Opleidingen
56.682
resultaten
Actualiteiten Privacyrecht
Zoom
di 27 okt. 2026
Cursusbeschrijving
Tijdens deze cursus worden de belangrijkste recente ontwikkelingen en actualiteiten binnen het privacyrecht besproken. De cursus biedt een overzicht van de meest relevante wetgeving en rechtspraak die van invloed zijn op de juridische praktijk.
De cursus is praktijkgericht opgezet en besteedt aandacht aan de betekenis van deze ontwikkelingen voor de dagelijkse juridische praktijk. Na afloop bent u weer volledig op de hoogte van de belangrijkste privacyrechtelijke actualiteiten en kunt u deze kennis direct toepassen in uw advisering en procesvoering.
Sprekers
Friederike van der Jagt is advocaat bij Hunter Legal te Amsterdam. Zij werkte eerder als privacyrechtadvocaat bij de advocatenkantoren Van Doorne en Stibbe en in het bedrijfsleven als Senior Legal Counsel Privacy bij een internationaal beveiligingssoftwarebedrijf. Friederike publiceert en spreekt geregeld over privacygerelateerde onderwerpen, is een van de redacteurs van het tijdschrift Jurisprudentie Bescherming Persoonsgegevens en auteur van de Kluwer Themapagina AVG. Verder is zij voorzitter van de Stichting Take Back Your Privacy, bestuurslid van de Nederlandse Juristen-Vereniging en plaatsvervangend lid van het Hof van Discipline. Daarnaast is zij hoofddocent van de Grotius specialisatieopleiding Privacyrecht en docent privacy bij de Stichting Beroepsopleiding Bedrijfsjuristen. Voorts is zij arbiter bij de Stichting Geschillenoplossing Automatisering en fellow bij het Onderzoekscentrum Onderneming & Recht van de Radboud Universiteit Nijmegen.
Certificaat en opleidingspunten
Na afloop van de cursus ontvangt u een certificaat van deelname (2 opleidingspunten / PO/PE).
Lesmateriaal
Als onderdeel van de cursus ontvangt u lesmateriaal (kort voorafgaand aan de cursus of direct na afloop van de cursus).
€155
Klassikaal
max 25
Nieuwe EU antiwitwaswetgeving in de praktijk
Zoom
vr 30 okt. 2026
Cursusbeschrijving
Tijdens deze cursus wordt ingegaan op de nieuwe Europese antiwitwaswetgeving die op 10 juli 2027 in werking treedt en die ingrijpende gevolgen heeft voor de praktijk. Diverse onderdelen van deze regelgeving treden al eerder in werking. Zo krijgt de Financial Intelligence Unit (FIU) per 1 juli 2026 de bevoegdheid om bij Wwft-instellingen transacties tijdelijk op te schorten.
De cursus biedt inzicht in de belangrijkste wijzigingen en laat zien hoe u zich hier tijdig en adequaat op kunt voorbereiden, zowel binnen uw eigen organisatie als richting cliënten. Daarbij wordt onder meer aandacht besteed aan de verlaging van de UBO-grens naar 25% en de daarmee samenhangende aanpassingen van het UBO-register.
De cursus heeft een uitgesproken praktische insteek en richt zich op de concrete gevolgen van de nieuwe wetgeving voor de dagelijkse praktijk. Er is ruime gelegenheid voor het stellen van vragen.
Sprekers
Birgit Snijder-Kuipers is adviseur en opleider bij Eye on AML B.V. Daarvoor was zij kandidaat-notaris bij De Brauw Blackstone Westbroek. Daarnaast is zij o.a. Hoogleraar Corporate Compliance & Anti-Money Laundering aan de Radboud Universiteit Nijmegen. Zij publiceert regelmatig over de WWFT.
Certificaat en opleidingspunten
Na afloop van de cursus ontvangt u een certificaat van deelname (2 opleidingspunten / PO/PE).
Lesmateriaal
Als onderdeel van de cursus ontvangt u lesmateriaal (kort voorafgaand aan de cursus of direct na afloop van de cursus).
€160
Klassikaal
max 25
AI+ Program Director – Practitioner™
Master AI Leadership with Practical Program Management
* AI Strategy Development: Learn to design and implement AI strategies that align with business goals, driving innovation and performance.
* Leading AI Projects: Gain skills in managing AI projects, ensuring timely execution, resource allocation, and effective collaboration.
* AI Program Integration: Understand how to integrate AI into business processes for seamless transitions and maximum value.
* Managing AI Teams: Lead cross-functional teams, fostering collaboration and driving continuous improvement in AI initiatives.
* Future-Proofing AI Programs: Stay ahead of AI trends and adapt strategies to ensure long-term competitiveness in the evolving landscape.
