Executive Development Programme in Clustering & Data Interpretation
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⢠Introduction to Clustering: Defining clustering, its importance, and use cases.
⢠Data Preprocessing: Data cleaning, normalization, and transformation for effective clustering.
⢠Cluster Analysis Techniques: K-means, hierarchical, density-based, and DBSCAN.
⢠Evaluation Metrics: Silhouette score, elbow method, and other assessment techniques.
⢠Python for Clustering: Implementing clustering algorithms using popular libraries.
⢠Data Visualization: Visualizing clusters using matplotlib, seaborn, and plotly.
⢠Real-world Applications: Case studies on customer segmentation, anomaly detection, and more.
⢠Advanced Clustering Methods: Subspace and co-clustering, spectral clustering, and mean-shift.
⢠Interpretation of Clustering Results: Deriving insights and making data-driven decisions.
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