A. Data Warehouses

  1. Introduction, and DMX
    • BI in brief,
    • Querying vs. On Line Processing vs. Data Mining
    • Data Warehose
    • OLTP vs. OLAP
    • Data Warehousing
    • ETL
    • Data Warehouse Architecture
    • Data Cube
    • OLAP Operators
    • MultiDimensional eXpressions
  2. Conceptual and Logical Design
    • Conceptual Level Design
      • A Conceptual Design Language: DFM
    • Logical Design
      • ROLAP, MOLAP, Star Schema, SnowFlake Schema
    • Mondrian + JasperServer

B. Data Mining

  1. Introduction
    • Introduction
      • Querying vs. Mining, CWA vs. OWA, Classification, Clustering, Associative Rules
    • Basic Concepts
      • Bayes Theorem, Sampled Gaussian Distribution
  2. Static Reporting
    • Introduction, Basic Concepts, Diagrams (Histogram, Box and wisker plot, Scatterplot matrix, Radar chart)
  3. Classification
    • Classification vs. Clustering
    • Types of classifiers: Linear Classifiers and Entropy Measure Based
    • Na├»ve Bayes
    • Decision Trees: C4.5 Algorithm
    • Other statistical techniques: Multilinear and Logistic Regression
  4. Clustering
    • K-means clustering
    • UPGMA Algorithm
    • Dendogram
  5. Data Cleaning and Outlier Detection
    • Data Cleaning
    • Outlier Detection
      • Proximity Based Outlier Detection, Density Based Outlier Detection, Clustering Based Outlier Detection
  6. Associative Rules
    • FP-Growth Algorithm
    • Rule Generation: A Priori techniques
    • Pattern Evaluation

C. Case Studies

  * Graph Data Mining
  * IBM Watson