Microsoft Power BI Training
المقدمة
اهداف البرنامج
محتوى البرنامج
الشهادات
This course examines “Microsoft Power BI Training” through training needs, learning objectives, instructional activities, assessment and evaluation. Participants review practical workflows, assess assumptions and risks, and develop evidence-based improvement actions relevant to the stated subject and professional context.
الحصول على الكتيب
للحصول على كتيب بكامل محتويات الدورة برجاء ملء النموذج التالي ببياناتك الصحيحة
يونيو
، 2027
الإنجليزية
جورجيا
$4,500.00
Programme Framework
Microsoft Power BI Training
Programme overview
This course examines “Microsoft Power BI Training” through training needs, learning objectives, instructional activities, assessment and evaluation. Participants review practical workflows, assess assumptions and risks, and develop evidence-based improvement actions relevant to the stated subject and professional context
Learning outcomes
- ✓Diagnose a learning gap, with application to Semantic models, relationships and DAX measures in Microsoft Power BI Training and a documented assessment of evidence and limitations.
- ✓Write measurable learning outcomes, with application to Business and analytical questions in Microsoft Power BI Training and a documented assessment of evidence and limitations.
- ✓Design an activity and assessment, with application to Data cleaning and missing information in Microsoft Power BI Training and a documented assessment of evidence and limitations.
- ✓Evaluate workplace transfer, with application to Descriptive statistics and key trends in Microsoft Power BI Training and a documented assessment of evidence and limitations.
- ✓Explain the core disciplinary concepts of Power BI data ingestion and Power Query transformations and distinguish them from adjacent professional issues, with application to Data modelling and transformations in Microsoft Power BI Training and a documented assessment of evidence and limitations.
- ✓Apply a documented method from Semantic models, relationships and DAX measures to a case related to Microsoft Power BI Training.
- ✓Assess the assumptions and supporting evidence for Business and analytical questions and identify limitations, with application to Semantic models, relationships and DAX measures in Microsoft Power BI Training and a documented assessment of evidence and limitations.
- ✓Compare approaches to Data cleaning and missing information using relevance, risk and feasibility criteria, with application to Business and analytical questions in Microsoft Power BI Training and a documented assessment of evidence and limitations.
Who should attend
- Practitioners and specialists whose work relates to the course subject
- Team leaders and supervisors responsible for related processes
- Professionals seeking evidence-informed continuing development
Detailed programme outline
01Power BI data ingestion and Power Query transformations
- Define the operational boundaries of Power BI data ingestion and Power Query transformations and distinguish foundational concepts from assumptions. Connect the scope to practical decisions in Microsoft Power BI Training, documenting the stakeholders, evidence sources and important limitations.
- Compare alternative approaches to Power BI data ingestion and Power Query transformations through a matrix of cost, benefit, feasibility and risk. Justify the preferred course of action using stated constraints rather than unverified claims about Microsoft Power BI Training.
- Communicate the findings of an analysis of Power BI data ingestion and Power Query transformations to both technical and nontechnical stakeholders. Distinguish verified facts, reasoned judgments and unresolved questions in the context of Microsoft Power BI Training.
- Construct an actionable workflow for Power BI data ingestion and Power Query transformations, indicating roles, information needs, checkpoints and outputs. Test the workflow against a scenario from Microsoft Power BI Training and identify conditions that would invalidate the result.
- Design a practical review checklist for Power BI data ingestion and Power Query transformations, separating mandatory criteria from desirable features. Apply it to a professional scenario related to Microsoft Power BI Training and record common errors or bias.
02Semantic models, relationships and DAX measures
- Design a practical review checklist for Semantic models, relationships and DAX measures, separating mandatory criteria from desirable features. Apply it to a professional scenario related to Microsoft Power BI Training and record common errors or bias.
- Compile an auditable learning record for Semantic models, relationships and DAX measures, covering baseline, decisions, feedback and lessons. Explain the follow-up evaluation needed to determine whether improvements in Microsoft Power BI Training are sustained.
