CADA Certification Specification | Data Science Institute

Certification specification

Certified Associate Data Analyst

The Certified Associate Data Analyst (CADA™) is the Data Science Institute's entry-level professional credential in applied business analytics, reporting and AI-enabled analyst practice.

CADA certification page

CADA specification at a glance
Level	Associate
Structure	3 Parts, 9 Units
Assessment	One Knowledge Examination
Pass mark	50%
Exam delivery	Online, remotely proctored
Progression	CPDA®

Section 1Certification overview

The Certified Associate Data Analyst (CADA™) is the Data Science Institute's entry-level professional credential in applied business analytics, reporting and AI-enabled analyst practice. It validates foundational competence in understanding business problems, working with business data, preparing reliable reporting outputs, using Excel, SQL and Power BI appropriately, and supporting evidence-based workplace decisions.

CADA™ is designed as a business-problem-led qualification. Learners begin with recognisable workplace problems and then learn how analysts use data, dashboards, reports, AI-supported workflows and professional judgement to support decisions. The programme is intentionally practical, accessible and motivating for learners who are new to analytics or who use data as part of a wider business role.

AI fluency is embedded throughout CADA™ rather than presented as a separate topic at the end of the programme. Learners encounter AI-supported workflows from the beginning, including AI-assisted problem framing, Copilot-style support in Excel and Power BI, natural-language-to-SQL and natural-language BI exploration, generative narrative support, and responsible validation of AI outputs. AI is treated as a productivity and thinking support layer, not as a replacement for analyst judgement.

Responsible data and AI practice is also embedded throughout the programme. Learners are introduced repeatedly to safe use of data, privacy awareness, data minimisation, data quality, responsible prompting, validation of outputs, transparent communication of limitations, and avoidance of misleading claims. Governance is taught as a practical workplace habit rather than as a separate compliance block.

CADA™ audiences include working professionals adding applied data skills to their current roles, recent graduates moving into analytical work, domain specialists in areas such as finance, insurance, retail, logistics, manufacturing, business process outsourcing, healthcare, marketing, operations and the public sector, and learners preparing to progress to the Certified Professional Data Analyst (CPDA®).

CADA™ sits below CPDA® on the DSI certification pathway and is intentionally lighter in scope. It builds confidence, workplace readiness and the core language of analytics before learners progress to more technical professional study. Advanced Python analytics, statistical modelling, data reduction and predictive modelling remain within the scope of CPDA®.

Section 2Programme structure

2.1 Structural model

The programme is organised as Parts → Units → Lessons, with unit scope defined in the unit-level syllabus and lesson-level learning materials delivered through the Certifications Platform. This specification defines the certification overview, part structure, unit scope, intended learning outcomes and assessment requirements. Detailed lesson maps and delivery schedules are maintained separately from this specification.

2.2 Parts and weightings

Parts and weightings for CADA
Part	Share of the certification	Weighting
Business Analytics FoundationsPart 1		33.3%
Reporting and Business IntelligencePart 2		33.3%
Data Preparation and Analytics PracticePart 3		33.3%

2.3 Parts and units

Part 1 · 33.3%

Business Analytics Foundations

Unit 1 Business Problems and the Analyst Role
Unit 2 Metrics, KPIs and Business Data
Unit 3 Exploring Problems with Data and AI

Part 2 · 33.3%

Reporting and Business Intelligence

Unit 1 Reporting Visuals and Data Storytelling
Unit 2 Power BI for Workplace Reporting
Unit 3 Stakeholder Communication and Reporting Decisions

Part 3 · 33.3%

Data Preparation and Analytics Practice

Unit 1 Excel for Analyst Workflows
Unit 2 SQL for Business Reporting
Unit 3 Integrated Analytics Practice

2.4 Programme scale

CADA™ is designed as a light, practical and motivating entry point into analytics. It is structured to give learners visible workplace progress through a sequence of applied outputs: analytics briefs, KPI interpretation, reporting visuals, dashboard-style outputs, prepared datasets, SQL query artefacts and a final integrated workplace analytics activity.

The consolidated specification includes 3 Parts and 9 Units. The certification is supported by lesson learning materials, knowledge checks, unit practices, part-level applied activities where appropriate, and certification examination preparation delivered through the Certifications Platform.

