Data wharehousing and Mining

Course Objectives:

 The course aims to introduce students to methods used in data mining and characterization needs to mechanization and the establishment of computer systems, and also aims to be the exploration of complex data types and understand the applications of structural query language

Course Content:

• Principles and structure of data warehousing, methods of analysis process (OLTP) and direct (OLAP).
• Extraction and representation, cleaning and delivery of data.
• Digging and cutting, data management and data warehouse architecture.
• Patterns of data warehouses (star - SnowFlake - tower).
• Ways to represent data warehouses (ROLAP - MOLAP - HOLAP).
• Improve performance and compilation of data warehouses and division.
• The importance of clean data in data mining techniques and basic statistics.
• Techniques for exploring data (tree resolution - neural networks - a market basket analysis).
• Application areas such as cross-selling – customer retention - detect fraud - pricing - the allocation of sources).
• Use of software ready to explore the data.

Skills expected from this course:

Upon finishing this course, the student should: 

• Identify the foundations and techniques for integrating and cleaning of data.
• Know the role of OLAP techniques and explore data to solve business information problems.
• Design and establish  data warehouses to solve the problems of different kinds of information.
• Learn about the benefits and techniques of accessing data stores through the Web.
• Know assessment techniques to support exploration and prospecting data (KDD).

Apply data mining tools on actual situations.

Textbook:
Data Mining Practice Machine Learning Tools and Techniques Ian witten Morgan Kaufmann bookstore 2nd Edition 2005
Reference:
Elliot King, "Data Warehousing and Data Mining: Implementing Strategic Knowledge Management," 1st Edition, 2000, Computer Technology Research Corporation, ISBN 1566070782