Data Analytics - Mining and Analysis of Big Data - Revised
Learn how to analyse big data using mining and clustering techniques, in this free online big data analytics course.
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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.Start Course Now
Introduction to Associative Rule Mining
Introduction to Associative Rule Mining Learning Outcomes
Association Rule Mining
Association Rule Mining (cont'd)
Introduction to Associative Rule Mining Lesson Summary
Introduction to Big Data
Introduction to Big Data Learning Outcomes
Big Data , A small introduction
Big Data , A small introduction(cont'd)
Introduction to Big Data Lesson Summary
Introduction to Clustering Analysis
Introduction to Clustering Analysis Learning Outcomes
Introduction to Clustering Analysis Lesson Summary
Experimentation and Active Learning
Experimentation and Active Learning : Learning Outcomes
Introduction to Experimentation and Active Learning
Introduction to Experimentation and Active Learning(cont'd)
An Introduction to Online Learning - Reinforcement Learning
An Introduction to Online Learning - Reinforcement Learning(cont'd)
Experimentation and Active Learning : Lesson Summary
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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