Data are observation of real-world phenomena. E.g. stock market data might involve observations of daily stock prices, announcements of earnings by individual companies, opinion articles from pundits.
Data can help us answer some questions. E.g. which stocks should I invest in? The tasks are how we get to the answers.
Frequent characteristics of data: wrong, redundant, missing. A mathematical model of data describes the relationships between the different aspects of the data. E.g. a model that predicts stock prices might be a formula that maps a company's earnings history, past stock prices, and industry to the predicted stock price. E.g. a spam detection algorithm.
A feature is a numeric representation of raw data. A feature is an individual measurable property or characteristic of a phenomenon being observed. Feature engineering is the process of formulating the most appropriate features given the data, the model and the task. E.g. in spam detection algorithms, features may include the presence or absence of certain email headers, the email structure, the language, the frequency of specific terms, the grammatical correctness of the text.
