Training Program on Data Mining / Predictive Analytics
Methodological Aspects of Data Mining / Predictive Analytics
Methodological Aspects of Data Mining / Predictive Analytics
- What is data mining?
- What is NOT data mining?
- What is a foundation of data mining?
- What are data mining myths?
- What is the difference between statistics and data mining?
- Why is intuition not enough?
- Why is traditional statistics not enough?
- Who uses data mining?
- Which project is a good candidate for a data mining application?
- What is the taxonomy of data mining? Data mining vs. text mining vs. web mining.
- How many targets can be considered simultaneously? Binary/categorical target vs. continuous target.
- What is ‘soft' modeling? Soft vs. hard modeling.
- What is an Occam's Razor?
- What is the Breiman uncertainty principle?
- What is a curse of dimensionality?
- What are Data Mining Processes (CRISP-DM and SEMMA)?
- Does data mining require tons of data?
- Can data mining identify a causal relationship? Correlation vs. causality.
- Are data mining tools industry-specific? Are data mining tools domain-specific?
- What is the trend in the Data Mining Industry?
- Is data mining a threat to privacy and data security?
