CDSM®
Certified Data Science Manager
Lead Data Teams. Govern AI Responsibly. Transform Analytics into Enterprise Value.
Offered by the International Board for AI, Cybersecurity and Technology Professionals (IBACTP®)
Prioritize, Govern and Scale Data Science Impact.
Advanced Manager Level
For data science leaders, managers and decision-makers
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.
Team Structure, Hiring & Capability Building
Research-to-Production Governance
Model Risk & Interpretability Oversight
Experimentation Culture & Decision Frameworks
Platform, Tooling & Investment Decisions
Stakeholder Communication & Influence
Business Impact & Value Realisation
About the credential
Become a Data Science professional the market trusts.
The Certified Data Science Manager (CDSM)® is an advanced professional certification for experienced data, analytics, artificial intelligence, technology, and business professionals who lead data science teams, manage analytical programs, govern AI systems, and convert data investments into measurable organizational value.
CDSM® moves beyond individual technical proficiency. It builds the managerial, strategic, governance, financial, operational, and leadership competencies needed to lead enterprise data science initiatives.
Participants learn to establish data and AI strategies, evaluate use cases, manage multidisciplinary teams, oversee analytical projects, govern model risk, allocate resources, communicate with executives, and create scalable systems for responsible data-driven decision-making.
Advanced Manager level — Three-year certification cycle with continuing professional education
Move from Data Science Practitioner to Data Science Leader.
[Apply for CDSM® Certification] [Download the Program Brochure] [Request Corporate Training]
CDSM®
Who Should Earn the CDSM® Certification?
CDSM® is designed for experienced professionals who lead—or are preparing to lead—data science, analytics, AI, business intelligence, or data-driven transformation initiatives.
The certification is appropriate for:
CDSM® is not designed as an introductory data science certification. Participants should possess foundational knowledge of analytics, statistics, data management, machine learning, information technology, or organizational decision-making.
- Data Science Managers
- Senior Data Scientists
- Lead Data Scientists
- Analytics Managers
- Business Intelligence Managers
- AI Program Managers
- Machine-Learning Managers
- Data and AI Governance Managers
- Data Engineering Managers
- Technical Project Managers
- Product Managers responsible for data or AI products
- Digital Transformation Managers
- Technology Consultants
- Information Systems Managers
- Data Governance Professionals
- Risk and Compliance Managers
- Directors of Analytics
- Heads of Data Science
- Chief Data Officers
- Chief Analytics Officers
- Chief AI Officers
- Technology executives
- Experienced professionals transitioning into data science leadership
CDSM®
Certification Testing Outcomes
The CDSM® examination assesses whether participants can apply management-level knowledge and judgment to realistic data science situations.
- Skills and Competencies Tested
CDSM®
Capstone Evaluation Criteria
Assessment area
Weight
Organizational problem and strategic alignment
15%
Data and AI maturity assessment
10%
Proposed analytical approach
15%
Implementation and resource plan
15%
Governance, ethics, privacy, and security
15%
Financial analysis and organizational value
10%
Performance and monitoring framework
10%
Executive communication and professional quality
10%
Total
100%
Capstone performance standards
Participants should demonstrate:
- Clear strategic reasoning
- Appropriate management judgment
- Technical understanding
- Realistic implementation planning
- Responsible AI awareness
- Financial and resource discipline
- Evidence-based recommendations
- Professional documentation
- Effective executive communication
Assessment
Assessment Options
CDSM®
Assessment Integrity and Professional Standards
All CDSM® assessments should be administered in accordance with IBACTP examination-security and professional-conduct requirements.
Participants must:
Plagiarism, impersonation, unauthorized assistance, examination-content sharing, falsification, or other misconduct may result in assessment invalidation, suspension, or denial of certification.
- Complete examinations and individual assignments honestly.
- Avoid unauthorized collaboration.
- Properly acknowledge external sources.
- Protect confidential and proprietary information.
- Refrain from sharing examination questions.
- Disclose the use of generative AI when required.
- Follow capstone submission and originality requirements.
- Comply with the IBACTP Code of Ethics.
CDSM®
Why Data Science Management Matters
Organizations are investing in artificial intelligence, machine learning, cloud platforms, data engineering, predictive analytics, and business intelligence. However, technology investments alone do not guarantee organizational value.
Successful data science programs require managers who can:
CDSM® prepares professionals to lead these responsibilities with technical understanding, managerial discipline, ethical judgment, and strategic insight.
- Align data initiatives with organizational strategy.
- Select high-value and feasible analytical opportunities.
- Build and lead multidisciplinary teams.
- Establish data, analytics, and AI governance.
- Manage budgets, vendors, risks, and stakeholder expectations.
- Oversee model development, deployment, and monitoring.
- Create standards for quality, documentation, security, and ethics.
- Communicate analytical value to executive decision-makers.
- Scale successful data products across the organization.
- Manage organizational change and promote data literacy.
CDSM®
The CDSM® Value Proposition
Lead beyond the model
Develop the ability to manage the people, processes, technologies, risks, and decisions surrounding enterprise data science.
Connect analytics with strategy
Learn to prioritize initiatives that support revenue, efficiency, innovation, customer experience, risk reduction, and public or organizational value.
Manage multidisciplinary teams
Build collaborative teams involving data scientists, analysts, data engineers, software developers, cybersecurity professionals, business specialists, legal advisers, and executive sponsors.
Govern AI responsibly
Establish policies and controls for privacy, security, fairness, transparency, explainability, accountability, regulatory compliance, and human oversight.
Deliver measurable value
Define performance indicators, evaluate costs and benefits, monitor adoption, and demonstrate the organizational impact of data and AI investments.
Strengthen leadership credibility
Earn an advanced IBACTP credential that validates management-level competency in data science strategy, operations, governance, and organizational leadership.
