Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules
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 Excel-based tools to implement the models covered in this course, and how to combine supervised and unsupervised models.
- 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
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.
- 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
- Skill: Introductory, Intermediate
- Credit Options: ACE, CAP, CEU
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The material covered in the Analytics for Data Science Certificate will be indispensable in my work. I can’t wait to take other courses. Great work!
I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.
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This course greatly benefited me because I am interested in working in AI. It has given me solid foundational knowledge…After completing this last course, I feel I have gained valuable skills that will enhance my employability in Data Science, opening up diverse career opportunities.
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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 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 receive a license for Analytic Solver Data Mining (previously XLMiner) for nominal cost – this is a special version, for this course. IMPORTANT: Do NOT download the free trial version of XLMiner from solver.com as it may conflict with the special course version.
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