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Design of suitable architecture

Discussion on architecture

Overall Architecture

In Data Analytics (and computer science in general), an architecture is the organisation of the different components which are used to create a system. They are usually organised sequentially, where each component is likely to contain several other sub-components performing specialised tasks.

In this course, we shall consider a simple architecture, consisting of the following components

  • Input
  • Data pre-processing
  • Model and data analysis
  • Visualisation of results and findings

More specifically, the input will comprise the data we need to analyse, which, depending on its size, structure, and any inconsistencies. In fact, data usually contains raw information from sensors, images, texts, etc., which can be corrupted by hardware and software malfunctions, as well as erroneous or missing records due to human error. Data pre-processing can be a lengthy process, and in some cases, it needs to be carried out manually. There are, however, some automated techniques that can speed up the whole process. The model and data analysis stage contains the analytical engine to extract intelligent insights from the data. There are many approaches that can be used, both supervised and unsupervised based on the type of analysis, the available data and the output that needs to be produced. Finally, the results and findings need to be displayed and visualised according to specific constraints, needs and requirements

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Introduction to Python for Big Data Analytics

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