Course Overview
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Course Synopsis
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The focal area of this course is to provide awareness of data warehouse basic components, importance of data warehouse in business, important steps and techniques to be considered during data warehouse development, and future trends and usage of data warehouse. This course will provide the knowledge and skills to design and implement a Data Warehouse. Presented in the regular lectures and 5 lab lectures, participants will experience all phases of a Data Warehouse implementation from Extract, Transform and Load (ETL) of the data to running queries on the final database. The course will have a look at OLAP, MOLAP, ROLAP and other systems and how they differ from one another. It will provide an understanding of how to use data warehousing techniques in implementing it on web.
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Course Learning Outcomes
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After completing this course you should be able to:
- Design and implement a Data Warehouse
- Define the basic concepts and importance of data warehouse
- Identify the business areas where data warehouse is required
- Use data warehouse for data mining projects
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Course Calendar
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1
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Introduction & Overview
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2
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Introduction & Overview
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3
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Introduction & Overview
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4
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Introduction & Overview
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5
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Introduction & Overview
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8
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De-Normalization Techniques
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9
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Issues of De-Normalization
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Assignment No. 1
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10
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Online Analytical Processing (OLAP)
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Quiz No. 1
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11
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Multidimensional OLAP (MOLAP)
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12
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Relational OLAP (ROLAP)
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13
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Dimensional Modeling (DM)
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14
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Process of Dimensional Modeling
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15
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Issues of Dimensional Modeling
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GDB
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16
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Extract Transform Load (ETL)
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Quiz No. 2
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18
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ETL Detail: Data Extraction & Transformation
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Mid-Term Exam
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19
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ETL Detail: Data Cleansing
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20
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Data Duplication Elimination & BSN Method
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21
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Introduction to Data Quality Management (DQM
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22
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DQM: Quantifying Data Quality
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24
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Need for Speed: Parallelism
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25
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Need for Speed: Hardware Techniques
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26
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Conventional Indexing Techniques
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27
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Need for Speed: Special Indexing Techniques
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Assignment No. 2
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29
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A Brief Introduction to Data mining (DM)
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30
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What Can Data Mining Do?
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31
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Supervised Vs. Unsupervised Learning
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32
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DWH Lifecycle: Methodologies
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33
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DWH Implementation: Goal Driven Approach
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34
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DWH Implementation: Goal Driven Approach
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35
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DWH Life Cycle: Pitfalls, Mistakes, Tips
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Quiz No. 3
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37
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Case Study: Agri-Data Warehouse
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38
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Case Study: Agri-Data Warehouse
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39
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Web Warehousing: An introduction
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40
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Web Warehousing: Issues
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41
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Data Transformation Services
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Quiz No. 4
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43
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Extracting Data Using Wizard
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45
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Data Transformation and Standardization
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Final Term Exam
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