Information originates from varied sources, reminiscent of sensor measurements, occasions, textual content, photos, and movies, with the Web of Issues (IoT) producing steady streams of data.
A lot of this knowledge is unstructured, like photos consisting of pixel RGB values, texts composed of phrases and characters, and clickstreams of person actions. A key problem in knowledge science is changing this uncooked, unstructured knowledge into actionable and structured kind.
Structured knowledge is available in two primary sorts: numeric and categorical.
Numeric knowledge may be steady, like temperature or distance, and discrete, just like the variety of vehicles in a parking zone.
Categorical knowledge has a set set of values, reminiscent of automotive fashions (sedan, SUV, hatchback) or metropolis names (New York, Los Angeles). A particular case of categorical knowledge is binary knowledge, which has two values, reminiscent of 0/1 or on/off. Ordinal knowledge, one other kind of categorical knowledge, has ordered classes, like instructional ranges (highschool, bachelor’s, grasp’s, doctorate).
References
- Sensible Statistics for Information Scientists: 50+ Important Ideas utilizing R and Python