Module 1: Foundations of AI for Program Strategy – Introduction
* 1.1 Understanding of AI, ML, and Deep Learning
* 1.2 AI Lifecycle & Real-World Applications
* 1.3 Societal Impact of AI
* 1.4 Use Case: Triage System (AI for Emergency Services)
* 1.5 Case Study: Retail Recommendation System (Personalizing Customer Experience)
* 1.6 Hands-on: Use Teachable Machine to Build a Simple AI Classifier Module 2: Identifying AI Opportunities & Use Cases
* 2.1 Introduce AI Strategy Alignment Frameworks: AI Canvas, Value vs Feasibility Matrix
* 2.2 Signs That a Process May Benefit from AI: Repetitive Tasks, Data-Rich Environments, Personalization Needs
* 2.3 Prioritization Techniques: Weighted Scoring, Risk-Adjusted ROI
* 2.4 Use-Case: Financial AI – Fraud Detection Systems Using AI
* 2.5 Case Study: AI-Driven Project Management System for a Program Director
* 2.6 Hands-on: Use Trello to Create a Board and Prioritize AI Opportunities Within a Given Scenario Module 3: Governance & Ethics in AI
* 3.1 Responsible AI Principles
* 3.2 AI Bias & Risk Mitigation
* 3.3 Use-case: Auditing Bias in AI-Powered Recruitment to Ensure Fair Hiring
* 3.4 Case Study: Mitigating Algorithmic Bias in Credit Scoring Models to Ensure Fair Lending Practices
* 3.5 Hands-on: Use Google’s What-If Tool in Google Colab to Evaluate Model Fairness and Bias Module 4: AI Project Lifecycle & Integration
* 4.1 AI Project Planning & CRISP-DM
* 4.2 Integration: Build vs Buy vs Partner
* 4.3 AI Project Management Tools
* 4.4 Use Cases: AI for Predictive Maintenance (Asset Management in Manufacturing)
* 4.5 Tool-Based Hands-on Activity: Simulate an AI Project in Asana Module 5: Data Strategy & Infrastructure for AI
* 5.1 Data Governance & Quality
* 5.2 Setting up Data Pipelines for AI
* 5.3 Sensitive Data Management
* 5.4 Use Case: Retail Inventory System — AI-driven Restocking and Demand Prediction
* 5.5 Case Study: Healthcare Data Security — Managing Patient Privacy in AI-Based Healthcare Systems
* 5.6 Tool-Based Hands-on Activity: Set up Airbyte Cloud and Build a Basic Data Pipeline Module 6: AI Integration — Build vs Buy vs Partner
* 6.1 Evaluating AI Solutions
* 6.2 Vendor Evaluation & Management
* 6.3 Use Case: AI Vendor Selection — Choosing Predictive Maintenance Solutions for a Manufacturing Plant
* 6.4 Tool-Based Hands-on Activity: Use a Vendor Selection Template to Evaluate AI Vendors (Google Sheets) Module 7: AI Risk Management & Compliance
* 7.1 Regulatory Frameworks
* 7.2 Bias Detection & Mitigation
* 7.3 Use Case: Facial Recognition Bias (Law Enforcement Systems)
* 7.4 Case Study: AI in Finance: Ensuring Compliance in AI Deployments
* 7.5 Tool-Based Hands-on Activity: Bias Testing & Fairness Evaluation Using KNIME and Google PAIR Facets Fairness Explorer Module 8: AI Tools & Techniques for Project Management
* 8.1 AI Project Management Tools
* 8.2 Data Management Tools
* 8.3 Case Study and Use Case: AI Workflow Management: Using project management tools for AI deployment in the retail sector
* 8.4 Tool-Based Hands-on Activity: Use Asana to simulate project timelines, setting up tasks and milestones for an AI initiative Module 9: Leadership in AI
* 9.1 Leading AI Teams & Change Management
* 9.2 Managing Stakeholders & Communication
* 9.3 Use Case: AI in Manufacturing: Leading AI Implementation in a Large-Scale Manufacturing Operation
* 9.4 Tool-Based Hands-on Activity: Use Miro to Map Stakeholder Communication Strategies and Identify Key Influencers Module 10: Scaling AI Initiatives
* 10.1 From Pilot to Full-Scale Deployment
* 10.2 Organizational Maturity Models for AI
* 10.3 Use Case: Scaling AI in Retail: Expanding AI-driven Recommendations Globally
* 10.4 Tool-Based Hands-on Activity: Create a Scaling Roadmap Using Lucidchart Outlining Key steps in Scaling AI Initiatives. Module 11: Future Trends in AI
* 11.1 Emerging AI Technologies
* 11.2 Use Case / Case Study: AI in Autonomous Vehicles: The future of AI in self-driving cars
* 11.3 Tool-Based Hands-on Activity: Explore Hugging Face Transformers for NLP and TensorFlow for Deep Learning Applications Module 12: Capstone Project & Presentation
* 12.1 Capstone Project Overview
* 12.2 Presentation & Feedback
* 12.3 Final Review & Certification – Method, Process, and Feedback Mechanism Tools you will explore
* Microsoft Project
* JIRA
* Trello
* Asana
* Monday.com
* Basecamp
* Wrike
* ClickUp
* GitLab
* Confluence
* Smartsheet
* Slack
* Power BI
* Tableau
* Azure DevOps
* AWS CloudFormation
* Google Cloud AI Platform
* TIBCO Jaspersoft
* RapidMiner
* Minitab
* Balsamiq
* Miro
* Zoom
* Jenkins
* Salesforce
* Lucidchart
* ServiceNow
* Redmine
* Airtable
* Workfront
* Notion
* QlikView
* Klipfolio
* Hootsuite
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€3.450
Klassikaal
max 12
5 dagen
AI+ Program Director – Practitioner™ eLearning
Master AI Leadership with Practical Program Management
* AI Strategy Development: Learn to design and implement AI strategies that align with business goals, driving innovation and performance.
* Leading AI Projects: Gain skills in managing AI projects, ensuring timely execution, resource allocation, and effective collaboration.
* AI Program Integration: Understand how to integrate AI into business processes for seamless transitions and maximum value.
