Computational Thinking and Data Science

In a continuation of my series of posts clearing up the meanings of different keywords used in the STEM workforce, computational thinking was one that I really want to address. There is a lot of confusion in relation to computer-related terms. When describing computing, computer science, computational thinking, and programming, a wide variety of terminology is applied. Computing involves both computer science and computational thinking methods and tools. While computer science is an academic discipline in its own right, computational thinking is a problem-solving strategy that integrates across activities, and programming is the process of creating a set of instructions that a computer can comprehend and execute, as well as debugging, organizing, and applying that code to appropriate problem-solving situations. Computational thinking requires a broader set of skills and practices which pull from computer science and can be used in other areas like English, math, and decision-making in general.

Down below is a table provided in Lecture One in my Honors Introduction to Computational Science course from the North Carolina School of Science and Math.

So how does this apply to data science? Let's split up the broad terms of data science using the skills that computational thinking encompasses.

Decomposition: Are you able to break down data into chunks?	Data Analysis
Pattern Recognition: Are you able to spot patterns in your data?	Data Visualization
Abstraction: Can you create a model that represents the important bit? Can you generalize the bits that are different from each other?	Data Modeling, Pattern Generalization
Algorithm Design: Can you create instructions for solving a this/related problem	Automation, Artificial Intelligence, Simulation