Target group
Business analysts who want an overview of the types of methods used in data science
Course structure
Introduction to Data Science
- Introduction to data science.
- Analytical life cycle.
Preparing the Data for Modeling
- Analytical challenges to predictive modeling.
- Feature engineering.
Supervised Models
- Introduction to supervised machine learning models.
- Regression.
- Decision trees.
- Neural networks.
- Support vector machines.
- Factorization machines.
- Ensemble models.
- Two-stage models.
Unsupervised Models
- Introduction to unsupervised models.
- Hierarchical clustering.
- K-means clustering.
- Self-organizing maps.
- Market basket analysis.
- Path analysis.
- Network analysis.
- Text analytics.
Model Assessment
- Introduction to model assessment.