* Managing AI Teams: Lead cross-functional teams, fostering collaboration and driving continuous improvement in AI initiatives.
* Future-Proofing AI Programs: Stay ahead of AI trends and adapt strategies to ensure long-term competitiveness in the evolving landscape.
Module 1: Foundations of AI for Program Strategy – Introduction
* 1.1 Understanding of AI, ML, and Deep Learning
* 1.2 AI Lifecycle & Real-World Applications
* 1.3 Societal Impact of AI
* 1.4 Use Case: Triage System (AI for Emergency Services)
* 1.5 Case Study: Retail Recommendation System (Personalizing Customer Experience)
* 1.6 Hands-on: Use Teachable Machine to Build a Simple AI Classifier Module 2: Identifying AI Opportunities & Use Cases
* 2.1 Introduce AI Strategy Alignment Frameworks: AI Canvas, Value vs Feasibility Matrix
* 2.2 Signs That a Process May Benefit from AI: Repetitive Tasks, Data-Rich Environments, Personalization Needs
* 2.3 Prioritization Techniques: Weighted Scoring, Risk-Adjusted ROI
* 2.4 Use-Case: Financial AI – Fraud Detection Systems Using AI
* 2.5 Case Study: AI-Driven Project Management System for a Program Director
* 2.6 Hands-on: Use Trello to Create a Board and Prioritize AI Opportunities Within a Given Scenario Module 3: Governance & Ethics in AI
* 3.1 Responsible AI Principles
* 3.2 AI Bias & Risk Mitigation
* 3.3 Use-case: Auditing Bias in AI-Powered Recruitment to Ensure Fair Hiring
* 3.4 Case Study: Mitigating Algorithmic Bias in Credit Scoring Models to Ensure Fair Lending Practices
* 3.5 Hands-on: Use Google’s What-If Tool in Google Colab to Evaluate Model Fairness and Bias Module 4: AI Project Lifecycle & Integration
* 4.1 AI Project Planning & CRISP-DM
* 4.2 Integration: Build vs Buy vs Partner
* 4.3 AI Project Management Tools
* 4.4 Use Cases: AI for Predictive Maintenance (Asset Management in Manufacturing)
* 4.5 Tool-Based Hands-on Activity: Simulate an AI Project in Asana Module 5: Data Strategy & Infrastructure for AI
* 5.1 Data Governance & Quality
* 5.2 Setting up Data Pipelines for AI
* 5.3 Sensitive Data Management
* 5.4 Use Case: Retail Inventory System — AI-driven Restocking and Demand Prediction
* 5.5 Case Study: Healthcare Data Security — Managing Patient Privacy in AI-Based Healthcare Systems
* 5.6 Tool-Based Hands-on Activity: Set up Airbyte Cloud and Build a Basic Data Pipeline Module 6: AI Integration — Build vs Buy vs Partner
* 6.1 Evaluating AI Solutions
* 6.2 Vendor Evaluation & Management
* 6.3 Use Case: AI Vendor Selection — Choosing Predictive Maintenance Solutions for a Manufacturing Plant
* 6.4 Tool-Based Hands-on Activity: Use a Vendor Selection Template to Evaluate AI Vendors (Google Sheets) Module 7: AI Risk Management & Compliance
* 7.1 Regulatory Frameworks
* 7.2 Bias Detection & Mitigation
* 7.3 Use Case: Facial Recognition Bias (Law Enforcement Systems)
* 7.4 Case Study: AI in Finance: Ensuring Compliance in AI Deployments
* 7.5 Tool-Based Hands-on Activity: Bias Testing & Fairness Evaluation Using KNIME and Google PAIR Facets Fairness Explorer Module 8: AI Tools & Techniques for Project Management
* 8.1 AI Project Management Tools
* 8.2 Data Management Tools
* 8.3 Case Study and Use Case: AI Workflow Management: Using project management tools for AI deployment in the retail sector
* 8.4 Tool-Based Hands-on Activity: Use Asana to simulate project timelines, setting up tasks and milestones for an AI initiative Module 9: Leadership in AI
* 9.1 Leading AI Teams & Change Management
* 9.2 Managing Stakeholders & Communication
* 9.3 Use Case: AI in Manufacturing: Leading AI Implementation in a Large-Scale Manufacturing Operation
* 9.4 Tool-Based Hands-on Activity: Use Miro to Map Stakeholder Communication Strategies and Identify Key Influencers Module 10: Scaling AI Initiatives
* 10.1 From Pilot to Full-Scale Deployment
* 10.2 Organizational Maturity Models for AI
* 10.3 Use Case: Scaling AI in Retail: Expanding AI-driven Recommendations Globally
* 10.4 Tool-Based Hands-on Activity: Create a Scaling Roadmap Using Lucidchart Outlining Key steps in Scaling AI Initiatives. Module 11: Future Trends in AI
* 11.1 Emerging AI Technologies
* 11.2 Use Case / Case Study: AI in Autonomous Vehicles: The future of AI in self-driving cars
* 11.3 Tool-Based Hands-on Activity: Explore Hugging Face Transformers for NLP and TensorFlow for Deep Learning Applications Module 12: Capstone Project & Presentation
* 12.1 Capstone Project Overview
* 12.2 Presentation & Feedback
* 12.3 Final Review & Certification – Method, Process, and Feedback Mechanism Tools you will explore
* Microsoft Project
* JIRA
* Trello
* Asana
* Monday.com
* Basecamp
* Wrike
* ClickUp
* GitLab
* Confluence
* Smartsheet
* Slack
* Power BI
* Tableau
* Azure DevOps
* AWS CloudFormation
* Google Cloud AI Platform
* TIBCO Jaspersoft
* RapidMiner
* Minitab
* Balsamiq
* Miro
* Zoom
* Jenkins
* Salesforce
* Lucidchart
* ServiceNow
* Redmine
* Airtable
* Workfront
* Notion
* QlikView
* Klipfolio
* Hootsuite
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€510
E-Learning
max 999
5 dagen
AI+ Project Management Practitioner™
Nieuwegein
ma 1 feb. 2027
Build stronger project foundations by combining AI-assisted planning with practical decision support.
