonstrate the use of simple descriptive analytics in spreadsheets or basic statistical software to produce insights for AI-supported decision-making.

Assessed by Evaluation of descriptive analytics report; assessed for correct use of spreadsheets or basic statistical software, appropriate descriptive analytics, and relevance of generated insights for AI-supported decision-making.

5.4 Recognize ethical and privacy considerations when handling personal, sensitive, or AI- relevant data.

Assessed by Ethics and privacy checklist audit; assessed for recognition of ethical and privacy considerations when handling personal, sensitive, or AI-relevant data, including identification of basic data-handling risks.

5.5 Create basic visualizations such as charts or dashboards to communicate AI-relevant insights.

Assessed by Assessment of data visualizations or dashboard; assessed for accuracy, clarity, appropriateness of chart or dashboard type, and effectiveness in communicating AI- relevant insights.

9.1 Analyse data from multiple sources such as databases, APIs, or log files to identify patterns and trends relevant for AI-enhanced decision-making.

Assessed by Evaluation of integrated datasets, assessed for analysis of data from multiple sources such as databases, APIs, or log files and identification of patterns and trends relevant for AI- enhanced decision-making.

9.2 Integrate structured and unstructured datasets using data integration tools or programming languages to improve AI model performance.

Assessed by Assessment of analytics report, assessed for integration of structured and unstructured datasets using data integration tools or programming languages and for contribution to AI model performance.

9.3 Apply predictive analytics and basic machine learning methods in Python, R, or equivalent tools to support AI-assisted decision-making.

Assessed by Review of predictive model outputs, assessed for appropriate application of predictive analytics and basic machine learning methods in Python, R, or equivalent tools to support AI- assisted decision-making.

9.4 Evaluate datasets for quality, bias, and compliance in accordance with ethical, privacy, or regulatory standards.

Assessed by Audit of dataset evaluation report, assessed for evaluation of dataset quality, bias, and compliance with ethical, privacy, or regulatory standards.

9.5 Communicate findings effectively through reports, dashboards, or presentations to support organisational AI-driven processes.

Assessed by Assessment of findings presentation, assessed for effective communication of findings and relevance to organizational AI-driven processes.

9.6 Produce data storytelling artifacts such as visual narratives or annotated dashboards to convey insights for AI-enabled decisions.

Assessed by Evaluation of data storytelling artifacts, assessed for clarity, accuracy, and effectiveness of visual narratives or annotated dashboards in conveying insights for AI-enabled decisions. DEVELOPMENT & OPERATIONS [2]

4.1 Design AI-augmented analytics workflows and solutions for complex, multi-factor problems in organisational or research contexts.

Assessed by Review of AI workflow documentation, including data acquisition, preprocessing, modelling, and evaluation stages; assessed for completeness, coherence of workflow design, traceability of decisions, and alignment with analytical objectives.

4.2 Apply advanced analytics, predictive models, and machine learning methods using large- scale or multi-source data to support AI-enabled decision-making.

Assessed by Evaluation of analytical model outputs, including performance metrics, validation results, and interpretation; assessed for correctness, appropriateness of evaluation metrics, and justification of model performance in relation to the problem context.

4.3 Evaluate data integrity, model performance, and ethical implications in AI and analytics initiatives within professional or organisational projects.

Assessed by Assessment of a data integrity and ethics report, including data quality analysis, bias identification, and compliance considerations; evaluated for methodological soundness, completeness, and alignment with ethical and regulatory standards.

4.4 Generate actionable insights and recommendations to improve decision-making and outcomes in business, organisational, or research settings.

Assessed by Evaluation of an insights and recommendations report, translating analytical results into actionable business or organisational recommendations; assessed for analytical depth, relevance, clarity, and strength of evidence-based argumentation.

4.5 Coordinate team activities to implement AI and data-driven analytics projects efficiently.

Assessed by Review of team coordination and collaboration documentation, including role allocation, workflow integration, and communication artefacts; assessed for effectiveness of coordination, clarity of responsibilities, and contribution to successful analytical outcomes.

4.6 Develop frameworks for performance metrics, risk assessment, and operational evaluation in AI-enabled analytics workflows.

Assessed by Assessment of a performance and risk evaluation framework, including definition of performance indicators, monitoring mechanisms, and risk mitigation strategies; evaluated for completeness, appropriateness, and integration into the analytical lifecycle.

4.7 Document reproducible AI and data science analysis pipelines including scripts, notebooks, and workflow documentation.

Assessed by Evaluation of reproducible AI pipelines, including code, data versioning, and documentation; assessed for reproducibility, robustness, scalability, and adherence to best practices in data science workflows.

