CDSP®
Certified Data Science Professional
Transform Data into Intelligence. Intelligence into Decisions. Decisions into Impact.
Offered by the International Board for AI, Cybersecurity and Technology Professionals (IBACTP®)
Explore. Model. Validate. Deploy. Govern.
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 science.
Statistics & Probability for Modeling
Data Wrangling & Feature Engineering
Supervised & Unsupervised Learning
Model Evaluation, Validation & Interpretability
Experimentation & A/B Testing
Programming for Data Science
Deployment & Model Lifecycle Management
Career Opportunities
CDSP® certification can support professional development for roles such as:
- Data Scientist
- Junior Data Scientist
- Data Analyst
- Senior Data Analyst
- Business Intelligence Analyst
- Machine-Learning Analyst
- AI and Analytics Specialist
- Statistical Analyst
- Quantitative Analyst
- Research Data Analyst
- Marketing Data Analyst
- Risk Analytics Specialist
- Fraud Analytics Specialist
- Healthcare Data Analyst
- Supply-Chain Data Analyst
- Data Visualization Specialist
- Analytics Consultant
- Data Science Project Coordinator
Certification does not guarantee employment or promotion. It demonstrates professional preparation and can strengthen a candidate’s portfolio when combined with education, experience, practical projects, and effective communication.
View Career Outlook
About the credential
Become a Data Science professional the market trusts.
The Certified Data Science Professional (CDSP)® certification is a career-focused professional credential designed for individuals who want to collect, analyze, interpret, and communicate data to support intelligent business and organizational decisions.
The program combines statistical analysis, programming, machine learning, data visualization, artificial intelligence, data governance, and practical problem-solving. Candidates develop the competencies needed to transform complex datasets into meaningful insights, predictive models, and actionable recommendations.
Whether you are entering the data science profession, advancing from an analytics role, or strengthening your organization’s data capabilities, CDSP® provides a structured pathway to becoming a confident and responsible data science professional.
Turn Data into Decisions. Become CDSP® Certified.
[Apply for Certification] [Download Program Brochure] [Request Corporate Training]
Why Data Science Matters
Organizations generate enormous volumes of data through customer interactions, digital platforms, connected devices, enterprise applications, financial transactions, supply chains, and operational systems. However, data alone does not create value.
Value emerges when professionals can
- Identify meaningful business and research questions.
- Acquire and prepare reliable data.
- Discover patterns and relationships.
- Build statistical and machine-learning models.
- Evaluate the accuracy and limitations of analytical results.
- Communicate findings clearly to decision-makers.
- Apply data ethically, securely, and responsibly.
CDSP® prepares professionals to perform these functions while connecting technical analysis with measurable organizational outcomes.
The CDSP® Value Proposition
Professional level — Three-year certification cycle with continuing professional education
Build practical, job-relevant capabilities
Learn how to approach the complete data science lifecycle—from defining a problem and preparing data to developing models, presenting findings, and monitoring results.
Develop multidisciplinary competence
Integrate programming, statistics, machine learning, data management, visualization, artificial intelligence, and business analysis.
Demonstrate professional credibility
Earn an IBACTP® credential that validates your commitment to professional development and competency in data science.
Communicate with technical and business stakeholders
Learn to translate analytical findings into understandable recommendations for executives, managers, clients, policymakers, and technical teams.
Apply responsible data practices
Address data privacy, security, fairness, transparency, bias, regulatory expectations, and the responsible use of artificial intelligence.
CDSP®
Why Choose an IBACTP® Certification?
The International Board for AI, Cybersecurity and Technology Professionals supports professional development in rapidly evolving technology disciplines. The CDSP® program is designed around the practical, ethical, and strategic competencies required in modern data environments.
The IBACTP® approach emphasizes:
- Industry-relevant professional competencies
- Practical and scenario-based learning
- Responsible and ethical technology practices
- Integration of technical and business knowledge
- Continuing professional development
- Global professional applicability
- Clear certification and renewal requirements
- Workforce and institutional partnerships
CDSP®
Who Should Earn the CDSP® Certification?
The CDSP® certification is suitable for professionals and graduates seeking to establish or advance a career in data science, analytics, artificial intelligence, or data-driven decision-making.
Ideal candidates include:
No single professional background defines a data scientist. CDSP® supports candidates from technology, business, healthcare, finance, education, government, engineering, cybersecurity, logistics, and other data-intensive fields.
- Aspiring data scientists
- Data analysts
- Business intelligence analysts
- Business analysts
- Database professionals
- Software developers
- Machine-learning practitioners
- AI professionals
- Research analysts
- Financial and risk analysts
- Cybersecurity analysts
- Marketing and customer-insight professionals
- Operations and supply-chain analysts
- IT professionals transitioning into data science
- Managers overseeing analytics or AI initiatives
- Recent graduates in technical, scientific, or business disciplines
- Faculty members and researchers working with quantitative data
CDSP®
Skills and Competencies Tested
CDSP®
Certification Testing Outcomes
The CDSP® certification examination is designed to determine whether participants can apply data science knowledge in realistic professional situations. Successful participants should demonstrate competence in the following areas.
CDSP®
Assessment Option 2: Applied Data Science Capstone Project
Required for Virtual Instructor-Led Training
Participants enrolled in the five-day virtual instructor-led CDSP® training program complete an applied capstone project.
The capstone requires participants to address a realistic organizational or research problem using an end-to-end data science process. Participants demonstrate their ability to define a problem, prepare data, perform analysis, develop and evaluate a model, communicate findings, and address ethical and operational concerns.
The capstone may be completed:
Participants must not submit confidential, proprietary, regulated, personally identifiable, or security-sensitive data without appropriate authorization.
- Individually
- In an instructor-approved team
- Using an IBACTP®-provided dataset
- Using an instructor-approved public dataset
- Using authorized organizational data
- Through an approved industry case study
CDSP®
Capstone Project Objectives
The capstone determines whether participants can:
- Define an appropriate data science problem.
- Formulate analytical questions and success criteria.
- Identify and evaluate relevant data.
- Prepare and document a reliable dataset.
- Conduct exploratory and statistical analysis.