Discover how AI enhances project planning, scheduling, task prioritization, and progress tracking while reducing manual effort and improving consistency. Learn to use data-driven insights for timeline forecasting, workload balancing, capacity planning, and early risk detection. Understand how AI supports accurate documentation, change control, audit readiness, compliance, and continuous risk monitoring. Develop the leadership skills needed to manage AI-augmented projects through automated reporting, real-time insights, and stronger alignment between teams and stakeholders.
Module 1: Project Management Overview
* 1.1 Introduction to Project Management
* 1.2 Project Management Lifecycle
* 1.3 Advanced Project Management Tasks
* 1.4 Project Management Frameworks
* 1.5 Project Manager’s Roles and Responsibilities Module 2: Introduction to AI and ML
* 2.1 Introduction to Artificial Intelligence (AI)
* 2.2 Introduction to Machine Learning (ML)
* 2.3 Neural Networks
* 2.4 AI and ML Applications and Trends
* 2.5 Case Studies on AI and ML Projects Module 3: Data Driven Decision Making
* 3.1 The Importance of Data in Artificial Intelligence
* 3.2 Data Analysis Techniques
* 3.4 Applying Data Insights to Project Decisions
* 3.5 Tools for Data Visualization and Reporting
* 3.6 Challenges and Best Practices Module 4: AI-Driven Project Risk Management
* 4.1 AI in Risk Management – An Introduction
* 4.2 AI for Risk Mitigation and Response
* 4.3 AI for Financial and Resource Risk Management
* 4.4 AI in Risk Management: The Future Scope
* 4.5 Case Study – AI-based Project Risk Management Module 5: Planning Project Work Breakdown and Structuring and Project Scheduling by AI
* 5.1 Introduction to Work Breakdown Structure (WBS)
* 5.2 AI for WBS Creation
* 5.3 AI in Project Scheduling
* 5.4 AI for Resource-Constrained Scheduling
* 5.5 Case Studies: AI-based WBS and AI Algorithms for Project Scheduling Module 6: Effective Project Budgeting Using AI
* 6.1 Introduction to AI in Budgeting
* 6.2 AI for Estimating Costs and Budget Allocation
* 6.3 AI for Budget Optimization
* 6.4 Future of AI in Project Budgeting
* 6.5 Case Study: AI Algorithms for Project Scheduling, AI- Based Model for Estimating Costs and Budget Allocation Module 7: AI for Planning Human Resources
* 7.1 Introduction to AI in Human Resource Planning
* 7.2 AI for Workforce Allocation
* 7.3 AI in Skill Matching and Employee Performance Analysis
* 7.4 The Future of AI in Human Resource Planning
* 7.5 Case Studies: Designing AI-Based Models for HR Planning Module 8: Stakeholder Management Using AI
* 8.1 Introduction to Stakeholder Management and AI
* 8.2 Identifying and Categorizing Stakeholders Using AI
* 8.3 Stakeholder Conflicts Management with AI
* 8.4 Ethics and Future Prospects in AI-based Stakeholder Management
* 8.5 Case Studies: AI Tools for Stakeholder Management Module 9: AI-based Project Monitoring
* 9.1 Introduction to Project Monitoring and AI
* 9.2 AI-based Tools for Monitoring Project Progress
* 9.3 AI for Risk Monitoring
* 9.4 Case Studies: AI Tools for Project Monitoring Module 10: Transformative Role of Project Management
* 10.1 Current State of AI in Project Management
* 10.2 Ethical Considerations in AI-Based Project Management
* 10.3 Technical Challenges in AI Integration Additional Module: AI Agents for Project Management Practitioner
* 1. Understanding AI Agents
* 2. How Does an AI Agent Work
* 3. Applications and Trends of AI Agents in Project Management
* 4. Core Characteristics of AI Agents
* 5. Significance of AI Agents in Project Management
* 6. Types of AI Agents
* 7. Case Study-AI Agents for Agile Project Delivery – Atlassian in Action
* 8. Hands-On Activity Tools you will explore
* Python for Project Analytics
* Machine Learning Libraries for Project Insights (Scikit-learn, TensorFlow)
* Project Data Handling Tools (Pandas, NumPy)
* Visualization Platforms for Project Dashboards (Power BI, Tableau)
* Project Data Storage using SQL & NoSQL Databases
* APIs for Project and Workflow Integration
* Cloud Platforms for AI-Enabled Project Management (AWS & Azure Services)
* OpenAI & LangChain for AI-Assisted Project Tools
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€3.450
Klassikaal
max 12
5 dagen
AI+ Project Management Practitioner™ eLearning
Build stronger project foundations by combining AI-assisted planning with practical decision support.
