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Description
What topics are covered in this Data Literacy for Business online course?
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
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
Entry requirements
There are no entry requirements.
Course assessment and certification
On successful completion of the final assessment a downloadable certificate is immediately available.