2.5 Embedded AI fluency and governance

AI fluency and responsible practice are cross-programme threads. Learners use AI-supported tools to clarify business questions, suggest metrics, explain formulas, draft or troubleshoot SQL, explore BI outputs, improve reporting narratives and communicate findings. Across these activities, learners are required to check AI outputs against source data, avoid sharing sensitive information, document assumptions, and communicate limitations clearly.

Section 3Learning and assessment requirements

3.1 Standard learning pathway

The programme combines instruction, applied practice and objective checks at lesson level; consolidation at unit level; and certification readiness activities supported by one summative knowledge examination at certification level.

3.2 Progression requirements

To be eligible for award:

All Unit Practices must be completed (ungraded but mandatory).
At least 90% of platform learning activities must be marked complete, including part-level applied activities where present in the pathway.
The CADA™ Knowledge Examination must be passed.

3.3 Summative certification exam

Knowledge Examination

Type: Knowledge
Format: Computer-based, objective examination
Question types: Multiple choice, multiple-select, true/false
Reporting: Reported as a percentage
Pass mark: 50%

Assessment delivery environment is online with remote proctoring.

CADA™ certification exam structure
Attribute	Knowledge Examination
Type	Knowledge
Format	Computer-based, objective examination
Question types	Multiple choice, multiple-select, true/false
Reporting	Reported as a percentage
Pass mark	50%

3.4 Use of the Certifications Platform

CADA™ is delivered through the Institute's Certifications Platform, which structures each certification consistently. Each Part contains units, units contain lessons, and supporting practice and case-study material is provided at the appropriate level of the structure.

At Part level — applied activities or case-study material integrating the units within the Part, where appropriate.
At Unit level — a Unit Practice that demonstrates capability against the unit's intended learning outcomes.
At Lesson level — Learn content, Lesson Practice, and a Knowledge Check aligned to the lesson's intended learning outcomes.
Across the programme — curated datasets, worked examples, scenario-based practice, AI-supported workflow activities and quizzes that support both lesson-level practice and revision for the Knowledge Examination.

Section 4Part descriptors

Each part below sets out its scope, its intended learning outcomes, and the units it contains. Unit entries give the unit purpose, the applied outputs expected, the tools and environment used, the scope of the unit, and the unit intended learning outcomes.

4.1 Part 1 · 33.3% of the certificationBusiness Analytics Foundations

Part 1 establishes the business-problem-led foundation for CADA™. Learners begin with recognisable workplace problems, the role of the analyst, the language of business data, KPIs and metrics, and the use of AI-supported workflows to explore questions responsibly. Governance is embedded through responsible scoping, safe data use, data minimisation, careful interpretation and transparent communication of limitations.

Part intended learning outcomes

Describe the role of the analyst in supporting evidence-based business decisions.
Frame common business problems as clear analytics questions and intended outputs.
Identify relevant business data, KPIs and metrics for simple workplace scenarios.
Apply basic numerical literacy to interpret business figures and recognise misleading summaries.
Use AI-supported workflows to clarify problems and explore possible explanations while validating outputs critically.
Recognise responsible data and AI considerations when scoping, interpreting and communicating analytical work.

Unit 1

Business Problems and the Analyst Role

Unit purpose: Introduce the role of the analyst as a business decision-support function and establish the principle that analytics begins with a business problem rather than a tool.
Applied outputs: A short analyst orientation artefact for a workplace problem, identifying the business question, stakeholder audience, likely data required, possible output format, AI support opportunities and responsible data considerations.
Tools and environment: Conceptual unit supported by examples from Excel, dashboards, reporting tools and AI-assisted analytics tools.
Unit scope: Common business problems addressed by analysts; reporting, business intelligence, analytics and data science; the analyst role in evidence-based decision-making; business questions and analytics briefs; AI as a support tool for clarifying questions and suggesting analytical approaches; responsible scoping, data relevance, privacy awareness and human judgement.

Unit intended learning outcomes

Describe common business problems that analysts help organisations investigate and address.
Distinguish between reporting, business intelligence, analytics and data science at an introductory level.
Translate a broad business request into a clear analytics question and intended output.
Identify the stakeholder audience and decision context for an analytical task.
Use AI support to clarify a business problem or generate possible analytical questions while checking relevance and accuracy.
Recognise basic responsible data considerations when scoping an analytical task.

