Module 1: Data Analytics Foundations and Business Problem Definition
Understand analytical thinking, business questions, analytics types, KPIs, metrics, and the analytics lifecycle.
CDAP®
Certified Data Analytics Professional
Turn Data Into Insight. Turn Insight Into Better Decisions.
Data is now one of the most valuable assets in modern organizations.
Analyze. Visualize. Explain. Decide.
Professional Level
For practitioners, specialists, analysts and engineers
Vendor-Neutral
Skills and knowledge that apply across platforms and tools
Global Recognition
Recognized internationally across industries and regions
Digital Credential
Shareable, verifiable credential and certificate
What You Will Learn
Master the core areas of data analytics.
- 8 Comprehensive Data Analytics Modules
Module 2: Data Acquisition, Preparation, Quality, and Management
Learn how to collect, clean, transform, integrate, validate, and document analytical data.
Module 3: Exploratory, Descriptive, and Diagnostic Analytics
Explore distributions, trends, anomalies, relationships, segmentation, correlation, and root-cause analysis.
Module 4: Statistical, Predictive, and Decision Analytics
Apply statistics, hypothesis testing, regression, classification, forecasting, scenario analysis, and decision methods.
Module 5: Data Analytics Tools, SQL, and Analytical Technologies
Use spreadsheets, SQL, Python, databases, cloud analytics, and AI-assisted analytical technologies.
Module 6: Business Intelligence, Data Visualization, and Dashboards
Create dashboards, reports, scorecards, KPIs, visualizations, and data stories.
Module 7: Data Governance, Privacy, Ethics, Security, and Responsible Analytics
Apply governance, security, privacy, quality, ethical, bias, and accountability principles.
Module 8: Enterprise Analytics, Communication, AI-Assisted Analytics, and Professional Practice
Use analytics across organizational functions and communicate insights for effective decision-making.
Career Opportunities
CDAP® can support professional development toward roles such as:
- Data Analyst
- Business Analyst
- BI Analyst
- Reporting Analyst
- Operations Analyst
- Financial Analyst
- Marketing Analyst
- Supply Chain Analyst
- Procurement Analyst
- Healthcare Analyst
- Risk Analyst
- Cybersecurity Analyst
- Performance Analyst
- Data Visualization Specialist
- Analytics Consultant
- Decision Support Analyst
About the credential
Become a Data Analytics professional the market trusts.
But data alone does not create value.
Organizations need professionals who can collect, clean, analyze, interpret, visualize, and communicate data in ways that improve business performance and support better decisions.
Professional level — Three-year certification cycle with continuing professional education
The Certified Data Analytics Professional (CDAP®) is a professional certification designed to validate practical, vendor-neutral competency across the modern data analytics lifecycle.
Offered by the:
Why this credential
Why Earn the CDAP® Certification?
Modern data analysts need more than spreadsheet skills.
They must understand:
CDAP® brings these capabilities together into one comprehensive professional credential.
The certification is designed to validate your ability to:
- Business questions
- Data quality
- Statistical reasoning
- SQL
- Data visualization
- Business Intelligence
- Predictive analytics
- Data governance
- Responsible analytics
- AI-assisted analysis
- Executive communication
- Decision support
- Translate business questions into analytical objectives
- Collect and prepare data
- Evaluate data quality
- Apply descriptive and diagnostic analytics
- Use statistical methods
- Understand predictive analytics
- Query databases using SQL
- Use modern analytical technologies
- Build dashboards and reports
- Communicate findings clearly
- Apply data governance and privacy principles
- Support evidence-based decisions
CDAP®–IBACTP® Data Analytics Professional Competency Model
The CDAP®–IBACTP® Data Analytics Professional Competency Model defines the eight core competencies required to perform data analytics effectively, responsibly, and with a clear focus on organizational decision-making.
The model is designed to reflect the complete professional analytics lifecycle, from defining the problem and preparing reliable data to generating insights, communicating findings, and supporting evidence-based decisions.
1. Analytical Problem Definition and Business Understanding
Translate organizational challenges, operational issues, and business objectives into clearly defined analytical questions, measurable outcomes, relevant KPIs, and appropriate evidence requirements.
2. Data Acquisition, Preparation, and Quality Management
Collect, clean, transform, integrate, validate, structure, and document data while addressing completeness, accuracy, consistency, integrity, and other data-quality requirements.
