Difference between Data Warehouse and Data Mining - DWDM Lectures



Difference between Data Warehouse and Data Mining – DWDM Lectures

Data Warehouse and Data Mining Lectures in Hindi for Beginners

#DWDM Lectures

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9 thoughts on “Difference between Data Warehouse and Data Mining – DWDM Lectures”
  1. Can u guys create sperate playlist of all the videos including DWDM als which helps us a lot in finding

  2. Introduction : Introduction to the multidisciplinary field of data mining,. Discussion on the

    evolution of database technology that has led to the need for data warehousing and data mining.

    Applications of Data Mining. [4]

    Data Warehousing And OLAP: Evolution of Data Warehousing, Data warehousing Concepts,

    Benefits of Data Warehousing, Data Warehouse Queries, Problems of Data Warehousing,

    Architecture of Data Warehouse, Data Warehouse Tools and Technologies, Data Mart, Reasons

    for creating Data Mart, Issues in Data Mart, Designing Data Warehouse, Dimensionality

    Modeling, Star Schema, Introduction to Online Analytical Processing (OLAP), OLAP
    Applications, Benefits of OLAP, Representation of Multidimensional Data, OLAP Tools ,

    MOLAP, ROLAP, HOLAP, DOLAP [8]

    Data Mining Primitives: Data preprocessing including data cleaning, data integration, data

    transformation. Definition and Specification of a generic data mining task. Description of Data

    mining query language with few example queries. Relationship between data warehouse and data

    mining.[5]

    Association Analysis: Different methods (algorithms) for mining association rules in transaction

    based databases. Classification of association rules, Apriori, frequent pattern growth algorithm.

    [5]

    Classification and Predictions: Different Classification algorithm, including C4.5, CART., use

    of genie index, decision tree induction, Bayesian classification [5]

    Clustering: Different types of clustering Methods -Partition based clustering, Density based

    clustering, and Distribution based clustering, Hierarchical clustering. K-Means and DBSCAN

    Clustering Algorithm.[5]

    Business Intelligence: Improvement in Decision Making Process, Need of Business Intelligence

    Program, Introduction to Business Intelligence, Analytics Spectrum, Value Drivers and

    Information Use, Performance Metrics and Key Performance Indicators, Horizontal and Vertical

    Use Case for Business Intelligence (BI). Applications of BI.

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