Python Cell Viability Data Analysis -- 2

I’m looking for a skilled data scientist who can take my raw cell-viability assay output and turn it into clear, publication-ready insights using Python, specifically Pandas for wrangling and Seaborn for plotting. The focus of this job is straightforward: perform comprehensive descriptive statistics on each experimental group or time point, visualise the results in an intuitive way, and give me a short written interpretation of what the numbers mean biologically.

You will start from the CSV files I provide, tidy them as needed, calculate essentials such as mean, median, standard deviation, coefficient of variation and 95 % confidence intervals, then visualise the findings (e.g. boxplots, bar charts with error bars). All code should be organised in a well-commented Jupyter notebook so I can reproduce every step later.

Deliverables
• Cleaned dataset (CSV)
• Annotated Jupyter notebook (.ipynb) using Pandas + Seaborn
• High-resolution figures (PNG/SVG) ready for slides or manuscripts
• One-page summary explaining key observations and any outliers or anomalies you noticed

I will supply the data and any metadata as soon as we start; you return the items above and we can iterate once if tweaks are needed on the visuals.