3. Exploratory and Diagnostic Analytics
Examine data to identify trends, patterns, relationships, segments, anomalies, performance drivers, and potential explanations for observed outcomes.
4. Statistical, Predictive, and Analytical Modeling
Apply appropriate statistical methods, regression, classification, forecasting, probability, and predictive techniques to evaluate relationships, estimate future outcomes, and support analytical conclusions.
5. Analytics Tools and Technology Application
Use spreadsheets, SQL, databases, programming environments, Business Intelligence platforms, visualization tools, and modern analytical technologies to perform efficient and reproducible analysis.
6. Data Visualization and Business Intelligence
Transform analytical results into clear dashboards, reports, scorecards, KPIs, charts, and visual narratives that enable stakeholders to understand performance and make informed decisions.
7. Data Governance and Responsible Analytics
Apply data quality, privacy, security, ethics, transparency, bias awareness, accountability, appropriate data use, and responsible analytical practices throughout the analytics lifecycle.
8. Insight Communication and Decision Support
Interpret analytical results, communicate uncertainty and limitations, develop evidence-based recommendations, and present insights effectively to technical, business, and executive stakeholders.
CDAP® Professional Analytics Progression
The CDAP® competency framework follows a practical progression from business problem to evidence-based decision:
Define → Acquire → Prepare → Explore → Analyze → Visualize → Interpret → Recommend → Decide
This progression reflects the professional responsibility of a data analyst to move beyond simply processing data and toward creating reliable, understandable, and actionable organizational insight.
CDAP® Professional Competency Objective
Transform Business Questions into Data → Data into Insight → Insight into Actionable Decisions.
CDAP®
Who Should Earn CDAP®?
CDAP® is designed for professionals who work with data or use analytics to support decisions.
Ideal candidates include:
- Data Analysts
- Business Analysts
- Business Intelligence Analysts
- Reporting Analysts
- Operations Analysts
- Financial Analysts
- Marketing Analysts
- Supply Chain Analysts
- Procurement Analysts
- Healthcare Analysts
- HR Analysts
- Risk Analysts
- Cybersecurity Analysts
- IT Analysts
- Information Systems Professionals
- Research Analysts
- Performance Analysts
- Analytics Consultants
- Data Visualization Specialists
- BI Developers
- Professionals transitioning into data analytics
Data Analytics
Recommended Candidate Background
CDAP® is a professional-level certification.
Candidates should preferably have basic familiarity with:
Advanced programming experience is not required.
- Computers
- Spreadsheets
- Tables and databases
- Basic mathematics
- Basic statistics
- Business processes
- Charts and reports
CDAP® Course Learning Outcomes
Upon successful completion of the Certified Data Analytics Professional (CDAP®) course, participants will be able to demonstrate practical and professional competency across the complete data analytics lifecycle.
1. Define Business Problems and Analytics Requirements
Translate business challenges, organizational objectives, and stakeholder needs into clearly defined analytical questions, measurable objectives, data requirements, Key Performance Indicators (KPIs), and appropriate success metrics.
2. Acquire, Prepare, and Manage Data for Analysis
Collect, integrate, clean, transform, validate, structure, and document data from multiple sources while applying appropriate data-quality and preparation techniques to create reliable, analysis-ready datasets.
3. Perform Exploratory, Descriptive, and Diagnostic Analytics
Apply exploratory and descriptive analytical techniques to identify distributions, trends, patterns, relationships, segments, anomalies, and performance drivers, and use diagnostic analysis to investigate potential causes of observed outcomes.
4. Apply Statistical, Predictive, and Decision Analytics
Apply and interpret appropriate statistical methods, probability concepts, regression, classification, forecasting, scenario analysis, and predictive techniques to evaluate relationships, estimate future outcomes, and support evidence-based decisions.
5. Apply Data Analytics Tools and Technologies
Use spreadsheets, SQL, databases, Python or equivalent analytical environments, Business Intelligence platforms, visualization tools, and AI-assisted analytics technologies to acquire, manipulate, analyze, and interpret data efficiently.
6. Develop Business Intelligence, Dashboards, and Data Visualizations
Design effective KPIs, reports, scorecards, dashboards, charts, and interactive visualizations that accurately communicate analytical findings and provide meaningful decision support to organizational stakeholders.
