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Introduction to the module
Welcome to Big Data Analytics course. This course is designed to support you in understanding not just the basics of Big Data analytics but how it is so crucial in various fields.

However, due to its highly specialised content, Big Data Analytics is often left to computer scientists and mathematicians. But does it have to be like that?

First of all, let me introduce what I mean by big data analytics. As you may have already come across, it is a relatively new area motivated by the increasing availability of large quantities of data. But what is data? And why now?

Data is everything. It’s what surrounds us and what our brain needs to function. It’s images, sounds, touch, as well as their (more structured) derivatives, such as music, texts, etc. But the key issue here is that data is not information. It must be turned into information via a set of processes and methods to identify trends and patterns that can pull all the different data together to make something meaningful.

Another important aspect of big data analytics is that it is an applied science. In other words, the power of data analytics is often seen in its applications. In fact, despite being motivated and backed-up by sophisticated mathematical and statistical techniques, we, data scientists, need to experiment, manipulate, and ultimately apply it to the real-world. In fact, big data analytics can be applied to digital humanities, sociology, psychology, criminology, cybersecurity, to name but a few.

This is why, during this short course, we shall consider specific case studies, which hopefully should provide an insightful overview on the main methods and applications of big data analytics.

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Introduction to Python for Big Data Analytics

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