Introduction to Data Science Statistical Methods

Target group

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.