- Select an appropriate analytical or machine-learning method.
- Train, validate, and evaluate a model.
- Interpret model performance.
- Develop meaningful visualizations.
- Translate results into recommendations.
- Identify privacy, security, bias, fairness, and ethical risks.
- Communicate findings to technical and nontechnical stakeholders.
CDSP®
Capstone Project Components
CDSP®
Capstone Deliverables
Problem, objectives, scope, data requirements, and success measures
Cleaned and documented data, subject to sharing restrictions
Issues identified, corrections made, and remaining limitations
Methods, performance measures, comparisons, and limitations
- Deliverable
- Recommended requirement
- Project proposal
- Analytical notebook
- Documented Python or approved analytical workflow
- Prepared dataset
- Data-quality summary
- Model evaluation
- Visualization package
- Charts, dashboard, or approved visual report
- Responsible AI assessment
- Privacy, security, bias, fairness, ethics, and oversight
- Written report
- Approximately 8–12 pages, excluding appendices
- Presentation
- Approximately 10–12 slides
- Presentation time
- 10–15 minutes
- Question-and-answer session
- Approximately 5–10 minutes
CDSP®
Capstone Evaluation Criteria
A minimum score of 70% is recommended for successful capstone completion.
- Assessment area
- Weight
- Problem definition and analytical objectives
- 10%
- Data acquisition, understanding, and quality assessment
- 10%
- Data preparation and documentation
- 15%
- Exploratory and statistical analysis
- 15%
- Model selection, development, and evaluation
- 20%
- Visualization and communication
- 10%
- Responsible AI, privacy, security, and ethics
- 10%
- Recommendations and professional presentation
- 10%
- Total
- 100%
CDSP®
Capstone Performance Standards
Successful participants should demonstrate:
- Accurate problem formulation
- Appropriate use of data
- Reproducible analytical methods
- Sound statistical reasoning
- Appropriate model selection
- Correct interpretation of results
- Awareness of model limitations
- Responsible and ethical data practices
- Clear visual communication
- Actionable recommendations
- Professional documentation and presentation
CDSP®
Tools and Technologies Covered
Depending on the approved training-provider environment, candidates may gain exposure to:
IBACTP® does not require candidates to purchase every listed platform. Authorized providers may use suitable open-source or institutionally available alternatives.
- Python
- Jupyter Notebook
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
- SQL
- Relational databases
- Spreadsheet-based analytical tools
- Power BI or comparable visualization platforms
- Git and version-control concepts
- Cloud data and AI services
- API-based data collection
- Natural-language processing tools
- Generative AI-assisted analytical workflows
CDSP®
What You Will Be Able to Do
After completing the CDSP® program, candidates should be able to:
- Explain the role of data science in business, government, research, and technology environments.
- Formulate data science problems based on organizational objectives and stakeholder requirements.
- Collect, integrate, clean, transform, and validate structured and unstructured data.
- Apply descriptive and inferential statistical methods to analyze datasets.
- Use Python and relevant analytical libraries to conduct data science activities.
- Perform exploratory data analysis to identify patterns, anomalies, relationships, and trends.
- Develop and evaluate supervised and unsupervised machine-learning models.
- Create meaningful data visualizations, dashboards, and analytical narratives.
- Work with databases, SQL, APIs, cloud platforms, and large-scale data environments.
- Apply responsible AI, privacy, security, governance, and ethical principles.
- Communicate analytical findings to technical and nontechnical audiences.
- Complete an end-to-end data science project that addresses a practical organizational problem.
CDSP®
CDSP® Competency Framework
The certification is organized around eight professional competency domains.
Competency domain
What candidates learn
Data Science Foundations
Data science concepts, roles, workflows, problem formulation, and organizational applications
Programming and Data Management
Python, SQL, data structures, databases, APIs, and data-processing techniques
Statistics and Mathematics
Descriptive statistics, probability, inference, regression, and analytical reasoning
Data Preparation and Exploration
Data collection, cleaning, transformation, validation, feature engineering, and exploratory analysis
Machine Learning and AI
Supervised and unsupervised learning, model selection, validation, explainability, and responsible AI
Data Visualization and Communication
Visual design, dashboards, storytelling, reporting, and stakeholder communication
Data Engineering and Deployment
Pipelines, cloud platforms, model deployment, monitoring, scalability, and MLOps foundations
Governance, Ethics, and Security
Data quality, privacy, security, bias, fairness, compliance, accountability, and professional conduct
CDSP®
Comprehensive Program Curriculum
Module 1: Foundations of Data Science
This module introduces data science as an interdisciplinary profession connecting statistics, computer science, information systems, domain knowledge, and business strategy.
Topics include:
- Definition and evolution of data science
- Differences among data science, analytics, business intelligence, AI, and machine learning
- Roles within a modern data team
- The data science lifecycle
- Translating organizational problems into analytical questions
- Developing hypotheses and measurable objectives
- Identifying stakeholders and decision requirements
- Understanding structured, semi-structured, and unstructured data
- Common data science applications across industries
- Evaluating the feasibility and potential value of a data science project
Practical outcome
Candidates develop a data science project charter that defines the problem, stakeholders, data requirements, analytical approach, expected value, risks, and success criteria.
Module 2: Python Programming for Data Science
Candidates develop the programming capabilities required to manipulate data, automate analysis, and build reproducible data science workflows.
Topics include:
- Python syntax and programming fundamentals
- Variables, data types, operators, and expressions
- Conditional statements and loops
- Functions, modules, and reusable code
- Lists, dictionaries, tuples, and sets
- File handling and exception management
- Working with notebooks and development environments
- NumPy arrays and numerical computing
- Pandas DataFrames and data manipulation
- Importing CSV, spreadsheet, JSON, and text files
- Writing clear, maintainable, and documented code
- Reproducibility and version-control concepts
Practical outcome
Candidates create a Python-based data-processing workflow that imports, validates, transforms, summarizes, and exports a dataset.
Module 3: Data Management and SQL
This module examines how data is organized, stored, accessed, integrated, and governed within modern information environments.
