Predictive Analytics 3: Dimension Reduction, Clustering and Association Rules

Predictive Analytics 3: Dimension Reduction,
Clustering and Association Rules

taught by Anthony Babinec and Galit Shmueli


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Aim of Course:

Data mining, the art and science of learning from data, covers a number of different procedures. In this online course, “Predictive Analytics 3 - Dimension Reduction, Clustering, and Association Rules,” you will cover key unsupervised learning techniques: association rules, principal components analysis, and clustering. Predictive Analytics 3 will include an integration of supervised and unsupervised learning techniques.

This is a hands-on course -- participants in the course will have access to an Excel-based comprehensive tool for data-mining, XLMiner, the use of which will be explained in the course. Participants will apply data mining algorithms to real data, and will interpret the results.  Note:  If you prefer to work in R or Python, this course is offered using R or Python.

A final project will integrate an unsupervised task with supervised methods covered in predictive Analytics 1 and 2 (though the unsupervised methods taught in the rest of the course stand on their own and can be studied without having taken those courses).

Anticipated learning outcomes:

  • Understand the issues related to using too many predictors (the "curse of dimensionality")
  • Use principal components analysis to reduce the number of predictors to a smaller number of "components" of correlated predictors
  • Use hierarchical clustering and k-means clustering to find and describe clusters of similar records
  • Use association rules to find patterns of "what goes with what" in transaction data
  • Combine unsupervised and supervised learning methods in a final project
This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

WEEK 1: Dimension Reduction

  • Detecting information overlap using domain knowledge and data summaries and charts
  • Removing or combining redundant variables and categories
  • Dealing with multi-category variables
  • Automated dimension reduction techniques
    • Principal Components Analysis (PCA)
    • Predictive algorithms with variable selection techniques

WEEK 2: Cluster Analysis

  • Popular uses of cluster analysis
  • Clustering approaches
  • Hierarchical Clustering
    • Distances between records
    • Distances between clusters
    • Dendrograms
    • Validating clusters
    • Strengths and weaknesses
  • K-Means Clustering
    • Initializing the k clusters
    • Distance of a record from a cluster
    • Within-cluster homogeneity
    • Elbow charts

WEEK 3: Association Rules and Recommender Systems

  • Discovering association rules in transaction databases
    • Support, confidence and lift
    • The apriori algorithm
    • Shortcomings
  • Collaborative filtering
    • Item-based
    • Person-based

WEEK 4: Integrating Supervised and Unsupervised Methods; Introduction to Network and Text Analytics

  • The role of unsupervised methods in predictive analytics
    • Dimension reduction of predictor space
    • Predictive models on subsets of homogeneous records
  • Advantages and weaknesses of combining unsupervised and supervised methods
  • Network analytics
  • Text analytics
  • Unsupervised methods used in network and text analytics


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

In addition to assigned readings, this course also has supplemental video lectures and an end of course data modeling project.

Predictive Analytics 3: Dimension Reduction, Clustering and Association Rules

Who Should Take This Course:
Marketers seeking to specify customer segments and identify associations among products purchased, environment scientists seeking to cluster observations, analysts who need to identify the key variables out of many, MBA's seeking to update their knowledge of quantitative techniques, managers and scientists who want to see what data-mining can do, and anyone who wants a practical hands-on grounding in basic data-mining techniques.

In addition, there is a lesson in the course where supervised and unsupervised learning techniques are used in combination, so, unless you do not need this portion, you should be familiar with supervised learning methods, such as those presented in Predictive Analytics 1.
Organization of the Course:
Options for Credit and Recognition:
Course Text:

The required text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 3rd Edition, by Shmueli, Patel and Bruce.



This is a hands-on course, and participants will apply data mining algorithms to real data.  The course is built around XLMiner, which is available:

  • For Windows versions of Excel, or
  • Over the web

Course participants will have receive a no-cost license for XLMiner - this is a special version, for this course.  IMPORTANT:  Do NOT download the free trial version of XLMiner from (it may conflict with the special course version).



September 06, 2019 to October 04, 2019 January 03, 2020 to January 31, 2020 April 10, 2020 to May 08, 2020 July 31, 2020 to August 28, 2020

Predictive Analytics 3: Dimension Reduction, Clustering and Association Rules


September 06, 2019 to October 04, 2019 January 03, 2020 to January 31, 2020 April 10, 2020 to May 08, 2020 July 31, 2020 to August 28, 2020

Course Fee: $549.00

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

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