Predictive Analytics 3: Dimension Reduction, Clustering and Association Rules - with Python

Predictive Analytics 3: Dimension Reduction,
Clustering and Association Rules - with Python
taught by Peter Gedeck
 

 
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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 with Python,” 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.

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

SOFTWARE: This is a hands-on course, and participants will apply data mining algorithms to real data.  The course will use Python, a free software environment for with statistical computing and graphics capabilities.  We also offer:

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
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
    • Person-based
    • Item-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:

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 - with Python

Who Should Take This Course:
Marketers seeking to specify customer segments, identify associations among products purchased and design recommender systems, 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.
Level:
introductory/intermediate
Prerequisite:

You should be familiar with introductory statistics.  Try these self tests to check your knowledge.

Do you need to take Predictive Analytics 1 and 2 (supervised learning) first?  Most of the topics in Predictive Analytics 3 do not require it, but here is a lesson in the course where supervised and unsupervised learning techniques are used in combination, so, if you want to complete that topic, you should be familiar with supervised learning methods, such as those presented in Predictive Analytics 1 - Python.

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.

 Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. Certificate - 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. CEUs and/or proof of completion - 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.
  4. Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses
Course Text:
Materials will be provided online.
Software:

SOFTWARE: This is a hands-on course, and participants will apply data mining algorithms to real data.  The course will use Python, a free software environment for with statistical computing and graphics capabilities.  We also offer:

Instructor(s):

Dates:

January 04, 2019 to February 01, 2019 April 12, 2019 to May 10, 2019 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 - with Python

Instructor(s):

Dates:
January 04, 2019 to February 01, 2019 April 12, 2019 to May 10, 2019 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

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