Through this course, the students will study how to use RapidMiner in data analyzing and prediction. Besides the basic skills in data preprocessing, learners will be educated to apply various algorithms and operators in solving real world problems. Every student is required to submit (including an oral presentation) a term paper with the subject of his/her interest. In addition, students are also required to survey and collect data from numerous on-line free access data resources.  


Week 1	
I.1 Retrieve Data              
I.2 Data Filtering and Sorting
Week 2	
I.3 Joining and grouping the attributes                    
I.4 Add and select attributes
Week 3	
I.5 Change attribute type and role 
I.6 Model building
Week 4	
II.1 Missing Variables
II.2 Normalizing the data and find the outliers
Week 5	
II.3 Variables Aggregation, pivoting and renaming
II.4 Macro and Sampling
Week 6	
II.5 Looping, Branching and Setting Sample Limit 
II.6 Looping, Branching and Setting the Smallest Sample
Week 7		
II.7 Append and Store Files
II.8 Join Variables from Multiple Files
Week 8	
II.9 Store and Apply ZIP Files
III.1 Classification Model
Week 9	
III.2 Model Scoring
III.3 Split Data and Forecasting
Week 10	
III.4 Cross Validation
III.5 Visualization of Model Comparision
Week 11	
IV.1 Price Risk Clustering
IV.2 Credit Risk Modeling
Week 12	
IV.3 Optimize Credit Risk Modeling
IV.4 Customer Churn Model
Week 13	
IV.5 Linear Regression Model 
IV.6 Optimizing Linear Regression Model
Week 14	
IV.7 Logistic Regression Model 
IV.8 Time Series Moving Average and Trend
Week 15	
IV.11 Basket Analysis 
IV.12 Text Mining-Prediction
Week 16	
IV.13 Text Mining-Association Rule
IV.14 Machine Maintain Prediction  
Week 17	
IV.15 Web Crawling
IV.16 Web Mining
Week 18	
Term Paper Presentation