Zinia-Store

Day 22 of 30:

At first glance, physics vectors and data science vectors seem unrelated. In physics, a vector is a tangible arrow defining magnitude and direction, like wind blowing 20mph East. In data science, it’s an abstract list of numbers representing features like a product defined by [price, weight, rating].

The bridge connecting them is coordinate geometry.

A physics vector representing movement in 2D space can be written as coordinates (x,y). This pair of numbers is literally a data vector of length two.

The fundamental similarity is that both represent a position in space relative to an origin. Physics usually limits this to tangible 2D or 3D space. Data science simply expands this concept into high-dimensional "feature space."

A product vector of [10, 2kg, 4.5 stars] is a single point plotted in a 3D abstract space where the axes are price, weight, and rating. Whether it's physical movement or abstract attributes, a vector is ultimately just an ordered list of coordinates defining a specific location in a defined space.