This repository contains the work for the midterm project for CSC2621 - Intro to Data Science. It focuses on data cleaning, exploration, and analysis techniques. The assignment involves analyzing a dataset provided by Dr. Rachel Reid, aimed at identifying species-specific traits in bobcats, coyotes, and foxes using scat samples. The goal is to apply data science methods to answer key ecological research questions.

Conservation ecologists track species population levels to understand population health, risks, and conservation efforts. Dr. Reid’s research introduces studying scat morphological and biogeochemical traits to distinguish between species. This project requires analyzing these traits to determine how they can be used for species identification and understanding the differences across species.

• Data Cleaning & Preprocessing: Conversion of data into appropriate types and formats for analysis.
• Trait Categorization: Organizing traits into morphological, biogeochemical, and contextual categories.
• Data Visualization & Analysis: Statistical tests and visualizations to determine which traits can distinguish between species.
• Biological Interpretation: Research into species biology to explain why specific traits vary across bobcats, coyotes, and foxes.

1. Introduction and Background: Literature review on species biology and population tracking methods.
2. Analysis: Data cleaning, trait categorization, and statistical analysis to identify distinguishing features.
3. Discussion and Interpretation: Exploration of how biological differences explain trait variation and their ecological significance.
4. Conclusion: Summary of findings and suggestions for future research.

• The project includes a Jupyter notebook (.ipynb) with all code and analysis, along with a detailed write-up in PDF format. The format I used to write the report was LaTeX, so the .tex source files are also included in the repository.