Excel & Power BI Project
Provides an inside look on 2-bedroom and 3-bedroom Airbnb properties based on pricing in and around downtown New Orleans, more specifically the Marigny neighborhood, to help retired couples Candace and Owen explore the realities of utilizing Airbnb to help cover a targeted travel budget. The data was cleaned in Excel where pivot tables provided an in-depth analysis of property revenue, average days booked, and average pricing in the neighborhood in order to develop a baseline price for the assigned business scenario. For visualizations, data was then imported into Power BI to effectively present findings.​
SQL & Tableau Project
This project utilized SQL and Tableau to answer BLUEbikes business scenario of converting one-time customers into subscription riders. An in-depth analysis included identifying the popular start/end stations in the Boston Metro area, subscription vs. customer (one-time users), along with peak season, days, and times from 2016-19. All four years worth of data was combined using JOINs and UNIONs in SQL where an EDA was performed across all factors listed above. Queries and secondary population data was then uploaded into Tableau to develop visuals such as heat maps and dual line charts.
Capstone Project
In my capstone exploring the validity of home field advantage in Major League Baseball (MLB), six seasons of data with a total of over 13,000 games played were analyzed from 2016-21. Three specific factors were looked at in how they impacted home win percentage with travel in between time zones, fan attendance, and umpire biases shaping the analysis. The data was cleaned in Excel then moved to Python where the use of pandas was utilized to create several new columns including a home win/loss column, run differential, and time zones in which the home and away teams are located in. Finally, the datasets were moved to Tableau to generate supporting evidence and to present the findings.