- Analyze technical, organizational and human risks arising in Semantic models, relationships and DAX measures. Separate immediate causes from systemic factors and propose preventive controls, escalation thresholds and documentary evidence for Microsoft Power BI Training.
- Translate findings on Semantic models, relationships and DAX measures into a prioritized improvement plan with ownership, milestones and verification evidence. Select indicators that demonstrate real outcomes in Microsoft Power BI Training, rather than activity counts alone.
- Examine the inputs required for Semantic models, relationships and DAX measures, including data quality and uncertainty. Compare two realistic situations related to Microsoft Power BI Training and explain why their operating decisions may legitimately differ.
03Dashboard interactivity and report validation
- Examine the inputs required for Dashboard interactivity and report validation, including data quality and uncertainty. Compare two realistic situations related to Microsoft Power BI Training and explain why their operating decisions may legitimately differ.
- Interpret a worked example involving Dashboard interactivity and report validation, explicitly identifying what the evidence supports and what remains uncertain. Draft a brief decision note suitable for a practitioner dealing with Microsoft Power BI Training.
- Challenge a proposed approach to Dashboard interactivity and report validation with a counterexample and examine the limits of the method. Revise the recommendation for Microsoft Power BI Training in response to the new evidence and constraints.
04Business and analytical questions
- Evaluate the ethical, safety, compliance and stakeholder implications of decisions involving Business and analytical questions. Identify the circumstances that require specialist sign-off or reference to applicable local rules in Microsoft Power BI Training.
- Define the operational boundaries of Business and analytical questions and distinguish foundational concepts from assumptions. Connect the scope to practical decisions in Microsoft Power BI Training, documenting the stakeholders, evidence sources and important limitations.
- Compare alternative approaches to Business and analytical questions through a matrix of cost, benefit, feasibility and risk. Justify the preferred course of action using stated constraints rather than unverified claims about Microsoft Power BI Training.
05Data discovery and source reliability
- Construct an actionable workflow for Data discovery and source reliability, indicating roles, information needs, checkpoints and outputs. Test the workflow against a scenario from Microsoft Power BI Training and identify conditions that would invalidate the result.
- Design a practical review checklist for Data discovery and source reliability, separating mandatory criteria from desirable features. Apply it to a professional scenario related to Microsoft Power BI Training and record common errors or bias.
06Data cleaning and missing information
- Translate findings on Data cleaning and missing information into a prioritized improvement plan with ownership, milestones and verification evidence. Select indicators that demonstrate real outcomes in Microsoft Power BI Training, rather than activity counts alone.
- Examine the inputs required for Data cleaning and missing information, including data quality and uncertainty. Compare two realistic situations related to Microsoft Power BI Training and explain why their operating decisions may legitimately differ.
- Interpret a worked example involving Data cleaning and missing information, explicitly identifying what the evidence supports and what remains uncertain. Draft a brief decision note suitable for a practitioner dealing with Microsoft Power BI Training.
- Challenge a proposed approach to Data cleaning and missing information with a counterexample and examine the limits of the method. Revise the recommendation for Microsoft Power BI Training in response to the new evidence and constraints.
07Measures, dimensions and calculation logic
- Develop measurable acceptance criteria for Measures, dimensions and calculation logic across quality, timeliness and outcomes. Assess how each metric could mislead interpretation when applied to the professional context of Microsoft Power BI Training.
- Evaluate the ethical, safety, compliance and stakeholder implications of decisions involving Measures, dimensions and calculation logic. Identify the circumstances that require specialist sign-off or reference to applicable local rules in Microsoft Power BI Training.
08Descriptive statistics and key trends
- Communicate the findings of an analysis of Descriptive statistics and key trends to both technical and nontechnical stakeholders. Distinguish verified facts, reasoned judgments and unresolved questions in the context of Microsoft Power BI Training.
- Construct an actionable workflow for Descriptive statistics and key trends, indicating roles, information needs, checkpoints and outputs. Test the workflow against a scenario from Microsoft Power BI Training and identify conditions that would invalidate the result.