Topics include:
- Relational database concepts
- Tables, keys, relationships, and constraints
- Entity-relationship modeling
- SQL queries and filtering
- Joins, subqueries, aggregation, and grouping
- Common table expressions and window functions
- Data warehouses, data marts, and data lakes
- Extract, transform, and load processes
- Data integration and interoperability
- APIs and external data sources
- NoSQL database concepts
- Metadata, lineage, and data catalogs
- Data quality and master-data management
Practical outcome
Candidates use SQL to retrieve, combine, aggregate, and analyze data from multiple related tables.
Module 4: Statistics and Probability for Data Science
Candidates learn how statistical reasoning supports reliable interpretation, experimentation, forecasting, and model development.
Topics include:
- Types of variables and levels of measurement
- Measures of central tendency and dispersion
- Probability concepts and distributions
- Sampling methods and sampling error
- Confidence intervals
- Hypothesis testing
- Correlation and association
- Simple and multiple regression
- Analysis of variance
- Parametric and nonparametric techniques
- Statistical significance versus practical significance
- Experimental and observational studies
- A/B testing
- Errors, assumptions, uncertainty, and limitations
Practical outcome
Candidates conduct a statistical investigation, interpret the results, and explain their significance to a nontechnical audience.
Module 5: Data Preparation and Exploratory Data Analysis
Because analytical results depend on data quality, this module addresses the systematic preparation, examination, and validation of data.
Topics include:
- Data profiling and quality assessment
- Missing-value analysis
- Duplicate-record detection
- Outlier identification
- Data-type conversion
- Standardization and normalization
- Categorical-variable encoding
- Data transformation
- Data integration
- Feature creation and feature selection
- Dimensionality-reduction concepts
- Exploratory data analysis
- Pattern and trend identification
- Preventing data leakage
- Documenting preparation decisions
Practical outcome
Candidates transform a raw dataset into a clean, documented, model-ready analytical dataset.
Module 6: Data Visualization and Storytelling
This module prepares candidates to communicate data through clear, accurate, and persuasive visual narratives.
Topics include:
- Principles of effective visualization
- Selecting the appropriate chart for a question
- Designing tables, charts, and dashboards
- Visualizing comparisons, distributions, trends, and relationships
- Interactive visualization concepts
- Dashboard performance indicators
- Accessibility and inclusive visual design
- Avoiding misleading visualizations
- Data storytelling and narrative structure
- Presenting uncertainty and limitations
- Executive summaries and analytical reports
- Communicating with technical and nontechnical audiences
Practical outcome
Candidates develop a professional dashboard and deliver an evidence-based presentation containing findings, implications, and recommendations.
Module 7: Supervised Machine Learning
Candidates learn how to build predictive models using labeled data and how to select appropriate evaluation techniques.
Topics include:
- Machine-learning terminology and workflows
- Training, validation, and test datasets
- Regression algorithms
- Classification algorithms
- Decision trees and ensemble methods
- Nearest-neighbor approaches
- Support vector machine concepts
- Feature engineering
- Model selection and hyperparameter tuning
- Cross-validation
- Overfitting and underfitting
- Regularization
- Evaluation metrics for regression
- Evaluation metrics for classification
- Class imbalance
- Model explainability and interpretation
Practical outcome
Candidates develop, compare, evaluate, and explain multiple predictive models for a defined business problem.
Module 8: Unsupervised Learning and Advanced Analytics
This module focuses on discovering hidden patterns and structures when predefined outcome labels are unavailable.
Topics include:
- Clustering concepts
- K-means clustering
- Hierarchical clustering
- Density-based clustering
- Cluster evaluation
- Principal component analysis
- Association-rule concepts
- Anomaly and fraud detection
- Customer and operational segmentation
- Recommendation-system foundations
- Time-series analysis
- Forecasting fundamentals
- Text analytics and natural-language processing
- Introduction to deep learning
- Generative AI applications in data science
Practical outcome
Candidates apply an unsupervised or advanced analytical technique to identify patterns and generate actionable insights.
Module 9: Data Engineering, Cloud Computing, and MLOps
Candidates examine the infrastructure and operational practices required to move analytical solutions from experimentation into production.
Topics include:
- Data pipeline architecture
- Batch and streaming data
- Data ingestion and processing
- Distributed-computing concepts
- Cloud data platforms
- Containers and deployment concepts
- Model APIs
- Model versioning
- Continuous integration and delivery concepts
- Model monitoring
- Performance degradation and model drift
- Retraining and retirement strategies
- Documentation and operational handoffs
- Reliability, scalability, and cost considerations
Practical outcome
Candidates design a deployment and monitoring plan for an analytical model or data product.
Module 10: Responsible AI, Data Ethics, Privacy, and Security
This module ensures that candidates understand the professional responsibilities associated with collecting data and developing automated systems.
Topics include:
- Principles of responsible data science
- Data ownership and stewardship
- Privacy by design
- Informed consent and appropriate data use
- Data minimization and retention
- De-identification and anonymization concepts
- Algorithmic bias and discrimination
- Fairness assessment
- Transparency and explainability
- Human oversight
- Secure data handling
- Access control and encryption concepts
- Data and model threats
- Regulatory and compliance considerations
- Professional accountability
- Ethical decision-making frameworks
Practical outcome
Candidates complete a risk and ethics assessment for a proposed data science or AI solution.
Module 11: Data Science Strategy and Business Value
This module connects technical analysis with strategic planning, performance management, and organizational decision-making.
Topics include:
- Developing a data strategy
- Aligning analytical initiatives with organizational objectives
- Selecting high-value use cases
- Defining analytical performance indicators
- Estimating costs, benefits, and risks
- Building versus acquiring analytical solutions
- Managing cross-functional data projects
- Stakeholder engagement
- Communicating return on investment
- Data-driven organizational culture
- Managing change and adoption
- Data science team structures
- Vendor and platform evaluation
- Scaling data science capabilities
Practical outcome
Candidates prepare a business case and implementation roadmap for a data science initiative.
Module 12: Data Science Professional Practice and Consulting
This module prepares candidates to operate effectively as data science professionals, consultants, and members of multidisciplinary analytics teams. It connects technical competence with stakeholder engagement, project planning, professional communication, and delivering business-focused analytical services.