CDSM®
What Participants Will Be Able to Do
After completing the CDSM® program, participants should be able to:
- Develop an enterprise data science and AI strategy.
- Align analytical initiatives with organizational priorities.
- Evaluate and prioritize data science use cases.
- Build and lead multidisciplinary data teams.
- Establish data science operating models and centers of excellence.
- Plan and manage analytical projects, programs, and portfolios.
- Evaluate data readiness, technical feasibility, and organizational risk.
- Oversee model development, validation, deployment, and monitoring.
- Establish responsible AI, data governance, and model-risk controls.
- Manage budgets, resources, vendors, platforms, and technology investments.
- Define technical, operational, financial, and business-performance indicators.
- Communicate data science strategy and value to executives.
- Manage stakeholder expectations and organizational change.
- Develop policies for secure, ethical, transparent, and accountable AI.
- Scale analytical capabilities across business units and geographical locations.
- Create a culture of evidence-based decision-making and data literacy.
CDSM®
CDSM® Competency Framework
Competency domain
Management capabilities
Data and AI Strategy
Developing strategies, operating models, roadmaps, and investment priorities
Use-Case and Portfolio Management
Selecting, prioritizing, funding, monitoring, and retiring initiatives
Data Science Leadership
Building teams, defining roles, coaching professionals, and managing performance
Analytical Program Management
Managing scope, schedules, resources, risks, dependencies, and stakeholders
Technical Oversight
Reviewing data, models, architecture, validation, deployment, and monitoring decisions
Data and AI Governance
Establishing accountability, quality, privacy, security, fairness, and compliance controls
MLOps and Operational Management
Managing production models, monitoring, drift, incidents, retraining, and retirement
Financial and Vendor Management
Developing budgets, business cases, sourcing strategies, and vendor controls
Communication and Change Leadership
Influencing executives, managing adoption, and developing organizational data literacy
Organizational Value
Defining outcomes, measuring benefits, and connecting analytics to performance
CDSM®
Comprehensive Program Curriculum
Module 1: Strategic Data Science Leadership
This module introduces the responsibilities of data science managers and the role of analytics and AI in organizational strategy.
Topics include:
- Evolution of data science leadership
- Responsibilities of data science managers
- Differences between technical leadership and people management
- Data science as an organizational capability
- Connecting data strategy with business strategy
- Assessing organizational data maturity
- Defining a data science vision
- Identifying strategic opportunities and constraints
- Centralized, decentralized, and federated operating models
- Developing a data science center of excellence
- Establishing decision rights and accountability
- Aligning data, AI, cybersecurity, and technology strategies
- Preparing a multiyear data science roadmap
- Communicating strategy to executives and governing boards
Management outcome
Participants prepare a strategic data science vision and capability roadmap aligned with organizational priorities.
Module 2: Data Science Use-Case and Portfolio Management
This module focuses on identifying, evaluating, prioritizing, and governing a portfolio of analytical and AI initiatives.
Topics include:
- Discovering organizational data science opportunities
- Conducting stakeholder discovery sessions
- Translating organizational problems into analytical use cases
- Evaluating strategic alignment
- Assessing data and technology readiness
- Estimating implementation complexity
- Evaluating expected benefits and risks
- Prioritization frameworks
- Proof-of-concept and pilot selection
- Managing project dependencies
- Balancing experimentation with operational delivery
- Portfolio dashboards and executive reporting
- Stage-gate review processes
- Stopping or redirecting low-value initiatives
- Scaling successful analytical products
- Managing technical debt across a data science portfolio
Management outcome
Participants develop a prioritized data science portfolio supported by documented selection criteria, value measures, risk ratings, and governance decisions.
Module 3: Building and Leading Data Science Teams
Participants learn how to design, recruit, develop, motivate, and retain multidisciplinary data teams.
Topics include:
- Data science team structures
- Roles of data scientists, analysts, engineers, architects, developers, and product managers
- Workforce planning and competency mapping
- Writing role descriptions
- Recruiting and interviewing technical talent
- Evaluating technical and professional competencies
- Onboarding data professionals
- Establishing team goals and performance expectations
- Coaching and mentoring
- Managing high-performing technical professionals
- Supporting creativity and experimentation
- Psychological safety and constructive challenge
- Conflict resolution
- Inclusive team leadership
- Performance evaluation
- Career progression and succession planning
- Managing employees, contractors, and external consultants
- Leading geographically distributed and virtual teams
Management outcome
Participants create a workforce and team-development plan for an enterprise data science function.
Module 4: Managing the Data Science Lifecycle
This module examines the managerial controls required across the complete lifecycle of a data science initiative.
Topics include:
- Business understanding and problem definition
- Requirements gathering
- Data discovery and feasibility assessment
- Data preparation and feature development
- Experimental design
- Model development and validation
- Deployment planning
- User acceptance
- Operational integration
- Performance monitoring
- Model maintenance and retraining
- Model retirement
- Documentation standards
- Approval authorities
- Quality checkpoints
- Change control
- Agile, iterative, and hybrid delivery approaches
- Managing uncertainty in research-oriented projects
- Lessons learned and continuous improvement
Management outcome
Participants design a governed lifecycle containing required activities, approvals, documentation, quality reviews, and accountability assignments.
Module 5: Technical Oversight for Data Science Managers
CDSM® managers are not expected to develop every model personally, but they must be able to evaluate technical decisions and challenge unsupported conclusions.
