Blog – Peter Amerkhanian

The following are a collection of my informal notes and tutorials on topics in programming, math, statistics, and policy analysis. If you find errors or else find them helpful, I’d love to hear from you.
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Grouping Problems with SQL

SQL

Data Management

Using DuckDB to look at some GROUP BY and PARTITION BY statements in SQL.

Building Time-series Utilities for `matplotlib`

Python

Writing utility functions for matplotlib and using them to visualize BART’s ridership data.

The Covariance Matrix from Scratch

Python

Linear Algebra

Notes on matrix algebra and covariance, with a mix of formal notation and python code.

Integrals in Probability

R

Calculus

Probability

Notes on the definite integral and probability modeling, with a mix of formal notation, plotly visuals, and sympy computation.

Benchmarking parquet and `dask`

Python

Data Management

Using parquet+dask to efficiently read and manipulate BART’s historical ridership data.

Production Maximization with Lagrange Mutlipliers

R

Calculus

Solving an optimization problem in (Strang and Herman 2016) using lagrange multipliers, with some plotly visuals mixed in.

Simple Optimization in 3D

R

Calculus

Solving an optimization problem in (Strang and Herman 2016), with plotly visuals.

Linear Approximation in 3D

Python

Calculus

Using taylor series to approximate function output, with python visuals.

Overfitting and The Train-Test Split

Python

Statistics

Using re-sampling to better estimate model performance.

Iterated Expectations

Python

Probability

Notes on conditional probability and adam’s law, with a mix of formal notation and python code.

Newton’s Method From The Ground Up

Python

Calculus

Using newton’s method for root-finding and optimization, with python code and a .gif of the search.

Simple Constrained Optimization in 2D

Python

Calculus

Introductory optimization, with formal notation, python code, and matplotlib visuals.

Buy vs. Rent, A Financial Modeling Workflow in Python

Python

Probability

Using numpy-financial and monte-carlo simulation to evaluate investments.

Reliable PDF Scraping with tabula-py

Python

Data Management

Using tabula in python to read in tables from .pdf files