Training course

Overview

Practical AI-Powered Analytics is a hands-on professional training course designed to help participants apply artificial intelligence, generative AI, automation, and modern analytics techniques to real-world data and workplace problems. The course focuses on practical end-to-end workflows that professionals can use to transform raw data into validated insights, visualisations, reports, predictions, and decision-support outputs. Participants work progressively through realistic analytical tasks while learning how to combine AI capabilities with Microsoft Excel, structured analytical methods, professional judgement, and responsible data practices.

The programme provides practical experience with tools and techniques including Excel formulas, sorting and filtering, pivot tables, charts, dashboards, AI assistants, natural-language analytics, AI-assisted data cleaning, automated summaries, prompt engineering, anomaly detection, scenario analysis, and AI-supported reporting. Participants learn how to apply practical frameworks such as CRISP-DM, PDCA, SMART objectives, Results-Based Management, Balanced Scorecard principles, root-cause analysis, evidence-to-action, and risk-based decision-making. Exercises and workplace scenarios demonstrate how these tools can be integrated into repeatable analytical workflows rather than used as isolated AI experiments.

The course places strong emphasis on validating AI-generated analysis before it is used for professional purposes. Participants learn practical methods for checking data quality, identifying missing values and outliers, validating calculations and formulas, reviewing assumptions, interpreting descriptive statistics, examining trends and relationships, and assessing uncertainty. The programme introduces correlation, regression, probability, confidence intervals, forecasting concepts, predictive analytics, and anomaly detection while highlighting the difference between correlation and causation. Participants also learn to recognise hallucinations, unsupported claims, misleading visualisations, bias, data leakage, overfitting, automation bias, privacy concerns, and other risks associated with AI-assisted analytics.

Through guided exercises, practical datasets, case studies, workplace simulations, group activities, and an applied capstone, participants build a complete practical AI-powered analytics workflow. The course progresses from foundational concepts and data preparation to AI-assisted exploration, visualisation, statistical interpretation, predictive analytics, automation, responsible AI, decision support, and implementation. By the end of the five-day programme, participants will be able to apply AI tools to practical analytical tasks, validate and improve AI-generated outputs, produce professional analytical deliverables, communicate evidence clearly, and establish responsible AI-assisted analytics processes that can be applied directly in real-world work environments.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:
• Professionals working with operational, business, financial, research, or performance data
• Data analysts and reporting professionals
• Business intelligence and management information 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 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
• Supervisors and team leaders responsible for operational information
• Professionals responsible for KPIs, dashboards, reports, forecasts, and analytical outputs
• Professionals seeking practical applications of generative AI for analytics
• Professionals involved in data preparation, reporting, performance analysis, and decision support
• Academics and postgraduate researchers working with applied data
• Professionals seeking hands-on AI-powered analytics skills without requiring advanced programming expertise

Course Objectives

By the end of the training, participants will be able to:
• Explain the practical foundations, applications, capabilities, and limitations of AI-powered analytics
• Distinguish between traditional analytics, AI-assisted analytics, machine learning, predictive analytics, and generative AI
• Identify practical workplace problems that can be addressed through AI-powered analytical workflows
• Define analytical questions, objectives, expected outputs, decision requirements, and success criteria
• Identify, import, structure, clean, and validate datasets for AI-assisted analysis
• Assess data accuracy, completeness, consistency, relevance, timeliness, provenance, and context
• Use Microsoft Excel and AI assistants for sorting, filtering, formulas, calculations, pivot tables, and data transformation
• Develop effective prompts for data analysis and validate AI-generated formulas, calculations, summaries, and interpretations
• Apply descriptive statistics, comparisons, trends, variance analysis, ratios, and anomaly detection
• Create practical charts, dashboards, summary tables, reports, and data stories using AI-assisted workflows
• Apply correlation, regression, probability, confidence intervals, and uncertainty concepts to practical analytical problems
• Explain basic machine learning concepts including classification, regression, clustering, prediction, training, testing, and validation
• Interpret predictive analytics outputs, forecasts, probabilities, and model performance measures
• Identify missing data, outliers, bias, measurement errors, data leakage, overfitting, and other analytical risks
• Evaluate AI-generated insights for accuracy, relevance, consistency, unsupported claims, hallucinations, and practical usefulness
• Distinguish correlation from causation and recognise when additional evidence is required
• Apply root-cause analysis, Five Whys, Fishbone, Pareto, PDCA, SMART, and evidence-to-action techniques
• Use AI to support scenario analysis, sensitivity analysis, what-if analysis, and practical decision support
• Automate appropriate repetitive data preparation, analysis, reporting, and documentation activities using AI-assisted workflows
• Apply responsible AI principles relating to privacy, confidentiality, security, fairness, transparency, accountability, and human oversight
• Communicate analytical findings, assumptions, limitations, uncertainty, and recommendations clearly to professional audiences
• Establish practical validation, monitoring, documentation, quality assurance, and continuous-improvement processes
• Complete an end-to-end capstone demonstrating the practical application of AI-powered analytics to a real-world scenario

