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Data Warehouse and Data Mining Fundamentals

(BWC-DATAWM.AOP1) / ISBN : 978-1-64459-791-0
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1

Modeling for Analytical Databases

  • Data Warehouses
  • Data Marts
  • Modeling Analytical Data Structures
  • Loading Data into Analytical Databases
2

Enterprise Data Modeling

  • Enterprise Data Management
  • The Enterprise Data Model
3

Introduction to Analytics

  • What’s in a Name?
  • Why the Sudden Popularity of Analytics and Data Science?
  • The Application Areas of Analytics
  • The Main Challenges of Analytics
  • A Longitudinal View of Analytics
  • A Simple Taxonomy for Analytics
  • The Cutting Edge of Analytics: IBM Watson
  • Summary
  • References
4

Introduction to Predictive Analytics and Data Mining

  • What Is Data Mining?
  • What Data Mining Is Not
  • The Most Common Data Mining Applications
  • What Kinds of Patterns Can Data Mining Discover?
  • Popular Data Mining Tools
  • The Dark Side of Data Mining: Privacy Concerns
  • Summary
  • References
5

Standardized Processes for Predictive Analytics

  • The Knowledge Discovery in Databases (KDD) Process
  • Cross-Industry Standard Process for Data Mining (CRISP-DM)
  • SEMMA
  • SEMMA Versus CRISP-DM
  • Six Sigma for Data Mining
  • Which Methodology Is Best?
  • Summary
  • References
6

Data and Methods for Predictive Analytics

  • The Nature of Data in Data Analytics
  • Preprocessing of Data for Analytics
  • Data Mining Methods
  • Prediction
  • Classification
  • Decision Trees
  • Cluster Analysis for Data Mining
  • k-Means Clustering Algorithm
  • Association
  • Apriori Algorithm
  • Data Mining and Predictive Analytics Misconceptions and Realities
  • Summary
  • References
7

Algorithms for Predictive Analytics

  • Naive Bayes
  • Nearest Neighbor
  • Similarity Measure: The Distance Metric
  • Artificial Neural Networks
  • Support Vector Machines
  • Linear Regression
  • Logistic Regression
  • Time-Series Forecasting
  • Summary
  • References
  • Summary

1

Modeling for Analytical Databases

  • Designing a Star Schema Fact Table
2

Enterprise Data Modeling

  • Developing an Enterprise Conceptual Model
3

Introduction to Predictive Analytics and Data Mining

  • Creating a Decision Tree in Python
  • Creating a Decision Tree in KNIME
4

Data and Methods for Predictive Analytics

  • Running k-Means Clustering Algorithm in KNIME
5

Algorithms for Predictive Analytics

  • Using the k-Nearest Neighbor Algorithm
  • Using ANN and SVM for Prediction Type Analytics Problems
  • Implementing Linear Regression in Python
  • Implementing Linear Regression Model in KNIME

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