Cluster Analysis- Theory & workout using SAS and R
What you'll learn
- Learn cluster analysis in crystal clear and simple way
- Learn hierarchical and non-hierarchical clustering
- Know theory, business apllication, sas program and interpretation of output
- R syntax for clustering
Requirements
- Basic understanding of statistics
- Basic knowledge of SAS
- Basic knowledge of R
Description
- About the course - Cluster analysis is one of the most popular techniques used in data mining for marketing needs. The idea behind cluster analysis is to find natural groups within data in such a way that each element in the group is as similar to each other as possible. At the same time, the groups are as dissimilar to other groups as possible.
- Course materials- The course contains video presentations (power point presentations with voice), pdf, excel work book and sas codes.
- Course duration- The course should take roughly 10 hours to understand and internalize the concepts.
- Course Structure (contents) The structure of the course is as follows.
Part 01 - cluster analysis theory and workout using SAS
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Motivation –
- Where one applies cluster analysis. Why one should learn cluster analysis?
- How it is different from objective segmentation (CHAID / CART )
Statistical foundation and practical application: Understand
- Different type of cluster analysis
- Cluster Analysis – high level view
- Hierarchical clustering –
- Agglomerative or Divisive technique
- Dendogram – What it is? What does it show?
- Scree plot - How to decide about number of clusters
- How to use SAS command to run hierarchical clustering
- When and why does on need to standardize the data?
- How to understand and interpret the output
- Non-hierarchical clustering (K means clustering).
- Why do we need k means approach
- How does it work?
- How does it iterate?
- How does it decide about combining old clusters?
- How to use SAS command to run hierarchical clustering
- When and why does on need to standardize the data?
- How to understand and interpret the output
Part 02
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Learn R syntax for hierarchical and non hierarchical clustering
Part 03
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Cluster analysis in data mining scenario
Part 04
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Assignment on cluster analysis
Who this course is for:
- statistics and analytics professionals / students
Instructor
- 4.4 Instructor Rating
- 12,896 Reviews
- 120,672 Students
- 18 Courses
I am a seasoned analytics professional and the CEO of ProData Science AI Consultancy Pvt Ltd, with over 24 years of professional experience. I specialize in impactful and actionable analytics, data science, decision strategy, and enterprise-wide data strategy.
ProData Science offers outstanding solutions for building machine learning models. These solutions make the model-building process enjoyable, accurate, and efficient.
Additionally, I am a dedicated trainer who believes that effective training ensures users understand the concepts thoroughly. If students remain confused after the training, it is ineffective. I ensure that my students or participants are crystal clear on how to apply their learning in real business scenarios.
My expertise includes:
-- Credit card business
-- Scoring ( Machine learning / econometrics-based model development)
-- Score management
-- Loss forecasting / Time series forecasting
-- Business intelligence solutions such as Tableau, SAS Visual Analytics, Microsoft Power BI etc.
-- Enterprise-wide big data framework and streaming analysis
Please refer to my course for
- Data analysis and Data science using SAS / R / Python
- SAS / R / Python program details (syntax and options)
- SAS / R / Python output deep dive
- Practical usage in Industrial situation