Data Analytics - Mining and Analysis of Big Data - Revised

Data Science
Free Course
Data Analytics the process of studying and evaluating data, is widely used in business, commercial industries and even government, it helps decision makes make more informed decisions. It is a growing part of modern business practices, from mining data, to defining big data, to creating clustering data models study this course to get a start on Data Analytics and Big Data.
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  • Duration

    2-3 Hours
  • Assessment

  • Certification

  • Responsive

  • Publisher





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In this online data analysis course Data Analytics - Mining and Analysis of Big Data you will be introduced to the concept of big data and to a number of techniques that are used to analyse and interpret big data.

The course starts off with introducing you to big data and lists the four V’s of big data. You will learn about associative rule mining, and about when association can be applied and the patterns that arise in mining.

In the second module, you will learn about clustering analysis. You will examine the difference between clustering and classification and the different types of clustering. You will also learn about K-means clustering and K-meloids.

In the last module, you will learn about online and active learning, learn about experimentation and the difference between an online and offline context of creating data. You will be introduced to the n-arm bandit problem and how to find solutions for the multi-arm bandit problem.

This free online course will be of great interest to professionals involved in data science and data analysis and any learner who wants to learn more about analysing big data using mining and clustering techniques.

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Learning Outcomes

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
  • 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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