Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data!
Learn how to apply selected statistical and machine learning techniques and tools to analyse big data. Everyone has heard of big data. Many people have big data. But only some people know what to do with big data when they have it. So what’s the problem? Well, the big problem is that the data is big—the size, complexity and diversity of datasets increases every day. This means that we need new technological or methodological solutions for analysing data. There is a great demand for people with the skills and know-how to do big data analytics.
Extract information from large datasets
This free online course equips you for working with these solutions by introducing you to selected statistical and machine learning techniques used for analysing large datasets and extracting information.
Of course, we can’t teach everything in one course, so we have focused on giving an overview of a selection of common methods. You will become familiar with predictive analysis, dimension reduction, machine learning and clustering techniques. You will also discover how simple decision trees can help us make informed decisions and you can dive into statistical learning theory.
Explore real-world big data problems
These methods will be described through case studies that explain how each is applied to solve real-world problems. You can also develop your coding skills by applying the techniques you’ve just learnt to complete hands-on tasks and obtain results.
Just as there are many statistical and machine learning methods for big data analytics, there are also many software packages (see ‘Requirements’ below) that can be used for this purpose. In this course, we will expose you to three such packages, so that you can start to become familiar with using different tools, and can gain confidence in going further with these packages or using others that may come your way.