Data Mining - R

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Data Mining in R 

taught by Inbal Yahav

Aim of Course:

In this online course, “Data Mining in R,” you will learn how to partition data and use a holdout sample, how to measure the performance of predictive models, and what to do about the problem of overfitting.  Popular classification methods (logistic regression, k-nearest-neighbors, classification trees) and prediction methods (linear regression and regression trees) are discussed.  Collaborative filtering and association rules are also covered.

This course may be taken individually (one-off) or as part of a certificate program.

Course Program:

WEEK 1: Getting Started

  • Data prep
  • Data partitioning (holdout data)
  • Measuring the performance of classification and prediction models
  • K-nearest neighbors classification

WEEK 2: Linear Regression and CART

  • Multiple linear regression
  • Classification and regression trees

WEEK 3:  Logistic Regression

  • Propensities and ranking

WEEK 4: Recommender Systems

  • Association Rules - Apriori Algorithm
  • Collaborative Filtering - k-Nearest neighbors


Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

Data Mining - R

Who Should Take This Course:

R users who want to learn how to apply R to data mining.  Data mining analysts in search of new tools.  Students in's PASS program in Data Mining seeking an affordable data mining tool.  Note that working in R will be more involved than using a specially designed interface for data mining, such as those found in major commercial data mining programs.



1+ years of programming using R, or
R Programming Introduction 1 and R Programming Introduction 2
plus 1+ years using R or another programming language.

Organization of the Course:

This course takes place online 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.

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.

Time Requirement:
About 15 hours per week, at times of  your choosing.

Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  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,  CEU's and a record of course completion will be issued by The Institute, upon request.
This course is also recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam, and can help CAP®analysts accrue Professional Development Units to maintain their certification .

Course Text:

All required study materials will be provided in the course.


You must have a copy of R for the course. Click here for information on obtaining a free copy. After installing R in your computer you must also install several R add-on packages. Instructions for this installation will be provided as needed.

Data Mining - R

June 23, 2017 to July 21, 2017October 13, 2017 to November 10, 2017February 02, 2018 to March 02, 2018June 22, 2018 to July 20, 2018October 12, 2018 to November 09, 2018

Course Fee: $549