IE 348 AI for engineering applications
Budget: $25 – $100 USD
I will hand over a cleaned CSV containing thermal-cycling measurements for a single electronic component. What I need back are four polished figures—one line plot, one histogram, one box plot, and one scatter plot—that follow best-practice data-viz rules (Tufte-style minimal ink, readable labels, colour-blind-safe palette, logical ordering of axes, etc.) so that I can drop them straight into an AI-for-mining lecture.
The narrative each figure should support is:
• Line plot – show the full temperature profile across cycles so students can see macro trends and any step changes.
• Histogram – reveal the distribution of peak temperatures.
• Box plot – let us discuss spread and potential thermal-stress outliers.
• Scatter plot – correlate cycle number with a secondary variable (e.g., ΔT or measured strain) to spark a conversation on degradation.
Python is my usual teaching stack, so pandas for wrangling and seaborn or matplotlib for plotting will integrate perfectly, but if you prefer tidyverse in R that’s fine as long as the export is high-resolution PNG and an editable source file (Jupyter notebook or .Rmd).
Please annotate the code clearly, keep functions modular, and include a short README that explains how to reproduce the figures from the raw CSV.
The narrative each figure should support is:
• Line plot – show the full temperature profile across cycles so students can see macro trends and any step changes.
• Histogram – reveal the distribution of peak temperatures.
• Box plot – let us discuss spread and potential thermal-stress outliers.
• Scatter plot – correlate cycle number with a secondary variable (e.g., ΔT or measured strain) to spark a conversation on degradation.
Python is my usual teaching stack, so pandas for wrangling and seaborn or matplotlib for plotting will integrate perfectly, but if you prefer tidyverse in R that’s fine as long as the export is high-resolution PNG and an editable source file (Jupyter notebook or .Rmd).
Please annotate the code clearly, keep functions modular, and include a short README that explains how to reproduce the figures from the raw CSV.