Key terms

While we have already discussed some common data terms, there will be others ones too that we will use throughout the course, so we’re going to introduce these now as a useful glossary, with some terms explained below and more in the Glossary of terms PDF download.

  • Artificial Intelligence (AI): the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. (OED)

  • Big Data: According to the UK Cabinet Office, refers to both “large volumes of data with high levels of complexity” and the “more advanced techniques and technologies required to gain meaningful information and insights in real time”.

  • Cognitive Computing: refers to systems that learn at scale (i.e. have the ability to process very large volumes of all types of data), in real time, and interact with humans naturally.

  • Data Analytics: the extraction of insights and meaning from raw data using specialised tools and techniques.

  • Data Analyst vs Data Scientist: In general, a data analyst will help you query, summarise, and process data, and a data scientist will apply analytic tools and techniques to solve specific problems.

  • Data Visualisation: the art of communicating and making sense of data using images.

  • Machine Learning: a branch of Artificial Intelligence that enables systems to learn and improve from experience, without being explicitly programmed. Great at spotting patterns and generalising to other cases based on the data inputs and outputs. (Here’s a short guide from Harvard on how Machine Learning can work for local government.)

  • Small Data: refers to data which is small enough to be processed inside a single computer, using simple tools such as spreadsheet applications.

  • Structured Data: data which is in a traditional row-column tabular format.

  • Unstructured Data: is data that needs to be cleaned and processed before analysis, or where the structure of the data is not tabular. An example of the first type would be text, and of the second, a social network.

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This article is from the free online course:

Understanding Data in the Tourism Industry

Edinburgh Napier University