#datascience — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #datascience, aggregated by home.social.
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Organizations that invest in shared infrastructure help keep R strong, reliable, and ready for the future.
Mike K. Smith, R Consortium Board Chair and Senior Director, Statistical Data Science and Analytics, Pfizer:
"Using open source without contributing back is not a sustainable model."
The views expressed are Mike's own and do not necessarily reflect those of his employer.
Learn more: https://lnkd.in/ggvCEfbe
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🌳 Is Calgary actually as green as it looks?
While open lawns (28.6% citywide) give Calgary a visual "green appearance", dry summer conditions quickly strip unirrigated grass of its cooling capacity. Meanwhile, true cooling infrastructure — dense and sparse tree canopy — accounts for only ~17%.
To quantify structural heat risks, I introduced the Solid-to-Tree Ratio index across 193 residential communities:
🔹 Critical Deficit: Downtown Core (56.2) and Beltline (24.6), where hard surfaces outnumber tree canopy by tens of times.
🔹 Suburban Pressure: New communities like Redstone (23.8) and Seton (20.8) feature high-density lots with minimal mature shade.
🔹 Ecological Buffers: River valley communities like Discovery Ridge (0.5) and Wildwood (0.6), where canopy exceeds concrete.👇 Link to the full study and interactive dataset:
https://www.datastory.org.ua/calgarys-microclimatic-anatomy-what-lies-beneath-the-citys-green-appearance/#Calgary #YYC #DataScience #GIS #RStats #RemoteSensing #UrbanHeat #DataViz #SpatialAnalysis #MachineLearning #GreennessOfCalgary #FOSSGIS #Alberta #Canada
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🌳 Is Calgary actually as green as it looks?
While open lawns (28.6% citywide) give Calgary a visual "green appearance", dry summer conditions quickly strip unirrigated grass of its cooling capacity. Meanwhile, true cooling infrastructure — dense and sparse tree canopy — accounts for only ~17%.
To quantify structural heat risks, I introduced the Solid-to-Tree Ratio index across 193 residential communities:
🔹 Critical Deficit: Downtown Core (56.2) and Beltline (24.6), where hard surfaces outnumber tree canopy by tens of times.
🔹 Suburban Pressure: New communities like Redstone (23.8) and Seton (20.8) feature high-density lots with minimal mature shade.
🔹 Ecological Buffers: River valley communities like Discovery Ridge (0.5) and Wildwood (0.6), where canopy exceeds concrete.👇 Link to the full study and interactive dataset:
https://www.datastory.org.ua/calgarys-microclimatic-anatomy-what-lies-beneath-the-citys-green-appearance/#Calgary #YYC #DataScience #GIS #RStats #RemoteSensing #UrbanHeat #DataViz #SpatialAnalysis #MachineLearning #GreennessOfCalgary #FOSSGIS #Alberta #Canada
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mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
https://thierrymoudiki.github.io/blog/2024/05/06/python/r/lsboost/lsboost-feature-reduction -
mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
https://thierrymoudiki.github.io/blog/2024/05/06/python/r/lsboost/lsboost-feature-reduction -
mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
https://thierrymoudiki.github.io/blog/2024/05/06/python/r/lsboost/lsboost-feature-reduction -
mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
https://thierrymoudiki.github.io/blog/2024/05/06/python/r/lsboost/lsboost-feature-reduction -
mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
https://thierrymoudiki.github.io/blog/2024/05/06/python/r/lsboost/lsboost-feature-reduction -
AI & Expert Systems | @DrJimCarey | Flipboard
AI and Expert Systems news, developments, recommendations, tips and tutorials.
https://flipboard.com/@drjimcarey/ai-expert-systems-erpftjj3y?utm_source=dlvr.it&utm_medium=mastodon
#AI #ExpertSystems #ArtificialIntelligence #Technology #TechNews #FutureOfWork #Tutorials #MachineLearning #Innovation #DataScience -
AI & Expert Systems | @DrJimCarey | Flipboard
AI and Expert Systems news, developments, recommendations, tips and tutorials.
