Data Science by R programming (Interme­diate level) R006

Instructor : Vivian Zhang, CTO@Supstat Inc

Outline:

Week 1:
Introducing Data mining

• what is and How to data mining
• steps to apply data mining to your data
• supervised versus unsupervised learning
• regression versus classification problems

Review of linear models

• simple linear regression
• logistic regression
• generalized linear models

Week 2:

evaluation model performance

• confusion matrices
• beyond accuracy
• estimating future performance

extension of linear models

• subset selection
• shrinkage methods
• dimension reduction methods

Week 3:

K-nearest neighbors models

• understanding kNN algorithm
• calculating distance
• choosing an appropriate k
• case study

Naive Bayes models

• understanding joint probability
• the naive bayes algorithm
• the laplace estimator
• case study

Week 4:
tree models

• regression trees- classification trees
• tree model with party
• tree model with rpart
• randomforest model
• GBM model

support vector machines

• maximal margin classifier
• support vector classifiers
• support vector machines
  Week 5:

market basket analysis

• understanding association rules
• the apriori algorithm
• case study

unsupervised learning

• K-means clustering
• Hierarchicl clustering
• case study

time series models

• fundamental concepts
• stationary time series
• ARIMA model
• seasonal model