7. Apply Data Governance, Privacy, Security, Ethics, and Responsible Analytics
Apply appropriate principles of data quality, governance, privacy, security, ethical data use, bias awareness, transparency, accountability, and responsible analytics throughout the analytical lifecycle.
8. Interpret, Communicate, and Translate Analytics into Action
Evaluate analytical findings, communicate assumptions, uncertainty, and limitations, develop evidence-based recommendations, use effective data storytelling techniques, and present actionable insights to technical, business, management, and executive stakeholders.
CDAP® Learning Progression
The CDAP® curriculum develops competency through an integrated professional learning progression:
Understand → Prepare → Explore → Analyze → Visualize → Interpret → Communicate → Recommend
By completing the program, participants should be prepared to move beyond simply working with data and develop the professional capability to transform organizational data into trustworthy insights and actionable business decisions.
CDAP® Certification Testing Outcomes — Skills and Competencies Assessed
The Certified Data Analytics Professional (CDAP®) certification assessment is designed to evaluate a candidate’s applied knowledge, analytical reasoning, technical skills, professional judgment, and ability to convert data into meaningful business insight.
Candidates are expected to demonstrate competency across the following eight assessment domains:
1. Analytics Foundations and Problem Definition
Define analytical problems, identify relevant stakeholders, establish analytical objectives, select meaningful KPIs and metrics, and translate business questions into structured data requirements.
2. Data Acquisition, Preparation, and Quality
Collect, clean, transform, integrate, validate, and prepare data while identifying and addressing missing values, inconsistencies, duplicates, outliers, and other data-quality concerns.
3. Exploratory, Descriptive, and Diagnostic Analytics
Apply descriptive statistics, segmentation, comparison, correlation, trend analysis, distribution analysis, anomaly detection, and diagnostic reasoning to understand what happened and why.
4. Statistical and Predictive Analytics
Interpret and apply statistical concepts, regression, classification, forecasting, probability, model outputs, uncertainty, and predictive techniques to support evidence-based conclusions.
5. Analytics Tools, SQL, and Data Technologies
Use spreadsheets, SQL, databases, programming-based analytical environments, Business Intelligence platforms, and modern analytics technologies to manipulate, analyze, and retrieve data effectively.
6. Business Intelligence, Visualization, and Dashboards
Develop clear and effective dashboards, reports, scorecards, KPIs, charts, and visualizations that communicate analytical results and support organizational decision-making.
7. Governance, Privacy, Ethics, and Responsible Analytics
Recognize and address data quality, privacy, security, bias, transparency, governance, appropriate data use, and ethical issues that may affect analytical work and outcomes.
8. Analytics Communication and Decision Support
Interpret analytical results, explain assumptions and limitations, communicate uncertainty, develop evidence-based recommendations, and present findings effectively to technical, business, and executive stakeholders.
CDAP® Certification Competency Standard
CDAP® assesses more than familiarity with analytical terminology.
Successful candidates are expected to demonstrate the ability to apply analytics across the complete professional lifecycle:
Define → Acquire → Prepare → Analyze → Visualize → Interpret → Communicate → Decide
This progression reflects the core expectation of a CDAP® professional: to transform business questions into trustworthy analysis and convert analytical findings into clear, actionable decision support.
What the CDAP® Assessment Validates
The certification assessment is intended to verify that candidates can:
- Frame the Right Analytical Question
- Work with Reliable Data
- Apply Appropriate Analytical Methods
- Use Modern Analytics Technologies
- Develop Meaningful Visualizations
- Interpret Results Responsibly
- Communicate Evidence Clearly
- Support Better Organizational Decisions
CDAP®
CDAP® Capstone Competency Progression
The Capstone evaluates the candidate's ability to complete the professional analytics lifecycle:
- Define
- Prepare
- Analyze
- Validate
- Visualize
- Interpret
- Recommend
- Decide
Successful completion demonstrates that the candidate can move beyond individual analytical techniques and integrate business understanding, data preparation, analytics, technology, visualization, responsible data practices, and professional communication into a complete decision-support solution.
.