https://flipboard.com/@drjimcarey/ai-expert-systems-erpftjj3y?utm_source=dlvr.it&utm_medium=mastodon
#AI #ExpertSystems #ArtificialIntelligence #Technology #TechNews #FutureOfWork #Tutorials #MachineLearning #Innovation #DataScience -
AI & Expert Systems | @DrJimCarey | Flipboard
AI and Expert Systems news, developments, recommendations, tips and tutorials.
https://flipboard.com/@drjimcarey/ai-expert-systems-erpftjj3y?utm_source=dlvr.it&utm_medium=mastodon
#AI #ExpertSystems #ArtificialIntelligence #Technology #TechNews #FutureOfWork #Tutorials #MachineLearning #Innovation #DataScience -
AI & Expert Systems | @DrJimCarey | Flipboard
AI and Expert Systems news, developments, recommendations, tips and tutorials.
https://flipboard.com/@drjimcarey/ai-expert-systems-erpftjj3y?utm_source=dlvr.it&utm_medium=mastodon
#AI #ExpertSystems #ArtificialIntelligence #Technology #TechNews #FutureOfWork #Tutorials #MachineLearning #Innovation #DataScience -
AI & Expert Systems | @DrJimCarey | Flipboard
AI and Expert Systems news, developments, recommendations, tips and tutorials.
https://flipboard.com/@drjimcarey/ai-expert-systems-erpftjj3y?utm_source=dlvr.it&utm_medium=mastodon
#AI #ExpertSystems #ArtificialIntelligence #Technology #TechNews #FutureOfWork #Tutorials #MachineLearning #Innovation #DataScience -
A detailed introduction to Deep Quasi-Randomized 'neural' networks
https://thierrymoudiki.github.io/blog/2024/05/19/python/r/deep-qrns -
A detailed introduction to Deep Quasi-Randomized 'neural' networks
https://thierrymoudiki.github.io/blog/2024/05/19/python/r/deep-qrns -
A detailed introduction to Deep Quasi-Randomized 'neural' networks
https://thierrymoudiki.github.io/blog/2024/05/19/python/r/deep-qrns -
A detailed introduction to Deep Quasi-Randomized 'neural' networks
https://thierrymoudiki.github.io/blog/2024/05/19/python/r/deep-qrns -
A detailed introduction to Deep Quasi-Randomized 'neural' networks
https://thierrymoudiki.github.io/blog/2024/05/19/python/r/deep-qrns -
Season 1 Lesson 32 Part 8 - Your First Steps in Python Python Launcher Find All Versions #dataengineer #jupyternotebook #pythoncode #machinelearning #aws #azure #gcp #softwaredeveloper #python #vibecoding #pythonprogramming #softwarengineer #codingtutorial #learncoding #datascience #dataanalysis
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Season 1 Lesson 32 Part 8 - Your First Steps in Python Python Launcher Find All Versions #dataengineer #jupyternotebook #pythoncode #machinelearning #aws #azure #gcp #softwaredeveloper #python #vibecoding #pythonprogramming #softwarengineer #codingtutorial #learncoding #datascience #dataanalysis
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Season 1 Lesson 32 Part 8 - Your First Steps in Python Python Launcher Find All Versions #dataengineer #jupyternotebook #pythoncode #machinelearning #aws #azure #gcp #softwaredeveloper #python #vibecoding #pythonprogramming #softwarengineer #codingtutorial #learncoding #datascience #dataanalysis
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Season 1 Lesson 32 Part 8 - Your First Steps in Python Python Launcher Find All Versions #dataengineer #jupyternotebook #pythoncode #machinelearning #aws #azure #gcp #softwaredeveloper #python #vibecoding #pythonprogramming #softwarengineer #codingtutorial #learncoding #datascience #dataanalysis
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The way you use AI can have a huge impact on how much value you get from it.
