Data Science Foundations Course

$1,380.00 excludes Tax

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Description

  • Introduction and business scenario
  • CRISP-DM
  • User interface
  • Creating and managing repositories
  • Loading data and summary statistics
  • Visualising data
  • Data preparation, handling missing values, normalisation, etc
  • k-Nearest Neighbour
  • Naïve Bayes
  • Linear Regression
  • Tree based methods, including bagging, random forest and GBM
  • Ensemble modelling
  • Bias, Variance, Overfitting and Underfitting
  • Split and cross validation
  • Evaluation methods & performance criteria
  • Optimisation and parameter tuning
  • Scoring models

Additional information

Delivery options

Point/click with RapidMiner, Code with R

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