Pharmaceutical Drug Project Analysis

Pymaceuticals, Inc. - Anti-Cancer Medication Study - Key Achievements:

Project Overview:
● Served as a data analyst at Pymaceuticals, Inc., a pharmaceutical company specializing in anti-cancer medications.
● Conducted a comprehensive analysis of a recent animal study involving 249 mice treated with various drug regimens to combat squamous cell carcinoma (SCC), a prevalent form of skin cancer.

Data Preparation:
● Merged mouse_metadata and study_results DataFrames to create a consolidated dataset for analysis.
● Ensured data integrity by identifying and addressing duplicate time points associated with specific mouse IDs.
● Created a cleaned DataFrame, removing data inconsistencies for further analysis.

Summary Statistics:
● Generated a detailed DataFrame of summary statistics, including mean, median, variance, standard deviation, and SEM of tumor volume for each drug regimen.

Bar Charts and Pie Charts:
● Produced identical bar charts showcasing the total number of rows (Mouse ID/Timepoints) for each drug regimen throughout the study.
● Created bar charts using both Pandas DataFrame.plot() method and Matplotlib's pyplot methods.
● Developed identical pie charts illustrating the distribution of female versus male mice in the study, utilizing both Pandas and Matplotlib.

Quartiles, Outliers, and Box Plot:
● Calculated the final tumor volume for mice treated with promising drug regimens (Capomulin, Ramicane, Infubinol, and Ceftamin).
● Determined quartiles, IQR, and identified potential outliers across all four treatment regimens.
● Generated a box plot using Matplotlib to visualize the distribution of final tumor volume, highlighting potential outliers.

Line Plot and Scatter Plot:
● Created a line plot showcasing tumor volume versus time point for a single mouse treated with Capomulin.
● Developed a scatter plot depicting mouse weight versus average observed tumor volume for the entire Capomulin treatment regimen.

Correlation and Regression:
● Calculated correlation coefficient and implemented a linear regression model between mouse weight and average observed tumor volume for the Capomulin treatment regimen.
● Visualized the linear regression model on top of the scatter plot, providing insights into the relationship between mouse weight and tumor volume.

Conclusion:
● Successfully fulfilled the executive team's request for comprehensive tables and figures for the technical report of the clinical study.
● Demonstrated advanced skills in data preparation, statistical analysis, and data visualization, contributing to the understanding of drug regimens' efficacy in treating squamous cell carcinoma.
● Provided actionable insights to the executive team for informed decision-making in the development of anti-cancer medications.