Working knowledge and practical application of data wrangling (handling missing data, data cleaning and normalization, transforming raw data into usable features, and working with structured + unstructured data. Knowledge of supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation (precision, recall, F1, ROC-AUC), feature engineering, and overfitting vs underfitting.