Introduction
Data science made easy: Introduction
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1. Data Science Overview
Topics
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Definition, terminology, and a simple taxonomy
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Data science process
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Data science methods and algorithms
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AI/ML evolution
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1. Data Science Tools
Topics
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Tool landscape
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Introduction to KNIME AP
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Nodes and extensions
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KNIME demo with iris dataset, part 1
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KNIME demo with iris dataset, part 2
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3. ML Model Development with KNIME
Topics
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Data ingestion and preparation, part 1
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Data ingestion and preparation, part 2
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ML model building and testing
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Comparative assessment
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4. Best Practices in Data Science and AI/ML
Topics
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Data balancing for class imbalance problem
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Cross-validation for bias-variance trade-off
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Model ensembles (with bagging and boosting)
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Model explainability (XAI)
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5. Text Analytics
Topics
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Overview of text mining and natural language processing (NLP)
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Text mining process
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TM applications: Sentiment analysis
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TM applications: Topic modeling
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Conclusion
Data science made easy: Summary
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