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Masters of Computer Science

DATA WAREHOUSING & BIG DATA | Microsyllabus | MCS

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1 Introduction:

  • What motivated data mining? What is data Mining?
  • Types of database (Relational database, data warehouses, transactional database)
  • Functionalities of data mining – what kinds of pattern can be mined?
  • Association Analysis, cluster Analysis, outlier analysis, evolution analysis
  • Stages of knowledge discovery in database (KDD)
  • Setting up a KDD Environment
  • Issues in data warehouse and Data Mining
  • Application of Data Warehouse and Data mining

2 Data Warehousing for Data Mining:

  • Differences between operational database systems and
  • data warehouses
  • Data Warehouse Architecture
  • Distributed and Virtual Data Warehouse
  • Data Warehouse Manager
  • Data marts, Metadata, Multidimensional data model
  • From Tables and Spread Sheets to Data Cubes
  • Star schema, Snowflake schema and Fact constellation
  • Schema

3 OLAP Technology for Data Mining:

  • On-line analytical processing models and operations
  • (drill down, drill up, slice, dice, pivot)
  • Types of OLAP Servers: ROLAP versus MOLAP versus HOLAP
  • OLTP

4 Tuning for Data Warehouse:

  • Computation of Data Cubes, modeling
  • OLAP data, OLAP queries
  • Data Warehouse back end tools
  • Tuning and testing of Data Warehouse

5 Data Mining Techniques:

  • Data Mining definition and Task
  • KDD versus Data Mining
  • Data Mining techniques, tools and application

6 Data Mining Query Languages:

  • Data mining query languages
  • Data specification, specifying knowledge, hierarchy
  • specification, pattern presentation & visualization
  • specification
  • Data mining languages and standardization of data
  • Mining

7 Association Analysis:

  • Association Rule Mining (Market basket analysis)
  • Why Association Mining is necessary?
  • Pros and Cons of Association Rules
  • Apriori Algorithm

8 Cluster Analysis, Classification and Predication:

  • What is classification? What is predication?
  • Issues regarding classification and prediction (Preparing
  • the data for classification and prediction, Comparing
  • classification methods)
  • Classification by decision tree induction (Extracting
  • classification rules from decision trees)
  • Bayesian Classification
  • Classification by back propagation
  • Introduction to Regression (Types of Regression)
  • Clustering Algorithm (K-mean and K-Mediod Algorithms)

9 Advanced Concepts in Data Mining:

  • Mining Text Databases.
  • Mining the World Wide Web
  • Mining Multimedia and Spatial Databases
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