Business Knowledge Series course
Presented by Gordon S. Linoff, co-founder of Data Miners, Inc. and co-author of Data Mining Techniques and Mastering Data Mining
Formats available | Duration | | |
Classroom: |
3.0 days | | |
|
This course addresses SAS Enterprise Miner software.
Introduction to Data Mining- what is data mining?
- directed and undirected data mining
- models
- profiling and prediction
Data Mining Methodology- why have a methodology?
- how data miners can inadvertently learn things that are not true
- translating business problems into data mining problems
- the importance of model stability
- finding the right input variables
- sampling to create balanced model sets
- partitioning to create training, validation, and test sets
- data preparation
- model assessment
Data Exploration- developing intuition about data
- data structure
- data types
- data values
- exploring distributions
- summary statistics
- histograms
- using SAS Enterprise Miner for data exploration
Regression Models- the null hypothesis
- statistical significance
- confidence bounds
- variance and standard deviation
- standardized values
- correlation
- linear regression
- logistic regression
- using SAS Enterprise Miner to build regression models
Decision Trees- decision trees as data exploration and classification tools
- decision trees for modeling and scoring
- decision trees for variable selection
- alternate representations of decision trees
- algorithms used to build decision trees
- splitting criteria
- recognizing instability and overfitting in decision tree models
- capturing interactions between variables
- using SAS Enterprise Miner to build decision trees
Neural Networks- origins of neural networks
- neural networks compared with regression
- algorithms used to train neural networks
- data preparation requirements for neural networks
- picking appropriate inputs for neural networks
- creating neural network models using SAS Enterprise Miner
Memory-Based Reasoning- similarity and distance
- distance metrics appropriate for different kinds of data
- the role of the training set in memory-based reasoning (MBR)
- combining the votes of several neighbors
- other K-nearest neighbor techniques
- collaborative filtering
- using the SAS Enterprise Miner MBR node
Clustering- more on similarity and distance
- the k-means algorithm
- divisive clustering
- agglomerative clustering
- data preparation for clustering
- interpreting clusters
- finding clusters with SAS Enterprise Miner
Survival Analysis- origins of survival analysis
- how business data is different from clinical data
- hazards and hazard charts
- retention curves and survival curves
- calculating survival from retention
- calculating hazards empirically
- parametric hazard models
- censoring
- competing risks
- survival-based forecasting
- using SAS code in SAS Enterprise Miner to create survival curves
Association Rules- market basket analysis
- association rules
- sequential pattern analysis
- using SAS Enterprise Miner to discover associations in retail data
Link Analysis- background on graph theory
- sphere of influence
- using link analysis to generate derived variables
- graph-coloring algorithm
- Kleinberg's algorithm
Genetic Algorithms- optimization techniques and problems (SAS/OR software)
- other algorithms
- linear programming problems
- genetic algorithms
Survey