Live Online RapidMiner Course for Data Analytics

Live Course Module: RapidMiner Course for Data Analytics

Total Duration: 24 Hours (4 Weeks)

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Week 1: Introduction to RapidMiner & Data Preprocessing (6 Hours)

Session 1 (2 hrs): Getting Started with RapidMiner

• Overview of RapidMiner platform and architecture

• Installing and setting up RapidMiner Studio

• Understanding the user interface and operators

• Working with RapidMiner repositories and projects

• Hands-on: Importing and exploring sample datasets

Session 2 (2 hrs): Data Loading and Exploration

• Importing data from various sources (CSV, Excel, databases)

• Data types and metadata in RapidMiner

• Handling missing values and errors

• Exploring data using statistics and visualization tools

• Hands-on: Data summary and quality analysis

Session 3 (2 hrs): Data Preprocessing Techniques

• Data cleaning (missing values, duplicates, outliers)

• Normalization, standardization, and binning

• Attribute selection and generation

• Filtering and sampling data

• Practical: Building a preprocessing workflow

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Week 2: Predictive Modeling – Classification & Regression (6 Hours)

Session 4 (2 hrs): Introduction to Predictive Modeling

• Machine Learning workflow in RapidMiner

• Splitting data into training and testing sets

• Overview of supervised learning

• Building a simple decision tree model

• Hands-on: Predicting customer churn

Session 5 (2 hrs): Classification Models

• Decision Trees, Naïve Bayes, K-NN, and SVM

• Model performance evaluation (confusion matrix, accuracy, ROC curve)

• Cross-validation and parameter optimization

• Hands-on: Comparing classification models

Session 6 (2 hrs): Regression Models

• Linear and polynomial regression

• Performance metrics (R², MAE, RMSE)

• Regularization techniques (Ridge, Lasso)

• Practical: Predicting sales performance

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Week 3: Unsupervised Learning & Advanced Analytics (6 Hours)

Session 7 (2 hrs): Clustering and Segmentation

• Introduction to clustering

• K-Means and hierarchical clustering in RapidMiner

• Evaluating clusters and visualizing groups

• Hands-on: Customer segmentation project

Session 8 (2 hrs): Association Rules & Market Basket Analysis

• Understanding association rule mining

• Apriori algorithm and rule interpretation

• Metrics: Support, confidence, lift

• Practical: Analyzing shopping basket data

Session 9 (2 hrs): Text Mining & Sentiment Analysis

• Text preprocessing (tokenization, stop words, stemming)

• Creating word vectors and term frequencies

• Sentiment classification with RapidMiner

• Hands-on: Analyzing product reviews dataset

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Week 4: Model Optimization, Automation & Deployment (6 Hours)

Session 10 (2 hrs): Model Validation and Optimization

• Grid search and parameter tuning

• Cross-validation workflows

• Performance comparison and model selection

• Hands-on: Selecting the best predictive model

Session 11 (2 hrs): Process Automation and Macros

• Building automated analytics workflows

• Using loops, parameters, and macros

• Scheduling and batch execution

• Practical: Creating a reusable automation pipeline

Session 12 (2 hrs): Capstone Project & Deployment

• End-to-end project: From data import to model deployment

• Exporting and deploying models (PMML, API, or RapidMiner AI Hub)

• Presenting insights and results

• Course recap and certification quiz

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🧠 Tools & Technologies Used

• RapidMiner Studio (latest version)

• RapidMiner AI Hub (optional)

• Sample datasets: Customer churn, retail sales, marketing campaign, text data

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🏁 Final Deliverables

• End-to-end data analytics project in RapidMiner

• Workflow files and documentation

• Model performance comparison report

• Certificate of completion

Learning Outcomes

By the end of this course, learners will be able to:
✅ Understand the RapidMiner platform and its visual workflow environment
✅ Perform data import, cleansing, transformation, and visualization
✅ Build and evaluate predictive analytics models
✅ Apply classification, regression, clustering, and text mining techniques
✅ Deploy models and automate analytics workflows