{"id":390949,"date":"2025-03-02T22:17:29","date_gmt":"2025-03-03T04:17:29","guid":{"rendered":"https:\/\/win-vector.com\/?p=18147"},"modified":"2025-03-02T22:17:29","modified_gmt":"2025-03-03T04:17:29","slug":"best-before-dates-by-bass","status":"publish","type":"post","link":"https:\/\/www.r-bloggers.com\/2025\/03\/best-before-dates-by-bass\/","title":{"rendered":"Best Before Dates by Bass"},"content":{"rendered":"\r\n\r\n
\r\n[This article was first published on R \u2013 Win Vector LLC<\/a><\/strong>, and kindly contributed to R-bloggers<\/a>]. (You can report issue about the content on this page here<\/a>)\r\n
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I was searching for one last real world example for my upcoming video talk March 13th on time series forecasting.<\/p>\n


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\n\"Ts<\/p>\n\n

Hope to see you there! Or reach out to Win Vector LLC for custom training!
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\n<\/center><\/p>\n

I had the seemingly harmless thought: \u201cLet\u2019s look at Stack Overflow trends<\/a>\u201c. In particular their pre-built \u201cdata science and big data trends\u201d query seemed fun.<\/p>\n

\"Ds<\/p>\n

This is a graph of the percentage of Stack Overflow questions tagged with data science terms such as R, Pandas, and so on. It seems to show exploding interest in R and Pandas, and maybe even Tensorflow. Pandas was likely chosen as a proxy for interest in Python for data science (versus a general interest in Python). I\u2019d prefer view counts over question percentages as a proxy of interest, but it is what it is.<\/p>\n

Then I thought, let\u2019s see if they have newer data. They do, and it is horrifying (though not unexpected to those of us in the industry).<\/p>\n

\"Ds<\/p>\n

The graph appears to show interest in R and Pandas rapidly falling. I know there are alternatives to Pandas (such as Polars), but my spot checks didn\u2019t show any of them taking Pandas\u2019s place. Likely we are seeing a big replacement of data science courses with LLM course work and projects. A relevant point is that ChatGPT was released in November of 2022.<\/p>\n

For laughs I digitized the results from the graph into numbers and used Sanjiv Ranjan Das\u2019s excellent book chapter \u201cProduct Market Forecasting using the Bass Model\u201d<\/a> to fit a good old Bass product diffusion model onto the data. The joke is that the Bass model assumes all products die. That isn\u2019t so much the prediction of the Bass model, but one of the assumptions of the model. The idea is: products go obsolete, and Bass helps estimate when.<\/p>\n

The Bass methodology gave me the following graph. Keep in mind: it forces this paraboloid like shape no matter what the data.<\/p>\n

\"Unnamed<\/p>\n

As dire as the Bass curves are, they are not that far off yet. I did the analysis in R, so I am pleased it chose R (itself) to outlast the other systems :). All jokes aside: forecasting helps you plan and adapt.<\/p>\n\n

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I was searching for one last real world example for my upcoming video talk March 13th on time series forecasting. Hope to see you there! Or reach out to Win Vector LLC for custom training! I had the seemingly harmless thought: \u201cLet\u2019s look at Stack Overflow trends\u201c. In particular …<\/div>\n
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