14.6: Assessments

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Page ID: 58358

Felix Amoruwa
De Anza College

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Learning Objectives

Explain advanced ML techniques: ensemble methods (bagging, boosting, stacking) and deep learning (CNNs, RNNs/LSTMs).
Describe AutoML platforms (Google Vertex AI, H2O.ai, Azure AutoML) and how they democratize machine learning.
Analyze AI-powered and augmented analytics: how AI is embedded in BI tools and assists human decision-makers.
Evaluate emerging trends: real-time/edge analytics, Explainable AI (LIME, SHAP), data mesh, and privacy-preserving analytics.
Compare traditional analytics methods with emerging approaches across speed, automation, transparency, and privacy.
Assess the implications of these trends for future analytics careers and organizational strategy.

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