Advanced Analytics with RapidMiner Training Course

Course Overview

• Introduction to RapidMiner Studio
• Familiarization with the RapidMiner interface and core capabilities

CRISP-DM Methodology in RapidMiner

• Comprehensive review of the CRISP-DM framework
• Implementation for value estimation and projection scenarios

Data Understanding and Preparation

• Procedures for data import and exploration
• Techniques for preprocessing and data cleansing
• Methods for advanced data transformation

Data Modeling with RapidMiner

• Foundational principles of data modeling
• Selection and execution of machine learning algorithms
• Application of supervised learning algorithms
• Application of unsupervised learning algorithms

Model Evaluation and Deployment

• Evaluation methodologies for analytical models
• Strategic approaches to model deployment within government systems
• Processes for model realignment and optimization

Time Series Analysis and Forecasting

• Core concepts of time series analysis
• Utilization of moving average models
• Data aggregation and preprocessing for temporal data

Advanced Time Series Techniques

• Analytical decomposition methods
• Forecasting using time windows
• Forecasting through feature engineering

ARIMA Modeling

• Theoretical foundation of ARIMA models
• Practical implementation in RapidMiner for government applications

Summary and Next Steps