Topics include:
- Reviewing analytical problem formulations
- Evaluating data suitability and representativeness
- Assessing data quality and lineage
- Reviewing statistical assumptions
- Understanding supervised and unsupervised learning
- Evaluating model-selection decisions
- Interpreting classification and regression measures
- Cross-validation and generalizability
- Overfitting, underfitting, and data leakage
- Class imbalance
- Feature engineering and selection
- Model explainability
- Reproducibility
- Comparing model performance with business outcomes
- Establishing technical review standards
- Independent model validation
- Evaluating generative AI and large-language-model solutions
- Recognizing when specialist review is required
Management outcome
Participants perform a structured management review of a proposed analytical solution and determine whether it is suitable for deployment.
Module 6: Data Governance, Quality, Privacy, and Security
This module addresses the management structures required to ensure that data are reliable, protected, traceable, and used appropriately.
Topics include:
- Data governance principles
- Ownership, stewardship, and custodianship
- Data policies and standards
- Data catalogs and metadata management
- Data lineage
- Master and reference data
- Data-quality dimensions
- Quality monitoring and remediation
- Data classification
- Access management
- Encryption and secure handling
- Privacy by design
- Consent, minimization, retention, and deletion
- De-identification and anonymization
- Third-party data risk
- Cross-border data considerations
- Security threats involving analytical environments
- Incident response and escalation
- Collaboration with privacy, legal, risk, and cybersecurity teams
Management outcome
Participants develop a data governance and control framework supporting secure, reliable, and authorized analytical use.
Module 7: Responsible AI and Model-Risk Governance
This module prepares managers to establish appropriate oversight for machine-learning, generative AI, and automated decision systems.
Topics include:
- Responsible AI principles
- AI risk classification
- Algorithmic bias and discrimination
- Fairness objectives and tradeoffs
- Transparency and explainability
- Human oversight and intervention
- Accountability for automated decisions
- Model documentation
- Model cards and system documentation
- Independent validation
- Acceptable-use policies
- Generative AI risks
- Hallucination, misinformation, and unreliable output
- Intellectual property and confidentiality
- Third-party and foundation-model risk
- High-impact AI applications
- AI incident management
- Regulatory and industry expectations
- Governance committees and approval structures
- Continuous control monitoring
Management outcome
Participants design a responsible AI governance framework with defined roles, risk tiers, approval requirements, monitoring controls, and escalation procedures.
Module 8: Data Platforms, Cloud Strategy, and Enterprise Architecture
This module equips managers to make informed decisions about the platforms and architecture supporting data science.
Topics include:
- Enterprise data architecture
- Data warehouses, lakes, and lakehouses
- Structured and unstructured data environments
- Batch and streaming architectures
- Cloud, on-premises, and hybrid environments
- Platform selection criteria
- Scalability, reliability, and performance
- Data integration and interoperability
- APIs and analytical services
- Computational-resource planning
- Storage and processing costs
- Build, buy, and partner decisions
- Open-source and commercial platforms
- Technical debt
- Architecture governance
- Business continuity and disaster recovery
- Environmental and sustainability considerations
- Vendor lock-in and portability
Management outcome
Participants develop an architecture decision proposal that addresses organizational requirements, risks, scalability, security, cost, and long-term sustainability.
Module 9: MLOps, Model Deployment, and Operational Resilience
This module examines the processes required to operate models reliably after development.
Topics include:
- Transitioning models into production
- MLOps operating principles
- Development, testing, and production environments
- Model registries and version control
- Continuous integration and delivery
- Automated testing
- Deployment strategies
- Performance baselines
- Data and concept drift
- Model degradation
- Monitoring dashboards and alerts
- Retraining triggers
- Rollback and recovery procedures
- Service-level objectives
- Production incident management
- Business continuity
- Model retirement and archival
- Operational ownership
- Documentation and auditability
Management outcome
Participants establish an operational management plan for model deployment, monitoring, incident response, retraining, and retirement.
Module 10: Financial Management, Procurement, and Vendor Governance
This module connects data science leadership with financial accountability and commercial decision-making.
Topics include:
- Developing data science budgets
- Cost estimation
- Staffing and infrastructure costs
- Cloud cost management
- Total cost of ownership
- Return on investment
- Cost-benefit and risk-adjusted value analysis
- Benefits realization
- Capital and operational expenditure considerations
- Procurement planning
- Developing technical requirements
- Requests for proposals
- Vendor due diligence
- Evaluating AI and analytics platforms
- Contractual performance measures
- Data ownership and intellectual-property clauses
- Privacy and security requirements
- Service-level agreements
- Third-party model risk
- Vendor performance and exit planning
Management outcome
Participants develop a business case and vendor-evaluation scorecard for a proposed data science investment.
Module 11: Executive Communication and Change Leadership
Data science initiatives succeed when stakeholders understand, trust, adopt, and appropriately use analytical solutions.
Topics include:
- Executive communication
- Communicating with boards and governing committees
- Translating technical findings into business language
- Presenting uncertainty and limitations
- Developing executive dashboards
- Building support for data investments
- Managing stakeholder expectations
- Resolving competing priorities
- Resistance to analytical change
- Organizational readiness
- Change-impact assessment
- Stakeholder mapping
- Communication and engagement planning
- Data literacy programs
- User training
- Adoption measurement
- Ethical persuasion
- Crisis and incident communication
- Establishing a data-driven culture
Management outcome
Participants prepare an executive briefing and change-adoption plan for an enterprise data science initiative.
Module 12: Enterprise Data Science Management Simulation
This module integrates strategic, managerial, technical, financial, governance, and leadership responsibilities through a structured organizational simulation. It does not require a traditional capstone project.
Participants assume the role of a data science manager responding to a realistic enterprise challenge. They evaluate competing use cases, allocate resources, address risks, review technical proposals, manage stakeholder disagreements, and present recommendations to an executive committee.
Simulation activities include:
- Assessing the organization’s data and analytics maturity.
- Identifying strategic data science opportunities.