Discover how AI enhances project planning, scheduling, task prioritization, and progress tracking while reducing manual effort and improving consistency. Learn to use data-driven insights for timeline forecasting, workload balancing, capacity planning, and early risk detection. Understand how AI supports accurate documentation, change control, audit readiness, compliance, and continuous risk monitoring. Develop the leadership skills needed to manage AI-augmented projects through automated reporting, real-time insights, and stronger alignment between teams and stakeholders.
Module 1: Project Management Overview
* 1.1 Introduction to Project Management
* 1.2 Project Management Lifecycle
* 1.3 Advanced Project Management Tasks
* 1.4 Project Management Frameworks
* 1.5 Project Manager’s Roles and Responsibilities Module 2: Introduction to AI and ML
* 2.1 Introduction to Artificial Intelligence (AI)
* 2.2 Introduction to Machine Learning (ML)
* 2.3 Neural Networks
* 2.4 AI and ML Applications and Trends
* 2.5 Case Studies on AI and ML Projects Module 3: Data Driven Decision Making
* 3.1 The Importance of Data in Artificial Intelligence
* 3.2 Data Analysis Techniques
* 3.4 Applying Data Insights to Project Decisions
* 3.5 Tools for Data Visualization and Reporting
* 3.6 Challenges and Best Practices Module 4: AI-Driven Project Risk Management
* 4.1 AI in Risk Management – An Introduction
* 4.2 AI for Risk Mitigation and Response
* 4.3 AI for Financial and Resource Risk Management
* 4.4 AI in Risk Management: The Future Scope
* 4.5 Case Study – AI-based Project Risk Management Module 5: Planning Project Work Breakdown and Structuring and Project Scheduling by AI
* 5.1 Introduction to Work Breakdown Structure (WBS)
* 5.2 AI for WBS Creation
* 5.3 AI in Project Scheduling
* 5.4 AI for Resource-Constrained Scheduling
* 5.5 Case Studies: AI-based WBS and AI Algorithms for Project Scheduling Module 6: Effective Project Budgeting Using AI
* 6.1 Introduction to AI in Budgeting
* 6.2 AI for Estimating Costs and Budget Allocation
* 6.3 AI for Budget Optimization
* 6.4 Future of AI in Project Budgeting
* 6.5 Case Study: AI Algorithms for Project Scheduling, AI- Based Model for Estimating Costs and Budget Allocation Module 7: AI for Planning Human Resources
* 7.1 Introduction to AI in Human Resource Planning
* 7.2 AI for Workforce Allocation
* 7.3 AI in Skill Matching and Employee Performance Analysis
* 7.4 The Future of AI in Human Resource Planning
* 7.5 Case Studies: Designing AI-Based Models for HR Planning Module 8: Stakeholder Management Using AI
* 8.1 Introduction to Stakeholder Management and AI
* 8.2 Identifying and Categorizing Stakeholders Using AI
* 8.3 Stakeholder Conflicts Management with AI
* 8.4 Ethics and Future Prospects in AI-based Stakeholder Management
* 8.5 Case Studies: AI Tools for Stakeholder Management Module 9: AI-based Project Monitoring
* 9.1 Introduction to Project Monitoring and AI
* 9.2 AI-based Tools for Monitoring Project Progress
* 9.3 AI for Risk Monitoring
* 9.4 Case Studies: AI Tools for Project Monitoring Module 10: Transformative Role of Project Management
* 10.1 Current State of AI in Project Management
* 10.2 Ethical Considerations in AI-Based Project Management
* 10.3 Technical Challenges in AI Integration Additional Module: AI Agents for Project Management Practitioner
* 1. Understanding AI Agents
* 2. How Does an AI Agent Work
* 3. Applications and Trends of AI Agents in Project Management
* 4. Core Characteristics of AI Agents
* 5. Significance of AI Agents in Project Management
* 6. Types of AI Agents
* 7. Case Study-AI Agents for Agile Project Delivery – Atlassian in Action
* 8. Hands-On Activity Tools you will explore
* Python for Project Analytics
* Machine Learning Libraries for Project Insights (Scikit-learn, TensorFlow)
* Project Data Handling Tools (Pandas, NumPy)
* Visualization Platforms for Project Dashboards (Power BI, Tableau)
* Project Data Storage using SQL & NoSQL Databases
* APIs for Project and Workflow Integration
* Cloud Platforms for AI-Enabled Project Management (AWS & Azure Services)
* OpenAI & LangChain for AI-Assisted Project Tools
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€510
E-Learning
max 999
5 dagen
AI+ Healthcare Administrator Practitioner™
Nieuwegein
vr 6 nov. 2026
en 1 andere data
Transform Healthcare Management with Intelligent Administration
* Operational Excellence: Learn how AI streamlines billing, scheduling, claims, and staff workflows to relieve administrative burden.
* Resource Optimization: Master predictive analytics, demand forecasting, and data-driven decision-making for efficient hospital operations.
* Compliance & Privacy: Understand regulatory frameworks, data security, and ethical standards essential for AI-powered administration.
* Leadership Impact: Equip yourself to lead organizational change — from digital record-keeping to optimized patient flow and cost control.