A new series of Statistics Globe Hub modules has just started, and the first module is ChatGPT for R Programming.
More information: https://statisticsglobe.com/hub
#AI #RStats #Python #DataScience #MachineLearning #Statistics
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The way you use AI can have a huge impact on how much value you get from it.
A new series of Statistics Globe Hub modules has just started, and the first module is ChatGPT for R Programming.
More information: https://statisticsglobe.com/hub
#AI #RStats #Python #DataScience #MachineLearning #Statistics
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The way you use AI can have a huge impact on how much value you get from it.
A new series of Statistics Globe Hub modules has just started, and the first module is ChatGPT for R Programming.
More information: https://statisticsglobe.com/hub
#AI #RStats #Python #DataScience #MachineLearning #Statistics
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The way you use AI can have a huge impact on how much value you get from it.
A new series of Statistics Globe Hub modules has just started, and the first module is ChatGPT for R Programming.
More information: https://statisticsglobe.com/hub
#AI #RStats #Python #DataScience #MachineLearning #Statistics
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The way you use AI can have a huge impact on how much value you get from it.
A new series of Statistics Globe Hub modules has just started, and the first module is ChatGPT for R Programming.
More information: https://statisticsglobe.com/hub
#AI #RStats #Python #DataScience #MachineLearning #Statistics
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I was out at a tech meetup last week when I mentioned I was going to a conference on stats education this week. “Sounds riveting” said someone, with the deepest sarcasm. The thing is, stats are riveting. Stats are powerful. If we’re not showing kids the magic of it all, how can we expect them to love it like we do?
https://adsei.org/2026/07/21/getting-past-the-fear-factor/ -
I was out at a tech meetup last week when I mentioned I was going to a conference on stats education this week. “Sounds riveting” said someone, with the deepest sarcasm. The thing is, stats are riveting. Stats are powerful. If we’re not showing kids the magic of it all, how can we expect them to love it like we do?
https://adsei.org/2026/07/21/getting-past-the-fear-factor/ -
I was out at a tech meetup last week when I mentioned I was going to a conference on stats education this week. “Sounds riveting” said someone, with the deepest sarcasm. The thing is, stats are riveting. Stats are powerful. If we’re not showing kids the magic of it all, how can we expect them to love it like we do?
https://adsei.org/2026/07/21/getting-past-the-fear-factor/ -
I was out at a tech meetup last week when I mentioned I was going to a conference on stats education this week. “Sounds riveting” said someone, with the deepest sarcasm. The thing is, stats are riveting. Stats are powerful. If we’re not showing kids the magic of it all, how can we expect them to love it like we do?
https://adsei.org/2026/07/21/getting-past-the-fear-factor/ -
I was out at a tech meetup last week when I mentioned I was going to a conference on stats education this week. “Sounds riveting” said someone, with the deepest sarcasm. The thing is, stats are riveting. Stats are powerful. If we’re not showing kids the magic of it all, how can we expect them to love it like we do?
https://adsei.org/2026/07/21/getting-past-the-fear-factor/ -
🌳 Quantifying Calgary’s Microclimatic Imbalance: The Solid-to-Tree Ratio
To evaluate the structural heat load across Calgary's communities, I calculated the ratio of high-thermal artificial surfaces to cooling tree canopy: Solid / (Park + Forest), based on my 2025 satellite land cover model (LULC v6.0).
Why this ratio matters:
🔹 Thermal Stress Indicator: It measures how many square meters of heat-absorbing surfaces (asphalt, concrete, roofs, bare soil) exist for every square meter of tree canopy.
🔹 Spatial Inequality: While mature western neighborhoods maintain ratios between 1.5 and 4, eastern and peripheral zones reach values from 10 to over 50.
🔹 Evidence-Based Planning: It moves the discussion from generic averages to identifying exact spatial boundaries where microclimatic mitigation is most needed.Note: The map uses a logarithmic scale.
