Applied AI for Data Analysis, Automation and Decision-Making Amsterdam 01.Jun.2027 (103600632_82236)

Course Overview:

The Applied AI for Data Analysis, Automation and Decision-Making course is a practical corporate Artificial Intelligence Training Course designed to strengthen analytical, automation, systems-analysis, and decision-support capabilities. It is particularly relevant as AI for Computer Analysts, Artificial Intelligence for Computer Analysts, and an Applied AI Course for Computer Analysis Professionals, while remaining suitable for broader technical, data, and operational roles.

The course introduces machine-learning workflows, AI-powered data analysis, predictive analytics, generative AI, intelligent automation, human-centered systems, and responsible AI governance. Participants examine how data moves from collection and preparation through model development, evaluation, deployment, and continuous improvement. The programme also connects AI initiatives to measurable organizational outcomes through business requirements, KPIs, process improvement, and technology-driven decision-making.

The content reflects the AI and data analytics lifecycle described in the AIDA Guidebook, including machine-learning methodology, data management, KPIs, human-machine interaction, organizational change, privacy, security, and governance. It also incorporates the NIST AI Risk Management Framework approach to governing, mapping, measuring, and managing AI risks throughout the system lifecycle.

Through applied exercises and business cases, participants learn to identify viable AI opportunities, assess data readiness, redesign processes for automation, evaluate AI outputs, and recommend intelligent solutions that improve accuracy, efficiency, service quality, and managerial decision-making.

Target Audience:

• Computer Analysts and Systems Analysts
• Data Analysts and Business Intelligence Analysts
• Business Systems Analysts
• Application and Software Analysts
• Process Improvement Analysts
• Automation and Digital Transformation Specialists
• Information Systems Specialists
• Technology Project Coordinators
• Database and Reporting Specialists
• Technical Business Analysts
• Operations Analysts
• Professionals seeking Computer Analyst AI Training

Targeted Organizational Departments:

• Information Technology and Information Systems
• Data Analytics and Business Intelligence
• Digital Transformation and Innovation
• Business Process Management
• Strategy and Corporate Planning
• Operations and Service Delivery
• Enterprise Architecture
• Software Development and Applications
• Risk, Compliance and Information Governance

Targeted Industries:

• Banking, Financial Services and Insurance
• Government and Public Administration
• Telecommunications and Technology
• Healthcare and Life Sciences
• Manufacturing and Industrial Operations
• Energy, Utilities and Oil and Gas
• Transportation and Logistics
• Retail and E-Commerce
• Education and Research

Course Offerings:

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

• Explain the practical roles of artificial intelligence, machine learning, generative AI, predictive analytics, and intelligent automation.
• Translate business and systems requirements into viable AI and analytics use cases.
• Apply Data Analysis with Artificial Intelligence to identify trends, patterns, anomalies, and operational insights.
• Assess data availability, quality, relevance, privacy, and readiness for AI applications.
• Distinguish between supervised, unsupervised, predictive, and generative AI approaches.
• Design conceptual machine-learning pipelines covering data preparation, model training, validation, deployment, and monitoring.
• Use AI-Powered Data Analysis to improve reporting, forecasting, classification, and decision support.
• Identify processes suitable for Business Process Automation with AI.
• Prioritize automation opportunities using business value, feasibility, risk, and performance criteria.
• Design AI-Driven Decision Support Systems that augment rather than replace informed human judgment.
• Apply human-in-the-loop principles when designing Human-Centered AI Systems.

Training Methodology:

The course uses a practical, workplace-oriented learning methodology combining instructor-led explanation, guided analysis, case studies, group work, scenario exercises, system-design discussions, and feedback sessions. Each major concept is connected to realistic organizational challenges such as inefficient reporting, repetitive workflows, inconsistent data quality, delayed decisions, unstructured documents, prediction requirements, and the need for responsible AI controls.

Participants work through an end-to-end AI use-case methodology. They begin by defining a business problem, identifying users and stakeholders, establishing measurable goals, assessing data requirements, selecting an appropriate AI approach, and evaluating possible operational risks. The human-centered machine-learning material emphasizes stakeholder involvement, measurable success criteria, responsible data collection, iterative testing, and human review throughout the system lifecycle.

For intelligent automation exercises, participants distinguish processes, procedures, and tasks before selecting automation candidates. This reflects business-process automation guidance that recommends treating automation as a business-analysis and change-management initiative rather than only a technology implementation.

Group activities include process mapping, data-readiness reviews, use-case prioritization, prompt design, model-evaluation discussions, risk identification, KPI definition, and implementation planning. Participants receive structured instructor feedback on the feasibility, value, governance, and human impact of their proposed solutions. Demonstrations may reference relevant AI tools, but the course remains platform-neutral and focuses on transferable analytical and implementation skills.

Course Toolbox:

• AI use-case identification framework
• AI opportunity prioritization matrix
• Business problem definition template
• Data-readiness assessment checklist
• Data quality and suitability review guide
• Machine-learning lifecycle reference model
• Predictive analytics use-case canvas
• Generative AI use-case assessment template
• Prompt design and output-validation guide
• Process, procedure and task mapping template

The course may provide insights, demonstrations, examples, and comparisons of relevant AI tools. Software licenses, commercial platforms, automation tools, generative AI subscriptions, and technical products are not provided as part of the course.

