Database

Expert guidance on data cleaning and preprocessing techniques for preparing raw data for analysis and modeling.

Expert guidance on feature engineering patterns for transforming raw data into predictive ML features.

Expert guidance on Jupyter notebooks for interactive data exploration, documentation, and reproducible analysis.

Expert guidance on Matplotlib for creating static, animated, and interactive visualizations in Python.

Expert guidance on NumPy for numerical computing, array operations, and linear algebra in Python.

Expert guidance on Pandas for tabular data manipulation, transformation, and analysis in Python.

Expert guidance on Polars for high-performance dataframe operations with a lazy query engine in Python.

Expert guidance on scikit-learn for building, evaluating, and deploying machine learning models in Python.