CDAP®
Tools and Technologies Covered
CDAP® is vendor-neutral but may expose candidates to widely used technologies such as:
CDAP®
Hands-On Practical Labs
The CDAP® Hands-On Practical Laboratory Component provides candidates with applied experience across the complete data analytics lifecycle. Each lab reinforces concepts covered in the CDAP® Body of Knowledge and develops practical competency using realistic business datasets, analytical tools, and decision-making scenarios.
CDAP®
CDAP® Applied Data Analytics and Decision Intelligence Capstone (Instructor-Led Training Only)
Candidates selecting the applied assessment pathway complete the CDAP® Applied Data Analytics and Decision Intelligence Capstone.
The Capstone provides candidates with an opportunity to demonstrate integrated professional competency by applying the CDAP® Body of Knowledge to a real or simulated organizational challenge.
Candidates are expected to progress from business problem definition through data preparation and analysis to actionable recommendations and executive communication.
The Capstone consists of three integrated parts:
Assessment
Flexible Certification Assessment Options
CDAP®
Certification Validity · Professional Designation
Certification Validity
3 Years
Recommended renewal requirement:
40 CPE Credits Every Three Years
Professional Designation
Successful candidates earn:
CDAP®
ISO and International Framework Alignment
The CDAP® Body of Knowledge is designed with consideration of frameworks relevant to analytics, data quality, security, privacy, AI, risk, and personnel certification.
Relevant areas may include:
- ISO/IEC 17024
- ISO/IEC 27001
- ISO/IEC 27701
- ISO/IEC 25012
- ISO 8000 family
- ISO 31000
- ISO/IEC 42001 where AI-assisted analytics applies
- ISO/IEC 23894 where AI-related analytics risk applies
- Relevant NIST cybersecurity, privacy, and AI risk-management guidance
Data Analytics
Global, Vendor-Neutral Design
CDAP® is designed around transferable analytics competencies.
It is not tied to one:
This supports professional applicability across diverse technology environments.
- BI platform
- Database
- Programming language
- Cloud provider
- Visualization tool
- Industry
Data Analytics
Global Recognition and Professional Portability
CDAP® is designed as an internationally relevant professional credential for modern data analysts.
Its competencies can be applied across industries such as:
Recognition remains subject to individual employer, institutional, governmental, and regulatory requirements.
For a public-facing certification webpage, I would strengthen this section while carefully distinguishing alignment from actual accreditation.
- Technology
- Banking
- Finance
- Healthcare
- Government
- Telecommunications
- Manufacturing
- Energy
- Retail
- Supply Chain
- Procurement
- Cybersecurity
- Consulting
- Insurance
- Information Technology
- Professional Services
Accreditation, Standards, and Credentialing Quality Alignment
The Certified Data Analytics Professional (CDAP®) certification framework is designed to support high standards of professional competency assessment, examination integrity, credential governance, and continuous improvement.
The CDAP® certification program is developed with consideration of internationally recognized principles and credentialing practices associated with:
- ISO/IEC 17024 — Conformity assessment requirements for bodies operating certification of persons
- ANSI National Accreditation Board (ANAB) — Accreditation principles applicable to personnel certification bodies
- National Commission for Certifying Agencies (NCCA) — Recognized practices for professional certification programs
- Institute for Credentialing Excellence (I.C.E.) — Credentialing quality, governance, and professional certification practices
- International conformity-assessment and personnel-certification practices relevant to competency-based professional credentials
CDAP® Credentialing Quality Framework
To support the validity, reliability, integrity, and professional value of the CDAP® credential, the certification framework incorporates or is intended to incorporate key credentialing practices including:
- Job Task Analysis (JTA) and role-based competency definition
- Defined certification eligibility and competency requirements
- Validated CDAP® Body of Knowledge
- Certification examination blueprint development
- Subject Matter Expert (SME) participation and technical review
- Psychometric and assessment-quality principles
- Standardized examination administration
- Examination security and confidentiality
- Candidate identity verification and assessment integrity
- Defined passing and certification decision requirements
- Impartial and consistent certification decisions
- Candidate appeals and complaints procedures
- Professional ethics and credential-holder conduct requirements
- Continuing Professional Education (CPE)
- Periodic recertification
- Credential verification and certification-status management
- Periodic Job Task Analysis and Body of Knowledge review
- Continuous monitoring and program improvement
Commitment to Certification Integrity
IBACTP® is committed to developing CDAP® as a rigorous, competency-based professional certification that evaluates whether candidates can apply data analytics knowledge, tools, techniques, professional judgment, and responsible practices in realistic organizational environments.
