Decoding Patterns in Markets, Data, & History

An inquiry into dynamic changes to the population of butterflies given initial resource (food, population, growth rate, etc) allocation.

This project is a python implementation of a commonly used population dynamic model (often shown on the example of population of foxes vs. rabbits). The model runs the simulation and outputs a .csv file with relevant information.

In the nature, some species of caterpillars are known to be cannibalistic. The model is a simulation of such behavior. The model is based on the Lotka-Volterra equations, which describe the dynamics of biological systems in which two species interact, one as a predator and the other as prey.

In this case, the caterpillars are both the prey and the predator. The model simulates the population dynamics of both species over time, taking into account factors such as birth rates, death rates, and the availability of resources (leaves).

The model is implemented in Python and uses the NumPy library for numerical calculations. The simulation runs for a specified number of iterations, and the results are stored in a CSV file for further analysis.

The model can be used to study the effects of different parameters on the population dynamics of the caterpillars and leaves, such as the initial population sizes, growth rates, and carrying capacities.

Overall, this project provides a valuable tool for understanding the complex interactions between species in an ecosystem and the factors that influence their population dynamics.

An exploration of business performance drivers using real-world CRM sales pipeline data, including account, product, and sales team information.

This project is a Python- and Tableau-based implementation of a data analytics workflow aimed at visualizing and diagnosing sales funnel efficiency. The data is processed in Python (using pandas and SQLite), where initial cleaning and merging is performed to create a relational structure across accounts, opportunities, sales reps, and products. The cleaned data is then used to power a Tableau dashboard, which delivers key performance indicators (KPIs) such as total revenue, win rate, average deal size, and sales cycle duration.

In large organizations, sales pipelines are complex systems with dozens of moving parts — from reps and products to sectors and buyer behaviors. Without a unified view, it's difficult to identify what’s working and where improvements are needed. This project simulates what a lightweight, data-informed pipeline management system could look like using public CRM datasets. It mimics how a business analyst or sales operations team would translate raw data into insights and strategy.

By segmenting performance across products, managers, and industries, this project serves as a model for how analytics can surface hidden patterns in pipeline quality, rep productivity, and customer segmentation. Ultimately, it’s a demonstration of using data not just to observe, but to act.