- Evaluating available data, technologies, and workforce capabilities.
- Prioritizing competing analytical initiatives.
- Developing a program budget and resource plan.
- Selecting a team and assigning responsibilities.
- Reviewing a proposed model and its validation evidence.
- Identifying privacy, security, bias, and operational risks.
- Responding to a production-model incident.
- Evaluating a third-party AI vendor.
- Developing performance and benefits-realization measures.
- Presenting an executive recommendation.
Required management outputs may include:
- Data science strategy summary
- Prioritized initiative portfolio
- Workforce and responsibility plan
- Risk and governance assessment
- Budget and resource recommendation
- Vendor evaluation
- Model-review decision
- Monitoring and incident-response plan
- Executive decision memorandum
Management outcome
Participants demonstrate the judgment required to balance innovation, value, cost, technical quality, risk, ethics, and stakeholder expectations.
CDSM®
Benefits for Participants
By completing the CDSM® program, participants can:
- Transition from technical contributor to data science manager.
- Develop enterprise-level data and AI strategy.
- Strengthen leadership and people-management capabilities.
- Learn to evaluate analytical initiatives from technical and business perspectives.
- Manage data science programs, portfolios, budgets, and resources.
- Improve executive communication and stakeholder-management skills.
- Establish data governance and responsible AI controls.
- Oversee model validation, deployment, monitoring, and retirement.
- Evaluate platforms, vendors, consultants, and technology investments.
- Develop business cases and demonstrate return on investment.
- Lead organizational change and data-literacy initiatives.
- Prepare for senior management and executive data roles.
- Strengthen professional credibility through an advanced certification.
- Apply management competencies across multiple industries.
- Demonstrate commitment to ethical, accountable, and secure AI leadership.
Participant Value Statement
CDSM® helps experienced professionals develop the strategic perspective, managerial judgment, technical oversight, and governance capabilities needed to lead data science at scale.
CDSM®
Benefits for Employers
Organizations can use CDSM® training and certification to:
- Develop qualified leaders for data science, analytics, and AI functions.
- Establish consistent management standards across data initiatives.
- Align data science investments with organizational strategy.
- Improve project selection and resource allocation.
- Strengthen accountability for analytical outcomes.
- Improve coordination among technical, business, risk, legal, and executive teams.
- Build responsible AI and model-risk governance.
- Reduce the risk of deploying inaccurate, biased, insecure, or poorly governed models.
- Improve data quality, documentation, lineage, and control.
- Develop internal leadership and succession pipelines.
- Strengthen vendor selection and third-party oversight.
- Improve budget management and cloud-cost accountability.
- Accelerate the transition of successful models into reliable operations.
- Establish consistent model-monitoring and incident-management practices.
- Improve user adoption and benefits realization.
- Develop scalable data science operating models.
- Strengthen organizational data literacy.
- Create measurable value from data and AI investments.
Employer Value Statement
CDSM® supports the development of managers who can govern the entire data science environment—not merely individual models or isolated projects.
CDSM®
CDSM® Program Learning Objectives
Upon successful completion, participants will be able to:
PLO 1: Develop Data and AI Strategy
Create a data science strategy that aligns analytical capabilities, technology investments, workforce priorities, and governance requirements with organizational objectives.
PLO 2: Assess Organizational Maturity
Evaluate an organization’s data, analytics, technology, workforce, governance, and cultural maturity and recommend an appropriate development roadmap.
PLO 3: Manage Data Science Portfolios
Identify, evaluate, prioritize, fund, monitor, scale, redirect, or terminate data science initiatives based on value, feasibility, risk, and strategic alignment.
PLO 4: Lead Multidisciplinary Teams
Design team structures, define responsibilities, manage performance, resolve conflicts, and support the development of data and AI professionals.
PLO 5: Govern the Data Science Lifecycle
Establish management controls, documentation requirements, approvals, quality reviews, and accountability throughout the analytical lifecycle.
PLO 6: Evaluate Technical Decisions
Critically assess data suitability, statistical methods, model-selection decisions, validation evidence, performance measures, explainability, and deployment readiness.
PLO 7: Direct Data and AI Governance
Develop policies and controls addressing data quality, ownership, privacy, security, fairness, transparency, accountability, and responsible AI.
PLO 8: Oversee Data Platforms and Architecture
Evaluate platform, cloud, integration, scalability, resilience, and architecture decisions supporting enterprise data science.
PLO 9: Manage Production Models
Establish practices for model deployment, monitoring, incident response, retraining, change control, and retirement.
PLO 10: Manage Financial Resources
Develop budgets, business cases, investment analyses, cost controls, and benefits-realization measures for data science initiatives.
PLO 11: Govern Vendors and Third Parties
Evaluate vendors, analytical platforms, consultants, third-party models, contractual requirements, performance measures, and exit risks.
PLO 12: Lead Organizational Change
Develop stakeholder, communication, training, adoption, and data-literacy strategies for data-driven transformation.
PLO 13: Communicate with Executives
Present analytical strategy, performance, risks, limitations, and recommendations clearly to executive and governing audiences.
PLO 14: Demonstrate Ethical Leadership
Apply professional judgment to complex situations involving privacy, bias, security, accountability, competing interests, and responsible technology use.
CDSM®
Assessment and Training Options
The Certified Data Science Manager (CDSM)® program offers flexible assessment and training pathways for experienced professionals, organizations, and institutional cohorts. Participants may prepare through self-paced learning or an intensive five-day virtual instructor-led program.
All participants must demonstrate the knowledge and management competencies defined in the CDSM® examination blueprint. Participants enrolled in virtual instructor-led training also complete an applied capstone project that integrates data science strategy, leadership, governance, technical oversight, and organizational value.