The certification framework emphasizes:
Validity • Reliability • Impartiality • Security • Transparency • Professional Competency • Continuous Improvement
Global Credentialing Alignment
The CDAP® framework is intended to support internationally relevant and portable professional competencies while maintaining a vendor-neutral approach to data analytics.
Its Body of Knowledge and assessment structure is designed around competencies that can be applied across industries, technologies, organizational environments, and geographic regions.
Important Accreditation Statement
Alignment with, consideration of, or preparation toward ISO/IEC 17024, ANAB, NCCA, I.C.E., or other credentialing standards do not constitute accreditation, endorsement, recognition, or approval by those organizations.
CDAP® Quality Commitment
Competency-Based. Vendor-Neutral. Professionally Governed. Globally Relevant. Built for Continuous Improvement.
.
CDAP®
Your Data Analytics Certification Pathway
- Professional Level
CDAP®
Build the Skills Behind Data-Driven Decision-Making
Organizations increasingly depend on data to answer critical questions:
What happened?
Why did it happen?
What is likely to happen next?
What should we do?
CDAP® is designed to help professionals answer these questions using structured analytical methods, modern tools, and evidence-based reasoning.
Start Your CDAP® Journey
APPLY FOR CERTIFICATION
REGISTER FOR THE CDAP® EXAM
ENROLL IN TRAINING
CHOOSE THE CAPSTONE PATHWAY
DOWNLOAD THE CERTIFICATION GUIDE
CDAP®
Real-World Data Analytics Applications
CDAP® competencies can be applied across:
- Financial analysis
- Revenue analysis
- Marketing performance
- Customer analytics
- Supply chain analytics
- Procurement analytics
- Healthcare analytics
- Workforce analytics
- Cybersecurity analytics
- IT operations
- Risk analysis
- Performance management
- Forecasting
- Demand planning
- Customer segmentation
- Fraud analysis
- Business Intelligence
- Decision support
CDAP®
Certified Data Analytics Professional (CDAP®)
Example:
Jane Smith, CDAP®
CDAP®
CDAP®
Certified Data Analytics Professional
↓
- Advanced Manager Level
CDAP®
Certified Data Analytics Professional (CDAP®)
An active credential holder may use CDAP® after their name in accordance with applicable IBACTP® credential-use policies.
Example: Jane Smith, CDAP®
What comes after CDAP®?
Professionals seeking to progress from performing analytics to leading and governing enterprise analytics may advance to:
CDAP®
Still Have Questions About CDAP®?
Take the next step toward building professional competency in modern data analytics.
Explore CDAP® Training
Review Certification Requirements
Register for the CDAP® Examination
Explore the Applied Capstone Pathway
Apply for CDAP® Certification
- CDAP® — Turn Data Into Insight. Turn Insight Into Decisions.
CDAP®
Certified Data Analytics Professional (CDAP®)
Offered by the International Board of AI, Cybersecurity & Technology Professionals (IBACTP®)
- Analyze Data. Discover Insight. Communicate Evidence. Drive Better Decisions.
The examination
Exam & Certification Details
Everything you need to plan your sitting.
CDAP-100
Exam code for the Professional-level Data Analytics credential.
100 questions (maximum)
Multiple choice, completed in 120 minutes.
700 out of 1000
Passing score. Delivered in English.
Recommended experience
A minimum of two years of experience in data analytics or a closely related technology discipline.
Where you sit it
IBACTP® approved testing centers and online proctored delivery
Staying certified
Three-year certification cycle with continuing professional education
Choose your route
Four ways to enroll. One credential.
Every route leads to the same CDAP® examination and the same designation.
Progression
Your Certification Pathway
Start as a Professional. Advance as a Leader.
Get started
Ready to certify as a CDAP®?
Self-Paced Learning
Exam fee only, with complimentary course materials provided — $400 USD.
Virtual Instructor-Led Training
4 days, 2 hours daily online. All course materials + Exam — $1,200 USD.
Bootcamps & Intensives
10 days, 2 hours daily. All course materials + Exam — $1,800 USD.
