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updating readme, as in a table we have to use html markup
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1-Introduction/01-defining-data-science/README.md

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@@ -45,23 +45,23 @@ Since data is a pervasive concept, data science itself is also a broad field, to
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<dt>Databases</dt>
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The most obvious thing to consider is **how to store** the data, i.e. how to structure them in a way that allows faster processing. There are different types of databases that store structured and unstructured data, which [we will consider in our course](../../2-Working-With-Data/README.md).
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The most obvious thing to consider is **how to store** the data, i.e. how to structure them 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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Often we need to store and process really large quantities of data with relatively simple structure. There are special approaches and tools to store that data in a distributed manner on a computer cluster, and process them efficiently.
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<dt>Machine Learning</dt>
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One of the ways to understand the data is to **build a model** that will be able to predict desired outcome. Being able to learn such models from data is the area studied in **machine learning**. You may want to have a look at our [Machine Learning for Beginners](https://github.com/microsoft/ML-For-Beginners/) Curriculum to get deeper into that field.
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One of the ways to understand the data is to **build a model** that will be able to predict desired outcome. Being able to learn such models from data is the area studied in **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 get deeper into that field.
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<dt>Artificial Intelligence</dt>
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As machine learning, artificial intelligence also relies on data, and it involves building high complexity models that will exhibit the behavior similar to a human being. Also, AI methods often allow us to turn unstructured data (eg. natural language) into structured by extracting some insights.
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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 - we can start making much more sense of data, and drawing some conclusions. Thus, it is important to know many ways to visualize information - something that we will cover in [Section 3](../../3-Data-Visualization/README.md) of our course. Related fields also include **Infographics**, and **Human-Computer Interaction** in general.
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Vast amounts of data are incomprehensible for a human being, but once we create useful visualizations - we can start making much more sense of data, and drawing 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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