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Data Visualisation Technique 5
Name of the Representation Bubble Map
Description It is a combination of a bubble chart, data visualisation and a map. It is used to
visualise location and proportion using circles over geographical regions with
the area of the circle being proportional to its value in the dataset.
How to draw?
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Suitable for which type of data? It is good for comparing proportions over geographic regions without the issues
caused by regional area size. For example, showing the earthquake-prone areas
in a country, the geographical distribution of whales in the northern hemisphere,
number of tigers in the national parks of India.
What is Modelling?
Modelling or data modelling is defined as the process of designing decision-making algorithms that have to
be trained on a set of data (which was acquired at the data acquisition stage for the problem you scoped in the
problem-scoping stage) and applying that learning to recognise certain types of patterns.
Once the data is visualised and trends are formed, we need to work with algorithms to prepare the AI model. This
can be done by designing our models or using the existing AI models. Before we go into the details of modelling,
let us first understand the following important terms:
• Artificial Intelligence: AI refers to any technique that enables computers to mimic or imitate, develop, and
demonstrate human intelligence. They are machines that can perform tasks that they are programmed for. AI
enables machines to think without any human intervention.
• Machine Learning: Machines need to learn the ways of humans by learning the techniques and processes. So
machine learning is a subset of artificial intelligence that uses statistical methods that enable machines to
improve with experiences. So machines learn from their mistakes and take them into consideration in the next
iteration, this way they keep improving with experience. For example, Snapchat filters and Netflix
recommendations. Artificial
Intelligence
• Deep Learning: Machines can draw meaningful inferences from large
volumes of datasets. In deep learning, the machine is trained with a huge Machine
Learning
amount of data, which helps it train itself. Deep learning is a machine
learning algorithm that is inspired by the functionality of our brain cells
Deep
called neurons. For example, Google Translate and image recognition in Learning
social media apps.
AI Reflection, Project Cycle and Ethics 129