Topics include:
- Roles and responsibilities of a data science professional
- Understanding client and stakeholder expectations
- Conducting stakeholder interviews and discovery sessions
- Translating business challenges into analytical requirements
- Defining project scope, objectives, assumptions, and constraints
- Assessing the feasibility of data science initiatives
- Developing analytical project plans and timelines
- Applying Agile and iterative methods to data science projects
- Collaborating with data engineers, analysts, developers, cybersecurity professionals, and business leaders
- Managing changing requirements and stakeholder expectations
- Documenting data sources, assumptions, methods, models, and decisions
- Presenting analytical findings to executive and technical audiences
- Developing actionable recommendations from analytical results
- Estimating the costs, benefits, risks, and resource requirements of data science solutions
- Evaluating third-party data, analytical tools, AI platforms, and technology vendors
- Managing confidentiality, conflicts of interest, and professional responsibilities
- Establishing performance indicators for analytical initiatives
- Supporting organizational adoption of data-driven solutions
- Building a professional data science portfolio
- Preparing for data science interviews and career advancement
- Maintaining professional competence through continuing education
Professional Case Simulation
Candidates participate in a structured data science consulting simulation involving a realistic organizational challenge. They review stakeholder requirements, assess available data, recommend an analytical approach, identify risks, and prepare a professional solution proposal.
Required activities may include:
- Analyzing a client or organizational problem statement.
- Identifying stakeholder needs and expected outcomes.
- Defining the analytical questions to be addressed.
- Evaluating the suitability and quality of available data.
- Recommending appropriate statistical or machine-learning methods.
- Identifying privacy, security, ethical, and operational risks.
- Developing a project scope, resource plan, and implementation schedule.
- Establishing model and business-performance indicators.
- Preparing an executive briefing or consulting proposal.
- Responding to stakeholder questions and defending the recommended approach.
Practical outcome
Candidates produce a professional Data Science Solution Proposal that includes:
This module ensures that CDSP® credential holders can contribute not only as technical practitioners but also as trusted professionals capable of aligning data science solutions with organizational objectives.
- Business problem definition
- Stakeholder requirements
- Analytical objectives
- Data requirements
- Recommended methodology
- Technology and resource requirements
- Risk and ethical assessment
- Implementation schedule
- Performance measures
- Executive recommendations
CDSP®
Industry Applications
CDSP® competencies can be applied across numerous sectors.
- CDSP® Certification Pathway
CDSP®
Eligibility Requirements
Applicants should satisfy at least one of the following recommended pathways:
Academic pathway
A diploma, associate degree, bachelor’s degree, postgraduate qualification, or equivalent credential in:
- Data science
- Computer science
- Information systems
- Statistics
- Mathematics
- Engineering
- Business analytics
- Economics
- Finance
- Cybersecurity
- A related discipline
Professional pathway
Relevant experience in data analysis, information technology, research, business intelligence, database management, programming, reporting, statistics, AI, or a related area.
Emerging-professional pathway
Students, recent graduates, and career changers may qualify after completing an IBACTP®-approved CDSP® training program and its required practical activities.
Recommended foundational knowledge
Candidates benefit from basic familiarity with:
Professional experience requirements may be adjusted according to the applicant’s education, approved training, and demonstrated competencies.
- Computers and information systems
- Algebra and introductory statistics
- Spreadsheets
- Logical problem-solving
- Basic programming concepts
CDSP®
CDSP® Assessment and Training Options
The Certified Data Science Professional (CDSP)® program offers flexible assessment and training pathways for participants seeking practical data science knowledge and professional certification.
Participants may prepare through:
All certification candidates must pass the CDSP® multiple-choice examination. Participants enrolled in the five-day virtual instructor-led training also complete an applied data science capstone project.
The two assessments serve different purposes:
Assessment
Primary purpose
Multiple-choice certification examination
Measures knowledge, analytical reasoning, and application of data science concepts
Capstone project
Measures the practical ability to complete and communicate an end-to-end data science solution
- Self-paced learning
- Five-day virtual instructor-led training
CDSP®
Assessment Option 1: CDSP® Certification Examination
The CDSP® certification examination assesses whether participants possess the knowledge, analytical reasoning, and professional judgment required to perform data science activities responsibly.
The examination includes knowledge-based, application-based, and scenario-based multiple-choice questions. Participants may be required to interpret code, datasets, statistical results, model-performance measures, visualizations, and professional scenarios.
- Types of Examination Questions
Examination Structure
Examination feature
CDSP® specification
Number of questions
100 questions
Examination duration
90 minutes
Question format
Multiple-choice questions
Question style
Knowledge-based, applied, interpretive, and scenario-based
Passing score
70%
Delivery method
Online proctored or approved testing center
Certification level
Professional
Primary language
English
Credential validity
Three years
Final registration, identity verification, retesting, accommodation, and examination-security requirements should be published in the official CDSP® Candidate Handbook.
Knowledge-based questions
These questions assess understanding of essential data science terminology, principles, methods, tools, and professional responsibilities.
Application-based questions
Participants apply data science principles to a defined problem, dataset, model, or organizational scenario.
Code-interpretation questions
Participants examine short Python, SQL, or pseudocode examples and determine the expected result, identify an error, or select the most appropriate solution.
Data-interpretation questions
Participants interpret tables, descriptive statistics, distributions, correlations, or analytical summaries.
Model-evaluation questions
Participants review model results and determine whether a model is accurate, reliable, appropriately validated, or suitable for a stated purpose.
Visualization questions
Participants select, evaluate, or interpret charts, dashboards, and graphical representations.
Scenario-based questions
Participants analyze realistic professional situations involving data quality, analytical methods, privacy, security, ethics, stakeholder communication, or responsible AI.
Professional-judgment questions
Participants identify the most appropriate action when a technical decision involves uncertainty, risk, ethical concerns, or conflicting stakeholder requirements.
