Course Introduction

This course will introduce the concepts, techniques, design and applications of data warehousing and data mining. Some systems for data warehousing and/or data mining will also be introduced. The course is expected to enable students to understand and implement classical algorithms in data mining and data warehousing. Students will learn how to analyze the data, identify the problems, and choose the relevant algorithms to apply. Then, they will be able to assess the strengths and weaknesses of the algorithms and analyze their behavior on real datasets.

Data warehousing testing or database testing is a sum of testing fundamentals + database skills. Its a challenging profile with lots of opportunities and scope isn't limited to db only as there is always a demand for this profile in warehousing and reporting.

Course Overview

  • Course Introduction
  • Knowledge discovery process
  • Why data warehouse & data mining

Data Warehouse

  • OLTP and OLAP
  • Data Cube
  • Data Warehouse modeling
  • Warehouse views
  • Data Warehouse Architectures

Data quality

Data preprocessing

Data Mining Techniques

  • Mining association rules
  • Rule based Classification

Data Mining Techniques

  • Rule based Classification
  • Rule based Classification

Data Mining Techniques

  • Other classification approaches
  • Cluster analysis
  • Hierarchical methods

Data preprocessing

  • Data cleaning
  • Data integration and transformation
  • Data reduction
  • Data compression
  • Discretization and concept hierarchy generation

Applications on Data Warehouse

  • Motivation and challenges of data mining
  • Data mining tasks
  • Types of Data
  • Data set types
  • Data mining applications

Distributed Data Base

Multimedia Data Base

Parallel Data Base








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