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.

Analysts of business data, consultants, MBAs seeking to update their knowledge of quantitative techniques, managers who want to see what data-mining can do, and anyone who wants a practical hands-on grounding in basic data-mining techniques.

Introduction to Data Mining

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Introduction to Data Mining

This course will introduce you to the basic concepts in data mining. Data mining, the art and science of learning from data, covers a number of different procedures. This course covers the two core paradigms that account for most business applications of data mining: classification and prediction. In both cases, data mining takes data where a variable of interest is known and develops a model that relates this variable to a series of predictor variables. In classification, the variable of interest is categorical ("purchased something" vs. "has not purchased anything"). In prediction, the variable of interest is continuous ("dollars spent"). Five techniques will be used: k-nearest neighbors, classification and regression trees (CART), neural nets, logistic regression and multiple linear regression. The course will also cover the use of partitioning to divide the data into training data (data used to build a model), validation data (data used to assess the performance of different models, or, in some cases, to fine tune the model) and test data (data used to predict the performance of the final model). The course includes hands-on work with XLMiner, a data-mining add-in for Excel.

• Survey of Statistics for Beginners, or Introduction to Statistics for Beginners
• Introduction to Statistics 1: Sampling and Inference
• Introduction to Statistics 2: Working with Bivariate Data)

If you are unclear as to whether you have mastered the requirements, test yourself with these placement exams here.

Participants should also be familiar with multiple linear regression, which can be studied in the "Regression Analysis" course.

SESSION 1: Introduction

• Core ideas in data mining
• Supervised and unsupervised learning
• The steps in data mining
• SEMMA
• Preliminary steps
  • Sampling from a database
  • Pre-processing and cleaning the data
  • Partitioning the data
• Building a model
  • An example with linear regression
• K-nearest neighbor

SESSION 2: Classification

• Judging the performance of classification algorithms
• Classification trees
• Logistic regression
• Lift

SESSION 3: Neural nets

• Neural nets
• Comparing different models

SESSION 4: Prediction

• Multiple linear regression
• Regression trees

This course takes place over the internet, at statistics.com 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 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 (Program in Advanced 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).

As you begin the class, you will be asked to specify your category.

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a record of course completion will be issued by Statistics.com, upon request.

The text is Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 2nd Edition by Shmueli, Patel and Bruce, from Wiley. Please PREORDER* this text. It can be ordered directly from Wiley using the previous link. Wiley offers statistics.com customers a 15% discount on this book (and all other statistics titles): enter the code aff15 in the Promotion Code field when prompted during checkout and click the Apply Discount button. (If you are located in Asia, the web procedure for your location may not accept this discount -- try calling your regional Wiley representative.) A six-month license to XLMiner comes bundled with the course text.

*If the text does not arrive in time for the course, please provide us with proof of purchase and we will supply you with a .pdf of appropriate chapters in the course itself.

This is a hands-on course. Participants will apply data mining algorithms to real data, and interpret the results. XLMiner - a data-mining add-in for Excel, will be illustrated in the course and its use and output will be explained. A six-month license to XLMiner comes bundled with the course text. The instructor is familiar with XLMiner and the illustrations and assignments are fully integrated with XLMiner. Any software capable of handling the routines covered (i.e. most data mining software) may be used. For more information on the above mentioned statistical software, please click here.

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