Live Online KNIME Course for Data Analytics

Live Course Module: KNIME Course for Data Analytics

Total Duration: 24 Hours (4 Weeks)

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

Session 1 (2 hrs): Getting Started with KNIME

• Overview of KNIME and its role in data analytics

• Installation and setup of KNIME Analytics Platform

• Understanding KNIME workbench: nodes, workflows, and connections

• Navigating KNIME Hub and extensions

• Hands-on: Creating your first workflow

Session 2 (2 hrs): Data Import and Exploration

• Importing data from CSV, Excel, databases, and web sources

• Data views and table manipulation

• Filtering and sorting data

• Hands-on: Exploring a sample dataset (e.g., sales or customer data)

Session 3 (2 hrs): Data Cleaning and Transformation

• Handling missing values and duplicates

• Normalization and standardization

• String manipulations and column transformations

• Row and column filters

• Practical: Building a data preprocessing pipeline

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Week 2: Data Visualization & Predictive Modeling (6 Hours)

Session 4 (2 hrs): Data Visualization in KNIME

• Creating charts and plots (bar, scatter, line, box)

• Using Color Manager and Rule Engine

• Correlation and distribution analysis

• Hands-on: Visual storytelling using KNIME views

Session 5 (2 hrs): Supervised Learning – Classification Models

• Introduction to supervised learning

• Decision Trees, Logistic Regression, and Naïve Bayes

• Model evaluation: confusion matrix, accuracy, ROC curve

• Hands-on: Predicting customer churn

Session 6 (2 hrs): Regression Models in KNIME

• Linear and multiple regression

• Evaluating regression models (RMSE, R²)

• Regularization (Ridge, Lasso)

• Practical: Predicting sales revenue

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Week 3: Unsupervised Learning & Model Optimization (6 Hours)

Session 7 (2 hrs): Clustering Techniques

• Understanding unsupervised learning

• K-Means and hierarchical clustering in KNIME

• Cluster visualization and evaluation

• Hands-on: Customer segmentation

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

• Introduction to association rule mining

• Support, confidence, and lift metrics

• Apriori algorithm implementation in KNIME

• Practical: Retail market basket analysis

Session 9 (2 hrs): Model Evaluation and Optimization

• Cross-validation and partitioning data

• Parameter optimization and tuning

• Comparing multiple model performances

• Practical: Selecting the best predictive model

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Week 4: Advanced Analytics, Automation & Deployment (6 Hours)

Session 10 (2 hrs): Text Mining and Sentiment Analysis

• Text preprocessing: tokenization, stop words, stemming

• Creating document vectors

• Sentiment scoring and classification

• Hands-on: Analyzing customer reviews

Session 11 (2 hrs): Workflow Automation and Integration

• Using loops and flow variables

• Automating repetitive analytics processes

• Integrating Python/R scripts in KNIME

• Hands-on: Automating a full data-to-report workflow

Session 12 (2 hrs): Capstone Project & Deployment

• End-to-end analytics project

• Exporting and sharing workflows

• Publishing results using KNIME Server / KNIME Hub

• Capstone presentation, discussion, and certification

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🧠 Tools & Technologies Used

• KNIME Analytics Platform (latest version)

• KNIME Hub (for nodes and workflow templates)

• Optional: Python Integration, KNIME Server (for deployment)

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🏁 Final Deliverables

• End-to-end KNIME workflow project

• Visual dashboards and model reports

• Workflow documentation and presentation

• Certificate of completion

Learning Outcomes

By the end of this course, learners will be able to:
✅ Understand KNIME Analytics Platform and its visual workflow interface
✅ Import, clean, and transform real-world datasets
✅ Perform classification, regression, clustering, and text mining
✅ Automate analytics workflows and integrate external tools
✅ Deploy and share analytical solutions effectively