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Course List

Data Mining

  • Course Code :
    ISY 443
  • Level :
    Undergraduate
  • Course Hours :
    3.00 Hours
  • Department :
    Department of Information Systems

Instructor information :

Area of Study :

This Data Mining course aims to extract knowledge in large data sets that is always needed for future predictions. Data mining has important and growing significance in many applications like security, banking, bioinformatics, medicine, social networks, optimization, and web page design and analysis. Course Goals:  Understand data mining as a set of analyzing concepts and techniques within different applications.  Build the required knowledge in many recent areas like fuzzy algorithms, rough sets, genetic algorithms, and neural networks.  Implement and elaborate different mining algorithms to get the required skills.  Be an effective member of teamwork through the assigned projects and assignments

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Data Mining

Knowledge discovery in databases, Data mining process, Data cleaning and preparation, Mining association rules, Classification, Prediction, Clustering, Web mining, Applications of data mining, Mining advanced databases. The course focuses on two subjects the essential data mining and knowledge representation techniques used to extract intelligence from data and expense and common problems from the fields of finance marketing, and operations/ service that demonstrate the use of the various techniques and the tradeoffs involved in choosing form among them. The area explicitly covered in the course is OLAP, Neural networks, Genetic algorithms, rule induction, fuzzy logic, Case- based reasoning, and rule- bases systems. Recent correlated software packages should be used thrush labs.

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Data Mining

Course outcomes:

a. Knowledge and Understanding:

1- Describe the data mining process as a KDD
2- Explain the need and scope of technical indicators for data mining
3- Demonstrate Machine Learning techniques
4- Identify Data mining tools in different context

b. Intellectual Skills:

1- Analyze large data sets
2- Integrate Data Mining techniques for supporting user decision
3- Select suitable Data mining techniques regarding the context

c. Professional and Practical Skills:

1- Handle large data sets using suitable tools
2- Design and develop knowledge extraction approach based on Data Mining techniques
3- Evaluate performance of Data Mining techniques in different context

d. General and Transferable Skills:

1- Work in a team

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Data Mining

Course topics and contents:

Topic No. of hours Lecture Tutorial/Practical
What is Data Mining? What Motivated Data 3 2 2
Mining? Data processing 3 2 2
Decision Making Systems, Modeling and Support 3 2 2
Decision Support Systems: An overview 3 2 2
Data Warehousing, Access, Analysis, etc 3 2 2
Modeling and Analysis 3 2 2
Decision Support Systems Development 3 2 2
Collaborative Computing Technologies 3 2 2

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Data Mining

Teaching And Learning Methodologies:

Teaching and learning methods
• Lectures
• Exercises
• Lab Work
• Cases

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Data Mining

Course Assessment :

Methods of assessment Relative weight % Week No. Assess What
Assignments 15.00 2
Class Discussion and Presentation 15.00 3
Final-term Examination 40.00 15
Midterm 1 15.00 6
Midterm 2 15.00 12

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Data Mining

Books:

Book Author Publisher
Data Mining Concepts and Techniques Jiawei Han MK
Introduction to Data Mining Pang-Ning Tan, Michael Steinbach and Vipin Kumar Addison-Wesley

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