Course Agenda:

Day 1: Applied AI Foundations and Business Use Cases

• Topic 1: Artificial Intelligence, Machine Learning, Analytics and Automation
• Topic 2: Practical AI Applications for Computer Analysts
• Topic 3: Translating Business Problems into AI Use Cases
• Topic 4: AI for Systems Analysis and Requirements Definition
• Topic 5: Evaluating AI Value, Feasibility and Organizational Readiness
• Topic 6: Building an AI Use-Case Prioritization Matrix
• Reflection & Review: Participants review the distinctions between AI, machine learning, analytics, generative AI and automation, then present one workplace problem suitable for further AI analysis.

Day 2: AI Data Analytics and Predictive Modeling

• Topic 1: Data Collection, Preparation and Quality for AI
• Topic 2: Data Analysis with Artificial Intelligence
• Topic 3: Exploratory Analysis, Patterns, Trends and Anomalies
• Topic 4: Supervised and Unsupervised Machine Learning
• Topic 5: Predictive Analytics for Forecasting and Classification
• Topic 6: Evaluating Models and Analytical Outputs
• Reflection & Review: Participants assess a sample dataset, identify data-quality risks and select an appropriate analytical or machine-learning approach for the stated business objective.

Day 3: Generative AI and Intelligent Decision Support

• Topic 1: Generative AI Capabilities, Limitations and Use Cases
• Topic 2: Prompt Design for Analytical and Systems Tasks
• Topic 3: AI Tools for Computer Analysts
• Topic 4: Designing AI-Driven Decision Support Systems
• Topic 5: Human-Centered AI Systems and Human-in-the-Loop Controls
• Topic 6: Validating AI Outputs, Recommendations and Explanations
• Reflection & Review: Participants compare AI-generated outputs, identify unsupported conclusions or risks, and define where human review must remain within the decision process.

Day 4: AI Automation and Process Transformation

• Topic 1: Business Process Automation with AI
• Topic 2: Distinguishing Processes, Procedures and Repetitive Tasks
• Topic 3: RPA, OCR, NLP, Machine Learning and Workflow Automation
• Topic 4: Selecting Processes for Intelligent Automation
• Topic 5: Designing Human–Automation Handoffs and Exception Paths
• Topic 6: Measuring Automation Benefits, Costs and Performance
• Reflection & Review: Participants map an existing business process, identify eligible automation steps and define measurable performance improvements such as cycle time, accuracy, exception rates and service quality.

Day 5: Responsible AI, Risk Management and Implementation

• Topic 1: Responsible AI and Data Governance
• Topic 2: Privacy, Security, Fairness and Algorithmic Transparency
• Topic 3: AI Risk Management using Govern, Map, Measure and Manage
• Topic 4: AI System Evaluation and Continuous Monitoring
• Topic 5: Building an Applied AI Implementation Roadmap
• Topic 6: Presenting the AI Business Case and Final Recommendations
• Reflection & Review: Participants present a complete AI or automation proposal covering the business problem, data requirements, proposed solution, expected value, human oversight, risks, KPIs and phased implementation plan.

FAQ:

What specific qualifications or prerequisites are needed for participants before enrolling in the course?

No advanced qualification in artificial intelligence, machine learning or programming is required. Participants should have a general understanding of business processes, information systems, data analysis, reporting, technology projects or operational decision-making. Familiarity with spreadsheets, databases, dashboards or process documentation is helpful but not mandatory. The course is suitable both for professionals beginning their AI learning journey and for experienced analysts seeking a structured Artificial Intelligence Training Course for Computer Analysts.

How long is each day's session, and is there a total number of hours required for the entire course?

Each day's session is generally structured to last around 4–5 hours, with breaks and interactive activities included. The total course duration spans five days, approximately 20–25 hours of instruction.

Does the course teach participants to build AI models, or to evaluate and apply them?

The course focuses primarily on applied analysis, use-case definition, data readiness, intelligent automation, model evaluation, decision support, risk management and implementation planning. Participants learn how machine-learning models and generative AI systems work at a practical level, but the programme is not designed as an advanced coding or data-science course.

This distinction is important because successful AI deployment requires more than technical model construction. It also requires clear objectives, suitable data, measurable success criteria, user involvement, privacy controls, governance, organizational readiness and continuous monitoring. The course therefore prepares participants to contribute effectively to multidisciplinary AI projects and to evaluate whether proposed solutions are useful, trustworthy and aligned with operational requirements.

How This Course is Different from Other Applied AI for Data Analysis, Automation and Decision-Making Courses:

This course differs from many introductory AI programmes because it does not focus only on tool demonstrations, general AI terminology or isolated prompt-writing exercises. It combines four connected capabilities: AI-powered data analysis, intelligent automation, decision-support design and responsible system implementation.

The programme is structured around the actual responsibilities of analysts and systems professionals. Participants learn how to define requirements, assess data, analyze workflows, identify automation candidates, interpret model outputs, evaluate risks and communicate recommendations. This makes it a Professional AI Training for Computer and Systems Analysts rather than a generic awareness programme.

Its human-centered approach also distinguishes it from purely technical Machine Learning and Automation for Computer Analysts courses. Participants examine how people provide data, feedback, validation and operational judgment across the AI lifecycle. The human-centered machine-learning reference emphasizes that users and stakeholders should be involved during conceptualization, implementation, evaluation and deployment.