Module 1: Fundamentals of AI for Healthcare Administrators
* 1.1 Understanding of AI
* 1.2 AI in Healthcare Operations
* 1.3 Case Study
* 1.4 Hands-On: No-Code AI-Based Chest X-ray Classification for COVID-19 and Lung Conditions Using Google Teachable Machine Module 2: Data Literacy for Healthcare Admins
* 2.1 Understanding Healthcare Data Types
* 2.2 Using Data for Decisions
* 2.3 Case Study 1: Apollo Hospital’s AI-Based Discharge Management System
* 2.4 Case Study 2: Cleveland Clinic’s AI Integration for Medical Billing Optimization
* 2.5 Hands-On: No-Code Exploration of a Hospital Analytics Dashboard Using Vizly.ai Module 3: AI in Operations Optimization
* 3.1 Streamlining Patient Flow and Resource Optimization
* 3.2 Inventory, Maintenance, and Procurement
* 3.3 Case Study 1: AI-Powered Emergency Interhospital Transfers
* 3.4 Case Study 2: AI for Inventory Waste Reduction in Hospital Supply Chains (Mayo Clinic, Cleveland Clinic & Rush University)
* 3.5 Hands-On: AI-Driven Hospital Operations Optimization: A No-Code Predictive Interface Using Julius AI Module 4: NLP and Generative AI in Admin Work
* 4.1 Foundations of NLP and Chatbots
* 4.2 Writing and Communication Tasks with Generative AI
* 4.3 Case Study: Alleviating Physician Burnout via Clinical Documentation Assistance
* 4.4 Hands-On: Meeting Summarization Assistant for Healthcare Admin Module 5: AI in Billing, Coding & Claims
* 5.1 AI in Medical Coding and Documentation
* 5.2 Claims Management and Fraud Detection
* 5.3 Hands-On: No-Code AI-Powered Medical Claims Denial Prediction Module 6: Ethics, Bias & Regulation in Admin AI
* 6.1 Identifying Bias in Administrative AI Tools
* 6.2 Legal & Compliance Considerations
* 6.3 Case Study: AI Triage Failure and Legal Exposure at North Bridge Hospital
* 6.4: Hands-On: Analyzing Hospital Admission Bias with Claude AI Module 7: Evaluating and Procuring AI Tools
* 7.1 Assessing AI Tools for Quality and Relevance
* 7.2 Implementation Planning and Procurement
* 7.3 Case Study 1: AI-Powered Cancer Detection at Tata Memorial Hospital
* 7.4 Case Study 2: AI-Powered Eye Screening by Forus Health and Microsoft
* 7.5 Hands-On: Healthcare Data Visualization with No-Code BI Tools Module 8: Telehealth, Virtual Care, and Cybersecurity in the Age of AI
* 8.1 Understanding Cyber Threats in AI-Driven Healthcare
* 8.2 Building a Secure AI-Operations Environment
* 8.3 Case Study 1: WannaCry Attack on NHS (2017)
* 8.4 Case Study 2: Universal Health Services Ransomware Attack (2020)
* 8.5 Hands-On: AI Cybersecurity Risk Dashboard Implementation Using Google Looker Studio Module 9: Becoming an AI Champion in Admin Settings
* 9.1 Introduction: Why This Module Matters Now
* 9.2 Leading Small-Scale AI Pilots
* 9.3 Identifying Pilot Opportunities in Departments
* 9.4 Stakeholder Alignment: IT, Compliance, Frontline Staff
* 9.5 Building Organizational Readiness
* 9.6 Step-by-Step Guide: No-Code AI for Medical Claim Denial Prediction Using Relevance AI Tools you will explore
* TensorFlow
* Keras
* Apache Spark
* Hadoop
* Power BI
* Python
* Tableau
* Matplotlib
* SQL
* Electronic Health Record (EHR) Management Tools
* Healthcare Workflow Automation Platforms
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€895
Klassikaal
max 12
1 dag
AI+ Healthcare Administrator Practitioner™ eLearning
Transform Healthcare Management with Intelligent Administration
* Operational Excellence: Learn how AI streamlines billing, scheduling, claims, and staff workflows to relieve administrative burden.
* Resource Optimization: Master predictive analytics, demand forecasting, and data-driven decision-making for efficient hospital operations.
* Compliance & Privacy: Understand regulatory frameworks, data security, and ethical standards essential for AI-powered administration.
* Leadership Impact: Equip yourself to lead organizational change — from digital record-keeping to optimized patient flow and cost control.