Corporate Training
Certify a whole team on a schedule that suits your organization. Fees negotiable.
Questions
Frequently Asked Questions — CDAP®
Find answers to common questions about the Certified Data Analytics Professional (CDAP®) certification, training, assessment, technologies, eligibility, and professional development pathway.
What is the CDAP® certification?
The Certified Data Analytics Professional (CDAP®) is a professional-level certification designed to validate practical competency across the modern data analytics lifecycle.
The certification covers data preparation, exploratory analytics, statistics, SQL, predictive analytics, Business Intelligence, dashboards, data visualization, AI-assisted analytics, data governance, responsible analytics, and decision support.
CDAP® emphasizes the ability to transform organizational data into meaningful insights and actionable recommendations.
Who should pursue the CDAP® certification?
CDAP® is suitable for current and aspiring:
It is also appropriate for professionals whose roles increasingly require data-driven decision-making.
- Data Analysts
- Business Analysts
- Business Intelligence Analysts
- Reporting Analysts
- Operations Analysts
- Financial Analysts
- Marketing Analysts
- Supply Chain and Procurement Analysts
- Healthcare Analysts
- HR and Workforce Analysts
- Risk and Cybersecurity Analysts
- IT and Information Systems Professionals
- Data Visualization Specialists
- Analytics Consultants
- Professionals transitioning into data analytics
Is CDAP® vendor-neutral?
The certification focuses on transferable analytics concepts, methodologies, competencies, and professional practices rather than requiring candidates to specialize in one commercial technology.
Candidates may therefore apply CDAP® competencies across different databases, analytical environments, Business Intelligence platforms, programming tools, and cloud technologies.
Yes. CDAP® is designed as a vendor-neutral certification.
Do I need programming experience to pursue CDAP®?
Basic familiarity with spreadsheets, data, tables, charts, and quantitative concepts is beneficial. Candidates are introduced to programming-based analytics where appropriate, but the certification emphasizes analytical competency rather than advanced software development.
Advanced programming experience is not required.
Does CDAP® cover SQL?
Yes. SQL is an important component of the CDAP® Body of Knowledge.
Candidates develop competency in using SQL to retrieve, filter, aggregate, join, summarize, and analyze structured organizational data.
Does CDAP® cover Microsoft Power BI?
Yes. Business Intelligence, dashboards, visualization, KPIs, reporting, and decision-support technologies are important components of the program.
Microsoft Power BI may be used during training and practical laboratory activities. Other platforms such as Tableau, Qlik, Looker, or equivalent BI technologies may also be used.
The CDAP® certification itself remains vendor-neutral.
Does CDAP® include Python?
Yes. Python or an equivalent analytical programming environment may be incorporated into practical activities.
Candidates may gain exposure to technologies and libraries such as:
The emphasis is on using analytical technologies effectively rather than advanced software engineering.
- Python
- Jupyter
- pandas
- NumPy
- Matplotlib
- scikit-learn
Does CDAP® cover statistical analysis?
Candidates develop competency in descriptive statistics, distributions, relationships, correlation, statistical reasoning, hypothesis concepts, regression, variability, uncertainty, and interpretation of analytical results.
Yes.
Does CDAP® cover predictive analytics?
The curriculum introduces predictive analytics concepts and techniques such as:
The objective is to help candidates understand how historical data can be used appropriately to support predictions and organizational decisions.
Yes.
- Regression
- Classification
- Forecasting
- Predictive modeling
- Model evaluation
- Scenario analysis
- Prediction uncertainty
Does CDAP® cover Artificial Intelligence and AI-assisted analytics?
Modern analytics professionals increasingly work with AI-enabled analytical technologies. CDAP® therefore addresses responsible applications of AI in areas such as:
Candidates are also expected to understand the importance of validating AI-generated analytical outputs and applying appropriate human oversight, privacy, security, governance, and ethical controls.
Yes.
- AI-assisted data analysis
- Natural-language querying
- AI-assisted SQL
- Automated insight generation
- Analytics copilots
- Generative reporting
- Visualization assistance
- Predictive analytics
- Machine learning
Does CDAP® cover Business Intelligence?
Candidates learn concepts involving:
Yes. Business Intelligence is a core component of CDAP®.
