Day 1: Foundations of Data Mining: Concepts, Methods & Toolkits

• Understand the data mining lifecycle (CRISP-DM, SEMMA)

• Learn preprocessing techniques: missing values, encoding, normalization

• Explore sampling and feature selection strategies

• Set up your toolkit: Python, Pandas, Scikit-learn, Seaborn

• Visualize data distributions, correlations, and outliers

• Apply basic unsupervised techniques (k-means preview, PCA)

Tools: Python, Pandas, Scikit-learn, Jupyter, Seaborn, NumPy
Focus: Preprocessing • Foundations • Tool Setup