Analytics Practitioner Course
Data Mining: Principles and Best Practices
Data mining is an advanced science that can be difficult to do correctly. The instructor-led Analytics Practitioner course introduces participants to the power and potential of data mining and how to properly build reliable predictive models and interpret the results with confidence. Examples are drawn from several industries, including credit scoring, fraud detection, biology, investments, and cross-selling. The course is intended for participants with a strong interest in solving analytical business problems and who have a technical background, especially familiarity with computer programming and statistics.
Participants will learn how to:
- Identify projects with a high probability of success
- Translate a business problem into a closely related set of technical tasks
- Transform raw data to create higher-order features that reveal information
- Build models using powerful nonlinear techniques, such as decision trees and neural networks
- Use multiple re-sampling techniques to avoid overfit and predict how well models will perform in actual use
- Interpret and validate modeling results
- Become aware of the most common analytic mistakes and how to avoid them.
Intended Audience: Data Scientists, Data Analysts, Analytics Managers, Business Intelligence Analysts
Duration: 2-3 Days