Data Mining Technical Guide Course Content

CHAPTER 1. Data cleaning, data quality and input preparation

1.1 Attribute types

1.2 Missing values

1.3 Inaccurate values

1.4 Gathering the data together

1.4.1 Denormalize relational data
1.4.2 Integrating data from different sources
1.4.3 Overlay data
1.4.4 Degree of aggregation

CHAPTER 2. Data Mining learning algorithms and knowledge representation

2.1 Decision trees

2.1.1 Defining decision trees
2.1.2 Decision tree knowledge representation
2.1.3 Examples and exercises

2.2 Classification rules

2.2.1 Defining classification rules
2.2.2 Classification rules knowledge representation
2.2.3 Examples and exercises

2.3 Association rules

2.3.1 Defining association rules
2.3.2 Association rules and knowledge representation
2.3.3 Examples and exercises

2.4 Clustering

2.4.1 Defining clustering
2.4.2 Clustering and knowledge representation
2.4.3 Examples and exercises

CHAPTER 3. Evaluating credibility of results

3.1 Performance evaluation

3.2 Lift charts

3.3 ROC curves

CHAPTER 4. Implementing Data Mining models

4.1 Good to know: bagging, boosting and stacking

4.2 Rules and functions

4.3 Translating knowledge into practical business cases