Effective plots are important to synthesize the information into relevant and actionable information. The following tutorial details some of the common data plotting functions within Microsoft Excel.

This tutorial can also be completed with scripting programming languages like MATLAB and Python. Click on the appropriate link for additional information and source code.

Generative AI Learning

Use these prompts to test your understanding after completing the tutorial. Direct the AI - make the plots yourself first, then let it critique them.

Modern data: Grid operators publish real solar and wind generation time series (for example, hourly output for a day from your local utility or a public dataset). Plotting one day of solar output vs time, and daily energy vs day-of-year, is the same scatter-plot skill applied to data your generation of engineers will actually use.

What to Turn In

Submit a short report (PDF, 1-2 pages) with your homework that curates your results into a demonstration of what you learned. You may use Generative AI to help write the report, but you must guide it to the correct visualizations, justifications, and assumptions. Answer these questions:

1. Include one XY scatter chart of engineering or energy data (your homework data, or a solar/wind time series) with complete axis labels, units, and a title. State why scatter was the right chart type.
2. Take one data set that looks curved on linear axes and re-plot it with a log axis (or plot log of the values). What functional form does the straightened plot suggest, and why?
3. From the reviewer prompt: what was the most substantive criticism of your chart, and what did you change?
4. From the quiz prompt: one question you missed and the corrected answer.