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Data Mining Algorithms in SSAS, Excel, and R

Course Summary

Don't use data mining as a black box. Get a deep understanding of how the data mining algorithms work. This knowledge is not only theoretical; it helps you developing better models in production.


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    Course Syllabus

    Introduction to Data Mining
    - 31m 0s

    —Introduction and Modules 2m 5s
    —Assumptions and Overview 0m 53s
    —What Is Data Mining? 1m 34s
    —Data Mining Types and Tasks 2m 18s
    —Demo: SQL Server Queries 2m 0s
    —Demo: SSRS Report 2m 23s
    —Virtuous Cycle and CRISP Model 1m 44s
    —Data Flow 1m 24s
    —Types of Analyses and SQL Server Tools 2m 12s
    —Demo: SSAS Cube 6m 37s
    —Demo: Data Mining Model 4m 52s
    —Excel Tools and R 2m 4s
    —Summary 0m 50s
    Naive Bayes and Decision Trees
    - 34m 39s

    —Introduction 0m 30s
    —Naive Bayes Prior 2m 27s
    —Naive Bayes Posterior 2m 25s
    —Example and Usage 1m 31s
    —Demo: Naive Bayes in SSAS 4m 41s
    —Demo: Naive Bayes in Excel 3m 35s
    —Demo: Naive Bayes in R 5m 45s
    —Decision Trees 1m 59s
    —Decision Trees Example 1m 40s
    —Decision Trees Example and Usage 2m 40s
    —Demo: Decision Trees in SSAS 2m 56s
    —Demo: Decision Trees in Excel 2m 7s
    —Demo: Decision Trees in R 1m 44s
    —Summary 0m 34s
    Linear Regression, Regression Trees, and Support Vector Machines
    - 20m 48s
    Linear Regression, Neural Network, and Models Evaluation
    - 26m 19s
    Time Series
    - 24m 36s
    Clustering
    - 17m 36s
    Association Rules and Sequence Clustering
    - 24m 42s


Course Fee:
USD 29

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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