Unit 2

Metrics, KPIs and Business Data

Unit purpose: Develop learners' understanding of the business data, KPIs, metrics and numerical summaries used in everyday reporting and decision support.
Applied outputs: A short KPI and metrics interpretation exercise using a supplied business scenario, including calculation and interpretation of basic measures and identification of possible misleading summaries.
Tools and environment: Excel and AI-supported explanation tools used appropriately for calculation support, interpretation checking and reporting clarity.
Unit scope: Business data types including transactions, customers, products, categories, dates, departments and regions; KPIs, metrics, targets, benchmarks and scorecards; totals, percentages, proportions, growth rates, mean, median and range; outliers and misleading averages; AI-supported explanation of calculations; independent verification of numerical outputs; responsible communication of figures.

Unit intended learning outcomes

Identify common types of business data used in reporting and analytics.
Define KPIs, metrics, targets, benchmarks and scorecards and explain when each is used.
Select appropriate metrics for simple business problems and recognise when a metric may be misleading.
Calculate and interpret basic numerical summaries, including totals, percentages, proportions, growth rates, mean, median and range.
Identify outliers and explain their impact on reported figures.
Use AI explanations to support understanding while verifying all calculations against source data.

Unit 3

Exploring Problems with Data and AI

Unit purpose: Develop the habit of using data and AI-supported thinking to explore possible explanations for business problems while maintaining evidence-based judgement.
Applied outputs: A short exploratory analysis plan for a workplace problem, including possible drivers, follow-up questions, evidence required, assumptions and limitations.
Tools and environment: AI-assisted analytics tools, Excel or dashboard examples, and structured problem-framing templates.
Unit scope: Moving from broad problems to specific analytical questions; comparisons, trends, segments and exceptions; using AI to brainstorm hypotheses and follow-up questions; distinguishing possibilities from evidence; identifying assumptions, missing information and limitations; responsible interpretation and avoidance of unsupported claims.

Unit intended learning outcomes

Break a broad business problem into specific analytical questions.
Identify relevant comparisons, trends, segments and exceptions to investigate.
Use AI to generate possible explanations or follow-up questions while recognising that suggestions must be tested against data.
Distinguish between evidence, assumption and interpretation.
Identify limitations in available data and explain why they matter.
Communicate exploratory findings responsibly without overclaiming.

4.2 Part 2 · 33.3% of the certificationReporting and Business Intelligence

Part 2 develops the capability to present data clearly and use business intelligence tools to support decisions. Learners move from questions and metrics into visuals, Power BI reports, dashboards and stakeholder communication. AI and governance are embedded through natural-language BI exploration, AI-assisted narratives, honest visual design, privacy-aware reporting and transparent communication of limitations.

Part intended learning outcomes

Design clear, accessible and honest reporting visuals that support informed decision-making.
Build simple Power BI reports or dashboard-style outputs for defined business audiences.
Use AI-supported narrative, natural-language and insight-generation features responsibly.
Translate dashboard findings into concise business recommendations.
Adapt reporting outputs to stakeholder needs and decision context.
Communicate limitations, uncertainty and data quality concerns transparently.

Unit 1

Reporting Visuals and Data Storytelling

Unit purpose: Equip learners to turn data into clear, honest and audience-appropriate visual explanations that support business decisions.
Applied outputs: A short visual insight report using a supplied dataset, including appropriate chart selection, clear design, an AI-assisted but validated narrative summary and a statement of limitations.
Tools and environment: Excel and Power BI used as reporting output tools alongside AI-assisted narrative support where appropriate.
Unit scope: Chart selection for comparison, trend, ranking, composition and distribution questions; visual clarity, accessibility, titles, labels, scale and colour; common visual mistakes; simple data narratives for business audiences; AI-assisted narrative drafting; checking claims against source visuals and data; responsible visual communication.

Unit intended learning outcomes

Select an appropriate chart type for a given business question.
Apply principles of clarity, accessibility and honest scaling to reporting visuals.
Identify and correct misleading or cluttered charts.
Structure a short data narrative around a defined audience and decision.
Use AI-assisted narrative support to improve communication while checking that all claims are accurate.
Present visual insight responsibly, including relevant limitations.

