This course gives an overview of the statistical methods used by data scientists, with an emphasis on the applicability to business problems. No software is shown in the course, and the mathematical details are kept to a minimum.

Learn how to

• Identify the need for feature engineering such as sampling, variable transformations, imputation, and variable selection.
• Describe applications of supervised models such as decision trees, neural networks, support vector machines, factorization machines, and logistic regression.
• Describe the usefulness of unsupervised models such as clustering, text mining, network analysis, and path analysis.
• Implement model assessment and deployment such as visualization and monitoring.

Who should attend

Business analysts who want an overview of the types of methods used in data science

Formats available	Standard Duration
Classroom:	1.0 day
Live Web Classroom:	2 half-day session(s)	System Requirements

Before attending this course, you should have some exposure to model building and quantitative analyses.

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.