Advanced Analytics with RapidMiner Training Course

Introduction

• Overview of RapidMiner Studio
• Familiarization with the RapidMiner interface and key features

Applying the CRISP-DM Methodology in RapidMiner

• Comprehending the CRISP-DM framework
• Practical application in value estimation and forecasting

Data Understanding and Preparation

• Importing and exploring datasets
• Preprocessing and data cleaning methods
• Advanced data transformation techniques

Data Modeling with RapidMiner

• Introduction to data modeling concepts
• Choosing and implementing machine learning algorithms
• Supervised learning techniques
• Unsupervised learning techniques

Model Evaluation and Deployment

• Methods for assessing model performance
• Strategies for deploying models
• Model recalibration and optimization

Time Series Analysis and Forecasting

• Foundations of time series analysis
• Implementing moving average models
• Time series preprocessing and data aggregation

Advanced Time Series Techniques

• Decomposition analysis
• Forecasting using time windows
• Forecasting via feature generation

ARIMA Modeling

• Understanding ARIMA models
• Practical implementation within RapidMiner

Summary and Next Steps