Data Science and Data Ethics Business Course

Name: Data Science and Data Ethics for Business Professionals Course
Availability: InStock

Connect business analytics with practical ethics, governance, and responsible AI controls for evidence-based decisions.

Data Science and Data Ethics Business Course

At a glance

Duration: 2 Weeks
Format: Classroom
Cities: Trabzon, Bali, Porto, Milan, Prague, Abu Dhabi and more
Next session: 8 – 19 November 2026, Trabzon
Average fee: 9,800 €

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Data Science and Data Ethics for Business Professionals Course

Overview

Data Science and Data Ethics for Business Professionals Course is a two-week foundation-to-intermediate course for managers, analysts, data stewards, governance functions, and decision-makers who leave with a Responsible Business Data Science Decision Pack. Participants connect business problem framing and analytics workflows with model interpretation, bias and fairness, privacy, transparency, accountability, and responsible AI controls. Agile Leaders Training Center develops data science and data ethics capabilities.

Who Should Attend

Business functions responsible for framing decisions, benefits, constraints, and evidence needs
Analytics functions responsible for data preparation, analysis, model interpretation, and reporting
Data stewardship functions responsible for quality, provenance, access, and permitted use
Governance functions responsible for policies, accountability, risk review, and escalation
Product and operations functions responsible for deploying data-supported decisions into processes
Leadership functions responsible for oversight, stakeholder communication, and decision approval

The course assumes participants use data or analytical outputs at work, and leaves out advanced programming, production model engineering, legal advice, and certification.

Departments and Industries

The course supports departments and industries that use analytics to guide decisions affecting customers, employees, assets, services, or resources.

Strategy, analytics, data offices, governance, risk, and internal audit
Marketing, customer experience, sales, product, and digital operations
Financial services, insurance, healthcare, education, and professional services
Manufacturing, utilities, transport, logistics, retail, and hospitality
Technology, telecommunications, public services, infrastructure, and nonprofit organizations

Learning Objectives

By the end of this course, participants will be able to:

Apply business problem-framing and data-suitability methods
Analyze descriptive and predictive outputs for decision use
Evaluate model interpretation, bias, fairness, privacy, and transparency
Build accountability, risk, monitoring, and escalation controls
Compare stakeholder impacts and responsible-use options
Build a Responsible Business Data Science Decision Pack

Course Agenda

Day 1: Frame Business Data Science

Business Decision and Evidence Canvas
Problem Statement and Success-Criteria Template
Data Science Lifecycle Map
Stakeholder and Decision-Rights Matrix
Analytics Assumption Register

Day 2: Assess Data Fitness

Data Source Inventory Card
Data Provenance and Lineage Diagram
Data Quality Dimension Scorecard
Sampling and Representation Review
Permitted-Use and Access Checklist

Day 3: Build Business Analytics Workflows

Descriptive Analytics Question Tree
Segmentation and Cohort Analysis Grid
Indicator Definition Sheet
Correlation and Causation Challenge Card
Analytics Workflow Control Map

Day 4: Interpret Predictive Models

Prediction Target and Outcome Map
Training, Validation, and Test Data Diagram
Performance Metric Selection Matrix
Model Interpretation Evidence Card
Decision Threshold Tradeoff Table

Day 5: Examine Data Ethics

Ethical Question-Framing Canvas
Stakeholder Impact Assessment Map
Benefits, Harms, and Rights Matrix
Data Minimization Decision Card
Human Oversight Design Checklist

Day 6: Evaluate Bias and Fairness

Bias Source Taxonomy
Representation Gap Analysis
Fairness Criterion Comparison Table
Disparate Outcome Review Card
Bias Mitigation Option Register

Day 7: Design Transparency and Privacy

Transparency Audience and Purpose Matrix
Model Explanation Plan
Data Notice Content Checklist
Privacy Risk Scenario Map
Decision Challenge and Redress Path

Day 8: Govern Responsible AI

NIST AI RMF Govern-Map-Measure-Manage Map
ISO/IEC 42001 AI Management System Context Card
Responsible AI Accountability Matrix
AI Risk and Control Register
Third-Party Analytics Due-Diligence Checklist

Day 9: Monitor and Communicate Decisions

Model and Decision Monitoring Dashboard
Data and Model Change Log
Incident Escalation Decision Tree
Stakeholder Communication Brief
Responsible-Use Review Meeting Template

Day 10: Practice Ethical Business Decisions

Exercise: Frame a Customer Analytics Decision
Exercise: Diagnose Data Fitness and Representation Gaps
Exercise: Review Model Evidence and Fairness Tradeoffs
Exercise: Design Governance, Monitoring, and Escalation Controls
Capstone: Responsible Business Data Science Decision Pack

Practical Exercises

The course uses suggested activities based on customer segmentation, workforce planning, service demand, credit assessment, asset maintenance, and supply decisions.

