Data Literacy

Data literacy means being able to understand, work with and talk about data. A data-literate person can turn raw data into useful decisions. The data pyramid shows the climb: data becomes information when it is organised, knowledge when we find patterns in it, and wisdom when we use it to act well.

Numbers and graphs can be used to mislead. A data-literate person asks: Where did this data come from? How many people were asked? Where does the axis start? What was left out? Asking these questions helps you make informed decisions instead of being fooled.

Becoming data literate is a process, not a single lesson. The Data Literacy Process Framework guides people at every level of skill to use data well. It is iterative: after evaluating, we plan again and keep improving.

Data privacy is about the proper handling of personal data: who may collect it, why, and with whose consent. Data security is about protecting data from unauthorised access, damage or theft. A data breach happens when data gets into the wrong hands. Strong passwords are your first line of defence.

Data can be textual (qualitative), made of words, or numeric (quantitative), made of numbers. Numeric data is discrete (counted in whole numbers) or continuous (measured, any value in a range). We acquire data by data discovery (finding existing datasets), data augmentation (making changed copies of data we have) and data generation (recording new data with surveys or sensors).

Good data has a clear structure, is clean (no duplicates, missing values or impossible values) and is accurate. Preprocessing cleans the data before we use it. The columns of a dataset are its features: independent features are the inputs a model uses and the dependent feature is the output it predicts.

Data processing turns raw data into usable information. Data interpretation means reviewing that information to draw conclusions. Qualitative interpretation works with words, like interview answers; quantitative interpretation works with numbers, using statistics like the mean. We can present data as text, in a table or as a graph.

A dashboard puts the most important numbers and charts on one screen, and lets you filter them. Good visualisation makes patterns easy to see and easy to share. Tools like Tableau, Datawrapper, Google Sheets and Excel can build dashboards. Explore this one, then build your own with your class's data.