September 15, 2019 Author:

In data science, we commonly encounter several composite data types. By composite, we mean that the data type contains multiple value. This is unlike scalar data types, which contain only a single value.

Here are the most common composite data types you will likely encounter in data science.

### Homogenous Data Types

Vector
A vector, also known as an array, is a one-dimensional sequence of homogenous data. Vectors are used to store a list of elements that are all of the same data type.

Matrix
A matrix is a two-dimensional grid of homogenous data. Matrices are typically used to store and process groups of related numbers using a set of mathematical operations known as matrix algebra.

Tensor
A tensor is a three-dimensional cube (or an n-dimensional hypercube) of homogenous data. Tensors are typically used to create deep neural networks in machine learning, which is where the deep learning framework TensorFlow gets its name.

### Tabular Data Types

Dictonary
A dictionary is a two-column table that stores a list of key-value pairs. A dictionary, also known as a look-up table, is used to quickly retrieve data by a unique identifier.

Table
A table stores data as a set of rows and columns. Tables are the most common way you will encounter structured data in data science.

### Semi-structured Data Types

Tree
A tree organizes data as a set of nodes and branches. Trees are used to represent hierarchical data (i.e. data that are organized into parent-child relationships).

Graph
A graph organizes data as a set of nodes and edges. Graphs are used to represent a network of data. They represent each item as a node and each relationship as an edge.

### Multimedia Data Types

There are also a variety of multimedia data types that we encounter in data science. These include text documents, images, audio, video, and shape data.