Course Content

Day 1: Practical Foundations of AI-Powered Analytics

Module 1: Practical Foundations of AI-Powered Analytics

1.      Understanding AI-Powered Analytics Through Practical Workplace Applications

2.      Artificial Intelligence, Machine Learning, Generative AI, and Modern Analytics

3.      Traditional Analytics Versus AI-Assisted and Automated Analytical Workflows

4.      Identifying Practical Analytical Problems and Defining Use Cases

5.      Defining Analytical Questions, Objectives, Data Requirements, and Expected Outputs

6.      Data Sources, Data Types, Metadata, Context, and Data Preparation Requirements

7.      Practical Data Quality Checks for Accuracy, Completeness, Consistency, Relevance, and Timeliness

8.      CRISP-DM, Human-in-the-Loop Validation, and Practical Analytics Workflow Design

9.      Case Study: Selecting an AI-Powered Analytics Solution for a Real Workplace Problem

10.  Practical Exercise: Designing an End-to-End AI-Powered Analytics Workflow

Day 2: Practical AI-Assisted Data Preparation, Analysis, and Visualisation

Module 2: Practical AI-Assisted Data Preparation, Analysis, and Visualisation

1.      Importing, Structuring, Cleaning, and Validating Practical Datasets

2.      Sorting, Filtering, Removing Duplicates, and Identifying Data Quality Problems

3.      Using AI Assistants for Excel Formulas, Calculations, and Data Transformation

4.      Pivot Tables, Summary Tables, Grouping, and AI-Assisted Data Exploration

5.      Descriptive Statistics, Trends, Comparisons, Ratios, and Variance Analysis

6.      AI-Assisted Pattern Recognition, Anomaly Detection, and Exception Identification

7.      Selecting and Creating Effective Charts, Visualisations, and Dashboards

8.      AI-Assisted Reporting, Data Summaries, Narratives, and Professional Presentations

9.      Case Study: Analysing a Practical Business or Operational Dataset with AI

10.  Practical Exercise: Building a Complete AI-Assisted Data Analysis and Visualisation Workflow

Day 3: Practical Statistical Analysis, Predictive Analytics, and Evidence Evaluation

Module 3: Practical Statistical Analysis, Predictive Analytics, and Evidence Evaluation

1.      Practical Statistical Thinking for AI-Assisted Analytics

2.      Correlation, Relationships, and Introductory Regression Analysis

3.      Probability, Confidence Intervals, and Practical Interpretation of Uncertainty

4.      Correlation Versus Causation in Real-World Analytical Problems

5.      Foundations of Predictive Analytics and Practical Machine Learning

6.      Classification, Regression, Clustering, Forecasting, and Anomaly Detection Applications

7.      Training, Testing, Validation, and Basic Model Performance Evaluation

8.      Evaluating AI-Generated Analysis, Formulas, Code, Insights, and Recommendations

9.      Case Study: Validating AI-Powered Statistical and Predictive Analysis Before Use

10.  Practical Exercise: Testing, Correcting, and Improving AI-Assisted Analytical Outputs

Day 4: Advanced Practical AI Analytics, Automation, and Responsible AI

Module 4: Advanced Practical AI Analytics, Automation, and Responsible AI

1.      Generative AI for Practical Data Analysis, Research, Reporting, and Problem Solving

2.      Prompt Engineering for Data Analysis and Repeatable AI Workflows

3.      AI-Assisted Root-Cause Analysis Using Five Whys, Fishbone, and Pareto Techniques

4.      Applying PDCA, SMART, and Evidence-to-Action Frameworks to AI Analytics

5.      Automating Repetitive Data Preparation, Quality Checks, Analysis, and Reporting

6.      AI-Assisted Scenario Analysis, Sensitivity Analysis, and What-If Modelling

7.      Evaluating AI Risks Including Hallucinations, Bias, Automation Bias, and Unsupported Outputs

8.      Responsible AI, Privacy, Confidentiality, Security, Fairness, Transparency, and Accountability

9.      Case Study: Diagnosing and Managing Risks in an AI-Assisted Analytical Workflow

10.  Practical Exercise: Building a Responsible and Automated AI Analytics Process

Day 5: Practical AI Decision Support, Implementation, and Capstone

Module 5: Practical AI Decision Support, Implementation, and Capstone

1.      Integrating AI Analytics, Professional Knowledge, Evidence, and Human Judgement

2.      Developing AI-Powered Dashboards, Analytical Reports, Decision Briefs, and Data Stories

3.      Applying KPIs, Performance Indicators, Balanced Scorecard Principles, and Results-Based Management

4.      Using AI-Assisted Root-Cause Analysis to Develop Evidence-Based Improvement Actions

5.      Applying Decision Matrices, Prioritisation, Scenario Analysis, and Risk Assessment

6.      Communicating AI-Assisted Findings, Assumptions, Limitations, Uncertainty, and Recommendations

7.      Establishing Practical AI Analytics Quality Controls, Validation Procedures, and Documentation

8.      Monitoring Data Quality, AI Outputs, Analytical Performance, and Continuous Improvement

9.      Integrated Case Study: End-to-End Practical AI-Powered Analytics and Decision Simulation

10.  Capstone Exercise: Develop, Clean, Analyse, Validate, Visualise, Communicate, and Implement an AI-Powered Analytics Solution for a Real-World Scenario