CDSP®
Examination Domain Weighting
- Examination domain
- Weight
- Data Science Foundations and Problem Formulation
- 10%
- Python, SQL, and Data Management
- 15%
- Statistics and Probability
- 15%
- Data Preparation and Exploratory Analysis
- 15%
- Machine Learning and Advanced Analytics
- 20%
- Data Visualization and Communication
- 10%
- Data Engineering, Cloud, and MLOps
- 5%
- Responsible AI, Privacy, Security, and Ethics
- 10%
- Total
- 100%
CDSP®
CDSP® Certification Completion Requirements
The capstone project complements the certification examination. It does not replace the 100-question examination unless IBACTP® formally authorizes an alternative assessment arrangement.
- Requirement
- Self-paced learning
- Five-day virtual instructor-led training
- Meet certification eligibility requirements
- Required
- Required
- Complete assigned learning modules
- Required
- Required
- Complete module knowledge checks
- Required
- Required
- Attend live training sessions
- Not applicable
- Required
- Complete instructor-led practical activities
- Not applicable
- Required
- Complete capstone project
- Optional unless separately selected
- Required
- Pass the 100-question certification examination
- Required
- Required
- Accept the IBACTP® Code of Ethics
- Required
- Required
CDSP®
Training Option 1: Self-Paced Learning
The self-paced CDSP® program is designed for participants who require scheduling flexibility and prefer to progress independently.
Participants receive access to structured learning content covering the complete CDSP® competency framework.
Self-Paced Learning May Include
- Digital course modules
- Recorded instructional presentations
- Downloadable study materials
- Python demonstrations
- SQL examples
- Guided analytical exercises
- Data science case studies
- Practice datasets
- Module knowledge checks
- Sample examination questions
- Examination blueprint
- Study guides
- Data preparation templates
- Model-evaluation checklists
- Responsible AI assessment tools
- Progress tracking
- Participant support
Recommended Practical Activities
Self-paced participants should complete exercises involving:
- Importing and inspecting datasets
- Cleaning and transforming data
- Writing SQL queries
- Calculating descriptive statistics
- Conducting exploratory analysis
- Creating visualizations
- Building classification and regression models
- Performing clustering
- Comparing model performance
- Interpreting model results
- Evaluating bias and ethical risks
- Preparing analytical summaries
Recommended Study Commitment
Participants should plan for approximately 50–70 hours of study and practical work, depending on their previous experience with:
- Programming
- Statistics
- Databases
- Machine learning
- Data visualization
- Data governance
- Artificial intelligence
Recommended Completion Period
A suggested completion period is six to ten weeks. Participants may progress more quickly or slowly within the applicable course-access period.
Self-Paced Learning Is Appropriate For
- Working professionals with changing schedules
- International participants in different time zones
- Independent learners
- Career changers requiring additional practice
- Analysts preparing to transition into data science
- Technical professionals seeking certification
- Organizations enrolling employees individually
Advantages of Self-Paced Learning
- Flexible study schedule
- Ability to repeat lessons and demonstrations
- Independent control over learning pace
- Reduced time away from work
- Access from any suitable location
- Structured preparation for the certification examination
- Opportunities to practice technical skills independently
Participant Responsibilities
Self-paced participants are expected to:
- Review all required learning materials.
- Complete module knowledge checks.
- Perform recommended practical activities.
- Identify and address competency gaps.
- Prepare for the certification examination.
- Comply with examination-security requirements.
- Follow the IBACTP® Code of Ethics.
CDSP®
Training Option 2: Five-Day Virtual Instructor-Led Training
The five-day virtual instructor-led CDSP® program provides an intensive, interactive learning experience led by a qualified instructor.
It combines live instruction, demonstrations, guided laboratories, analytical exercises, case studies, examination preparation, and capstone development.
Delivery Structure
Program feature
Description
Duration
Five training days
Delivery method
Live online instruction
Recommended daily contact time
Two to three hours daily
Total instructor-led time
Approximately 8-15 hours total
Learning methods
Lectures, demonstrations, laboratories, cases, discussions, and presentations
Technical activities
Instructor-guided
Capstone project
Required
Examination preparation
Included
Participant interaction
Live instructor and peer engagement
Attendance
Required according to IBACTP® policy
CDSP®
Five-Day Virtual Training Schedule
- Day 1: Data Science Foundations, Python, and Problem Formulation
- Day 2: SQL, Data Preparation, Statistics, and Exploratory Analysis
- Day 3: Machine Learning and Model Evaluation
- Day 4: Visualization, Responsible AI, Data Engineering, and Deployment
- Day 5: Professional Practice, Capstone Presentation, and Examination Preparation
Topics
- Data science roles and applications
- Data science lifecycle
- Translating organizational problems into analytical questions
- Defining objectives and success measures
- Structured and unstructured data
- Python foundations
- Variables, data types, conditions, loops, and functions
- NumPy and Pandas foundations
- Working with notebooks
- Reproducibility and documentation
Practical activities
- Problem-formulation exercise
- Python coding activities
- Importing and inspecting a dataset
- Dataset summary exercise
- Capstone topic and problem selection
Daily outcome
Participants define an analytical problem and use Python to examine an initial dataset.
Topics
- Relational databases
- SQL queries, joins, filters, and aggregation
- Data-quality assessment
- Missing values and duplicate records
- Outliers and inconsistent data
- Data transformation
- Descriptive statistics
- Probability and statistical inference
- Correlation and regression
- Exploratory data analysis
Practical activities
- SQL query laboratory
- Data-cleaning exercise
- Statistical interpretation exercise
- Exploratory-analysis laboratory
- Capstone data preparation
Daily outcome
Participants prepare, query, summarize, and explore data for analysis.
Topics
- Machine-learning workflow
- Training, validation, and test datasets
- Regression
- Classification
- Decision trees
- Ensemble methods
- Clustering
- Feature engineering
- Cross-validation
- Hyperparameter tuning
- Overfitting and underfitting
- Performance measures
- Model comparison
- Explainability
Practical activities
- Regression-model laboratory
- Classification-model laboratory
- Clustering exercise
- Performance-measure interpretation
- Capstone model development
Daily outcome
Participants build, compare, evaluate, and interpret analytical models.
