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What is data science?

Is 'data the new oil'? Reflect on this question and find out what to expect from this applied data science course.

Imagine planning a weekend trip to Barcelona. You rent a room near the old city centre which got plenty of positive reviews on an online rental. Using a maps app, you verify that it appears to lie in a relatively quiet cul-de-sac.

A popular recommendation app quickly finds several highly-rated restaurants nearby that match your taste, and a music event app notifies you that one of your favourite bands will play in Barcelona on the day of your arrival. Once there, you navigate the labyrinth of the old town with ease using your maps app and overcome any communication barriers by consulting an online translation service. After broadcasting a few spectacular pictures of the city taken on a video social networking platform, an old friend of yours proposes to meet up since they happen to be staying in Barcelona as well.

Our lives – whether leisure or work – are now so thoroughly infused with data that it is sometimes difficult to remember a time when data was not deeply intertwined with our daily life. Yet very few of us ever see the underlying data. What we receive are algorithmic recommendations, miraculous decisions handed down to us from the invisible cloud. Becoming more proficient in our understanding of what data is and how it is used will shed light on these background processes and provide us with some means of navigating and contributing to this new reality dominated by data.

What to expect from this course

In this short introductory course, we will take the perspective of an applied data scientist (the who), mining or analysing data (the what) that he or she is given or has obtained (the where), using learned methods (the how). We will, in fact, concentrate on the how, touching on the methods that the data scientist might use to analyse the data and produce palatable outputs.

By the end of this course, you will have a good idea of what data science ‘feels like’ and what the important tools are that we use.

© Coventry University. CC BY-NC 4.0
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Applied Data Science

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