How do we debate or add to this?

Understanding large datasets such as 50 years of historical weather metrics will require a mathematical way to digest the data. It's imperative to leverage statistics and/or linear algebra techniques to identify patterns in weather activity, year-over-year (YoY) trends, seasonal changes, and much more. With a timeline as large as 50 years, you will likely identify how frequently we experience spikes or record-breaking temperatures across the area of interest. As a result, you will discover mathematically sound ways to predict what to expect for the upcoming week, month, or season.

The first example of a use case where linear algebra could help is if you seek to predict the temperature for the next week based on historical weather data. To do this, you would take the days and label them as 1,2, 3, 4, etc., followed by previous temperatures and humidity (or something else if desired). You would add them to the equation using np.array, making it x. The y would be the corresponding temperatures and you would create a model such as model = LinearRegression().fit(x, y).

The second example could be to analyze the impact on asthma cases in relation to the air quality index (AQI). You would also use the np.arry to label your existing data and perform a principal component analysis (PCA) to identify the trend.