Topics
- Data visualization principles
- Selecting appropriate charts
- Dashboard design
- Data storytelling
- Privacy and security
- Bias and fairness
- Transparency and explainability
- Human oversight
- Data pipelines
- Cloud data concepts
- Model deployment
- MLOps foundations
- Model drift and monitoring
- Generative AI applications and risks
Practical activities
- Visualization and dashboard exercise
- Responsible AI risk assessment
- Model-monitoring scenario
- Capstone visualization and ethics assessment
Daily outcome
Participants communicate results effectively and evaluate the responsible deployment of analytical solutions.
Topics
- Communicating with stakeholders
- Developing analytical recommendations
- Professional reporting
- Data science consulting
- Project documentation
- Implementation considerations
- Certification examination review
- Examination strategies
- Continuing professional development
Practical activities
- Capstone presentations
- Instructor and peer feedback
- Question-and-answer sessions
- Comprehensive domain review
- Practice examination
- Individual development planning
Daily outcome
Participants present an end-to-end data science solution and prepare for the certification examination.
CDSP®
Virtual Training Participation Requirements
Participants should have:
Participants may be required to complete:
- A reliable computer and internet connection
- A webcam and microphone
- Access to the approved virtual-conferencing platform
- Permission to install or use required analytical tools
- A supported web browser
- Access to Python, Jupyter Notebook, or the approved cloud environment
- Access to the required course materials and datasets
- A quiet environment suitable for live participation
- Availability for all scheduled sessions
- Pre-course readings
- A technical-readiness check
- Software installation
- An introductory competency assessment
- A brief pre-course Python or statistics refresher
CDSP®
Comparison of CDSP® Training Options
- Feature
- Self-paced learning
- Five-day virtual instructor-led training
- Schedule
- Flexible
- Scheduled live sessions
- Learning pace
- Participant-controlled
- Instructor-directed
- Instructor interaction
- Limited or optional
- Continuous live interaction
- Technical demonstrations
- Recorded or documented
- Live
- Guided laboratories
- Independent
- Instructor-guided
- Peer collaboration
- Limited
- Extensive
- Live questions and feedback
- Limited
- Included
- Capstone project
- Optional unless specified
- Required
- Capstone presentation
- Not normally required
- Required
- Examination preparation
- Included
- Live and instructor-led
- Recommended for
- Flexible independent learning
- Intensive guided preparation
- Estimated study time
- 50–70 hours
- Five days plus capstone preparation
- Organizational customization
- Limited
- Available for private cohorts
CDSP®
Corporate and Institutional Training
Organizations may arrange private CDSP® training for employees, students, faculty members, association members, or workforce-development participants.
Private Cohorts May Include
Programs may be customized for:
Core CDSP® competencies and certification examination requirements should remain consistent across delivery formats.
- Customized industry datasets
- Organization-specific case studies
- Team-based analytical exercises
- Private virtual classrooms
- Customized scheduling
- Instructor office hours
- Capstone mentoring
- Organization-specific dashboards
- Responsible AI workshops
- Cohort performance reports
- Examination-preparation sessions
- Post-training support
- Financial services
- Healthcare
- Government
- Cybersecurity
- Education
- Supply chain and logistics
- Manufacturing
- Marketing
- Retail
- Energy
- Insurance
- Technology and consulting
CDSP®
Assessment Integrity and Responsible Use of AI
Participants must comply with IBACTP® assessment-security, academic-integrity, and professional-conduct requirements.
Participants are expected to:
Generative AI may support learning when authorized, but participants remain responsible for the accuracy, originality, security, and ethical integrity of their submissions.
Plagiarism, impersonation, unauthorized collaboration, falsification, prohibited AI assistance, or disclosure of examination content may result in assessment invalidation or denial of certification.
- Complete individual examinations independently.
- Submit original capstone work.
- Properly acknowledge external sources.
- Document the use of external code and libraries.
- Disclose the use of generative AI when required.
- Validate AI-generated code, analysis, and text.
- Protect confidential and personally identifiable information.
- Avoid submitting proprietary data without authorization.
- Refrain from sharing certification examination content.
- Follow the IBACTP® Code of Ethics.
CDSP®
Select Your CDSP® Training Path
Choose Self-Paced Learning
Best for participants who want:
[Enroll in Self-Paced CDSP® Learning]
- Maximum scheduling flexibility
- Independent technical practice
- Control over learning pace
- The ability to repeat lessons
- Structured examination preparation
Choose Five-Day Virtual Instructor-Led Training
Best for participants who want:
[Register for Five-Day Virtual CDSP® Training]
- Live expert instruction
- Guided technical laboratories
- Immediate feedback
- Peer collaboration
- A supervised capstone experience
- Intensive examination preparation
Develop an Organizational Cohort
Prepare employees or members to apply data science, machine learning, visualization, and responsible AI to practical organizational challenges.
[Request a Corporate CDSP® Training Proposal]
CDSP®
Maintaining the CDSP® Credential
Data science methods, technologies, and regulatory expectations continue to evolve. CDSP® credential holders are therefore expected to maintain professional competence.
Proposed recertification requirements
Qualifying activities may include professional training, conferences, webinars, academic coursework, publications, mentoring, teaching, research, and approved data science projects.
- Renew the certification every three years.
- Earn 30 continuing professional development units.
- Maintain compliance with the IBACTP® Code of Ethics.
- Report qualifying education, training, research, teaching, or professional activities.
- Pay the applicable renewal fee.
- Complete any required responsible-AI or professional-practice updates.
CDSP®
Benefits of the CDSP® Certification
The Certified Data Science Professional (CDSP)® certification creates value for both individual participants and employers. Participants develop practical, career-relevant data science competencies, while employers gain professionals who can transform organizational data into reliable insights, responsible AI solutions, and measurable business outcomes.
Benefits for Participants
By completing the CDSP® program, participants can:
- Develop practical competencies in Python, SQL, statistics, machine learning, data visualization, and responsible artificial intelligence.
- Understand the complete data science lifecycle, from problem definition and data collection to model development, deployment, and monitoring.
- Learn to clean, integrate, transform, validate, and analyze structured and unstructured data.
- Build statistical and machine-learning models that support prediction, classification, segmentation, forecasting, and decision-making.
- Gain experience using widely adopted data science tools, libraries, databases, and visualization platforms.
