One-sentence summary
Data is the raw record that describes the world — numbers, text, measurements and observations — and when we give those records meaning, they become information.
Why does it matter?
In the line-following robot project, the robot kept reading numbers from its sensor. Those values, telling it whether the surface was dark or light, were data. By looking at that data, the robot decided to "turn right" or "go straight."
Artificial intelligence works in much the same way. AI is not magic; it is a tool that finds patterns in large amounts of data. You cannot really understand AI without understanding data first. That is why we open the data and AI module with a simple question: what is data?
There is another important point. The results an AI gives depend on the quality of the data it is fed. Incomplete or biased data leads to wrong results. So getting to know data is the first step toward using its results with confidence.
What is data?
Data is the raw pieces we record about an event, an object or a situation. On its own, a piece of data usually carries no interpretation; it just sits there "as it is."
We meet data everywhere in daily life:
- The 18 °C you saw on the thermometer this morning is data.
- Last night's match score of 2 - 1 is data.
- The 8,240 steps your smartwatch shows is data.
All three examples point to the same idea: each one is a piece taken and recorded from the real world.
Four common kinds of data
Data comes in different forms. We can separate the four we see most often:
- Number: Digits that measure a value. Example: 18, 2, 8240.
- Text: Records made of letters. Example: "Ankara", "rainy", a student's name.
- Measurement: A numerical value taken with an instrument. Example: temperature, distance, weight.
- Observation: A situation we note by watching. Example: "the sky is cloudy", "the cat is asleep".
Notice that a measurement is also a number; but how it is obtained matters. A measurement is taken with the help of a sensor or tool. The "10 centimetres" the robot read from its distance sensor is exactly this kind of measurement data.
The difference between data and information
Data and information are often mixed up, but there is an important difference between them.
- Data is the raw record: 18, 21, 24, 22, 19.
- Information appears when we make sense of those records: "The weather warmed up this week, and the hottest day reached 24 °C."
So numbers standing on their own are data. They turn into information when we organise and interpret them to answer a question.
Here is a simple rule: data answers "what was recorded?", while information answers "what do these records tell us?"
The link to sensor data
In the line-following robot, the sensor read the surface dozens of times every second. Each of these readings was a small piece of data on its own. But when the robot gathered these pieces one after another and looked for a pattern, it reached the information "the line is drifting to the right."
The same idea holds in larger systems. A weather station collects thousands of measurements from temperature and humidity sensors. Those measurements are raw data. Bringing them together to conclude "there is a high chance of rain tomorrow" is turning data into information.
The step counter on your phone also uses a sensor. With every step you take, it records a small piece of movement data. At the end of the day it adds up these thousands of tiny records and shows the information "you took 8,240 steps today." So the sensor produces raw data; the useful information appears only when we bring that data together. AI learning is no different: first a great deal of data, then the pattern we look for inside it.
Mini practice
Let us record a week of midday temperatures in a list and pull simple information out of that raw data. The Python code below uses only the standard library; it does not train any AI model — it just counts the recorded data and takes its average.
# A week of midday temperatures (Celsius) - raw data
temperatures = [18, 21, 24, 22, 19, 23, 20]
total = sum(temperatures)
day_count = len(temperatures)
average = total / day_count
hottest = max(temperatures)
coldest = min(temperatures)
print("Number of days:", day_count)
print("Average temperature:", round(average, 1), "C")
print("Hottest day:", hottest, "C")
print("Coldest day:", coldest, "C")When this code runs, the temperatures list is the raw data. The average, hottest and coldest values are the information we draw from that data.
Try it yourself: put real temperatures you find into the list, or use another everyday value such as step counts or match scores. Watch how the result changes when the numbers change.
Common mistakes
Confusing data with information
"24" on its own is not information but data. We are the ones who give it the meaning "the hottest day of the week." Missing this difference leads to reading results wrongly.
Not asking where the data came from
Not every piece of data is reliable. A broken thermometer can give a wrong number. Before you use a result, it is a good habit to ask, "how was this data collected?"
Drawing firm conclusions from too little data
Looking at a single day's temperature and saying "this city is always hot" is wrong. A solid conclusion needs enough and balanced data.
Confusing a measurement with a guess
A measurement taken with an instrument is not the same as a guess like "I think it was cold." A measurement is data; a guess is an interpretation.
Safety note
Data is often about people, so we need to be careful.
- Protect personal data. Your name, address, phone number or a friend's details are personal data. Do not type them into online AI tools or public data lists.
- Adult guidance. Use AI and data tools together with an adult and follow the age rules of each platform.
- Be aware of the power of data. Collected data can be used in important decisions. Biased or incomplete data leads to biased results. Check that an important output is correct before you rely on it.
- Responsibility belongs to people. Who collects data, for what purpose, and how a result is used are human decisions. AI is a tool; the responsibility belongs to the person using it.
Lesson summary
- Data is the raw record that describes the world: numbers, text, measurements and observations.
- A temperature record, a match score and a step count are everyday examples of data.
- Data is raw; it becomes information when we interpret it to answer a question.
- Sensors constantly produce data; robots and AI make decisions by finding patterns in that data.
- Protecting personal data and questioning where data comes from are part of responsible use.
Check questions
- What is the difference between data and information? Give an example of each.
- Which kinds of data do "Ankara", 18 and "the sky is cloudy" belong to?
- What kind of data is the "10 centimetres" the robot read from its distance sensor?
- Why is it risky to draw firm conclusions from too little data?
- Why is it unsafe to type your name and address into an online AI tool?
Answers
- Data is the raw record (for example, 18). Information appears when we interpret that record (for example, "it is 18 °C today, cooler than yesterday"). Data answers "what was recorded?" and information answers "what does this tell us?"
- "Ankara" is text, 18 is a number (it could also be a measurement), and "the sky is cloudy" is an observation.
- Because it was taken with an instrument (a sensor), it is measurement data; it is also numerical.
- Because one or a few records may not reflect the general situation correctly. A solid conclusion needs enough and balanced data; too little data can be misleading.
- A name and address are personal data. Once typed into online tools, they may reach other people. Personal information should be kept private, and such tools should be used with adult guidance.
Source and verification note
For “What Is Data?”, verification focuses on whether the relationship between What is data? and The difference between data and information remains consistent across examples. Datasets in this module are small and educational; real personal data should not be used. An AI result should be evaluated not only for accuracy but also for data balance, error distribution and explainability.
Next lesson
Collecting and Cleaning Data: Where do we gather raw data from, and how do we spot and fix wrong or missing records?