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Elective Courses (4 required)
Advanced Logistic Regression
After taking this course, participants will be able to specify, implement and interpret the output of a variety of advanced logistic regression models not covered in the first course, "Logistic Regression."
Tuition: $369
Next dates available:
- To be Announced.
Financial Risk Modeling
This course teaches participants how to model financial events that have uncertainties associated with them.
Tuition: $369
Next dates available:
- To be Announced.
Forecasting - Advanced
This course covers multiple linear regression, autoregressive modesls (ARMA and ARIMA), seasonal adjustment, measures of forecast accuracy, and exponential smoothing. It goes into greater detail and depth that "Forecasting Time Series 1."
Tuition: $369
Next dates available:
- October 22, 2010
Introduction to Quantitative Risk Analysis
This course will cover the most important principles, techniques and tools used in modeling in Quantitative Risk Analysis.
Tuition: $369
Next dates available:
- October 01, 2010
- April 09, 2010
Logistic Regression
Logistic regression extends ordinary least squares (OLS) methods to model data with binary (yes/no, success/failure) outcomes. Rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.
Tuition: $369
Next dates available:
- September 10, 2010
- March 12, 2010
Cluster Analysis
This course will teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. Methods discussed include hierarchical clustering, k-means clustering, two-step clustering, and normal mixture models for continuous variables.
Tuition: $369
Next dates available:
- November 05, 2010
Discrete Choice Modeling and Conjoint Analysis
To design a product, respond to competitors or anticipate their moves, or develop pricing strategies, decision-makers need to integrate answers to such questions in a quantitatively useful fashion. Conjoint analysis is a marketing research technique that asks respondents to rank, rate, or choose among multiple products or services, where each product is described using multiple characteristics. The researcher uses experimental designs to manipulate the appearance of attribute levels in product concepts. After the data are collected, the researcher uses statistical methods to infer how the product attribute levels drive preference or choice. The researcher can use the resulting model to model how the market would choose among a set of competing product alternatives.
Tuition: $369
Next dates available:
- April 09, 2010
Decision Trees and Rule-Based Segmentation
Rule induction is an important component of data mining, and this course covers two main styles of generating rules.
Tuition: $369
Next dates available:
- January 22, 2010
Data Mining: Unsupervised Techniques
This course covers key unsupervised learning techniques - association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques.
Tuition: $369
Next dates available:
- October 15, 2010
Introduction to Data Mining
This course covers the two core paradigms that account for most business applications of data mining: classification and prediction. The course includes hands-on work with XLMiner, a data-mining add-in for Excel.
Tuition: $369
Next dates available:
- September 10, 2010
- March 05, 2010
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