Module 1: Fundamentals of AI for Healthcare Administrators
* 1.1 Understanding of AI
* 1.2 AI in Healthcare Operations
* 1.3 Case Study
* 1.4 Hands-On: No-Code AI-Based Chest X-ray Classification for COVID-19 and Lung Conditions Using Google Teachable Machine Module 2: Data Literacy for Healthcare Admins
* 2.1 Understanding Healthcare Data Types
* 2.2 Using Data for Decisions
* 2.3 Case Study 1: Apollo Hospital’s AI-Based Discharge Management System
* 2.4 Case Study 2: Cleveland Clinic’s AI Integration for Medical Billing Optimization
* 2.5 Hands-On: No-Code Exploration of a Hospital Analytics Dashboard Using Vizly.ai Module 3: AI in Operations Optimization
* 3.1 Streamlining Patient Flow and Resource Optimization
* 3.2 Inventory, Maintenance, and Procurement
* 3.3 Case Study 1: AI-Powered Emergency Interhospital Transfers
* 3.4 Case Study 2: AI for Inventory Waste Reduction in Hospital Supply Chains (Mayo Clinic, Cleveland Clinic & Rush University)
* 3.5 Hands-On: AI-Driven Hospital Operations Optimization: A No-Code Predictive Interface Using Julius AI Module 4: NLP and Generative AI in Admin Work
* 4.1 Foundations of NLP and Chatbots
* 4.2 Writing and Communication Tasks with Generative AI
* 4.3 Case Study: Alleviating Physician Burnout via Clinical Documentation Assistance
* 4.4 Hands-On: Meeting Summarization Assistant for Healthcare Admin Module 5: AI in Billing, Coding & Claims
* 5.1 AI in Medical Coding and Documentation
* 5.2 Claims Management and Fraud Detection
* 5.3 Hands-On: No-Code AI-Powered Medical Claims Denial Prediction Module 6: Ethics, Bias & Regulation in Admin AI
* 6.1 Identifying Bias in Administrative AI Tools
* 6.2 Legal & Compliance Considerations
* 6.3 Case Study: AI Triage Failure and Legal Exposure at North Bridge Hospital
* 6.4: Hands-On: Analyzing Hospital Admission Bias with Claude AI Module 7: Evaluating and Procuring AI Tools
* 7.1 Assessing AI Tools for Quality and Relevance
* 7.2 Implementation Planning and Procurement
* 7.3 Case Study 1: AI-Powered Cancer Detection at Tata Memorial Hospital
* 7.4 Case Study 2: AI-Powered Eye Screening by Forus Health and Microsoft
* 7.5 Hands-On: Healthcare Data Visualization with No-Code BI Tools Module 8: Telehealth, Virtual Care, and Cybersecurity in the Age of AI
* 8.1 Understanding Cyber Threats in AI-Driven Healthcare
* 8.2 Building a Secure AI-Operations Environment
* 8.3 Case Study 1: WannaCry Attack on NHS (2017)
* 8.4 Case Study 2: Universal Health Services Ransomware Attack (2020)
* 8.5 Hands-On: AI Cybersecurity Risk Dashboard Implementation Using Google Looker Studio Module 9: Becoming an AI Champion in Admin Settings
* 9.1 Introduction: Why This Module Matters Now
* 9.2 Leading Small-Scale AI Pilots
* 9.3 Identifying Pilot Opportunities in Departments
* 9.4 Stakeholder Alignment: IT, Compliance, Frontline Staff
* 9.5 Building Organizational Readiness
* 9.6 Step-by-Step Guide: No-Code AI for Medical Claim Denial Prediction Using Relevance AI Tools you will explore
* TensorFlow
* Keras
* Apache Spark
* Hadoop
* Power BI
* Python
* Tableau
* Matplotlib
* SQL
* Electronic Health Record (EHR) Management Tools
* Healthcare Workflow Automation Platforms
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€200
E-Learning
max 999
1 dag
AI+ Medical Assistant Practitioner™
Nieuwegein
do 3 sep. 2026
en 1 andere data
Formerly known as AI+ Medical Assistant™
Revolutionize Healthcare Support with AI-Powered Medical Assistance
* Patient Interaction Excellence: Learn how AI enhances patient communication, appointment scheduling, and follow-up care to improve the patient experience.
* Clinical Workflow Efficiency: Master AI tools for streamlining patient intake, medical record management, and lab result analysis to optimize clinical operations.
* Data-Driven Decision Support: Gain expertise in using AI to assist healthcare providers with accurate diagnostics, treatment suggestions, and patient monitoring.
* Enhanced Medical Administration: Prepare to support healthcare teams with AI-driven administrative tasks, reducing errors, improving accuracy, and enabling faster decision-making.
Module 1: Fundamentals of AI for Medical Assistants
* 1.1 Understanding AI and Its Healthcare Applications
* 1.2 The Role of AI in Medical Assistance
* 1.3 Case Studies
* 1.4 Hands-on Session: Functionality Survey and Stepwise Analysis of the Eka.care Patient-Side Application Module 2: Data Literacy for Medical Assistants
* 2.1 Healthcare Data Types and Management
* 2.2 Using Data Effectively in AI
* 2.3 Case Studies
* 2.4 Hands-On Session: Structured vs. Unstructured Data in Healthcare: A Practical Study Using Eka.Care Patient Health Record System Module 3: AI in Patient Care Optimization
* 3.1 Enhancing Patient Interactions with AI
* 3.2 Predictive Analytics and Workflow Management
* 3.3 Case Studies
* 3.4 Hands-On Session: Eka.care in Action: Appointment Management, Smart Reminders & Tele-Consult Dashboards Module 4: NLP and Generative AI in Medical Documentation
* 4.1 Foundations of NLP for Medical Assistants
* 4.2 Practical Applications and Risks
* 4.3 Case Studies
* 4.4 Hands-On Simulation Exercise
* 4.5 Hands-On Session: Automating Clinical Documentation Using Eka.care: Notes, Summaries, and Communication Workflows Module 5: AI in Diagnostics and Screening
* 5.1 Diagnostic Support Tools
* 5.2 Real-World Applications and Simulation
* 5.3 Use Cases
* 5.4 Hands-On: AI-Powered Detection of Common Health Conditions: Review and Analysis of AI-Suggested Diagnostic Insights using Eka Care Module 6: Ethics, Bias, and Regulation in AI for Healthcare
* 6.1 Recognizing and Addressing Bias in AI
* 6.2 Legal, Ethical, and Compliance Frameworks
* 6.3 Hands-On Exercise: Analyzing and Visualizing Bias in Artificial Intelligence Systems — Exploring Racial, Socioeconomic, and Demographic Disparities using Google’s What-If Tool Module 7: Evaluating and Implementing AI Tools
* 7.1 Selecting and Planning for AI Adoption
* 7.2 Best Practices and Stakeholder Engagement
* 7.3 Case Study: Procurement and Early Deployment of AI Tools for Chest Diagnostics in a National Health Service Setting
* 7.4 Hands-On Simulation Exercise: Recognizing Red Flags in Vendor Solutions for AI in Medical Assistant
* 7.5 Hands-On Exercises: Evaluating the Relevance and Effectiveness of AI Models using the Zoho Analytics Module 8: Cybersecurity and Emerging Trends in AI
* 8.1 Cybersecurity Risks and Protection
* 8.2 Future Trends and Preparing for Innovation
* 8.3 Case Studies: EY’s Strategic Transformation: Adapting to Emerging AI Technologies
* 8.4 Hands-On Exercises: Common Cybersecurity Threats in AI-Enabled Healthcare: A Hands-On Exploration Using Google Sheets Tools you will explore
* TensorFlow
* Keras
* Python
* Natural Language Processing (NLP) Tools
* SQL
* Matplotlib
* Power BI
* Healthcare Data Integration Tools
* Electronic Health Record (EHR) Systems
* Patient Scheduling and Coordination Platforms
* AI-Powered Diagnostic Tools
* Medical Imaging Analysis Tools
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€895
Klassikaal
max 12
1 dag
AI+ Medical Assistant Practitioner™ eLearning
Formerly known as AI+ Medical Assistant™
Revolutionize Healthcare Support with AI-Powered Medical Assistance
* Patient Interaction Excellence: Learn how AI enhances patient communication, appointment scheduling, and follow-up care to improve the patient experience.
