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#datascience

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Data graphs, computation graphs, property and #knowledgegraphs, #graphdatabase, probabilistic graph models, #gnn, there is currently an explosion of terms involving "graphs". What are their relations if any?

The Open Risk Academy course "An overview of graph methods in #datascience " aims to put some order in the graph chaos by introducing and relating these concepts in a structured way, resulting in, you've guessed it, a "graph of graphs"!

openriskacademy.com/course/vie

We tested different AI models to identify the largest of three numbers with the fractional parts .11, .9, and .099999. You'll be surprised that some AI mistakenly identifying the number ending in .11 as the largest. We also test AI engines on the pronunciation of decimal numbers. #AI #ArtificialIntelligence #MachineLearning #DecimalComparison #MathError #AISafety #DataScience #Engineering #Science #Education #TTMO

youtu.be/TB_4FrWSBwU

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Take, for example, the rich Viking Period graves of Birka. They are absolutely essential for research into that period. But Stolpe excavated and documented them in the 1870s and 1880s. Then the finds and bones and paper archive were not very well curated in a museum until the 1930s, when Arbman collated the information, and published it in the 1940s. Finally both versions of what these graves were like was put online ten years ago.

Now and then people from data science suggest to me that they should be able to do something interesting with big archaeological data. But there's a naïveté in their concept of "data". I always sigh and tell them that they have no idea how unstandardised our data are. Even if you could scan it all from the paper media it's on, any pattern-seeking algorithm would primarily catch the history of changing archaeological documentation practices.

#bigdata #datascience #archaeology

Imagine being an undergraduate and because you were bored, you upended one of the core principles of data science? I would like to say that I grokked everything in this article, but it may be smarter than I am. It’s a super cool story, and nothing in it about the world’s dumpster fires, so that’s nice! Just cool, logical #mathematics.

quantamagazine.org/undergradua

#datascience
#AndrewKrapivin

A tool chest with a tray full of different sized arrows against a red-orange backdrop.
Quanta Magazine · Undergraduate Upends a 40-Year-Old Data Science ConjectureBy Steve Nadis

The #Python language is a true catalyst, making numerically intensive #datascience tasks more far approachable than before.

Economic input-output analysis is one such domain, enabled by the Python Pymrio package which can fetch and process large EEIO databases.

An Open Risk Academy course provides a step-by-step introduction to its use:

openriskacademy.com/course/vie

The course repo: github.com/Open-Risk-Academy/A

The pymrio repo: github.com/IndEcol/pymrio

Dear LazyWeb: is there a C/C++, #RustLang or #Zig equivalent of #SciPy’s `stats` module for statistical analysis? Namely:
• a collection of common PDFs (probability density functions);
• MLE (maximum likelihood estimation) for these common distributions;
• KDE (kernel density estimation).

SciPy’s API is a pleasure to work with. Anything that comes close but usable from C/C++/Rust/Zig would make my life so much easier. Boosts appreciated for visibility.

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Most users are not math, stats, or tech inclined. E.g., consider #healthcare cases or otherwise predominantly med / biomed folk. Hence, users want reassurance that the model is performing as expected and considering the relevant characteristics in the decision-making process. For example, dominant features, correlations, why does the model return the predictions it does, or any other feature of #XAI

datatofu.wordpress.com

Tags: #ai #linux #python #datascience #tech #rstats #opensource

Digestible Data Analytics (DDA)Digestible Data Analytics (DDA)Serving you digestible big data analysis and analytics systems.

#ai tools are innocuously used everywhere. From music and video recommendations to healthcare and industry automation, algorithms efficiently solve clustering, transfer learning, classification, or regression problems. Essentially limited to task-specialized algorithms trained on a specific dataset, they mostly fall into the category of black box models that provide a succint answer.

datatofu.wordpress.com

Tags: #datascience #linux #tech #python #rstats #opensource

Digestible Data Analytics (DDA)Digestible Data Analytics (DDA)Serving you digestible big data analysis and analytics systems.