- Translate organizational challenges into appropriate analytical questions and measurable objectives.
- Communicate analytical findings clearly to technical specialists, managers, clients, and executive decision-makers.
- Identify privacy, security, bias, fairness, transparency, and ethical risks associated with data and AI systems.
- Develop competencies applicable to multiple industries, including finance, healthcare, government, cybersecurity, education, supply chain, marketing, and technology.
- Strengthen professional credibility through an industry-focused certification.
- Prepare for entry-level, transitional, and advancing roles in data science, analytics, business intelligence, and AI.
- Demonstrate commitment to continuing professional development and responsible professional practice.
- Build confidence in evaluating analytical results and recommending evidence-based solutions.
- Create professional data science documentation, reports, dashboards, and solution proposals.
- Become better prepared for technical interviews, consulting assignments, analytics projects, and multidisciplinary teamwork.
Participant Value Statement
CDSP® helps participants move beyond isolated technical skills. The program develops professionals who can connect data, technology, business requirements, ethical responsibilities, and organizational strategy.
Benefits for Employers
Organizations can use CDSP® training and certification to:
- Establish a consistent data science competency framework across departments and professional roles.
- Validate employees’ knowledge of data preparation, statistics, programming, machine learning, visualization, governance, and responsible AI.
- Strengthen evidence-based planning and organizational decision-making.
- Improve the quality, consistency, reliability, and documentation of analytical work.
- Develop internal data science, artificial intelligence, and business analytics capabilities.
- Identify and close workforce competency gaps.
- Create structured professional-development and career-progression pathways.
- Reduce dependence on fragmented, undocumented, or inconsistent analytical practices.
- Improve collaboration between data scientists, analysts, IT professionals, cybersecurity teams, managers, and organizational leaders.
- Promote responsible, ethical, private, and secure use of organizational data.
- Improve the ability of employees to translate business problems into practical analytical solutions.
- Develop professionals who can communicate analytical findings to nontechnical decision-makers.
- Support workforce development, employee retention, and succession planning.
- Build multidisciplinary teams capable of managing data-intensive initiatives.
- Strengthen organizational readiness for AI adoption and digital transformation.
- Improve evaluation of analytical platforms, AI tools, external vendors, and consulting services.
- Reduce the risks associated with poorly designed models, unreliable data, algorithmic bias, and insufficient governance.
- Accelerate the development and implementation of practical data products and analytical solutions.
- Establish performance indicators for measuring the organizational value of data science initiatives.
- Support innovation, operational efficiency, customer intelligence, forecasting, risk management, and strategic growth.
Employer Value Statement
CDSP® certification helps employers develop professionals who understand not only how to analyze data, but also how to connect analytical methods with organizational objectives, stakeholder requirements, responsible AI practices, and measurable performance outcomes.
CDSP®
CDSP® Program Learning Objectives
Upon successful completion of the CDSP® program, participants will be able to:
PLO 1: Explain Data Science Foundations
Explain the principles, terminology, professional roles, methodologies, and lifecycle activities associated with data science, artificial intelligence, machine learning, and business analytics.
PLO 2: Define Data Science Problems
Translate business, operational, research, or public-sector challenges into clearly defined analytical questions, hypotheses, requirements, and measurable success criteria.
PLO 3: Acquire and Manage Data
Identify, collect, query, integrate, and manage data from databases, files, APIs, cloud platforms, and other appropriate sources.
PLO 4: Prepare Data for Analysis
Profile, clean, transform, validate, encode, normalize, and document structured and unstructured data for statistical analysis and machine learning.
PLO 5: Apply Programming and Analytical Tools
Use Python, SQL, data science libraries, database technologies, and visualization tools to create reproducible analytical workflows.
PLO 6: Apply Statistical Methods
Use descriptive statistics, probability, statistical inference, hypothesis testing, correlation, regression, and experimental methods to investigate data and support valid conclusions.
PLO 7: Conduct Exploratory Data Analysis
Identify trends, distributions, relationships, anomalies, data-quality problems, and potentially useful predictive features through exploratory analysis.
PLO 8: Develop Machine-Learning Models
Select, develop, train, tune, compare, and evaluate supervised and unsupervised machine-learning models for appropriate analytical problems.
PLO 9: Evaluate Model Performance
Use suitable performance measures and validation techniques to assess model accuracy, reliability, generalizability, fairness, and operational suitability.
PLO 10: Visualize and Communicate Findings
Design effective visualizations, dashboards, reports, and presentations that communicate analytical findings to technical and nontechnical stakeholders.
PLO 11: Apply Data Engineering and Deployment Principles
Explain and apply foundational concepts related to data pipelines, cloud computing, model deployment, scalability, monitoring, model drift, retraining, and MLOps.
PLO 12: Apply Data Governance and Responsible AI
Evaluate data science solutions for privacy, security, bias, fairness, transparency, explainability, regulatory considerations, and ethical risk.
PLO 13: Align Data Science with Organizational Strategy
Develop analytical recommendations that align with stakeholder requirements, organizational objectives, operational constraints, and expected business value.
PLO 14: Demonstrate Professional Practice
Prepare professional project documentation, analytical reports, solution proposals, implementation plans, and executive recommendations while following appropriate ethical and professional standards.
CDSP®
Competencies Validated by the CDSP® Examination
Successful certification indicates that the credential holder has demonstrated knowledge or applied competency in:
Competency area
Validated capabilities
Analytical reasoning
Framing problems, evaluating evidence, interpreting results, and drawing defensible conclusions
Programming
Applying Python concepts and analytical libraries to data science tasks
Data management
Querying, integrating, transforming, validating, and documenting data
Statistical analysis
Selecting and interpreting appropriate statistical methods
Machine learning
Building, comparing, evaluating, and explaining analytical models
Data visualization
Presenting data accurately through charts, dashboards, and reports
Business alignment
Connecting analysis with stakeholder needs and organizational objectives
Responsible AI
Addressing fairness, transparency, bias, privacy, security, and human oversight
Professional communication
Explaining technical findings to technical and nontechnical audiences
Operational awareness
Understanding deployment, monitoring, model drift, retraining, and lifecycle management
Professional judgment
Selecting defensible methods while recognizing limitations, risks, and ethical obligations
Certification confirms successful completion of the established assessment requirements. It does not independently guarantee employment, promotion, licensure, or mastery of every technology platform.