3.1 Design AI-augmented analytics workflows and solutions for complex, multi-factor problems in organisational or research contexts.

Assessed by Review of AI workflow documentation, including data ingestion, curation, transformation, and integration processes; assessed for completeness, traceability of data handling decisions, alignment with data governance standards, and coherence across the data lifecycle.

3.2 Apply advanced analytics, predictive models, and machine learning methods using large- scale or multi-source data to support AI-enabled decision-making.

Assessed by Evaluation of analytical model outputs, including performance metrics and validation results; assessed for correctness, appropriateness of evaluation criteria, and consideration of data quality constraints impacting analytical outcomes.

3.3 Evaluate data integrity, model performance, and ethical implications in AI and analytics initiatives within professional or organisational projects.

Assessed by Assessment of a data integrity and ethics report, including data quality analysis, bias identification, and compliance considerations; evaluated for methodological soundness, completeness, and alignment with governance and regulatory frameworks.

3.4 Generate actionable insights and recommendations to improve decision-making and outcomes in business, organisational, or research settings.

Assessed by Evaluation of an insights and recommendations report, translating analytical results into actionable improvements for data curation and management processes; assessed for relevance, clarity, and strength of evidence-based reasoning.

3.5 Coordinate team activities to implement AI and data-driven analytics projects efficiently.

Assessed by Review of team coordination documentation, including collaboration artefacts, role allocation, and workflow integration; assessed for effectiveness of coordination, clarity of responsibilities, and contribution to consistent data curation practices.

3.6 Develop frameworks for performance metrics, risk assessment, and operational evaluation in AI-enabled analytics workflows.

Assessed by Assessment of a performance and risk framework, including data quality indicators, monitoring mechanisms, and risk mitigation strategies; evaluated for completeness, appropriateness, and integration into the data lifecycle and governance processes.

3.7 Document reproducible AI and data science analysis pipelines including scripts, notebooks, and workflow documentation.

Assessed by Evaluation of reproducible AI pipelines, including data versioning, metadata documentation, and workflow automation; assessed for reproducibility, robustness, scalability, and adherence to data curation and governance best practices.

4.1 Design AI-augmented analytics workflows and solutions for complex, multi-factor problems in organisational or research contexts.

Assessed by Review of AI workflow documentation, assessed for design of AI-augmented analytics workflows and solutions appropriate to complex, multi-factor problems in organizational or research contexts.

4.2 Apply advanced analytics, predictive models, and machine learning methods using large- scale or multi-source data to support AI-enabled decision-making.

Assessed by Evaluation of analytical model outputs, assessed for appropriate application of advanced analytics, predictive models, and machine learning methods using large-scale or multi- source data.

4.3 Evaluate data integrity, model performance, and ethical implications in AI and analytics initiatives within professional or organisational projects.

Assessed by Assessment of data integrity & ethics report, assessed for evaluation of data integrity, model performance, and ethical implications in professional or organizational AI and analytics initiatives.

4.4 Generate actionable insights and recommendations to improve decision-making and outcomes in business, organisational, or research settings.

Assessed by Evaluation of insights & recommendations report, assessed for actionability, evidence base, and relevance of recommendations for improving decision-making and outcomes in business, organizational, or research settings.

4.5 Coordinate team activities to implement AI and data-driven analytics projects efficiently.

Assessed by Review of team coordination documentation, assessed for evidence of task allocation, coordination, progress monitoring, and efficient implementation of AI and data-driven analytics projects.

4.6 Develop frameworks for performance metrics, risk assessment, and operational evaluation in AI-enabled analytics workflows.

Assessed by Assessment of performance & risk framework, assessed for integration of performance metrics, risk assessment, and operational evaluation in AI-enabled analytics workflows.

4.7 Document reproducible AI and data science analysis pipelines including scripts, notebooks, and workflow documentation.

Assessed by Evaluation of reproducible AI pipelines, assessed for completeness and reproducibility of scripts, notebooks, and workflow documentation.

5.1 Design AI-augmented analytics workflows and solutions for complex, multi-factor problems in organisational or research contexts.

Assessed by Review of AI workflow documentation assessing design of AI-augmented analytics workflows and solutions for complex, multi-factor problems.

5.2 Apply advanced analytics, predictive models, and machine learning methods using large- scale or multi-source data to support AI-enabled decision-making.

Assessed by Evaluation of analytical model outputs assessing application of advanced analytics, predictive models, and machine learning methods using large-scale or multi-source data.

5.3 Evaluate data integrity, model performance, and ethical implications in AI and analytics initiatives within professional or organisational projects.

