Training course
Overview
AI-Powered Analytics is a
professional training course designed to equip professionals, managers,
analysts, researchers, and decision-makers with the knowledge and practical
skills required to combine artificial intelligence, machine learning,
automation, and modern analytics techniques for improved organisational
performance. The course introduces the complete AI-powered analytics lifecycle,
from defining business and analytical questions through data preparation,
AI-assisted exploration, predictive modelling, insight generation, decision
support, and responsible implementation. Participants learn how AI can augment
conventional analytics while understanding where human judgement, validation,
and governance remain essential.
The course provides hands-on
exposure to practical AI and analytics tools, including Microsoft Excel, AI
assistants, natural-language analytics, automated data preparation,
prompt-based analysis, dashboards, visualisation platforms, predictive
analytics workflows, and machine learning concepts. Participants explore how
generative AI can assist with data exploration, formula generation, analytical
documentation, visualisation design, report preparation, anomaly detection, and
insight summarisation. The programme also introduces practical frameworks and
standards-oriented approaches including CRISP-DM, responsible AI principles,
data governance, model lifecycle management, risk-based controls,
human-in-the-loop review, and reproducible analytical practices.
AI-powered analytics requires
careful evaluation of data quality, analytical assumptions, model outputs, and
AI-generated recommendations. Participants therefore learn to assess data
completeness, accuracy, consistency, relevance, bias, missing values, outliers,
and potential leakage while examining predictive performance, validation,
uncertainty, explainability, and model limitations. The course addresses
important risks associated with AI-assisted analytics, including hallucinated
information, unsupported conclusions, algorithmic bias, privacy concerns,
security risks, overfitting, automation bias, model drift, and inappropriate
use of sensitive data. Emphasis is placed on combining AI capabilities with
sound statistical reasoning, analytical validation, professional judgement, and
responsible governance.
Through practical exercises,
realistic datasets, case studies, AI-assisted analytical activities, group
challenges, workflow simulations, and an applied capstone, participants
progressively build an end-to-end AI-powered analytics capability. The
programme moves from foundational AI and analytics concepts to automated data
preparation, exploratory analysis, predictive techniques, generative AI
applications, advanced decision support, model evaluation, governance, and
implementation. By the end of the five-day programme, participants will be able
to design practical AI-assisted analytics workflows, critically evaluate
AI-generated outputs, improve analytical productivity, communicate
evidence-based insights, and establish responsible processes for using
artificial intelligence in organisational analytics and decision making.
Course
Duration
5 Days (40 Hours)
Target
Participants
This course is suitable for:
• Data analysts, business analysts, and reporting professionals
• Business intelligence and management information professionals
• Data scientists and analytics professionals
• Researchers and research assistants
• Monitoring, Evaluation, Research and Learning (MERL/MEL) professionals
• Managers and decision-makers using analytical evidence
• Finance, accounting, audit, risk, and performance professionals
• Marketing, sales, customer experience, and market research professionals
• Operations and service-delivery professionals
• Human resources and workforce analytics professionals
• Strategy, planning, transformation, and organisational development
professionals
• Programme and project professionals
• Supply chain, procurement, logistics, and resource planning professionals
• Policy, planning, development, NGO, government, and public-sector
professionals
• Consultants, advisers, and professional services practitioners
• Professionals responsible for dashboards, KPIs, reports, forecasts, and
analytical models
• Professionals exploring generative AI and machine learning for analytics
• Managers supervising analysts, researchers, data teams, or AI-enabled
analytical workflows
• Professionals responsible for data governance, analytics quality, or digital
transformation
• Academics and postgraduate researchers applying AI to quantitative or
organisational data
Course
Objectives
By the end of the training,
participants will be able to:
• Explain the foundations, capabilities, limitations, and organisational
applications of AI-powered analytics
• Distinguish between traditional analytics, machine learning, generative AI,
predictive analytics, and AI-assisted decision support
• Define business problems, analytical objectives, use cases, decision
questions, and measurable outcomes for AI-powered analytics
• Identify appropriate data sources and assess data quality, relevance,
completeness, consistency, accuracy, and timeliness
• Apply practical data preparation, cleaning, transformation, validation, and
feature-engineering techniques
• Use AI assistants and natural-language interfaces to support data
exploration, analysis, documentation, and reporting
• Develop effective prompts for analytical tasks while validating AI-generated
formulas, interpretations, code, and recommendations
• Use Excel, dashboards, visualisation tools, and AI-enabled analytical
workflows to generate actionable insights
• Apply descriptive analytics, exploratory data analysis, trend analysis,
anomaly detection, and pattern recognition
• Explain core machine learning concepts including supervised learning,
unsupervised learning, training, testing, features, targets, and model
