Marcus Holloway

Marcus is an analytics engineer with 9 years in the dbt and warehouse-modeling trenches. He spent three years at dbt Labs as a senior solutions architect helping enterprise customers (a large US bank, two telecom carriers) untangle 4000-model projects, and before that ran the analytics platform at HelloFresh's North America org where he rebuilt the supply-chain mart on Snowflake + dbt. His writing focuses on dbt project structure at scale, incremental model patterns that actually survive backfills, and the unglamorous work of column-level lineage and contract testing. He is a regular contributor to the dbt-utils package and co-maintains a small open-source linter for SQL style. Marcus lives in Berlin, holds a master's in statistics from UNC Chapel Hill, and roasts his own coffee badly.

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Articles by Marcus Holloway

Tutorials May 31, 2026 9 min read

Polars apply alternative: when/then/otherwise for 80x speedups

Eight worked examples that swap pandas-style .apply() in Polars for native when/then/otherwise expressions, plus benchmarks showing the real speedup on a 50M-row dataset.

Tutorials Mar 16, 2026 14 min read

Hypothesis Testing in Python: t-Tests, Chi-Square, ANOVA, and Non-Parametric Tests with SciPy

A practical, code-driven guide to hypothesis testing in Python using SciPy 1.17. Covers t-tests, chi-square, ANOVA, Mann-Whitney U, and Kruskal-Wallis with working examples, assumption checking, and a decision framework for choosing the right test.

Tutorials Mar 11, 2026 12 min read

Automated EDA in Python: YData Profiling, SweetViz, DataPrep, and D-Tale Compared

Find out which automated EDA tool fits your Python workflow. We compare YData Profiling, SweetViz, DataPrep, and D-Tale with code examples, benchmarks, and a practical decision framework.

Tutorials Mar 2, 2026 14 min read

Cross-Validation in Python: Every Scikit-Learn Strategy Explained with Code

Master every cross-validation strategy in scikit-learn 1.8 — KFold, StratifiedKFold, GroupKFold, TimeSeriesSplit, and more. Includes working code, a decision flowchart, and tips for avoiding data leakage.

Tutorials Feb 25, 2026 14 min read

SHAP Values in Python: A Practical Guide to Explaining ML Models

Learn how to use SHAP values to explain machine learning predictions in Python. Practical guide with scikit-learn, XGBoost, and LightGBM code examples, visualization techniques, and production-ready interpretability patterns.

Tutorials Feb 24, 2026 20 min read

Gradient Boosting in Python: XGBoost, LightGBM, and CatBoost Compared

A hands-on guide to XGBoost 3.2, LightGBM 4.6, and CatBoost 1.2.10 in Python. Covers benchmarks, categorical feature handling, Optuna hyperparameter tuning, SHAP interpretability, and production deployment.

Tutorials Feb 23, 2026 20 min read

Time Series Analysis and Forecasting in Python: From Pandas to Foundation Models

A practical guide to time series forecasting in Python — from pandas fundamentals and classical ARIMA models to deep learning with NeuralForecast and zero-shot predictions with Amazon Chronos-2 foundation models.

Tutorials Feb 14, 2026 20 min read

Python Data Visualization in 2026: A Practical Guide to Matplotlib 3.10, Seaborn, and Plotly 6

A hands-on guide to Python data visualization in 2026 covering Matplotlib 3.10's accessible color cycles and Colorizer API, Seaborn's mature objects interface, and Plotly 6's Narwhals-powered zero-copy DataFrame support. Includes code examples, performance tips, and a library comparison.

Tools & Libraries Feb 7, 2026 20 min read

Beyond Pandas: A Practical Guide to Polars and DuckDB for Python Data Science

Master the modern Python data stack by learning when and how to use Polars and DuckDB alongside pandas. This hands-on guide covers real code examples, performance benchmarks, and hybrid workflow patterns for 2026.