Advanced Analytics with RapidMiner Training Course

Introduction

• Overview of RapidMiner Studio
• Navigation of the RapidMiner interface and key features

CRISP-DM Methodology in RapidMiner

• Comprehending the CRISP-DM framework
• Applying the methodology to value estimation and projection

Data Understanding and Preparation

• Data importation and exploration
• Preprocessing and cleansing techniques
• Advanced data transformation methods

Data Modeling with RapidMiner

• Foundations of data modeling
• Selection and application of machine learning algorithms
• Supervised learning algorithms
• Unsupervised learning algorithms

Model Evaluation and Deployment

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

Time Series Analysis and Forecasting

• Basics of time series analysis
• Implementing moving average models
• Preprocessing time series data and aggregation

Advanced Time Series Techniques

• Decomposition analysis
• Projection using time windows
• Projection involving feature generation

ARIMA Modeling

• Understanding ARIMA models
• Practical application within RapidMiner

Summary and Next Steps