Advanced Analytics with RapidMiner Training Course

Introduction

• Overview of RapidMiner Studio
• Getting acquainted with the RapidMiner interface and key features

CRISP-DM Methodology in RapidMiner

• Understanding the CRISP-DM framework
• Applying the framework for value estimation and projection

Data Understanding and Preparation

• Data importation and exploratory analysis
• Preprocessing and data cleaning strategies
• Advanced methods for data transformation

Data Modeling with RapidMiner

• Fundamentals of data modeling
• Choosing and applying machine learning algorithms
• Supervised learning approaches
• Unsupervised learning approaches

Model Evaluation and Deployment

• Best practices for assessing model performance
• Strategies for effective model deployment
• Model realignment and continuous optimization

Time Series Analysis and Forecasting

• Core principles of time series analysis
• Implementing moving average models
• Preprocessing time series data and aggregation techniques

Advanced Time Series Techniques

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

ARIMA Modeling

• Insights into ARIMA models
• Practical implementation using RapidMiner

Summary and Next Steps