Glossary of Terms
All complex subjects have their own terminology that sometimes makes it hard for new people to break into the field. This sometimes includes uncommon words, but more often than not a subject will have very specific meanings for common words - the discussion of errors vs mistakes in this video is a good example of this.
This glossary is a reference of some of the uncommon terms and specific definitions of more common words that you will encounter throughout Data Tree and your broader dealings with data.
Many of these definitions come from the course materials and experts that helped develop Data Tree. Others come from the CASRAI Dictionary. Those definitions are kindly made available under a Creative Commons Attribution 4.0 International License.
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The study and practice of designing systems that can learn, adjust, and improve automatically, based on the data fed to them. This typically involves implementation of predictive and statistical algorithms that focus on 'correct' behaviour and insights as data flows through the system.
A big data algorithm for scheduling work on a computing cluster. The process involves splitting the problem set up, mapping it to different nodes (map), and computing over them to produce intermediate results, shuffling the results to align like sets, and then reducing the results by outputting a single value for each set (reduce).
Prefix denoting a factor of 106 or a million
Representation of a real world situation. The word “model” is used in many ways and means different things, depending on the discipline. For example a meteorologist might think of a global climate model, used for weather forecasting, while an agronomist might think of a crop simulation model, used to estimate crop growth and yields. Statistical models form the bedrock of data analysis. A statistical model is a simple description of a process that may have given rise to observed data.