Program facts
- Study fee
- Free
- subjects
- 5
- lessons
- 22
- Estimated time
- 13 hours
- Access period
- 60 days
- Study language
- Arabic and English
About the program
A polished dashboard is not automatically a correct analysis. A number can be double-counted, change under an unnoticed filter, use an undefined metric, or create a visual comparison that implies more than the data supports. This diploma begins with the BI foundation: what question are we answering, what decision might change, and what level of detail should the data represent? Learners then build a simple analytical model, turn data into defined measures and KPIs, and examine why calculations change with filter context. The program then moves to visual choice, page layout, titles, accessibility, interaction, filters, drill paths, and data storytelling. The final subject connects shared definitions, lineage, access, refresh, and data quality to a dashboard lifecycle that can be reviewed and improved. Power BI and Tableau are examples, not dependencies. The intended level is foundational and applied, with no advanced programming or database experience required.
Practical relevance
The method can be applied to fictional sales, service, operations, people, or project data: start with a question, define the measure, check the data, and then choose the visual and interaction that answer the question. Program cases are educational and do not represent market statistics or any real organization’s performance.
Why this program
A common BI failure is not a shortage of charts but a disconnect between the chart, the metric definition, its filter context, and the underlying data. The program connects those layers into one workflow: a comprehensible data model, an interpretable calculation, a usable dashboard, and governance that clarifies who can see what and how trust is maintained over time.
Career relevance
The content is useful for people who build reports and dashboards, request them from data teams, or use them in operational and managerial decisions. It builds a shared vocabulary between business users and analysts around grain, measures, KPIs, filters, refresh, and access. This is content relevance, not a promise of employment, salary, or vendor certification.
What you will learn
- Connect a business question to the data needed and the decision the dashboard should support instead of starting with a chart.
- Model facts, dimensions, grain, and relationships in ways that avoid mixed detail and double counting.
- Define measures and KPIs with units, targets, direction, time basis, filter context, and aggregation checks.
- Choose visual forms and build a clear, non-misleading layout with titles and annotations that support interpretation.
- Design filters, interactions, and drill paths with clear scope and preserved context.
- Write an analytical narrative that links evidence with context, limitations, and a next action.
- Apply accessibility practices so meaning does not depend on color alone and information remains understandable.
- Apply governance, access, lineage, refresh, and data-quality principles across the BI content lifecycle.
Learning outcomes (10)
- Explain how business intelligence turns business questions and data into decision-usable information, distinguishing it from merely displaying numbers.
- Build a simple analytical model that defines grain, facts, dimensions, and appropriate relationships without mixing incompatible levels of detail.
- Assess data quality and fitness for purpose using relevant quality dimensions and identify how issues affect analysis and dashboards.
- Define interpretable measures and KPIs that specify calculation logic, target, direction, time basis, and unit.
- Validate calculations, aggregations, and filter context, and explain why results change when slicers or view levels change.
- Select visual encodings and design a clear, non-misleading dashboard with appropriate hierarchy and accessibility.
- Design interactions and filters with clear scope and preserved context so users can explore without surprising or ambiguous results.
- Build a concise analytical narrative that links question, evidence, context, limitations, and next action without overstating conclusions.
- Apply foundational BI governance principles covering definitions, ownership, lineage, access, refresh, and data quality.
- Design a tool-neutral dashboard solution from a business question through reviewable handoff and improvement, justifying modeling, metric, and presentation choices.
Who it is for
- Learners and professionals seeking a structured foundation in business intelligence and dashboards.
- Early-career business or data analysts and people who create recurring reports or dashboards.
- Managers and operations leads who need to define metrics, read dashboards, and request analysis clearly.
- Roles that work with KPIs, periodic reporting, or data quality without requiring advanced technical experience.
Prerequisites
- No advanced BI or programming experience is required.
- Basic familiarity with tables, percentages, and simple arithmetic is useful, together with willingness to work through small data cases.
What it does not offer
- Not dedicated interface training for Power BI, Tableau, or a specific vendor certification.
- Does not replace advanced data-warehouse engineering, cloud administration, or advanced statistics pathways.
Curriculum
- Subject 1 Open for free
Business Intelligence Foundations and Data Modeling
Builds the BI foundation from business question to star schema, data quality, relationships, and aggregation.
- Subject 2
Measures, KPIs, and Calculation Context
Turns data into well-defined measures and KPIs, with time comparisons, filter context, and total validation.
