Data Literacy

Data literacy is reading, cleaning and using data well, and keeping it private and secure.

Key ideas

Data can be numerical, categorical, text, image or audio; the type decides how it is analysed.
Data is acquired from surveys, sensors, observation, interviews, web scraping, open data portals and cameras.
Cleaning removes repeated rows, rows with missing values and impossible values such as an age of 150.
A missing value can sometimes be filled with the mean of the other values.
Trends show in the change from one period to the next; the biggest rise is the biggest positive change.
Privacy is about who may see and use data; security is about protecting it from attack or theft.

Percentage change	(new − old) ÷ old × 100: 200 → 250 is a 25% increase
Fill a gap with the mean	12, 15, —, 18, 15 → (12 + 15 + 18 + 15) ÷ 4 = 15
Strong password	at least 8 characters, an uppercase letter, a lowercase letter, a digit and a symbol
Privacy examples	asking before sharing location; collecting only the data needed
Security examples	strong passwords, encryption, updates, two-step sign-in

Calling blood group or favourite sport numerical: they are categorical.
Dividing the change by the new value instead of the old one.
Mixing up privacy (who may use data) with security (keeping it safe from attack).

Is each mainly about privacy or security? (a) Collecting only the data a survey really needs (b) Locking a database with strong passwords

Working: privacy: who may see and use data; security: protecting it from attack or theft

Answer: (a) privacy; (b) security