Data mining is the way of examining different types of large databases to generate new information. Knowing to mine data can benefit your organization in gathering essential data to improve the performance of its operation.
Taking this [course_title] will help you utilize database available on the internet. You will be provided with methods on figuring the algorithms for data mining for you to easily gather the necessary data your organization needs.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: MIT
|Data Mining Overview – Part 1||01:15:00|
|Data Mining Overview – Part 2||00:30:00|
|Classification and Bayes Rule, Naïve Bayes||01:15:00|
|Discriminant Analysis Example 2 Fisher’s Iris data||01:00:00|
|Logistic Regression Case – Part 1||01:30:00|
|Logistic Regression Case – Part 2||00:45:00|
|Multiple Regression Review||01:30:00|
|Multiple Linear Regression in Data Mining||01:15:00|
|Comparison of Data Mining Techniques||00:10:00|
|K-Means Clustering, Hierarchical Clustering||00:45:00|
|Association Rules (Market Basket Analysis)||00:45:00|
|Submit Your Assignment||00:00:00|
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