Data Mining - STATISTICA Data Miner Practicum

The purpose of this course is to take the knowledge learned in Introduction to Data Mining and Data Mining: Unsupervised Techniques and apply it using STATISTICA Data Miner, focusing on the automated and semi-automated modeling features of the package.

Analysts who are familiar with the data mining concepts, and who need to acquire facility in STATISTICA Data Miner.

Data Mining - STATISTICA Data Miner Practicum

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Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise. Multiple course registrations may be entitled to tuition discounts; read more.

Data Mining - STATISTICA Data Miner Practicum

The purpose of this course is to take the knowledge learned in Introduction to Data Mining and Data Mining: Unsupervised Techniques and apply it using STATISTICA Data Miner. There will be practical exercises in prediction and classification using neural nets, classification and regression trees, logistic regression, Naive Bayes, and k-nearest neighbor. There will also be exercises in unsupervised methods: clustering, principal components and association rules.

This course is a core requirement or elective in the following Program(s) in Analytics and Statistical Studies (PASS):

• Data Mining

SESSION 1: Introduction to CRM Modeling and the Statistica Data Miner

SESSION 2: Classification for Retention with Logistic Regression and Ensembles

SESSION 3: Behavioral Modeling with Temporal Abstractions

SESSION 4: Credit Risk Modeling and Credit Scoring

This course takes place over the internet at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

The text is Handbook of Statistical Analysis and Data Mining Applications by Robert Nisbet, John Elder, and Gary Miner, published by Elsevier and available here.

Course registrants will be given access to STATISTICA Data Miner for use during the course.

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