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.
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.
- 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
- Popular uses of cluster analysis
- Clustering approaches
- Hierarchical Clustering
- Distances between records
- Distances between clusters
- Validating clusters
- Strengths and weaknesses
- K-Means Clustering
- Initializing the k clusters
- Distance of a record from a cluster
- Within-cluster homogeneity
- Elbow charts
Association Rules and Recommender Systems
- Discovering association rules in transaction databases
- Support, confidence and lift
- The apriori algorithm
- Collaborative filtering
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
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.
- Skill: Introductory, Intermediate
- Credit Options: CEU
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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.
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.
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.
Literacy, Accessibility, and Dyslexia
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