evaluation
• Interpret predictive analytics outputs, performance metrics, probabilities,
forecasts, and uncertainty
• Recognise overfitting, data leakage, bias, model drift, unstable results, and
other common analytical and modelling risks
• Evaluate AI-generated analytical outputs for accuracy, relevance,
consistency, unsupported claims, and hallucinations
• Apply human-in-the-loop validation and professional judgement to AI-assisted analytics
• Apply responsible AI principles relating to fairness, transparency,
explainability, privacy, security, accountability, and governance
• Apply CRISP-DM and structured analytics lifecycle principles to AI-powered
analytical projects
• Develop practical AI-assisted dashboards, reports, analytical summaries, and
decision-support outputs
• Integrate AI-generated insights with statistical evidence, business context,
qualitative information, and domain expertise
• Assess model and AI-system limitations, assumptions, risks, and appropriate
use cases
• Communicate AI-powered analytical findings and limitations clearly to
technical and non-technical stakeholders
• Design monitoring, validation, governance, and continuous-improvement
processes for AI-enabled analytics
• Complete an applied capstone demonstrating an end-to-end AI-powered analytics
workflow
Course
Content
Day
1: Foundations of AI-Powered Analytics and Intelligent Data Workflows
Module 1: Foundations of
AI-Powered Analytics and Intelligent Data Workflows
1. Understanding
AI-Powered Analytics and Its Role in Modern Organisations
2. Artificial
Intelligence, Machine Learning, Generative AI, and Advanced Analytics
3. Traditional
Analytics Versus AI-Assisted and Automated Analytics
4. Identifying
Business Problems, Analytical Questions, and AI Analytics Use Cases
5. Data
Sources, Data Types, Metadata, Context, and Analytical Requirements
6. Data
Quality, Accuracy, Completeness, Consistency, Relevance, and Timeliness
7. CRISP-DM
and the AI-Powered Analytics Lifecycle
8. Human-in-the-Loop
Analytics and the Role of Professional Judgement
9. Case
Study: Identifying High-Value AI Analytics Opportunities in an Organisation
10. Practical
Exercise: Designing an AI-Powered Analytics Use Case and Workflow
Day
2: AI-Assisted Data Preparation, Exploration, and Visualisation
Module 2: AI-Assisted Data
Preparation, Exploration, and Visualisation
1. AI-Assisted
Data Collection, Structuring, Cleaning, and Validation
2. Handling
Missing Values, Duplicates, Outliers, Inconsistent Records, and Errors
3. AI-Assisted
Data Transformation and Feature Engineering
4. Using
AI Assistants for Excel Formulas, Data Tasks, and Analytical Workflows
5. Natural-Language
Data Exploration and Question-Based Analytics
6. AI-Assisted
Descriptive Statistics, Trend Analysis, and Pattern Detection
7. Automated
Anomaly Detection and Exception Identification
8. AI-Assisted
Charts, Dashboards, Data Visualisation, and Insight Summaries
9. Case
Study: Using AI to Explore and Diagnose a Real-World Operational Dataset
10. Practical
Exercise: Building an AI-Assisted Data Preparation and Visualisation Workflow
Day
3: Machine Learning, Predictive Analytics, and AI Evidence Evaluation
Module 3: Machine Learning,
Predictive Analytics, and AI Evidence Evaluation
1. Foundations
of Supervised and Unsupervised Machine Learning
2. Features,
Targets, Training Data, Testing Data, and Validation Concepts
3. Classification,
Regression, Clustering, and Practical Machine Learning Applications
4. Predictive
Analytics, Forecasting, Probability, and Risk Prediction
5. Model
Performance Metrics and Interpreting Predictive Results
6. Overfitting,
Underfitting, Data Leakage, Model Drift, and Generalisation
7. Bias,
Fairness, Sampling Problems, and Data Quality Risks in AI Models
8. Evaluating
AI-Generated Analysis, Interpretations, Code, and Recommendations
9. Case
Study: Assessing the Reliability of an AI-Powered Predictive Model
10. Practical
Exercise: Interpreting and Validating AI-Generated Analytical Results
Day
4: Advanced AI Analytics, Automation, and Responsible AI
Module 4: Advanced AI
Analytics, Automation, and Responsible AI
1. Generative
AI for Advanced Analytics, Research, and Decision Support
2. Prompt
Engineering for Data Analysis and Analytical Problem Solving
3. AI-Assisted
Analytical Automation and Reusable Workflow Design
4. Combining
AI, Statistical Analysis, Dashboards, and Predictive Models
5. AI-Assisted
Scenario Analysis, Sensitivity Analysis, and Decision Modelling
6. Explainability,
Interpretability, Model Assumptions, and Analytical Transparency
7. Responsible
AI, Fairness, Accountability, Privacy, and Data Governance
8. AI
Security, Confidential Data, Sensitive Information, and Risk Management
9. Case
Study: Managing AI Analytics Risk in a High-Impact Organisational Decision
10. Practical
Exercise: Designing a Responsible and Governed AI Analytics Workflow
Day
5: AI-Powered Decision Intelligence, Implementation, and Capstone
Module 5: AI-Powered
Decision Intelligence, Implementation, and Capstone
1. Integrating
AI Analytics, Statistical Evidence, Business Context, and Human Judgement
2. Designing
AI-Powered Dashboards, Reports, Decision Briefs, and Executive Insights
3. Communicating
AI-Generated Findings, Confidence, Uncertainty, and Limitations
4. Evaluating
AI Recommendations, Alternatives, Risks, and Potential Consequences
5. Designing
AI Analytics Implementation Plans, Roles, Controls, and Responsibilities
6. Establishing
Model Monitoring, Validation, Performance Review, and AI Governance
7. Managing
Model Drift, Changing Data, Analytical Errors, and Continuous Improvement
8. Developing
Organisational AI Analytics Policies, Standards, and Best Practices
9. Integrated
Case Study: End-to-End AI-Powered Analytics and Decision-Making Simulation
10. Capstone
Exercise: Develop, Validate, Communicate, and Govern an AI-Powered Analytics
Solution