Jacob PieniazekinTowards Data ScienceDouble Machine Learning, Simplified: Part 2 — Extensions & the CATELearn how to utilize DML for estimating individual level treatment effects to enable data-driven targetingJul 31, 2023Jul 31, 2023

Jacob PieniazekinTowards Data ScienceDouble Machine Learning, Simplified: Part 1 — Basic Causal Inference ApplicationsLearn how to utilize DML in causal inference tasksJul 12, 20233Jul 12, 20233

Jacob PieniazekinTowards Data Sciencet-SNE from Scratch (ft. NumPy)Acquire a deep understanding of the inner workings of t-SNE via implementation from scratch in pythonApr 14, 20234Apr 14, 20234

Jacob PieniazekinTowards Data ScienceOptimization, Newton’s Method, & Profit Maximization: Part 3 — Applied Profit MaximizationLearn how to apply optimization & econometric techniques to solve an applied profit maximization problemMar 1, 2023Mar 1, 2023

Jacob PieniazekinTowards Data ScienceOptimization, Newton’s Method, & Profit Maximization: Part 2— Constrained Optimization TheoryLearn how to extend Newton’s Method to and solve constrained optimization problemsFeb 2, 20232Feb 2, 20232

Jacob PieniazekinTowards Data ScienceOptimization, Newton’s Method, & Profit Maximization: Part 1 — Basic Optimization TheoryLearn the basics of Newton’s Method for multi-dimensional optimizationJan 10, 2023Jan 10, 2023

Jacob PieniazekinTowards Data SciencePredictive Parameters in a Logistic Regression: Making Sense of it AllAcquire a robust understanding of logit model parameters beyond the canonical odds ratio interpretationJun 14, 20221Jun 14, 20221

Jacob PieniazekinTowards Data ScienceControlling for “X”?Understanding linear regression mechanics via the Frisch-Waugh-Lovell TheoremMay 26, 2022May 26, 2022