Predictive Analytics workshop – John Elder

Tools for Discovering Patterns in Data: Extracting Value from Tables, Text, and Links

Some business applications:

• Reduce customer and member dropout with Churn Analysis
• Increase revenue with shopping cart analysis to find the product recommendations for more / cross selling
• Detecting fraudulent transactions and cases
• Risk management of credit scoring
• Segmentation of customers for direct marketing campaigns across multiple channels
• Predicting the need for operation and maintenance control
• Streamlining of processes by classifying cases for different treatment strategies
• Reputation analysis and monitoring of discussions about products and brands in social media, blogs and other websites
• Predicting the inventory and staffing requirements in stores.
• Use predictive analytics for effectively and efficiently accomplishment of mission.

Find the useful information hidden in your data!

This course surveys computer-intensive methods for inductive classification and estimation, drawn from Statistics, Machine Learning, and Data Mining. Dr. Elder will describe the key inner workings of leading algorithms, compare their merits, and (briefly) demonstrate their relative effectiveness on practical applications. We’ll first review classical statistical techniques, both linear and non-parametric, then outline the ways in which these basic tools are modified and combined into powerful modern methods.

The course emphasizes practical advice and focuses on the essential techniques of Re-sampling, Visualization, and Ensembles. Actual scientific and business examples will illustrate proven techniques employed by expert analysts. Along the way, major relative strengths and distinctive properties of the leading commercial software products for Data Mining will be discussed.

Intended Audience:

Those from industry and academia who work with data and wish to understand recent developments in pattern discovery, data mining, and inductive modeling. At the conclusion of this course, one should be able to discern the basic strengths of competing methods and select the appropriate tools for one’s applications.

Participants should have prior working experience with computers and interest in applied statistical techniques. (It helps, as well, to have a motivating application you wish to solve.)

A note about the course scope:

Each of the major topics discussed could comprise a semester-long course if presented in full detail! What this (intensive) short course provides is a broad overview of the highlights, drawing connections between major developments in the diverse fields that contribute to Predictive Analytics, including cutting-edge ways to mine text and graphical networks. Previous participants have found this “big picture” to be very useful for identifying techniques to use immediately, as well as approaches worthy of further exploration, for research or practical problem-solving.