Data in everyday life
Be able to explain what data is, give examples of data collected in daily life, distinguish between different types of values (numbers, text, yes/no), and read a simple table.
Prerequisites
- APatterns and categoriesrequired
Everyday explanation
Every time you measure, count, or note something down, you create data: the temperature outside, how many steps you have taken, which colour you chose. Data are observations that can be stored.
A table organises data: each row is an item (a day, a person, a film) and each column is an attribute (temperature, length, grade).
Intuition
Values come in different types:
| Type | Example | Can you do calculations with it? |
|---|---|---|
| Number | 17 °C, 8,200 steps | Yes |
| Category | red, blue, yellow | No — only compare |
| Yes/no | Did it rain? | Like 1/0 |
Computers are good with numbers. Categories and text must first be converted into numbers before a model can learn from them. You will see this when we get to features.
Mastery means
- Reads a table with rows and columns correctly
- Distinguishes between numerical and categorical values
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Sources
Leads to
Part of the goals (39)
- Data: collect, clean, document
- The developer's toolbox
- Statistics for experiments
- The mathematics behind the models
- Build an NLP system end to end
- Classical machine learning in practice
- AI for beginners
- Build an AI service that survives production
- Fine-tune and run your own models
- AI, ethics and society
- Builder — collect data, train a model and test AI critically
- Training neural networks for real
- Fine-tune a model with LoRA
- Build a RAG system you can trust
- AI in production
- Generative models in depth
- Classical ML for real
- Frontier Lab — an independent research project
- Evals in practice
- Language models in practice
- Understand how generative AI works
- Train an agent with reward
- An AI service in operation
- Computers that see and hear
- Build a memory system for an agent
- Build an agent you can trust
- AI safety in practice
- Responsible AI in practice
- Interpreting a language model
- Reproduce a paper
- Build a voice interface
- Train your first neural network
- Deep reinforcement learning
- Discoverer — build a game and train a machine
- Seeing and hearing with AI
- Image classification with convolutional networks
- Run models more cheaply: quantisation
- Multimodal systems
- Build a transformer from scratch