Data Analyst
Current1. Utilized Python and Jupyter Notebooks to analyze 95 events, achieving an average of 13+ tickets sold per event, with a maximum of 69 tickets sold for top events.2. Employed pandas and NumPy for statistical analysis, optimizing venue capacity utilization up to 37% and identifying key performance trends.3. Created data visualizations using Matplotlib and Seaborn, driving average earnings of $326 per event and a peak revenue of $1,955 for the highest-grossing… Show more 1. Utilized Python and Jupyter Notebooks to analyze 95 events, achieving an average of 13+ tickets sold per event, with a maximum of 69 tickets sold for top events.2. Employed pandas and NumPy for statistical analysis, optimizing venue capacity utilization up to 37% and identifying key performance trends.3. Created data visualizations using Matplotlib and Seaborn, driving average earnings of $326 per event and a peak revenue of $1,955 for the highest-grossing event.4.Used SQL for data querying, identifying average ticket earnings of $307 and average donation earnings of $18 per event, boosting overall revenue through targeted strategies.5. Performed regression analysis to predict future event success, improving marketing strategies and enhancing visibility for underperforming events in preparation for 2024. Show less