Overview

Data literacy is now a core business skill: this practical course teaches non-analysts to read, question and act on the reports and dashboards already on their desk. No spreadsheets, no coding, just how to spot a misleading chart, tell correlation from causation, and choose metrics that matter. Four short units, real examples, no hype.

Target Audience

All staff and teams, at any level and in any function. No technical background required.

Learning Objectives

Upon completion, learners will gain a full and clear understanding of:

• What data literacy means, and why it is now a core decision-making skill
• How to read charts, tables and dashboards accurately, and spot the common ways they mislead
• The difference between correlation and causation, and how to question data quality and bias
• How to choose metrics that actually matter, and avoid the classic traps in how they get used
• How to communicate a data-backed point clearly, in a way any audience can act on
• How to use data responsibly, and build a lasting personal habit of reading, questioning and acting on data

Course Contents

Unit 1: Foundations of Data Literacy

• What data literacy means, and why it is a decision-maker's skill, not just an analyst's
• The current data skills gap, and why it is widening even as expectations rise
• The journey from raw data to a decision: data, information and insight
• Key terms in plain English: dataset, variable, metric, KPI, sample and population
• Building the habit at the centre of this course: read, question, act

Unit 2: Reading and Interpreting Data

• Averages, percentages and rates, and how each can be misread
• Reading tables, bar charts, line charts, pie charts and dashboards properly
• The real, well documented ways axes and charts can mislead, including a genuine case study
• Distributions, outliers and small samples, and how much weight each can really carry
• A five-point scepticism checklist to run before trusting any figure

Unit 3: From Data to Decisions

• Asking good questions of data, and testing a figure against the right comparison
• Correlation versus causation, with real, well documented examples
• Data quality, bias and survivorship bias, and how each shapes a conclusion
• Choosing metrics that matter, and Goodhart's law: when a target stops being a good measure
• A simple, repeatable framework for making a genuinely data-informed decision

Unit 4: Using Data Well

• What a healthy, data-informed culture looks like, and what it is not
• Communicating with data: leading with the headline, and choosing the right chart
• Privacy and ethics basics for anyone handling data, in plain English
• The pitfalls that catch even experienced people out
• Building your own data confidence habit, with a personal action plan