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Data analysis & reporting
The technical support for big data analysis
We collaborate with SFU Big Data to provide SFU researchers the technical support in regards to data analysis and reporting, including data analysis, data processing, and data visualization.
We support various analytics techniques, such as
- Text analytics
- Machine learning
- Predictive analytics
- Data mining
- Statistics and natural language processing
- and more...
Understanding big data
Big data is not a single technology but a combination of technologies that helps researchers gain insight from their data. Therefore, big data is the capability to manage a huge volume of disparate or complex data, within a given timeframe to allow for real-time analysis and reaction.
Big data is typically broken down by three characteristics:
Volume: How much data
Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. Size of data plays very crucial role in determining value out of data. Also, whether a particular data can actually be considered as a Big Data or not, is not dependent upon volume of data, but could also be based on the complexity of the data and its interactions.
Velocity: How fast that data is processed
It refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data.
Variety: The various types of data
Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. From structured datasets of numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data, realtime sensor information.
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