- Subject 3
Data Visualization and Dashboard Design
Focuses on chart choice, layout, titles, accessibility, and task-serving interaction without clutter.
- Subject 4
Filters, Context, and Analytical Storytelling
Explains filter scope, cross-filtering, drill paths, and context preservation, then connects findings through disciplined analytical storytelling.
- Subject 5
BI Governance, Access, and Dashboard Lifecycle
Connects definitions, ownership, lineage, access, refresh, and data quality through an end-to-end capstone from question to handoff and improvement.
Subjects unlock in sequence as you progress.
Strengths
- Connects model, measure, filter, and visual in one reasoning chain rather than teaching each in isolation.
- Checks grain, calculations, and aggregation before interpreting visuals, including double-counting and denominator errors.
- Treats accessibility, context, and governance as part of dashboard quality rather than cosmetic additions.
- Tool-neutral by design: Power BI and Tableau documentation illustrate transferable principles rather than turning the diploma into a vendor manual.
Evidence basis
The design draws on Microsoft Learn documentation for star schema, filter context, accessibility, security, and governance; Tableau guidance for dashboard design and interaction; Kimball Group guidance on dimensional grain; and W3C guidance on use of color. The listed public pages were actually opened; linked books or paid materials are not treated as read.
Reference organizations
- Microsoft
- Kimball Group
- W3C
- Tableau
Frameworks used
- Dimensional modeling and fact-table grain
- Star-schema modeling for BI semantic models
Methodology
The design starts with decision questions and data models, then connects outcomes to measures, dashboards, interaction, and governance. Official product documentation illustrates shared principles rather than imposing one product, and time-sensitive facts are rechecked before final teaching use.
How the route works
Short Diplomas are relatively comprehensive learning pathways that cover a major subject through five connected learning subjects and structured assessments, with a diploma transcript available with the credential.
Self-paced sequential study. Content unlocks progressively and each subject combines reading, examples, exercises, knowledge checks, and assessment.
- Build integrated knowledge in a major subject.
- Study through five clear, connected learning subjects.
- Complete a defined assessment for each subject.
- Receive a diploma transcript showing subjects and results when the credential is issued.
Assessment
Each Short Diploma contains five subjects, each with an approved assessment. A sixth final exam is not required when the five assessments adequately cover the learning outcomes.
Assessment attempts: 2 per assessment, 7 days apart
The pass mark is announced before the program is published.
Certificate
Successful completion makes the learner eligible for the Short Diploma certificate and Diploma Transcript under the credential policy.
Every certificate has a public verification code. Verify a certificate
Disclaimer: Training completion credential when policy requirements are met; not a university degree, Power BI/Tableau certification, or professional licence.
Program questions
Do I need previous Power BI or Tableau experience?
No. The program teaches the reasoning first and uses products only as examples. Learners with a BI tool can transfer exercises into it, while others can complete the cases, calculations, and design work without depending on one interface.
Will I learn DAX or a vendor-specific calculation language?
You will learn measure, aggregation, and filter-context logic, and some conceptual examples may reference DAX documentation. The diploma is not an advanced DAX programming pathway and does not assess memorization of functions.
How is this different from business data analysis?
This diploma centers on the BI delivery layer: semantic modeling, measures and KPIs, dashboards, interaction, storytelling, and governance. Data analysis can go further into exploratory or statistical methods; here the measurement system and dashboard are the main application.
Does the program address misleading charts?
Yes. It examines chart choice, axes, comparison, density, and titles and connects visual decisions back to the business question and metric. The goal is to show magnitude, direction, and context without visually exaggerating a result.
How do I prevent a KPI from becoming just a number?
A KPI is defined with its formula, unit, improvement direction, target, time basis, source, and filter context. The program also checks denominators, aggregation, and comparison boundaries before adding status colors or labels.
Are filters and cross-filtering covered?
Yes. The program covers filter scope, interactions between visuals, drill paths, details, context preservation, and reset/default states because a numerically correct value can still mislead when viewers do not know what has been filtered.
Is dashboard accessibility included?
Yes. The program covers color, contrast, titles, alt text, navigation order, and the principle that color should not be the sole carrier of meaning. Implementation details vary by tool.
Will I finish with something applicable?
The fifth subject integrates the workflow into a tool-neutral capstone: a business question, simple model, measures, dashboard plan, interactions, access and refresh controls, and a review plan. Full vendor-specific implementation is not required.