Deutsch English Français Italiano |
<vkvffu$1sirn$3@dont-email.me> View for Bookmarking (what is this?) Look up another Usenet article |
Path: ...!feeder3.eternal-september.org!news.eternal-september.org!eternal-september.org!.POSTED!not-for-mail From: Lawrence D'Oliveiro <ldo@nz.invalid> Newsgroups: alt.comp.os.windows-11,comp.os.linux.advocacy Subject: Re: The problem with not owning the software Date: Tue, 31 Dec 2024 00:56:30 -0000 (UTC) Organization: A noiseless patient Spider Lines: 21 Message-ID: <vkvffu$1sirn$3@dont-email.me> References: <Tn39P.50437$%aWb.4583@fx18.iad> <nuy9P.28629$aTp4.27488@fx09.iad> <vkbuqd$18m92$2@toylet.eternal-september.org> <9OCcnRW7grqkbPT6nZ2dnZfqnPUAAAAA@earthlink.com> <vkfva1$28j6k$1@toylet.eternal-september.org> <vkhqif$2hvap$1@dont-email.me> <vkmd9n$3l76a$4@toylet.eternal-september.org> <vko7up$6qks$2@dont-email.me> <ltb26aFov8dU1@mid.individual.net> <fUYbP.143220$bYV2.129957@fx17.iad> <ltbjk1FrgqvU3@mid.individual.net> <zKecnbsivueyNO36nZ2dnZfqn_HTpa6r@earthlink.com> <vks1gq.ufk.1@ID-201911.user.individual.net> <ltdrjlF7nkqU6@mid.individual.net> <vkso91$12a03$10@dont-email.me> <vkt8va$1fes1$3@dont-email.me> <vkun59$1mknq$3@dont-email.me> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Injection-Date: Tue, 31 Dec 2024 01:56:31 +0100 (CET) Injection-Info: dont-email.me; posting-host="41fdacfd7af50925eaa1291753caf57e"; logging-data="1985399"; mail-complaints-to="abuse@eternal-september.org"; posting-account="U2FsdGVkX1/hc/1OwM+6lC6jSuTQfLUs" User-Agent: Pan/0.161 (Chasiv Yar; ) Cancel-Lock: sha1:+NeQMz/rRBgsGMI0p3cwhkVkU+s= Bytes: 2531 On Mon, 30 Dec 2024 13:01:13 -0500, -hh wrote: > On 12/29/24 11:52 PM, Lawrence D'Oliveiro wrote: > >> How much data was involved, really? I suspect a more sensible app would >> deal with the same data much more efficiently and easily. > > There was a pretty modest chunk of data ... maybe just 1000 unique data > points? > > What made it large & computationally intensive was that the dataset was > routed iteratively through a ~dozen different "Monte Carlo" statistical > exercises and filters to identify & glean signal from noise. I’m sure something could be whipped up in Python with NumPy/Pandas/ Matplotlib etc that would go through the same operations much more quickly and efficiently. Microsoft is even offering access to these Python toolkits to Excel users now -- at a cost. You know -- charging for something that the users could bypass Microsoft and access for free, only they’re too dumb to realize it.