Module 1
Data Literacy, Measurement and Evidence
- Data, information, evidence and decisions
- Variables, data types and scales of measurement
- Data collection, definitions and context
- Data quality and fitness for analytical purpose
Foundation and Data
Data Literacy, Statistics and Analytical Thinking
Strengthen data literacy, statistical reasoning and the ability to challenge analytical claims before they influence decisions.
Course overview
Build confidence in reading, questioning and reasoning with data.
Data Literacy, Statistics and Analytical Thinking helps professionals interpret data with greater discipline and avoid common errors in evidence-based decision-making.
Participants learn how data types, distributions, sampling, variability, relationships, uncertainty and statistical inference shape what can legitimately be concluded from analysis.
The course places particular emphasis on asking better questions, recognising misleading comparisons and visualisations, and separating association from causation.
Learning outcomes
What participants will be able to do.
- Classify common data types and levels of measurement.
- Summarise distributions using appropriate measures of centre, spread and position.
- Interpret probability and uncertainty in practical business contexts.
- Explain sampling, sampling error and common sources of bias.
- Interpret confidence intervals and hypothesis-test results at a practical level.
- Distinguish correlation, association and causation.
- Recognise common statistical and visualisation traps.
- Evaluate analytical claims and communicate evidence responsibly.
Who should attend
Designed for professionals who use data, analysis or evidence to improve decisions.
- Managers and decision-makers who consume reports and dashboards.
- Business analysts, performance analysts and reporting professionals.
- Project, finance, risk and operational professionals.
- L&D, HR and marketing professionals using survey or performance data.
- Anyone who needs stronger statistical judgement without becoming a statistician.
Prerequisites
Professionally relevant and application-focused.
No advanced mathematics is required. Participants should be comfortable with basic arithmetic and percentages and be willing to work through practical data examples.
Course structure
A five-Module journey from analytical understanding to workplace application.
The sequence may be delivered across five days or adapted to another approved format while preserving the learning outcomes and the five connected Modules.
Module 2
Describing Data and Understanding Variation
- Frequency distributions and visual summaries
- Mean, median, percentiles and spread
- Variance, standard deviation and outliers
- Comparing groups without hiding variability
Module 3
Probability, Sampling and Uncertainty
- Probability concepts for business decisions
- Populations, samples and sampling methods
- Sampling error, bias and representativeness
- Confidence intervals and uncertainty language
Module 4
Relationships, Inference and Causal Claims
- Correlation and association
- Regression interpretation at a practical level
- Hypothesis testing and statistical significance
- Causality, confounding and alternative explanations
Module 5
Analytical Thinking and Evidence Communication
- Misleading statistics and visualisations
- Base rates, denominators and comparison traps
- Critical questions for reports and models
- Evidence brief and analytical challenge workshop
Learning experience
Move from understanding to application, production and workplace value.
Understand
Connect concepts with business decisions
Clarify methods, assumptions, evidence requirements and the decision context before applying tools.
Apply
Work through realistic analytical cases
Use datasets, scenarios and decision questions to practise analytical judgement in context.
Produce
Create practical analytical outputs
Develop artefacts that can be adapted to reporting, modelling, governance or decision-support work.
Review
Challenge evidence and analytical choices
Use peer review, validation criteria and facilitated critique to improve analytical reasoning.
Transfer
Apply the learning at work
Identify how to adapt the methods to organisational data, decisions, systems and governance requirements.
Completion requirements
Demonstrate participation, application and professional judgement.
- Participate actively in case discussions, data exercises and analytical workshops.
- Complete the principal analytical or decision-support outputs assigned during the programme.
- Contribute to the integrated case, model, dashboard or application workshop.
- Complete knowledge checks and a workplace application or study plan.
Participant outputs
Leave with practical analytical artefacts.
- Data-literacy diagnostic checklist.
- Statistical summary worksheet.
- Sampling and bias assessment.
- Correlation-versus-causation review.
- Analytical claim challenge checklist.
- Evidence communication brief.
Delivery options
Select the learning format that fits your people and analytical environment.
Instructor-led
Live Classroom
Facilitated face-to-face learning with analytical cases, datasets, modelling tasks, discussion and immediate feedback.
Instructor-led
Live Virtual Classroom
Interactive online delivery using collaborative workspaces, data exercises, breakout analysis and guided model development.
Flexible
Blended Learning
A structured combination of preparation, live facilitation, applied assignments, analytical work and follow-up application.
Organisation-specific
Corporate and In-Company
Tailored delivery aligned with organisational datasets, measures, tools, governance, decisions and analytics maturity where appropriate.
Frequently asked questions
Course information and participation.
Is this a mathematics-heavy statistics course?
No. The emphasis is on interpretation, judgement and practical business use. Calculations are introduced where they help participants understand the reasoning.
Will the course cover statistical significance?
Yes, at a practical interpretation level, including what significance does and does not tell a decision-maker.
Is it suitable for managers rather than analysts?
Yes. It is particularly useful for people who must question dashboards, forecasts, research findings and data-driven recommendations.
Can examples be tailored to our industry?
Yes. Corporate delivery can use sector-specific metrics, reports and analytical claims.
Does completion provide a professional certification?
The course develops data-literacy and statistical-reasoning capability and may provide an INDENTRA course-completion record where applicable. It is not a third-party professional certification.
Take the next step
Build the judgement to use data without being misled by it.
Discuss a public course or a corporate cohort focused on the statistical and analytical decisions your teams face.