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

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

This course, with a focus on Python, will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.

Overview

In this course, you will cover key unsupervised learning techniques including association rules, principal components analysis, and clustering. You will also review integration of supervised and unsupervised learning techniques. Participants will apply data mining algorithms to real data, and will interpret the results. A final project will integrate an unsupervised task with supervised methods covered in predictive Analytics 1 and 2. Students will use Python, a free software environment with statistical computing and graphics capabilities. Note: If you prefer to work in R or XLMiner, this course is offered using R or XLMiner.

  • Introductory, Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

After completing this course students will understand issues relating to using too many predictors and how to reduce the number of predictors to a smaller number of usable “components.” You will use various clustering techniques and association rules to describe clusters of similar records, and to find patterns in your data. You will learn to use Python to implement the models covered in this course, and how to combine supervised and unsupervised models.

  • Use principal components analysis and variable selection techniques to reduce dimensionality
  • Cluster records using hierarchical and k-means clustering
  • Discover association rules in transaction databases
  • Specify how collaborative filtering can be used to develop automated recommendations
  • Integrate unsupervised and supervised data mining methods in a case study
  • Use Python’s sci-kit learn package to implement the models in the course

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.

Our Instructors

Course Syllabus

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

Class Dates

2022

09/09/2022 to 10/07/2022
Instructors:

2023

01/13/2023 to 02/10/2023
Instructors:
05/12/2023 to 06/09/2023
Instructors:
09/08/2023 to 10/06/2023
Instructors:

2024

01/12/2024 to 02/09/2024
Instructors:

Prerequisites

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 with Python.

Predictive Analytics 1 with Python – Machine Learning Tools

This course introduces the basic paradigm for predictive modeling: classification and prediction.
  • Skill: Introductory, Intermediate
  • Credit Options: CEU
Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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

Additional Information

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.

Course Text

The required text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python, by Shmueli, Bruce, Gedeck and Patel. This same text is also used in the previous courses: “Predictive Analytics 1 – Machine Learning Tools – with Python” and “Predictive Analytics 2 – Neural Nets and Regression – with Python”. Please order a copy of your course textbook prior to course start date.

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 and a R section and XLMiner section (Excel add-in) for this course.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:

 

Chrome

 

Firefox

 

Safari

  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

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