Data Mining and Analysis Schulung

Alle Preise zzgl. MwSt

Kurs Code

datama

Dauer

28 hours (üblicherweise 4 Tage inklusive Pausen)

Überblick

Zielsetzung: Die Teilnehmer können große Datensätze analysieren, Muster extrahieren und die richtige Variable auswählen, die sich auf die Ergebnisse auswirkt, damit ein neues Modell mit prädiktiven Ergebnissen prognostiziert wird .

Machine Translated

Schulungsübersicht

  1. Data preprocessing

    1. Data Cleaning
    2. Data integration and transformation
    3. Data reduction
    4. Discretization and concept hierarchy generation
  2. Statistical inference

    1. Probability distributions, Random variables, Central limit theorem
    2. Sampling
    3. Confidence intervals
    4. Statistical Inference
    5. Hypothesis testing
  3. Multivariate linear regression

    1. Specification
    2. Subset selection
    3. Estimation
    4. Validation
    5. Prediction
  4. Classification methods

    1. Logistic regression
    2. Linear discriminant analysis
    3. K-nearest neighbours
    4. Naive Bayes
    5. Comparison of Classification methods
  5. Neural Networks

    1. Fitting neural networks
    2. Training neural networks issues
  6. Decision trees

    1. Regression trees
    2. Classification trees
    3. Trees Versus Linear Models
  7. Bagging, Random Forests, Boosting

    1. Bagging
    2. Random Forests
    3. Boosting
  8. Support Vector Machines and Flexible disct

    1. Maximal Margin classifier
    2. Support vector classifiers
    3. Support vector machines
    4. 2 and more classes SVM’s
    5. Relationship to logistic regression
  9. Principal Components Analysis

  10. Clustering

    1. K-means clustering
    2. K-medoids clustering
    3. Hierarchical clustering
    4. Density based clustering
  11. Model Assesment and Selection

    1. Bias, Variance and Model complexity
    2. In-sample prediction error
    3. The Bayesian approach
    4. Cross-validation
    5. Bootstrap methods

Erfahrungsberichte

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