Data Mining Mistakes and How to Avoid Them

October 11, 2013 to October 25, 2013 October 10, 2014 to October 24, 2014

Data Mining Mistakes and How to Avoid Them

taught by John Elder, IV

The very nature of data mining makes it prone to error. There are a plethora of algorithms and techniques, which heighten the risks of overfitting, or misinterpreting chance phenomenon as real. Participants will review practical cases that illustrate the hazards - both obvious and subtle - of data mining, and they will learn how to avoid them.

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

This course follows an online seminar format - instructor video presentations together with a discussion forum for interaction among students, and between students and instructor.  The topics covered include:

- The data mining process

- Review of available software

- The top 10 data mining "mistakes," and how to avoid them.

There are no homework exercises in this course.  The main purpose of this course is to afford participants the opportunity to ask questions and discuss topics of interest with one of the leading data mining practitioners in the US.

Data Mining Mistakes and How to Avoid Them

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

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. Those registering for multiple courses, Statistics.com's PASS students, and those affiliated with other academic institutions may be entitled to tuition discounts; read more.

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Data Mining Mistakes and How to Avoid Them

taught by John Elder, IV

Data Miners, business analysts, instructors teaching data mining.  This course is aimed at the analyst who has some data mining knowledge and/or experience.  Those new to the field (for example, those who have taken an introductory course) will find it deepens their understanding across the board.  Those with more experience will find some topics familiar, but will also find useful perspective in many topics.

Intermediate

This course takes place over the internet, at statistics.com for 2 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.

This course typically requires 5-10 hours per week. At the beginning of each week, you receive the relevant material.   Discussion among participants is encouraged. The instructor will provide answers and comments.

Credit:
This course does not offer continuing education units (CEU's) like many of the other courses at Statistics.com. For those successfully completing the course,  a record of course completion will be issued by Statistics.com, upon request.

There is no required text for this course.  A useful companion text is Handbook of Statistical Analysis and Data Mining Applications by Robert Nisbet, John Elder, and Gary Miner, published by Elsevier and available here or at Amazon here.  Elsevier is pleased to offer a 15% discount for people taking the course - the discount code is: StatsEdu.  Good for all countries on ElsevierDirect. Follow the link and enter the promo code when checking out.

Software is discussed in the course, but students are not expected to work with software.