What is computer science?
Computer science is the study of computational problem solving, where computation refers to systems built for the purpose of making a calculation to some effect. These systems could be computers, their internal and/or external hardware that interacts with them, the operating system and software that runs on them, the programming languages used to build them, and the mathematical equations and proofs used to make them efficient.
While computer science is a broad topic that mostly refers to theory, it also serves as an umbrella term for a vast number of practices that require computation, including the practice of writing code, also known as programming. One such practice is data science, which happens to branch out into multiple practices as well. Data science involves the use of scientific methods, such as statistics, to extract knowledge from and understand the hidden relationships within a given set of data. This practice includes building a model, which is a system typically comprised of mathematical computations that will take data as an input and produce a result as an output. This is usually an algorithm, which is a special set of instructions that achieves a certain goal and is typically a computer program that the data scientist may create or use. The output of a model is used to gain actionable insights from the data and is often used to “teach” the model how to produce a more precise output.
The use of models feeds into another practice called machine learning, which focuses on creating models to uncover meaningful information and patterns from data to support rational decisions. Machine learning is a subset of artificial intelligence, where acquired knowledge, such as data, is applied and used to make rational decisions without human interaction. A common example of this is using applications that adopt speech-to-text, where the application will improve and become accustomed to a voice over time, making the conversion to text even more accurate. Artificial intelligence converts your spoken words into text, whereas machine learning uses a model to confirm and improve on words it may have gotten wrong, and data science uses your voice as the data to make sense of.
As you can see, practices within computer science can produce their own set of specialized terms. It’s common for professionals in the computer science field to have breadth in some of these areas and depth in others. For example, a data scientist may have experience building models and building AI applications. Computer science is a vast field of varied specializations but each path leads to computation for solving problems.