CDSM®
Certification Completion Requirements
The certification pathway may require participants to satisfy the following:
Complete required learning modules (Complimentary materials will be provided)
The capstone supplements the certification examination and does not automatically replace it unless IBACTP formally establishes an alternative assessment pathway.
- Requirement
- Self-paced learning
- Five-day virtual instructor-led training
- Required
- Required
- Complete knowledge checks
- Required
- Required
- Participate in live sessions
- Not applicable
- Required
- Complete practical class activities
- Recommended
- Required
- Complete capstone project
- Optional unless otherwise specified
- Required
- Pass certification examination
- Required
- Required
- Accept the IBACTP Code of Ethics
- Required
- Required
- Meet eligibility requirements
- Required
- Required
CDSM®
Training Options
Option 1: Self-Paced Learning
The self-paced CDSM® program is designed for experienced professionals who require flexibility and prefer to manage their own study schedule.
Participants receive structured access to certification content and complete the program independently within the applicable access period.
The self-paced program may include:
- Digital learning modules
- Recorded instructional presentations
- Downloadable study materials
- CDSM® competency framework
- Examination blueprint
- Management case studies
- Practical scenarios
- Knowledge checks
- Sample examination questions
- Templates and management tools
- Recommended readings
- Examination-preparation resources
- Progress tracking
- Access to participant support
Management tools and templates may include:
- Data and AI strategy template
- Organizational maturity-assessment tool
- Use-case prioritization scorecard
- Data science project charter
- Stakeholder-analysis matrix
- Responsibility assignment matrix
- AI risk-assessment form
- Model-governance checklist
- Vendor-evaluation scorecard
- Benefits-realization plan
- Model-monitoring framework
- Executive presentation template
Recommended study commitment
Participants should plan for approximately 40–60 hours of study, depending on their experience with data science, analytics, AI governance, project management, and organizational leadership.
Recommended completion period
Participants may complete the program within the published access period. A suggested study schedule is four to eight weeks, although participants may progress more quickly or slowly according to their professional commitments.
Self-paced learning is ideal for:
- Experienced professionals with unpredictable schedules
- International participants working across time zones
- Managers who prefer independent study
- Professionals preparing for certification while working full time
- Participants already familiar with some examination domains
- Organizations enrolling employees individually
Advantages of self-paced learning
- Flexible start and completion schedule
- Access from any suitable location
- Ability to repeat complex lessons
- Independent control over study pace
- Reduced time away from work
- Structured preparation for the certification examination
Participant responsibilities
Self-paced participants are expected to:
- Review all assigned learning materials.
- Complete module knowledge checks.
- Follow the recommended study plan.
- Use practice questions to identify competency gaps.
- Schedule the certification examination within the applicable eligibility period.
- Protect the confidentiality of examination materials.
- Comply with the IBACTP Code of Ethics.
Option 2: Five-Day Virtual Instructor-Led Training
The five-day virtual instructor-led CDSM® program provides an intensive and interactive learning experience led by a qualified instructor.
The program combines live instruction, management scenarios, group discussions, case analysis, practical exercises, examination preparation, and guided capstone development.
Delivery format
Program feature
Description
Duration
Five consecutive or scheduled training days
Delivery
Live online instruction
Recommended daily contact time
Six to seven hours
Total instructor-led time
Approximately 30–35 hours
Learning methods
Lectures, discussions, cases, simulations, exercises, and presentations
Capstone
Required
Examination preparation
Included
Participant interaction
Live instructor and peer engagement
Attendance
Required according to IBACTP policy
CDSM®
Five-Day Training Schedule
- Day 1: Data Science Strategy and Organizational Alignment
- Day 2: Team Leadership and Data Science Program Management
- Day 3: Technical Oversight, Model Governance, and MLOps
- Day 4: Responsible AI, Governance, Finance, and Vendors
- Day 5: Executive Communication, Capstone Presentation, and Examination Preparation
Topics
- Role of the data science manager
- Enterprise data and AI strategy
- Organizational maturity assessment
- Data science operating models
- Centers of excellence
- Stakeholder identification
- Use-case discovery
- Strategic alignment
- Portfolio prioritization
- Data science roadmaps
Activities
- Organizational maturity-assessment exercise
- Stakeholder-mapping activity
- Use-case prioritization workshop
- Capstone problem selection
Daily outcome
Participants define a strategic data science opportunity and assess its organizational context.
Topics
- Building multidisciplinary data teams
- Workforce and competency planning
- Recruitment and talent development
- Leadership and performance management
- Agile and hybrid delivery methods
- Project scope, schedules, and resources
- Portfolio governance
- Managing uncertainty and technical debt
- Stakeholder expectations
- Program-performance reporting
Activities
- Team-structure design
- Responsibility-assignment exercise
- Project-risk analysis
- Capstone workforce and implementation planning
Daily outcome
Participants develop an appropriate team, delivery structure, and management plan for a data science initiative.
Topics
- Managerial review of data quality
- Statistical and machine-learning oversight
- Model validation
- Performance-measure interpretation
- Overfitting, data leakage, and generalizability
- Explainability
- Production deployment
- Model monitoring
- Data and concept drift
- Retraining and retirement
- MLOps governance
- Model incidents and operational resilience
Activities
- Model-review scenario
- Performance-dashboard interpretation
- Model-incident response exercise
- Capstone technical and operational design
Daily outcome
Participants evaluate whether an analytical solution is technically defensible and operationally ready.
Topics
- Responsible AI principles
- Bias and fairness
- Transparency and human oversight
- Privacy and cybersecurity
- Data governance and quality
- AI risk classification
- Financial planning
- Total cost of ownership
- Return on investment
- Benefits realization
- Build-versus-buy decisions
- Vendor due diligence
- Contractual and third-party risks
Activities
- Responsible AI risk assessment
- Vendor-comparison exercise
- Financial business-case activity
- Capstone governance, risk, and value planning
Daily outcome
Participants develop governance controls and evaluate the financial and ethical implications of a proposed solution.
