Data exploration, categorization and segmentation
In data analysis, we often have very large data (many observations), which are however similar to each other and we need to organize them into a few groups with similar observations within each group. For example, in the case of customer data, even though we may have data from millions of customers, these customers may belong to only a few segments: customers are similar within each segment but different from one segment to another. We may often want to analyze each segment separately because they may behave differently (for example, different market segments may have different product preferences and behavioral patterns). This training will present advanced analytical techniques to help businesses answer their questions.
This training allows participants to perform the data processing tasks, the exploration of important characteristics and the segmentations necessary to assist in decision making in a company. The objective of the training is to understand the use of advanced analytical techniques to enable businesses to meet their data mining needs.The training will provide knowledge on:
- Continuous data manipulation
- The necessity of data selection segmented
- Classification of the information contained in the data.
- Understanding pairwise visualizations.
- Mastering exploration and segmentation techniques.
- Choosing the appropriate method according to the nature of the data, and interpreting the results.
- Robustness analyses.
The details
Fundamentals of segmentation and classification (cluster analysis)
- The difference between standardization and localization.
- The concept of distance in classification models.
- Introduction of segmentation in business.
Preparation of data for segmentation models
- Data selection for classification models.
- Structure of data in classification models.
- Scaling and transforming data for classification models
Reducing the number of variables
- Factor analysis and principal component analysis.
- A method of principal component analysis for reducing the number of variables in a model.
- The choice of a distance measure
Creating a classification model
- Build a classification model to segment retail stores.
- View and validate your groups.
- Interpret the results and communicate the inferences of the analysis.
The clients
This training is intended for all analysts and professionals seeking to acquire the necessary skills that will help them conduct or understand data mining and segmentation in your business.
- Level: Beginner
- Duration: Two days
