Course Outline

Introduction

  • Overview of RapidMiner Studio
  • Orientation to RapidMiner UI and features

CRISP-DM Methodology in RapidMiner

  • Understanding CRISP-DM framework
  • Application in estimation and projection of values

Data Understanding and Preparation

  • Data import and exploration
  • Preprocessing and cleaning techniques
  • Advanced data transformation methods

Data Modeling with RapidMiner

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

Model Evaluation and Deployment

  • Techniques for model evaluation
  • Strategies for model deployment
  • Model realignment and optimization

Time Series Analysis and Forecasting

  • Fundamentals of time series analysis
  • Application of moving average models
  • Time series preprocessing and data aggregation

Advanced Time Series Techniques

  • Decomposition analysis
  • Projection with time windows
  • Projection with feature generation

ARIMA Modeling

  • Understanding ARIMA models
  • Practical application in RapidMiner

Summary and Next Steps

Requirements

  • Basic understanding of data analysis and machine learning concepts

Audience

  • Data Analysts
  • Business Analysts
  • Data Scientists
  14 Hours
 

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