This report explores Cyclistic bike-share data for Q1 2019 and Q1 2020 to uncover usage patterns between casual riders and annual members. The analysis includes data cleaning, descriptive statistics, and visualizations to support data-driven recommendations for increasing annual memberships.

We are analyzing the metagenomes from 2 soil samples that were used a greenhouse study prior to biofertilizer treatment. These will give us a sense of resident microbial (fungal and bacterial) communities residing in them.

This project demonstrates data cleaning techniques applied to the Nashville Housing dataset (2013–2016). Using R and packages like tidyverse, lubridate, and janitor, I standardized column names, handled missing values, formatted dates, cleaned text fields, and identified outliers to prepare the dataset for analysis and modeling. This cleaned dataset can now support exploratory data analysis, predictive modeling, and insights into regional housing trends. The project highlights essential data preparation skills required for real-world analytics work.

Final Project Code for Class DATA607