Training Program on Data Mining / Predictive Analytics

Methodological Aspects of Data Mining / Predictive Analytics

  1. What is data mining?
  2. What is NOT data mining?
  3. What is a foundation of data mining?
  4. What are data mining myths?
  5. What is the difference between statistics and data mining?
  6. Why is intuition not enough?
  7. Why is traditional statistics not enough?
  8. Who uses data mining?
  9. Which project is a good candidate for a data mining application?
  10. What is the taxonomy of data mining? Data mining vs. text mining vs. web mining.
  11. How many targets can be considered simultaneously? Binary/categorical target vs. continuous target.
  12. What is ‘soft' modeling? Soft vs. hard modeling.
  13. What is an Occam's Razor?
  14. What is the Breiman uncertainty principle?
  15. What is a curse of dimensionality?
  16. What are Data Mining Processes (CRISP-DM and SEMMA)?
  17. Does data mining require tons of data?
  18. Can data mining identify a causal relationship? Correlation vs. causality.
  19. Are data mining tools industry-specific? Are data mining tools domain-specific?
  20. What is the trend in the Data Mining Industry?
  21. Is data mining a threat to privacy and data security?