Suggested activity: frame a business decision, identify evidence needs, and test data suitability and assumptions.
Suggested activity: interpret model outputs, compare performance measures, and document decision-threshold tradeoffs.
Suggested activity: assess stakeholder impacts, representation, fairness, privacy, transparency, and human oversight.
Suggested activity: build accountability, risk, monitoring, communication, challenge, and escalation controls.

FAQs

Who suits data science and data ethics for business professionals, and what does it assume?

Managers, analysts, stewards, governance functions, and decision-makers suit the course; it assumes routine use of data or analytical outputs rather than advanced programming expertise.

How does data science and data ethics for business differ from advanced machine-learning engineering?

Business data science and ethics focuses on framing decisions, interpreting evidence, assessing impacts, and governing use. Advanced machine-learning engineering focuses on coding, model architecture, pipelines, infrastructure, and production deployment.

How should business professionals assess bias and fairness in data science?

Business professionals should examine representation, measurement choices, labels, sampling, error distribution, affected groups, decision thresholds, stakeholder impacts, mitigation options, and monitoring evidence in the context of use.

What makes a data-science decision transparent and accountable?

A transparent and accountable decision identifies purpose, data sources, assumptions, model role, responsible owners, limitations, explanations, approvals, monitoring measures, challenge routes, and escalation actions.

How should responsible AI controls support business analytics?

Responsible AI controls should connect decision rights, risk assessment, data governance, human oversight, documentation, monitoring, incident response, and periodic review to the analytics lifecycle and its context.

Conclusion

Participants take back a Responsible Business Data Science Decision Pack containing problem canvases, data-fitness checks, interpretation cards, impact maps, fairness reviews, explanation plans, accountability matrices, risk registers, monitoring measures, and escalation paths. It changes analytics review from informal judgment to documented decision practice. The pack supports traceable choices, stakeholder communication, and responsible follow-through.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 1-20 of 76 events

Location	Dates	Duration	Mode	Price
Trabzon	Week 45, 2026 8 – 19 November 2026	2 Weeks	Onsite	€10,000
Bali	Week 46, 2026 15 – 26 November 2026	2 Weeks	Onsite	€10,000
Porto	Week 47, 2026 16 – 27 November 2026	2 Weeks	Onsite	€10,000
Milan	Week 48, 2026 23 November – 4 December 2026	2 Weeks	Onsite	€10,000
Prague	Week 49, 2026 30 November – 11 December 2026	2 Weeks	Onsite	€10,000
Abu Dhabi	Week 49, 2026 30 November – 11 December 2026	2 Weeks	Onsite	€8,000
Phuket	Week 49, 2026 6 – 17 December 2026	2 Weeks	Onsite	€9,000
Montreux	Week 50, 2026 7 – 18 December 2026	2 Weeks	Onsite	€15,000
Lagos	Week 50, 2026 13 – 24 December 2026	2 Weeks	Onsite	€5,800
Tokyo	Week 51, 2026 14 – 25 December 2026	2 Weeks	Onsite	€15,000
London	Week 52, 2026 21 December 2026 – 1 January 2027	2 Weeks	Onsite	€10,000
Baku	Week 53, 2026 28 December 2026 – 8 January 2027	2 Weeks	Onsite	€8,800
Berlin	Week 53, 2026 28 December 2026 – 8 January 2027	2 Weeks	Onsite	€10,000
Paris	Week 01, 2027 4 – 15 January 2027	2 Weeks	Onsite	€10,000
London	Week 02, 2027 11 – 22 January 2027	2 Weeks	Onsite	€10,000
Cape town	Week 02, 2027 17 – 28 January 2027	2 Weeks	Onsite	€6,400
Amsterdam	Week 04, 2027 25 January – 5 February 2027	2 Weeks	Onsite	€10,000
Kuala Lumpur	Week 05, 2027 1 – 12 February 2027	2 Weeks	Onsite	€9,000
Manama	Week 05, 2027 7 – 18 February 2027	2 Weeks	Onsite	€8,000
Abu Dhabi	Week 06, 2027 8 – 19 February 2027	2 Weeks	Onsite	€8,000

Frequently asked questions

What does this course cover?

OverviewData Science and Data Ethics for Business Professionals Course is a two-week foundation-to-intermediate course for managers, analysts, data stewards, governance functions, and decision-makers who leave with a Responsible Business Data Science Decision Pack. Participants connect business problem framing and analytics workflows with model interpreta…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

Data Science and Data Ethics Business Course