Description

Learning Outcomes

• 1. Understand key principles of data mining, knowledge discovery, and data preprocessing.
• Apply classification algorithms such as Decision Trees, Naïve Bayes, KNN, and Logistic Regression.
• Use clustering techniques like K-Means and Hierarchical Clustering for pattern recognition.
• Perform association rule mining using Apriori and FP-Growth algorithms.
• Analyze large, unstructured, or noisy datasets and extract actionable insights.
• Build end-to-end data mining workflows for real business scenarios.

Program Highlights & Key Features

• 2. Hands-on practice with real datasets for classification, clustering, and rule mining.
• Training on essential data mining tools such as Python, R, Weka, or RapidMiner.
• Expert-led sessions on data preprocessing, feature engineering, and pattern discovery.
• Practical case studies across retail, finance, marketing, and healthcare.
• Coverage of emerging trends such as text mining, web mining, and AI-driven discovery.