Advanced Analytics with RapidMiner

• Basic understanding of data analysis and machine learning concepts

Audience

• Data Analysts
• Business Analysts
• Data Scientists

RapidMiner is an advanced analytics platform that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize analytical tools for time series forecasting.

By the end of this training, participants will be able to:

• Learn to apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
• Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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