Data Mining Mistakes and How to Avoid Them

taught by John Elder, IV

Brief Description:

The very nature of data mining makes it prone to error.  This course reviews and illustrates the typical sources of error, and how to avoid them.

Instructor(s):
Level: Intermediate

Who Should Take This Course:

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.

Dates:
October 12, 2012 to October 26, 2012
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

Register Online -$199
Register Online -$179 (you must be affiliated with a college, university or high school)

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. Multiple course registrations 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

Aim of Course:

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):

• Data Mining

Prerequisite(s):

If you are unclear as to whether you have mastered the requirements, try these placement tests here.

If you have not had any data mining experience, taking an introductory course, such as Introduction to Predictive Modeling, may allow you to participate more fully in discussions.

Course Program:

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.

Organization of the Course:

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.

Course Text:

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:

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

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Yes, I want to register for:

Data Mining Mistakes and How to Avoid Them

taught by John Elder, IV

Instructor(s):
Dates:
October 12, 2012 to October 26, 2012
Course Fee: $199
Academic Rate: $179

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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