Assessed by Assessment of data integrity & ethics report evaluating data integrity, model performance, and ethical implications in AI and analytics initiatives.

5.4 Generate actionable insights and recommendations to improve decision-making and outcomes in business, organisational, or research settings.

Assessed by Evaluation of insights & recommendations report assessing actionability, evidence base, and relevance for improving decision-making and outcomes.

5.5 Coordinate team activities to implement AI and data-driven analytics projects efficiently.

Assessed by Review of team coordination documentation assessing coordination of team activities and efficiency of implementation in AI and data-driven analytics projects.

5.6 Develop frameworks for performance metrics, risk assessment, and operational evaluation in AI-enabled analytics workflows.

Assessed by Assessment of performance & risk framework for coverage of performance metrics, risk assessment, and operational evaluation in AI-enabled analytics workflows.

5.7 Document reproducible AI and data science analysis pipelines including scripts, notebooks, and workflow documentation.

Assessed by Evaluation of reproducible AI pipelines assessing completeness of scripts, notebooks, workflow documentation, and reproducibility of the analysis process.

3.1 Develop innovative AI and data science methodologies for complex, multi-source and interdisciplinary challenges.

Assessed by Review of innovative AI methodology, assessed for originality, methodological rigour, and suitability for complex, multi-source and interdisciplinary challenges.

3.2 Integrate diverse datasets and apply advanced analytics to inform AI models and high-level decision-making.

Assessed by Assessment of integrated datasets & analytics outputs, assessed for integration of diverse datasets and application of advanced analytics to inform AI models and high-level decision- making.

3.3 Design and implement AI solutions using data-driven insights to enhance organisational, professional, or research outcomes.

Assessed by Evaluation of AI solution implementation, assessed for design and implementation quality and for use of data-driven insights to enhance organizational, professional, or research outcomes.

3.4 Evaluate ethical, operational, societal, and data quality implications of AI and analytics initiatives across multiple domains.

Assessed by Review of evaluation report, covering ethics, operational, societal, and data quality, assessed for evaluation of implications of AI and analytics initiatives across multiple domains.

3.5 Formulate original research questions, hypotheses, or frameworks in AI and data science contexts.

Assessed by Assessment of research questions & frameworks, assessed for originality, relevance, and coherence in AI and data science contexts.

3.6 Conduct experiments, analyses, or AI-driven studies using data science as input for AI models.

Assessed by Evaluation of experiment/analysis outputs, assessed for appropriate conduct of experiments, analyses, or AI-driven studies using data science as input for AI models.

3.7 Critically evaluate methods, AI model performance, data integrity, and research outcomes for validity, reliability, and ethical compliance.

Assessed by Critical appraisal of methods, AI models, and research outcomes, assessed for validity, reliability, data integrity, AI model performance, and ethical compliance.

3.8 Disseminate AI and data science insights through publications, presentations, collaborations, or practical applications.

Assessed by Review of dissemination outputs, including publications, presentations, collaborations, or practical applications, assessed for clarity, relevance, and effectiveness in communicating AI and data science insights.

4.1 Identify common structured and unstructured datasets such as CSV files, JSON, text, or sensor data relevant to AI-assisted applications.

Assessed by Review of dataset inventory or reference table; assessed for correct identification, classification, and relevance of structured and unstructured datasets such as CSV files, JSON, text, or sensor data for AI-assisted applications.

4.2 Apply basic data cleaning and preprocessing techniques using standard tools or scripts to prepare data for AI-enabled analysis.

Assessed by Inspection of cleaned and preprocessed dataset; assessed for appropriate use of standard tools or scripts, correct application of basic cleaning and preprocessing techniques, and readiness of the data for AI-enabled analysis.

4.3 Demonstrate the use of simple descriptive analytics in spreadsheets or basic statistical software to produce insights for AI-supported decision-making.

Assessed by Evaluation of descriptive analytics report; assessed for correct use of spreadsheets or basic statistical software, appropriate descriptive statistics, and relevance of generated insights for AI-supported decision-making.

4.4 Recognize ethical and privacy considerations when handling personal, sensitive, or AI- relevant data.

Assessed by Ethics and privacy checklist audit; assessed for recognition of ethical and privacy considerations when handling personal, sensitive, or AI-relevant data, including appropriate identification of handling risks.

4.5 Create basic visualizations such as charts or dashboards to communicate AI-relevant insights.

Assessed by Assessment of data visualizations such as charts or dashboard; assessed for accuracy, clarity, appropriateness of chart or dashboard type, and effectiveness in communicating AI-relevant insights.