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1-Introduction/01-defining-data-science/README.md

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 <dt>Databases</dt>
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 A critical consideration is **how to store** the data, i.e. how to structure it in a way that allows faster processing.  There are different types of databases that store structured and unstructured data, which <a href="../../2-Working-With-Data/README.md">we will consider in our course</a>.
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 <dt>Big Data</dt>
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 <dt>Machine Learning</dt>
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 One way to understand data is to **build a model** that will be able to predict a desired outcome.  Developing models from data is called **machine learning**. You may want to have a look at our <a href="https://aka.ms/ml-beginners">Machine Learning for Beginners</a> Curriculum to learn more about it.
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 <dt>Artificial Intelligence</dt>
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 <dt>Visualization</dt>
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 Vast amounts of data are incomprehensible for a human being, but once we create useful visualizations using that data, we can make more sense of the data, and draw some conclusions. Thus, it is important to know many ways to visualize information - something that we will cover in <a href="../../3-Data-Visualization/README.md">Section 3</a> of our course. Related fields also include **Infographics**, and **Human-Computer Interaction** in general. 
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 <dt>3) Data Processing</dt>
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 This is the most exciting part of the data journey, which involves converting the data from its original form into a form that can be used for visualization/model training.  When dealing with unstructured data such as text or images, we may need to use some AI techniques to extract **features** from the data, thus converting it to structured form.
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 <dt>4) Visualization / Human Insights</dt>