Measurement is the cornerstone of all science. Without it, how could we test our hypotheses?
Python, as the preeminent programming language for data science, makes it easy to gather, clean, and make sense of measurement data. With Python, we can back-test predictions, validate models, and hold prognosticators accountable.
Last year, an outdated meme taunting Al Gore showed up in my LinkedIn feed, marked with the hashtag “#catastrophizing.” The subject was his comments in 2007 and 2009 that the Arctic Sea would be devoid of summer ice within the span of seven years. Several fact-checking sites verified this statement as “mostly true” and referred to the following quote:
“Some of the models suggest to Dr. (Wieslav) Maslowski that there is a 75% chance that the entire north polar ice cap, during some of the summer months, could be completely ice-free within the next five to seven years.”
-Al Gore, December 2009
While many individuals accept memes at face value, data scientists possess the ability to delve into the data and draw their own conclusions. In this Quick Success Data Science project, we’ll use Python’s pandas and Matplotlib libraries to scrutinize the behavior of Arctic Sea ice over the last four decades and put the comments and memes to the test.
Please note that this is neither an anti- nor pro-climate change article, it is a pro-data article. Regardless of how you feel about anthropogenic climate change, I hope you agree that it’s in everyone’s best interest to validate models and confirm predictions.
It’s also important that thought leaders on critical subjects refrain from making extravagant or rash claims that are easily refuted. This not only tarnishes credibility but also politicizes the subject matter, making rational discussions difficult if not impossible.
In this case, Al Gore was wise enough to hedge his comments with probabilities and words like “suggest” and “could.”…