KasnebRevisions
Log inGet started
Papers in this syllabus

Paper 6

WAREHOUSING AND DATA MINING

Read study notes

This unit specifies competencies required to perform warehousing and data mining. It enables the learner to identify key concepts in warehousing and data, design and implement a data warehouse, mine and manage data and apply mined data

On completion, a candidate should be able to

  • Identify key concepts in Warehousing and Data
  • Design and implement a data warehouse
  • Mine and Manage Data
  • Utilize mined data

Content

  1. 1

    Identify key concepts in Warehousing and Data

    Identify key concepts in Warehousing and Data

    1. ❑ Definition of data warehousing ❑ Characteristics of a data warehouse ❑ The Data Warehousing Process ❑ Components of Data warehouse ✓ Load manager ✓ Warehouse Manager ✓ Query Manager ✓ End-user access tools ❑ Users and Uses of Data warehouse ❑ Basic Data Warehouse Architecture ❑ Data warehousing and online transaction processing systems (OLTP) ❑ Online Analytical Processing (OLAP) ✓ Basic analytical

      1. perations of OLAP - Roll-up - Drill-down - Slice and dice - Pivot (rotate) ❑ Data Warehousing Schemas ✓ Star Schema ✓ Snowflake Schema ✓ Fact Constellation Schema Advantages and Disadvantages Data warehousing

  2. 2

    Design and implement a data warehouse

    Design and implement a data warehouse

    1. ❑ Definition of: ✓ Metadata ✓ Data Mart ❑ Importance of metadata in data warehouses ❑ Types of Metadata ✓ Operational Metadata ✓ Extraction and Transformation Metadata ✓ End-User Metadata ❑ Metadata Interchange Initiative ❑ Metadata Interchange Standard Framework ❑ Metadata Repository ❑ Reasons for creating a data mart ❑ Types of Data Marts ✓ Dependent Data Marts ✓ Independent Data Marts ✓ Hybrid Data Marts ❑ Steps in Implementing a Data Mart ❑ Steps to Implement Data Warehouse ❑ Coping with business risks associated with a Data warehouse implementation ✓ Enterprise strategy ✓ Phased delivery ✓ Iterative Prototyping ❑ Data Warehouse Tools ✓ MarkLogic ✓ Oracle ✓ Amazon RedShift ❑ Best practices to implement a Data Warehouse

  3. 3

    Mine and Manage Data

    Mine and Manage Data

    1. ❑ Definition of Data Mining ❑ Data Mining tools ✓ R ✓ Python ✓ Orange Data Mining ✓ SAS Data Mining ✓ DataMelt Data Mining ✓ Rattle ✓ Rapid Miner ❑ Data Mining Functions ❑ Data Mining Techniques ✓ Cluster Analysis ✓ Induction ✓ Decision trees ✓ Rule induction ✓ Neural networks ❑ Criteria for choosing a Data Mining Software ❑ Data mining functionalities and the variety of knowledge they discover ✓ Characterization ✓ Discrimination ✓ Association analysis ✓ Classification ✓ Prediction ✓ Clustering ✓ Outlier analysis ✓ Evolution and deviation analysis ❑ Issues in Data Mining ✓ Security and social issues ✓ User interface issues ✓ Mining methodology issues ✓ Performance issues ✓ Data source issues

  4. 4

    Utilize mined data

    Utilize mined data

    1. ❑ Data Mining Applications Financial Analysis Telecommunication Industry Intrusion Detection Retail Industry Higher Education Energy Industry Spatial Data Mining Biological Data Analysis Other Scientific Applications Manufacturing Engineering Criminal Investigation Counter-Terrorism ❑ Technology Trends in Data Mining