Training Program on Data Mining / Predictive Analytics
Management Aspects of Data Mining / Predictive Analytics
Management Aspects of Data Mining / Predictive Analytics
- What business problems does data mining solve?
- Are there industry standards for applying data mining/predictive analytics to business problems?
- How to determine whether data mining can be applied to a business problem at hand?
- What projects are appropriate for data mining?
- How to form a successful data mining team?
- How to effectively manage data mining project?
- What is the role of data cleansing and data preprocessing in the success of a data mining project?
- What are the common project pitfalls and how to avoid them?
- Which data mining methods can handle irrelevant variables?
- Which data mining methods can handle redundant variables?
- Which data mining methods can deal with outliers/missing values?
- How to convert data mined information into actionable strategy?
- How to select a right data mining tool/software?
- What data structure is appropriate for applying data mining?
- Where does data mining work? Where does data mining not work?
- How to select the right data mining vendor/consultant?
- What information should a data mining consultant expect from a client in order to implement a project?
- What information/findings should you expect from a vendor?
- How to create an achievable data mining project plan?
- How to deploy a model?
- What is Predictive Model Markup Language (PMML)?
