Use Data Mining Techniques And Modern Tools To Discover Trends And Patterns In Realistic Datasets

  
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Data Mining (CSBP320) 

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This course introduces the concepts, issues, tasks and techniques of data mining process. Topics include data preparation and feature selection, association rules, classification, clustering, evaluation and validation, and sequence mining, and data mining applications. The course mainly focuses on data mining issues such as data selection and cleaning, machine learning techniques to ``learn" knowledge that is ``hidden" in data, and the reporting and visualization of the resulting knowledge. The course illustrates data mining process by examples of practical applications from the life sciences, computer science, and commerce. Several machine learning topics including classification, prediction, and clustering will be covered.

Learning Outcomes
At the end of the course, students will be able to :

  1. Evaluate Different Data Mining Models/Techniques With Respect To Their Performance Accuracy.
  2. Explain The Basic Principles Of The Data Mining Process.
  3. Function On Teams And Communicate Effectively In Written And Oral Forms.
  4. Prepare Data For Mining And Exploration
  5. Use Data Mining Techniques And Modern Tools To Discover Trends And Patterns In Realistic Datasets

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