The Great Big Data Science Glossary
To help those new to the field stay on top of industry jargon and terminology, we’ve put together this glossary of data science terms.
Getting started in data science can be overwhelming, especially when you consider the variety of concepts and techniques a data scienctist needs to master in order to do her job effectively. Even the term “data science” can be somewhat nebulous, and as the field gains popularity it seems to lose definition.
To help those new to the field stay on top of industry jargon and terminology, we’ve put together this glossary of data science terms. We hope it will serve as your handy quick reference whenever you’re working on a project, or reading an article and find you can’t quite remember what “ETL” means.
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These are some baseline concepts that are helpful to grasp when getting started in data science. While you probably won’t have to work with every concept mentioned here, knowing what the terms mean will help when reading articles or discussing topics with fellow data lovers.
An algorithm is a set of instructions we give a computer so it can take values and manipulate them into a usable form. This can be as easy as finding and removing every comma in a paragraph, or as complex as building an equation that predicts how many home runs a baseball player will hit in 2018.
The back end is all of the code and technology that works behind the scenes to populate the front end with useful information. This includes databases, servers, authentication procedures, and much more. You can think of the back end as the frame, the plumbing, and the wiring of an apartment.
Big data is a term that suffers from being too broad to be useful. It’s more helpful to read it as, “so much data that you need to take careful steps to avoid week-long script runtimes.” Big data is more about strategies and tools that help computers do complex analysis of very large (read: 1+ TB) data sets. The problems we must address with big data are categorized by the 4 V’s: volume, variety, veracity, and velocity.
Classification is a supervised machine learning problem. It deals with categorizing a data point based on its similarity to other data points. You take a set of data where every item already has a category and look at common traits between each item. You then use those common traits as a guide for what category the new item might have.
As simply as possible, this is a storage space for data. We mostly use databases with a Database Management System (DBMS), like PostgreSQL or MySQL. These are computer applications that allow us to interact with a database to collect and analyze the information inside.
A data warehouse is a system used to do quick analysis of business trends using data from many sources. They’re designed to make it easy for people to answer important statistical questions without a Ph.D. in database architecture.
The front end is everything a client or user gets to see and interact with directly. This includes data dashboards, web pages, and forms.
Algorithms that use fuzzy logic to decrease the runtime of a script. Fuzzy algorithms tend to be less precise than those that use Boolean logic. They also tend to be faster, and computational speed sometimes outweighs the loss in precision.
An abstraction of Boolean logic that substitutes the usual True and False and for a range of values between 0 and 1. That is, fuzzy logic allows statements like “a little true” or “mostly false.”
A greedy algorithm will break a problem down into a series of steps. It will then look for the best possible solution at each step, aiming to find the best overall solution available. A good example is Dijkstra’s algorithm, which looks for the shortest possible path in a graph.
A process where a computer uses an algorithm to gain understanding about a set of data, then makes predictions based on its understanding.
