We have created a glossary to help you with some specific terms used in this course that you may be unfamiliar with. You will also find a downloadable PDF version of this glossary in the downloads section at the end of the step.
Algorithm: a sequence of operations to be carried out by a computer.
Big data: a set of data with characteristics that cannot be handled with standard computer hardware and software.
Bluetooth: a technology to transfer data wirelessly over a short distance between sender and receiver.
Citizen science: scientific work carried out with public involvement.
Cookie: a small package of data, stored on a user’s computer, which contains information about the user’s web browsing activity.
Data analytics: an investigation of data to characterise the data or derive new insights from it.
Data mining: a specific type of data analytics that aims to discover patterns in large datasets, often using machine learning techniques.
Data science: an interdisciplinary field, combining data analytics methods from computer science with other domain sciences.
GDPR (General Data Protection Regulation): a regulation covering the processing of data related to individuals in the European Union.
GPS (Global Positioning System): a navigation system to determine the location of a compatible receiver.
Machine learning: algorithms and statistical methods implemented on a computer, allowing the computer to automatically improve on the analysis of the data it is given.
NFC (Near-Field Communication): a technology to transfer data wirelessly over a very short distance between sender and receiver.
RFID (Radio-Frequency Identification): a technology to transfer data wirelessly over a short to medium distance between sender and receiver.
Sentiment analysis: a method to detect the feelings of users in written data toward a topic.
Wearable technology: devices worn on the body that collect data, usually about the physical state of the person wearing them.
Browse the terms in the glossary.
Having looked at some of the language we will be using in this course, are you more aware of the dimensions that big data and analytics cover?
Share your thoughts in the comments.
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