Data Analytics - Mining and Analysis of Big Data
Learn the most effective and simple methods of mining and analysing big data, from the four V’s to clustering procedures
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The course begins by introducing you to the four V’s of big data. You will first learn about associative rule mining and when the association technique can be applied. This will include a clear overview of the key patterns that arise in mining. Next, you will learn about clustering analysis for big data. Here, the course will examine the difference between clustering and classification and guide you through the different types of clustering, including K-means clustering and K-meloids.
Next, you will learn about online and active learning. This will include a section covering experimentation in big data and the difference between an online and offline context of data creation. You will then be introduced to the concept of stochastic multi-choice problems and will be shown key techniques for navigating these problems. Finally, you will be introduced to the n-arm bandit problem and how to find solutions for the multi-arm bandit problem.
With big data being as important as it is for modern business, understanding data science and big data mining will make you a very valuable employee and bring your business to new heights. This free course will give you the skills you need to bring advanced data analysis to whatever business you are working with. So get started, and in just 3 hours you’ll have gained a great boost for your résumé and your career development.
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Having completed this course you will be able to: - Define association rule mining. - Explain mining frequent patterns and rules. - List when association rules can be applied. - Define the apriori algorithm. - List the four V’s of big data. - Explain why social media data can be hard to disambiguate . - Distinguish between clustering and classification. - Explain why clustering is used. - List the different types of clustering. - Define K-Means clustering. - Explain k-meloids. - Distinguish between online and offline context of creating data. - Define what stochastic multi-choice problems are. - Explain the n-arm bandit problem. - Describe some solutions for the multi-arm bandit problem.
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This Course has been revised!
For a more enjoyable learning experience, we recommend that you study the mobile-friendly republished version of this course.Take me to revised course.