Unit 2

Power BI for Workplace Reporting

Unit purpose: Develop introductory capability in Power BI as a workplace reporting tool, including natural-language and AI-supported exploration where available.
Applied outputs: A simple Power BI report or dashboard-style output for a defined business audience, including basic data preparation, relationships, visuals, simple measures, interactivity and a checked narrative summary.
Tools and environment: Microsoft Power BI Desktop and the Power BI Service, with introductory use of natural-language or AI-supported features where available.
Unit scope: The role of Power BI in business reporting; connecting to common data sources; preparing fields; creating basic visuals; simple relationships; measures versus calculated columns; basic DAX expressions; filters, slicers, drill-down, bookmarks and tooltips at introductory level; Power BI Q&A, Copilot-style prompts, AI-generated summaries and responsible validation of generated outputs. Advanced DAX, complex time intelligence, enterprise deployment and administration are outside the scope of CADA™.

Unit intended learning outcomes

Connect Power BI to common data sources and prepare fields for reporting.
Build simple report pages that answer defined business questions.
Apply simple relationships and basic measures to support reporting outputs.
Use filters, slicers and introductory interactivity to support dashboard exploration.
Use introductory natural-language or AI-supported BI features to explore reports or generate draft narratives.
Validate AI-supported BI outputs against source data and communicate findings responsibly.

Unit 3

Stakeholder Communication and Reporting Decisions

Unit purpose: Develop the communication skills needed to match reporting outputs to stakeholder needs and turn findings into clear, responsible recommendations.
Applied outputs: A short stakeholder reporting brief and recommendation based on a supplied dashboard or report, including decision context, audience needs, key findings, limitations and AI-assisted communication checks.
Tools and environment: Reporting examples, dashboard outputs, AI-assisted drafting tools and structured stakeholder communication templates.
Unit scope: Stakeholder needs and decision context; executive summaries and operational reports; adapting level of detail for different audiences; writing concise business recommendations; using AI to improve clarity and tone; checking accuracy and evidence; communicating risk, uncertainty, missing data, data quality concerns and assumptions.

Unit intended learning outcomes

Identify the audience and decision a report is intended to support.
Match reporting outputs to different stakeholder needs and levels of detail.
Write concise business recommendations based on evidence from data or dashboards.
Use AI-assisted drafting to improve clarity while validating accuracy and professional tone.
Communicate uncertainty, missing data and limitations transparently.
Avoid unsupported or misleading recommendations.

4.3 Part 3 · 33.3% of the certificationData Preparation and Analytics Practice

Part 3 develops the practical data preparation and analyst workflow skills needed to produce reliable reporting outputs. Learners work with Excel and SQL, document preparation decisions, validate outputs, and complete an integrated workplace analytics activity. AI and governance are embedded through safe data handling, traceability, formula and query checking, validation of AI-generated outputs and responsible presentation of findings.

Part intended learning outcomes

Prepare, clean, summarise and validate business data using Excel-based analyst workflows.
Retrieve, filter, summarise and join business data using introductory SQL for reporting.
Document data preparation decisions and quality checks clearly.
Use AI assistance to support formulas, transformations, SQL and narrative development while retaining responsibility for validation.
Complete an integrated workplace analytics task from business question to reporting output and recommendation.
Recognise the progression from CADA™ foundations into CPDA® professional-level analytics.

Unit 1

Excel for Analyst Workflows

Unit purpose: Develop practical competence in using Excel to clean, structure, summarise and validate business data for reporting and analysis.
Applied outputs: An analyst-ready Excel workbook demonstrating data cleaning, Power Query transformations, pivot summaries, lookup functions, validation checks and documentation of key preparation steps.
Tools and environment: Microsoft Excel, including Power Query, pivot tables, lookup functions and relevant AI-supported assistance where available.
Unit scope: Tables and structured ranges; common data quality issues including missing values, duplicates, inconsistent categories and formatting problems; Power Query for importing, cleaning and reshaping data; pivot tables and pivot charts; lookup functions including XLOOKUP; simple validation checks; AI support for explaining formulas, suggesting checks or clarifying transformations; traceability and responsible handling of business data.

Unit intended learning outcomes

Identify and correct common data quality issues in spreadsheet-based business datasets.
Use Power Query to import, clean and reshape data from common sources.
Build pivot tables and pivot charts to summarise business data.
Apply lookup functions to combine or enrich data across tables.
Apply simple validation checks to improve reliability of reporting outputs.
Use AI-supported assistance to explain spreadsheet logic or suggest checks while validating all outputs independently.

