Analysis Techniques

There are a large number of techniques which can be used for data
mining and statistical analysis. We pride ourselves in choosing the
technique which is most appropriate for a particular business
challenge, rather than always applying the same technology.

Techniques we use include:

  • Bayesian Statistics (eg. Naive Bayes)
  • Tree Based Methods (e.g. CART, Random Forests)
  • Clustering Methods (e.g. Hierarcical Clustering, K-means)
  • Neural Networks (e.g. Multi-Layer Perceptrons, RBF-Networks)

 Data Formats

We can read data in a wide variety of formats including

  • Comma Separated Variable (CSV) 
  • Microsoft Excel
  • SQL Database (e.g. Microsoft SQL Server, PostgresSQL, Oracle, MS-Access, MySQL and many others)
  • SPSS
  • rdata
  • SAS
  • Text files
  • A large number of other statistical and data analysis formats