* Clinical Workflow Efficiency: Master AI tools for streamlining patient intake, medical record management, and lab result analysis to optimize clinical operations.
* Data-Driven Decision Support: Gain expertise in using AI to assist healthcare providers with accurate diagnostics, treatment suggestions, and patient monitoring.
* Enhanced Medical Administration: Prepare to support healthcare teams with AI-driven administrative tasks, reducing errors, improving accuracy, and enabling faster decision-making.
Module 1: Fundamentals of AI for Medical Assistants
* 1.1 Understanding AI and Its Healthcare Applications
* 1.2 The Role of AI in Medical Assistance
* 1.3 Case Studies
* 1.4 Hands-on Session: Functionality Survey and Stepwise Analysis of the Eka.care Patient-Side Application Module 2: Data Literacy for Medical Assistants
* 2.1 Healthcare Data Types and Management
* 2.2 Using Data Effectively in AI
* 2.3 Case Studies
* 2.4 Hands-On Session: Structured vs. Unstructured Data in Healthcare: A Practical Study Using Eka.Care Patient Health Record System Module 3: AI in Patient Care Optimization
* 3.1 Enhancing Patient Interactions with AI
* 3.2 Predictive Analytics and Workflow Management
* 3.3 Case Studies
* 3.4 Hands-On Session: Eka.care in Action: Appointment Management, Smart Reminders & Tele-Consult Dashboards Module 4: NLP and Generative AI in Medical Documentation
* 4.1 Foundations of NLP for Medical Assistants
* 4.2 Practical Applications and Risks
* 4.3 Case Studies
* 4.4 Hands-On Simulation Exercise
* 4.5 Hands-On Session: Automating Clinical Documentation Using Eka.care: Notes, Summaries, and Communication Workflows Module 5: AI in Diagnostics and Screening
* 5.1 Diagnostic Support Tools
* 5.2 Real-World Applications and Simulation
* 5.3 Use Cases
* 5.4 Hands-On: AI-Powered Detection of Common Health Conditions: Review and Analysis of AI-Suggested Diagnostic Insights using Eka Care Module 6: Ethics, Bias, and Regulation in AI for Healthcare
* 6.1 Recognizing and Addressing Bias in AI
* 6.2 Legal, Ethical, and Compliance Frameworks
* 6.3 Hands-On Exercise: Analyzing and Visualizing Bias in Artificial Intelligence Systems — Exploring Racial, Socioeconomic, and Demographic Disparities using Google’s What-If Tool Module 7: Evaluating and Implementing AI Tools
* 7.1 Selecting and Planning for AI Adoption
* 7.2 Best Practices and Stakeholder Engagement
* 7.3 Case Study: Procurement and Early Deployment of AI Tools for Chest Diagnostics in a National Health Service Setting
* 7.4 Hands-On Simulation Exercise: Recognizing Red Flags in Vendor Solutions for AI in Medical Assistant
* 7.5 Hands-On Exercises: Evaluating the Relevance and Effectiveness of AI Models using the Zoho Analytics Module 8: Cybersecurity and Emerging Trends in AI
* 8.1 Cybersecurity Risks and Protection
* 8.2 Future Trends and Preparing for Innovation
* 8.3 Case Studies: EY’s Strategic Transformation: Adapting to Emerging AI Technologies
* 8.4 Hands-On Exercises: Common Cybersecurity Threats in AI-Enabled Healthcare: A Hands-On Exploration Using Google Sheets Tools you will explore
* TensorFlow
* Keras
* Python
* Natural Language Processing (NLP) Tools
* SQL
* Matplotlib
* Power BI
* Healthcare Data Integration Tools
* Electronic Health Record (EHR) Systems
* Patient Scheduling and Coordination Platforms
* AI-Powered Diagnostic Tools
* Medical Imaging Analysis Tools
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€200
E-Learning
max 999
1 dag