Topics
- Executive communication
- Presenting uncertainty and limitations
- Building organizational support
- Change-management planning
- User adoption
- Data literacy
- Performance reporting
- Certification examination review
- Test-taking strategies
Activities
- Capstone presentations
- Executive question-and-answer sessions
- Peer and instructor feedback
- Comprehensive knowledge review
- Practice examination
Daily outcome
Participants present and defend a data science management recommendation and prepare for the certification examination.
CDSM®
Virtual Training Participation Requirements
Participants should have:
Participants may also be expected to complete pre-course readings, a competency self-assessment, or a preliminary organizational scenario before the first training day.
-
01
A reliable internet connection
A computer capable of running the selected conferencing platform
-
02
A webcam and microphone
Access to required digital materials
-
03
A quiet environment suitable for professional participation
Permission to install or access any required collaboration software
-
04
Sufficient availability to attend scheduled sessions
Basic familiarity with data science, analytics, AI, or technology management
CDSM®
Comparison of Training Options
- Feature
- Self-paced learning
- Five-day virtual instructor-led training
- Schedule
- Flexible
- Fixed live schedule
- Instructor interaction
- Limited or optional
- Live and continuous
- Peer collaboration
- Limited
- Extensive
- Live case discussions
- No
- Yes
- Practical exercises
- Independent
- Instructor-guided
- Capstone project
- Optional unless specified
- Required
- Executive presentation
- Not normally required
- Required
- Examination preparation
- Included
- Instructor-led
- Recommended for
- Independent professionals
- Professionals seeking intensive guided preparation
- Time away from work
- Minimal and flexible
- Five scheduled training days
- Organizational customization
- Limited
- Available for private cohorts
CDSM®
Organizational Cohort Options
Employers may arrange private CDSM® training for management teams, technical leaders, and professionals responsible for data and AI programs.
Private cohorts may include:
Confidential organizational information should only be used when appropriate authorization, privacy protections, and data-handling arrangements are in place.
- Customized industry case studies
- Organizational maturity assessments
- Company-specific management scenarios
- Responsible AI workshops
- Data governance exercises
- Leadership competency assessments
- Executive briefings
- Team-based capstone projects
- Customized scheduling
- Post-training advisory sessions
- Cohort-level performance reporting
CDSM®
Choose the Training Path That Fits Your Goals
Choose self-paced learning if you need:
[Enroll in Self-Paced Learning]
- Maximum scheduling flexibility
- Independent study
- Control over your learning pace
- Structured examination preparation
- Minimal interruption to professional responsibilities
Choose virtual instructor-led training if you want:
[Register for Five-Day Virtual Training]
- Live instruction from an experienced facilitator
- Real-time questions and feedback
- Management case discussions
- Collaborative learning
- Guided capstone development
- Intensive examination preparation
For organizations
Develop a private CDSM® cohort aligned with your workforce, governance, and data leadership priorities.
[Request a Corporate Training Proposal]
CDSM®
Eligibility Requirements
Because CDSM® is an advanced certification, applicants should satisfy one of the following pathways.
Standard pathway
- A bachelor’s degree or equivalent qualification; and
- At least three years of relevant professional experience in data science, analytics, AI, information technology, business intelligence, statistics, data engineering, research, or a related field; and
- At least one year of team leadership, project leadership, supervisory, consulting, or management responsibility.
Professional experience pathway
Applicants without a bachelor’s degree may qualify with:
- At least five years of relevant professional experience; and
- Demonstrated responsibility for projects, teams, technical decisions, governance, or organizational initiatives.
Certification progression pathway
Applicants may qualify through:
- An active CDSP® certification or another recognized data science credential;
- At least two years of relevant professional experience; and
- Evidence of project, team, consulting, or management responsibility.
Executive pathway
Senior executives and experienced managers responsible for technology, analytics, data governance, digital transformation, risk, or AI programs may qualify based on documented leadership experience.
Eligibility approval does not waive the examination or professional-conduct requirements.
CDSM®
Maintaining the CDSM® Credential
CDSM® credential holders should maintain their knowledge as data technologies, governance requirements, and management practices evolve.
Proposed renewal requirements
Qualifying activities may include:
- Renew the certification every three years.
- Complete 45 continuing professional development units.
- Maintain compliance with the IBACTP Code of Ethics.
- Complete periodic responsible AI and governance updates.
- Submit evidence of qualifying professional-development activities.
- Pay the applicable renewal fee.
- Advanced training
- Conferences and professional workshops
- Executive education
- Teaching or mentoring
- Research and publication
- Data or AI governance service
- Professional presentations
- Leadership of qualifying analytical initiatives
- Development of organizational policies or standards
CDSM®
Employment Outlook
CDSM® aligns with responsibilities found across data science management, analytics leadership, AI program management, and computer and information systems management. No single government occupational category represents every data science management role; therefore, related categories provide useful labor-market indicators.
