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Data Analysis Techniques - XML

Data analysis techniques are statistical and machine learning methods used to extract valuable insights from large datasets and support decision-making. Various techniques exist depending on the purpose, such as regression analysis, cluster analysis, and principal component analysis, applied for prediction, classification, segmentation, and dimensionality reduction. In the modern big data era, these techniques have become essential tools across all industries including marketing, healthcare, finance, and manufacturing.

data analysis statistics machine learning regression analysis cluster analysis principal component analysis data science predictive analytics

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<items>
  <item>
    <code>01</code>
    <slug>regression-analysis</slug>
    <name>Regression Analysis</name>
    <description>A statistical method that models relationships between variables for prediction.</description>
    <category>Predictive Analysis</category>
    <learningType>supervised</learningType>
  </item>
  <item>
    <code>02</code>
    <slug>cluster-analysis</slug>
    <name>Cluster Analysis</name>
    <description>An unsupervised learning method that groups data based on similarity.</description>
    <category>Segmentation</category>
    <learningType>unsupervised</learningType>
  </item>
  <item>
    <code>03</code>
    <slug>principal-component-analysis</slug>
    <name>Principal Component Analysis</name>
    <description>A dimensionality reduction technique that compresses high-dimensional data into lower dimensions.</description>
    <category>Dimensionality Reduction</category>
    <learningType>unsupervised</learningType>
  </item>
  <item>
    <code>04</code>
    <slug>factor-analysis</slug>
    <name>Factor Analysis</name>
    <description>A method that extracts latent factors underlying observed data.</description>
    <category>Dimensionality Reduction</category>
    <learningType>unsupervised</learningType>
  </item>
  <item>
    <code>05</code>
    <slug>discriminant-analysis</slug>
    <name>Discriminant Analysis</name>
    <description>A method that creates discriminant functions to classify data into groups.</description>
    <category>Classification</category>
    <learningType>supervised</learningType>
  </item>
  <item>
    <code>06</code>
    <slug>time-series-analysis</slug>
    <name>Time Series Analysis</name>
    <description>A method that analyzes trends and periodicity in data along a time axis.</description>
    <category>Predictive Analysis</category>
    <learningType>supervised</learningType>
  </item>
  <item>
    <code>07</code>
    <slug>decision-tree-analysis</slug>
    <name>Decision Tree Analysis</name>
    <description>A method that builds tree structures through conditional branching for classification and prediction.</description>
    <category>Classification and Prediction</category>
    <learningType>supervised</learningType>
  </item>
  <item>
    <code>08</code>
    <slug>association-analysis</slug>
    <name>Association Analysis</name>
    <description>A method that discovers association rules between items.</description>
    <category>Pattern Discovery</category>
    <learningType>unsupervised</learningType>
  </item>
  <item>
    <code>09</code>
    <slug>correlation-analysis</slug>
    <name>Correlation Analysis</name>
    <description>A method that measures the strength and direction of relationships between variables.</description>
    <category>Relationship Analysis</category>
    <learningType>unsupervised</learningType>
  </item>
  <item>
    <code>10</code>
    <slug>abc-analysis</slug>
    <name>ABC Analysis</name>
    <description>A method that classifies items into three groups based on importance.</description>
    <category>Prioritization</category>
    <learningType>unsupervised</learningType>
  </item>
</items>