Data Structures

Data Structures

With this being a data analytics course, it is important to start with a focus on data. Data comes in a lot of different forms and can be organized into many different structures. The list below covers some main, basic data structures, in general terms:

Scalar

A scalar is just a single value. For example:

$x=2$

Vector

A vector contains multiple scalars organized into a single row:

$x=\left[\begin{array}{c}123\end{array}\right]$

or column:

x=\left[\begin{array}{c}1\\ 2\\ 3\end{array}\right]

The defining feature of a vector is that it is one-dimensional, regardless of which direction it goes.

Matrix

A matrix contains multiple values organized into a 2-dimensional array:

$x=\left[\begin{array}{c}12\\ 34\end{array}\right]$

Tensor

A tensor is a multidimensional set of arrays. For example, a 3-dimensional tensor can be thought of as a stack of matrices:

Table

So far, all the data structures listed above are for a single data type (i.e., only numbers). A table contains multiple variables (or vectors) organized into columns, and the different variables can have different data types (e.g., numbers and text), although any single variable needs to contain only a single data type (we'll talk more about data types in Lesson 2). Furthermore, tables typically have labels for the variables, as column headings:

* Data Source: Residential Energy Consumption Survey (RECS), U.S. Energy Information Administration (accessed Nov. 15th, 2021)

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