09Exploratory comparisons and distributions
- Analyze technical, organizational and human risks arising in Exploratory comparisons and distributions. Separate immediate causes from systemic factors and propose preventive controls, escalation thresholds and documentary evidence for Microsoft Power BI Training.
- Translate findings on Exploratory comparisons and distributions into a prioritized improvement plan with ownership, milestones and verification evidence. Select indicators that demonstrate real outcomes in Microsoft Power BI Training, rather than activity counts alone.
- Examine the inputs required for Exploratory comparisons and distributions, including data quality and uncertainty. Compare two realistic situations related to Microsoft Power BI Training and explain why their operating decisions may legitimately differ.
10Data modelling and transformations
- Challenge a proposed approach to Data modelling and transformations with a counterexample and examine the limits of the method. Revise the recommendation for Microsoft Power BI Training in response to the new evidence and constraints.
- Develop measurable acceptance criteria for Data modelling and transformations across quality, timeliness and outcomes. Assess how each metric could mislead interpretation when applied to the professional context of Microsoft Power BI Training.
- Evaluate the ethical, safety, compliance and stakeholder implications of decisions involving Data modelling and transformations. Identify the circumstances that require specialist sign-off or reference to applicable local rules in Microsoft Power BI Training.
- Define the operational boundaries of Data modelling and transformations and distinguish foundational concepts from assumptions. Connect the scope to practical decisions in Microsoft Power BI Training, documenting the stakeholders, evidence sources and important limitations.
11Visual analytics and dashboard design
- Compare alternative approaches to Visual analytics and dashboard design through a matrix of cost, benefit, feasibility and risk. Justify the preferred course of action using stated constraints rather than unverified claims about Microsoft Power BI Training.
- Communicate the findings of an analysis of Visual analytics and dashboard design to both technical and nontechnical stakeholders. Distinguish verified facts, reasoned judgments and unresolved questions in the context of Microsoft Power BI Training.
- Construct an actionable workflow for Visual analytics and dashboard design, indicating roles, information needs, checkpoints and outputs. Test the workflow against a scenario from Microsoft Power BI Training and identify conditions that would invalidate the result.
12Analytical assumptions and validation
- Compile an auditable learning record for Analytical assumptions and validation, covering baseline, decisions, feedback and lessons. Explain the follow-up evaluation needed to determine whether improvements in Microsoft Power BI Training are sustained.
- Analyze technical, organizational and human risks arising in Analytical assumptions and validation. Separate immediate causes from systemic factors and propose preventive controls, escalation thresholds and documentary evidence for Microsoft Power BI Training.
- Translate findings on Analytical assumptions and validation into a prioritized improvement plan with ownership, milestones and verification evidence. Select indicators that demonstrate real outcomes in Microsoft Power BI Training, rather than activity counts alone.
- Examine the inputs required for Analytical assumptions and validation, including data quality and uncertainty. Compare two realistic situations related to Microsoft Power BI Training and explain why their operating decisions may legitimately differ.
- Interpret a worked example involving Analytical assumptions and validation, explicitly identifying what the evidence supports and what remains uncertain. Draft a brief decision note suitable for a practitioner dealing with Microsoft Power BI Training.
Training methodology
Case analysis of Microsoft Power BI Training with documented assumptions and evidence.
Guided practical exercise on Dashboard interactivity and report validation using appropriate discipline-specific examples.
Peer review of procedures, data and acceptance criteria for Measures, dimensions and calculation logic.
Applied assessment of Analytical assumptions and validation, relevant governance and professional limits.
Practical deliverable
Practical deliverable
An applied portfolio for Microsoft Power BI Training, including evidence analysis, a subject-specific documented procedure, risk evaluation and a measurable final recommendation.
Suggested assessment
Assessment combines a staged application exercise and a final case: analytical accuracy (30%), suitability of methods (30%), risk and ethics (20%), and documented reasoning (20%). Training does not confer regulated professional licensure.
Educational outline only. Scope of practical activities and assessment is subject to the centre’s confirmed delivery plan.