Computer and Information Systems Managers
The U.S. Bureau of Labor Statistics reports:
Employment indicator
BLS data
Employment, 2025
685,800 jobs
Projected employment, 2035
793,900 jobs
Projected numerical increase
108,100 jobs
Projected growth, 2025–2035
16%
Average openings each year
53,500
Median annual wage, May 2025
$175,140
Median hourly wage, May 2025
$84.20
Lowest 10% annual earnings
Below $107,550
Highest 10% annual earnings
Above $297,510
The projected 16% growth is substantially higher than the 3% average for all occupations. The BLS associates demand with the growing importance and complexity of cloud computing, cybersecurity, digital platforms, AI, and enterprise technology implementation. U.S. Bureau of Labor Statistics—Computer and Information Systems Managers
Data Scientists
The underlying technical profession is also expanding rapidly:
Employment indicator
BLS data
Employment, 2025
275,600 jobs
Projected employment, 2035
371,000 jobs
Projected numerical increase
95,400 jobs
Projected growth, 2025–2035
35%
Average openings each year
24,800
Median annual wage, May 2025
$120,230
Growth in the practitioner workforce can increase organizational demand for professionals who can manage teams, coordinate initiatives, govern models, and connect technical work with executive priorities. This is a reasonable inference rather than a separate BLS projection for the title “Data Science Manager.” U.S. Bureau of Labor Statistics—Data Scientists
These figures apply to the United States. Compensation and employment conditions vary according to location, industry, education, experience, organizational size, and job responsibilities.
CDSM®
Employment Opportunities
CDSM® competencies may support professional advancement toward roles such as:
Data science and analytics leadership
- Data Science Manager
- Senior Data Science Manager
- Analytics Manager
- Advanced Analytics Manager
- Business Intelligence Manager
- Predictive Analytics Manager
- Decision Science Manager
- Quantitative Analytics Manager
AI and machine-learning leadership
- AI Program Manager
- Machine-Learning Manager
- Applied AI Manager
- AI Product Manager
- Responsible AI Manager
- AI Governance Manager
- Model-Risk Manager
- MLOps Manager
Data and technology leadership
- Data Engineering Manager
- Data Platform Manager
- Data Governance Manager
- Data Quality Manager
- Information Systems Manager
- Technology Program Manager
- Digital Transformation Manager
- Cloud Analytics Manager
Senior and executive progression
With substantial leadership experience, professionals may progress toward roles such as:
Certification does not guarantee employment, promotion, compensation, or executive appointment. Senior positions typically require substantial professional experience, demonstrated leadership results, organizational knowledge, and appropriate academic or technical preparation.
- Director of Data Science
- Director of Analytics
- Director of Business Intelligence
- Head of Data Science
- Head of AI
- Vice President of Data and Analytics
- Chief Data Officer
- Chief Analytics Officer
- Chief AI Officer
- Chief Information Officer
- Chief Technology Officer
CDSM®
Industries Employing Data and Technology Managers
CDSM® competencies are relevant across:
- Financial services and insurance
- Computer systems and technology services
- Healthcare and life sciences
- Government and public administration
- Manufacturing
- Retail and electronic commerce
- Telecommunications
- Energy and utilities
- Transportation and logistics
- Supply chain and procurement
- Cybersecurity
- Education and research
- Consulting and professional services
- Media and digital platforms
- Pharmaceutical and biotechnology organizations
CDSM®
Corporate and Institutional Training
IBACTP may offer CDSM® through:
Customized programs may incorporate the organization’s strategy, maturity level, industry risks, governance structure, use cases, and workforce-development priorities.
[Request a Corporate Proposal] [Discuss an Executive Cohort]
- Executive virtual instruction
- In-person management workshops
- Private organizational cohorts
- Blended learning
- Leadership academies
- University executive-education partnerships
- Customized industry programs
- Responsible AI governance workshops
- Data leadership development programs
- Examination-preparation sessions
The examination
Exam & Certification Details
Everything you need to plan your sitting.
CDSM-200
Exam code for the Advanced Manager-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 five years of experience, including two years in a supervisory, lead or management role.
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 CDSM® examination and the same designation.
Progression
Your Certification Pathway
Start as a Professional. Advance as a Leader.
Get started
Ready to certify as a CDSM®?
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 CDSM® certification?
The Certified Data Science Manager (CDSM)® is an advanced professional certification for individuals responsible for managing data science teams, analytical projects, AI programs, data governance, and enterprise data strategies. It combines technical oversight with leadership, strategy, governance, risk management, and organizational decision-making.
Is CDSM® a technical certification?
CDSM® is a technical-management certification. Participants must understand data science, statistics, machine learning, data engineering, AI, and model deployment sufficiently to evaluate technical work and guide specialists.
However, the certification’s primary focus is managing the people, processes, technologies, risks, investments, and governance structures that support enterprise data science.
Is CDSM® suitable for beginners?
CDSM® is intended for experienced professionals and is not designed as an introductory certification. Participants should possess prior knowledge or professional experience in data science, analytics, AI, information technology, statistics, business intelligence, data engineering, research, or a related field.
Individuals beginning their data science careers should consider earning the Certified Data Science Professional (CDSP)® certification before progressing to CDSM®.
Must applicants hold the CDSP® certification first?
CDSP® is the recommended certification pathway for professionals who want to progress from data science practice to management. However, it is not necessarily a mandatory prerequisite.
Applicants may qualify for CDSM® by demonstrating equivalent education, relevant professional certifications, technical competency, data or analytics experience, and appropriate project or leadership responsibilities.
Who should pursue the CDSM® certification?
CDSM® is appropriate for:
- Data science managers
- Senior and lead data scientists
- Analytics managers
- Business intelligence managers
- AI and machine-learning managers
- Data engineering managers
- Data governance managers
- Technical project and program managers
- Data and AI product managers
- Digital transformation leaders
- Technology consultants
- Directors of data and analytics
- Professionals preparing for senior data leadership positions
What is the difference between CDSP® and CDSM®?
CDSP® concentrates on performing data science activities, including programming, data preparation, statistical analysis, machine learning, visualization, and communicating analytical findings.
CDSM® focuses on managing enterprise data science. It covers organizational strategy, team leadership, portfolio management, technical oversight, responsible AI, model-risk governance, financial management, vendor oversight, MLOps, stakeholder engagement, and organizational change.