CDSP®
Employment Outlook
A Rapidly Growing Professional Field
Demand for professionals who can interpret complex data and support AI-enabled decision-making remains strong. Organizations are expanding their use of analytics, machine learning, automation, and artificial intelligence to improve operations, understand customers, manage risks, develop products, and make strategic decisions.
According to the U.S. Bureau of Labor Statistics:
Employment indicator
Current BLS data
Data scientist employment, 2025
275,600 jobs
Projected employment, 2035
371,000 jobs
Projected numerical increase
95,400 jobs
Projected growth, 2025–2035
35%
Average projected openings annually
24,800
Median annual wage, May 2025
$120,230
Median hourly wage, May 2025
$57.80
Lowest 10% annual earnings
Below $67,240
Highest 10% annual earnings
Above $199,130
The projected 35% employment growth for data scientists is substantially higher than the 3% average projected growth for all U.S. occupations during the same period. The BLS associates this growth with increasing volumes of available data, demand for data-driven decisions, and continued organizational adoption of AI-based systems. U.S. Bureau of Labor Statistics, Occupational Outlook Handbook
These figures reflect the U.S. labor market. Employment opportunities, educational expectations, salaries, and job titles differ by country, industry, experience, employer, and location.
Industries Employing Data Scientists
BLS data indicate that major employers of data scientists include:
Industry
Share of U.S. data scientist employment
Publishing, broadcasting, and content providers
12%
Insurance carriers and related activities
10%
Computer systems design and related services
10%
Management of companies and enterprises
10%
Credit intermediation and related activities
6%
Data science skills are also used extensively in:
- Banking and financial services
- Healthcare and life sciences
- Government and public administration
- Cybersecurity and fraud prevention
- Retail and electronic commerce
- Manufacturing and engineering
- Telecommunications
- Transportation and logistics
- Supply chain and procurement
- Energy and utilities
- Marketing and advertising
- Education and academic research
- Consulting and professional services
- Insurance and risk management
- Agriculture and environmental management
CDSP®
Employment Opportunities
CDSP® participants may use their competencies to pursue or advance within positions such as:
CDSP®
Why Organizations Need CDSP® Professionals
Organizations increasingly require professionals who can do more than operate analytical software. They need individuals who can determine whether data are reliable, select suitable methods, recognize model limitations, communicate findings, and consider the ethical and operational consequences of AI-enabled decisions.
CDSP® professionals are prepared to contribute to:
- Revenue and demand forecasting
- Customer segmentation and retention
- Fraud and anomaly detection
- Operational performance improvement
- Financial and enterprise-risk analysis
- Supply-chain planning and optimization
- Cybersecurity threat analysis
- Healthcare and patient-outcome analytics
- Product and service innovation
- Marketing-performance analysis
- Quality assurance and process improvement
- Public-policy and program evaluation
- Responsible AI governance
- Executive decision support
CDSP®
Professional and Organizational Impact
For participants
CDSP® provides a structured pathway for developing and validating data science capabilities that can be applied across multiple professional environments.
For employers
CDSP® provides a competency-based mechanism for developing, evaluating, and strengthening the professionals responsible for organizational data, analytics, machine learning, and AI initiatives.
For the profession
CDSP® promotes technically sound, strategically relevant, secure, ethical, and responsible data science practice.
CDSP®
Corporate and Institutional Training
IBACTP® can position CDSP® as a flexible workforce-development program for corporations, universities, government agencies, nonprofit organizations, and professional associations.
Delivery options may include:
Organizations may request customized content for healthcare, finance, cybersecurity, government, education, supply chain, energy, retail, or other sectors.
[Request a Customized Training Proposal]
- Instructor-led virtual training
- In-person classroom training
- Blended learning
- Private corporate cohorts
- University partnership programs
- Faculty and staff development
- Customized industry case studies
- Examination-preparation workshops
- Capstone mentoring
- Train-the-trainer programs
The examination
Exam & Certification Details
Everything you need to plan your sitting.
CDSP-100
Exam code for the Professional-level Data Science 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 science 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 CDSP® examination and the same designation.
Progression
Your Certification Pathway
Start as a Professional. Advance as a Leader.
Get started
Ready to certify as a CDSP®?
Self-Paced Learning
Exam fee only, with complimentary course materials provided — $450 USD.
Virtual Instructor-Led Training
5 days, virtual instructor-led. All course materials + Exam — $1,800 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.
Take the next step in Data Science
Apply, choose your preparation route and book your examination with an approved provider.
Questions
Frequently Asked Questions
What is the CDSP® certification?
CDSP® is a professional data science certification offered by IBACTP®. It validates knowledge and applied competency in data preparation, statistics, programming, machine learning, visualization, governance, and data-driven decision-making.
Do I need programming experience?
Prior programming experience is helpful but may not be mandatory for candidates entering through an approved training pathway. The program introduces Python-based data analysis before progressing to machine learning and advanced applications.
Is CDSP® suitable for beginners?
Yes. It can support emerging professionals when delivered through a structured learning pathway. Candidates should have basic quantitative reasoning and computer-literacy skills.
Is the program only for technology professionals?
No. CDSP® is relevant to professionals in business, finance, healthcare, education, government, marketing, supply chain, engineering, cybersecurity, and other data-intensive fields.
Does the certification cover artificial intelligence?
Yes. The curriculum includes machine learning, AI applications, model evaluation, explainability, responsible AI, and generative-AI considerations.
Will candidates complete practical work?
Yes. The proposed program includes labs, case studies, analytical exercises, and an applied capstone project.
Can organizations arrange private training?
Yes. Corporate and institutional cohorts may be delivered virtually, in person, or through a blended format.
How long does preparation take?
Preparation time depends on the candidate’s experience and delivery format. A structured program may be completed through an intensive boot camp or an extended professional-development schedule.
How long is the credential valid?
The proposed credential-validity period is three years, subject to final IBACTP® certification policy.
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