How is an 'algorithm' defined in the context of data science?

In the context of data science, an algorithm is defined as a set of instructions or rules that outline the steps needed to solve a problem or carry out a specific task. This definition is critical because algorithms serve as the foundational building blocks for many data processing tasks, including data analysis, predictive modeling, and machine learning.

Algorithms can operate on various forms of data to perform operations, including sorting, filtering, and aggregating data, or even making predictions based on patterns detected within datasets. While they can be embedded within statistical models or implemented using programming languages, the essence of an algorithm lies in its structured approach to problem-solving that can be applied across various applications in data science.

The other options do not accurately capture the essence of what defines an algorithm. A statistical model, while important in data interpretation, is not synonymous with an algorithm, which is more about the procedural aspect. Data visualization techniques, although essential for interpreting data, are separate from algorithms. Lastly, a programming language serves as a tool to implement algorithms, but it does not define the algorithm itself.