Leading teams, programs, governance, and enterprise data strategy
- Certification
- Primary emphasis
- CDSP®
- Performing professional data science and analytical work
- CDSM®
Does the program cover artificial intelligence and generative AI?
Yes. The program addresses AI strategy, use-case selection, model governance, generative AI, large-language-model applications, responsible AI, and third-party AI services.
Participants also examine important risks involving inaccurate outputs, hallucinations, bias, privacy, security, intellectual property, confidential information, vendor dependence, explainability, human oversight, and regulatory expectations.
Does CDSM® include programming?
Programming is not the program’s primary focus. Participants are not expected to write every algorithm or personally develop every model.
They must, however, understand programming, statistical analysis, databases, machine learning, data engineering, and model deployment well enough to:
- Review technical proposals.
- Ask informed questions.
- Evaluate analytical methods.
- Interpret model-performance results.
- Identify technical and operational risks.
- Communicate effectively with technical teams.
- Make defensible management decisions.
What management competencies does CDSM® develop?
The program develops competencies in:
- Data and AI strategy
- Data science portfolio management
- Team formation and workforce development
- Analytical project and program management
- Technical and model oversight
- Data governance
- Responsible AI
- Privacy and cybersecurity
- Model-risk management
- Data platforms and cloud strategy
- MLOps and production-model management
- Financial and resource management
- Vendor and third-party governance
- Executive communication
- Change leadership
- Benefits realization
Is professional experience required?
Because CDSM® is an advanced management certification, relevant professional experience is recommended. Applicants should possess experience in data science, analytics, AI, information technology, research, business intelligence, statistics, data engineering, or a related area.
Applicants should also demonstrate project leadership, team leadership, consulting, supervisory, governance, or management responsibility. IBACTP may evaluate alternative combinations of education, certification, and professional experience.
Is there an applied assessment?
Yes. The proposed certification structure includes an Enterprise Data Science Management Simulation.
Participants respond to a realistic organizational scenario requiring them to:
This assessment evaluates management judgment and the ability to integrate strategic, technical, financial, ethical, and operational considerations.
- Assess organizational data maturity.
- Prioritize competing data science initiatives.
- Allocate budgets and resources.
- Evaluate technical recommendations.
- Address privacy, security, bias, and model risks.
- Review an external AI vendor.
- Respond to a model-performance incident.
- Develop performance measures.
- Present and defend an executive recommendation.
How is the certification examination structured?
The proposed CDSM® examination consists of multiple-choice, scenario-based, and management decision questions. It evaluates the participant’s ability to apply knowledge to realistic data science leadership situations.
The examination covers:
Final examination specifications, including the number of questions, duration, passing score, and delivery method, should be confirmed in the official CDSM® Candidate Handbook.
- Data and AI strategy
- Program and portfolio management
- Team leadership
- Technical and model oversight
- Data governance
- Responsible AI
- Privacy and security
- MLOps and operational management
- Financial and vendor management
- Executive communication and change leadership
How long does it take to complete the program?
Completion time depends on the participant’s professional experience, study schedule, and selected training format. CDSM® may be delivered through an intensive executive program, instructor-led cohort, blended-learning program, or extended self-paced schedule.
Training providers should clearly publish the duration, contact hours, assignment requirements, and examination schedule for each cohort.
Is the certification available online?
IBACTP may provide online training and remotely proctored certification testing, subject to examination-security and identity-verification requirements. In-person and blended delivery options may also be available through authorized training providers.
Can organizations arrange private training?
Yes. Corporations, government agencies, universities, nonprofit organizations, and professional associations may request customized CDSM® training for their employees or members.
Private training may be delivered:
Customized programs may incorporate the organization’s industry, data maturity, governance needs, technology environment, use cases, and strategic priorities.
- Virtually
- In person
- Through blended learning
- As an executive workshop
- As a leadership-development cohort
- As part of an organizational data or AI academy
Can the program be customized for a specific industry?
Yes. CDSM® training may incorporate industry-specific cases and management scenarios for sectors such as:
The certification’s core competency and examination standards should remain consistent across all delivery formats.
- Financial services
- Healthcare
- Government
- Cybersecurity
- Manufacturing
- Supply chain and logistics
- Energy
- Education
- Retail
- Telecommunications
- Insurance
- Technology and consulting
What career opportunities can CDSM® support?
CDSM® may support professional advancement toward positions such as:
Certification does not guarantee employment or promotion. Career outcomes depend on education, experience, technical competency, leadership achievements, industry knowledge, and employer requirements.
- Data Science Manager
- Analytics Manager
- AI Program Manager
- Machine-Learning Manager
- Data Governance Manager
- MLOps Manager
- Data and AI Product Manager
- Director of Data Science
- Director of Analytics
- Head of Data Science
- Head of AI
- Vice President of Data and Analytics
- Chief Data Officer
- Chief Analytics Officer
- Chief AI Officer
How long is the CDSM® certification valid?
The proposed CDSM® certification-validity period is three years. Credential holders must satisfy IBACTP renewal requirements to maintain active certification status.
How can CDSM® holders maintain their certification?
Proposed renewal requirements include:
Final renewal requirements should be published in the official IBACTP Certification and Recertification Policy.
- Completing continuing professional development activities
- Maintaining compliance with the IBACTP Code of Ethics
- Participating in relevant training, research, teaching, mentoring, or professional service
- Completing required responsible AI and governance updates
- Submitting renewal documentation
- Paying the applicable renewal fee
Does CDSM® guarantee employment or promotion?
No certification can guarantee employment, promotion, salary increases, or appointment to a management position. CDSM® validates defined professional knowledge and competencies. Its career value is strongest when combined with relevant education, technical expertise, management experience, measurable professional achievements, and effective leadership.
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