- BI environments
- Dashboards
- KPIs
- Scorecards
- Enterprise reporting
- Interactive visualization
- Self-service analytics
- Performance analysis
- Decision-support reporting
- Data storytelling
Are there hands-on practical labs?
The recommended CDAP® training includes five major applied laboratory experiences:
These activities are designed to connect theoretical knowledge with realistic analytical problems.
Yes.
- Data Preparation and Quality Assessment
- Exploratory and Statistical Data Analysis
- SQL and Database Analytics
- Business Intelligence Dashboard Development
- Predictive Analytics and Executive Data Storytelling
How is the CDAP® certification assessed?
Candidates may satisfy the certification assessment requirement through an approved assessment pathway.
The recommended options are:
A secure examination consisting of multiple-choice and scenario-based questions designed to evaluate knowledge, application, analytical reasoning, interpretation, and professional judgment.
Option 2 — CDAP® Applied Data Analytics and Decision Intelligence Capstone
An applied project requiring candidates to define an organizational problem, prepare and analyze data, develop visualizations or dashboards, interpret findings, and present evidence-based recommendations.
Applicable assessment options and requirements should follow current IBACTP® certification policies.
Option 1 — CDAP® Certification Examination
What does the CDAP® Capstone Project involve?
The CDAP® Applied Data Analytics and Decision Intelligence Capstone consists of three integrated components:
Part 3 — Insights, Recommendations, and Executive Presentation
The Capstone evaluates the candidate's ability to apply the complete analytics lifecycle to a realistic organizational problem.
- Part 1 — Business Problem, Data, and Analytical Preparation
- Part 2 — Analysis, Modeling, and Visualization
How long is the recommended CDAP® training?
The recommended CDAP® training program is approximately 60 instructional hours.
Training may be delivered through instructor-led, virtual instructor-led, blended, or other approved learning formats.
What competencies does CDAP® validate?
CDAP® is designed to validate competency across eight major areas:
The professional competency progression is:
Define → Acquire → Prepare → Analyze → Visualize → Interpret → Communicate → Decide
- Analytics foundations and problem definition
- Data acquisition, preparation, and quality
- Exploratory, descriptive, and diagnostic analytics
- Statistical and predictive analytics
- Analytics tools, SQL, and data technologies
- Business Intelligence, visualization, and dashboards
- Governance, privacy, ethics, and responsible analytics
- Analytics communication and decision support
Is CDAP® only for technology professionals?
Data analytics is used across virtually every organizational function. CDAP® competencies may be applicable to professionals working in:
No.
- Technology
- Finance
- Banking
- Healthcare
- Marketing
- Supply Chain
- Procurement
- Human Resources
- Cybersecurity
- Government
- Manufacturing
- Energy
- Telecommunications
- Insurance
- Education
- Consulting
- Operations
- Professional Services
Does CDAP® address data governance and responsible analytics?
Candidates are expected to understand professional responsibilities involving data quality, privacy, security, appropriate data use, bias, transparency, accountability, analytical integrity, and responsible communication of findings.
Yes.
Is CDAP® aligned with international standards and credentialing practices?
The CDAP® framework is designed with consideration of relevant international standards and professional credentialing practices, including areas addressed by ISO/IEC 17024, ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 25012, ISO 8000, ISO 31000, and relevant AI and data-governance frameworks where applicable.
The certification framework may also be developed with consideration of credentialing practices associated with organizations such as ANAB, NCCA, and I.C.E.
Alignment or consideration of these frameworks should not be interpreted as formal accreditation, endorsement, or approval unless such status has officially been granted.
How long is the CDAP® certification valid?
The recommended CDAP® certification cycle is three years, subject to current IBACTP® certification and recertification policies.
Credential holders may be required to maintain their professional competency through Continuing Professional Education (CPE) and other approved professional-development activities.
What professional designation can I use after certification?
Candidates who successfully satisfy all certification requirements earn the designation:
CDAP®
Become CDAP® Certified
CDAP® prepares professionals to transform raw data into meaningful insight through:
Data Preparation • SQL • Statistics • Predictive Analytics • Business Intelligence • Dashboards • Visualization • AI-Assisted Analytics • Governance • Decision Support
Certified Data Analytics Professional (CDAP®) · International Board of AI, Cybersecurity & Technology Professionals (IBACTP®)
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