Unit 2

SQL for Business Reporting

Unit purpose: Build working SQL capability for retrieving, filtering, summarising and joining business data for reporting outputs.
Applied outputs: A set of SQL query artefacts that retrieve, filter, aggregate and join data across tables to produce reporting-ready outputs, with evidence of query checking and validation.
Tools and environment: A relational database environment supporting standard SQL, accessed through a query tool, with optional AI-assisted query explanation or drafting used under supervision.
Unit scope: How databases organise business data; tables, fields, records, primary keys and foreign keys; simple ER diagrams; SELECT, WHERE and ORDER BY; calculated fields; aggregation functions COUNT, SUM, AVG, MIN and MAX with GROUP BY; HAVING at introductory level; INNER JOIN and LEFT JOIN; readable SQL for reporting; AI-assisted SQL drafting, explanation and troubleshooting; validation of syntax, logic and outputs. Advanced SQL techniques are outside the scope of CADA™.

Unit intended learning outcomes

Describe how relational databases organise and relate business data.
Read a simple ER diagram and identify the role of keys and relationships.
Write SELECT queries with filtering, ordering and simple calculated fields.
Apply common aggregation functions with GROUP BY and introductory HAVING.
Combine data across tables using INNER JOIN and LEFT JOIN.
Use AI support to draft or explain SQL queries while checking syntax, logic and results.

Unit 3

Integrated Analytics Practice

Unit purpose: Consolidate the CADA™ skill set through an integrated workplace analytics activity and create a motivating bridge into CPDA®.
Applied outputs: An integrated workplace analytics artefact combining a business question, prepared dataset, Excel and/or SQL outputs, reporting visuals or dashboard-style output, an AI-assisted but validated insight summary, limitations and a short professional recommendation.
Tools and environment: Excel, SQL environment, Power BI or dashboard-style reporting tools, and AI-assisted analytics tools used appropriately within the workflow.
Unit scope: Planning an end-to-end analysis workflow; defining the business question, data need, audience and output; preparing and checking data; querying and summarising data; creating reporting visuals or a dashboard; using AI to support formulas, SQL, narrative or troubleshooting; validating AI-generated claims; presenting findings and limitations; identifying the next-stage learning developed in CPDA®, including Python, statistics, data reduction and predictive modelling.

Unit intended learning outcomes

Plan a simple end-to-end analytics workflow for a workplace problem.
Prepare and summarise data using Excel and/or SQL-supported workflows.
Create reporting visuals or dashboard-style outputs suitable for a defined audience.
Use AI assistance appropriately to support explanation, narrative or workflow efficiency.
Validate AI-supported outputs against source data and communicate limitations clearly.
Explain how the CADA™ skill set prepares learners for deeper analytical study in CPDA®.

Section 5Appendices

Appendix A Position in the DSI Certification Pathway

CADA™ is the associate-level data analyst credential in the DSI certification pathway. It provides the entry-level foundation for learners who are new to applied business analytics, reporting, business intelligence, data preparation and AI-enabled workplace analytics, and prepares them for progression to CPDA® where the emphasis moves into professional-level analytics.

Learners who complete CADA™ will have encountered the business problem framing, KPI interpretation, Excel, SQL, Power BI, reporting communication, responsible AI use and practical data governance foundations that support progression into CPDA®. CADA™ is intentionally lighter in scope than CPDA® and is designed to build confidence, workplace readiness and the core language of data analysis before learners move into more technical analytical methods.

CADA certification page CPDA specification

Appendix B Platform Learning Terms

Lesson Practice refers to lesson-level applied activity that helps learners practise the concept, tool or workflow immediately after learning it. Knowledge Checks are objective checks aligned to lesson learning outcomes and support readiness for the CADA™ Knowledge Examination. Unit Practices are mandatory consolidation activities linked to the unit's intended learning outcomes. Part-level applied activities or case-study material integrate learning across units and support applied professional judgement in realistic business reporting and analytics scenarios.

Across the Certifications Platform, Learn content provides the core instructional material, supported by worked examples, datasets, guided practice and quizzes. These activities are designed to help learners move from understanding a concept to applying it in practical reporting, business intelligence, AI-assisted analytics and analyst-support contexts.