#Calgary #YYC #DataScience #GIS #RStats #RemoteSensing #UrbanHeat #DataViz #SpatialAnalysis #MachineLearning #GreennessOfCalgary #FOSSGIS #Sentinel1 #Sentinel2
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🌳 Quantifying Calgary’s Microclimatic Imbalance: The Solid-to-Tree Ratio
To evaluate the structural heat load across Calgary's communities, I calculated the ratio of high-thermal artificial surfaces to cooling tree canopy: Solid / (Park + Forest), based on my 2025 satellite land cover model (LULC v6.0).
Why this ratio matters:
🔹 Thermal Stress Indicator: It measures how many square meters of heat-absorbing surfaces (asphalt, concrete, roofs, bare soil) exist for every square meter of tree canopy.
🔹 Spatial Inequality: While mature western neighborhoods maintain ratios between 1.5 and 4, eastern and peripheral zones reach values from 10 to over 50.
🔹 Evidence-Based Planning: It moves the discussion from generic averages to identifying exact spatial boundaries where microclimatic mitigation is most needed.Note: The map uses a logarithmic scale.
#Calgary #YYC #DataScience #GIS #RStats #RemoteSensing #UrbanHeat #DataViz #SpatialAnalysis #MachineLearning #GreennessOfCalgary #FOSSGIS #Sentinel1 #Sentinel2
-
🌳 Quantifying Calgary’s Microclimatic Imbalance: The Solid-to-Tree Ratio
To evaluate the structural heat load across Calgary's communities, I calculated the ratio of high-thermal artificial surfaces to cooling tree canopy: Solid / (Park + Forest), based on my 2025 satellite land cover model (LULC v6.0).
Why this ratio matters:
🔹 Thermal Stress Indicator: It measures how many square meters of heat-absorbing surfaces (asphalt, concrete, roofs, bare soil) exist for every square meter of tree canopy.
🔹 Spatial Inequality: While mature western neighborhoods maintain ratios between 1.5 and 4, eastern and peripheral zones reach values from 10 to over 50.
🔹 Evidence-Based Planning: It moves the discussion from generic averages to identifying exact spatial boundaries where microclimatic mitigation is most needed.Note: The map uses a logarithmic scale.
#Calgary #YYC #DataScience #GIS #RStats #RemoteSensing #UrbanHeat #DataViz #SpatialAnalysis #MachineLearning #GreennessOfCalgary #FOSSGIS #Sentinel1 #Sentinel2
-
🌳 Quantifying Calgary’s Microclimatic Imbalance: The Solid-to-Tree Ratio
To evaluate the structural heat load across Calgary's communities, I calculated the ratio of high-thermal artificial surfaces to cooling tree canopy: Solid / (Park + Forest), based on my 2025 satellite land cover model (LULC v6.0).
Why this ratio matters:
🔹 Thermal Stress Indicator: It measures how many square meters of heat-absorbing surfaces (asphalt, concrete, roofs, bare soil) exist for every square meter of tree canopy.
🔹 Spatial Inequality: While mature western neighborhoods maintain ratios between 1.5 and 4, eastern and peripheral zones reach values from 10 to over 50.
🔹 Evidence-Based Planning: It moves the discussion from generic averages to identifying exact spatial boundaries where microclimatic mitigation is most needed.Note: The map uses a logarithmic scale.
#Calgary #YYC #DataScience #GIS #RStats #RemoteSensing #UrbanHeat #DataViz #SpatialAnalysis #MachineLearning #GreennessOfCalgary #FOSSGIS #Sentinel1 #Sentinel2
-
🌳 Quantifying Calgary’s Microclimatic Imbalance: The Solid-to-Tree Ratio
To evaluate the structural heat load across Calgary's communities, I calculated the ratio of high-thermal artificial surfaces to cooling tree canopy: Solid / (Park + Forest), based on my 2025 satellite land cover model (LULC v6.0).
Why this ratio matters:
🔹 Thermal Stress Indicator: It measures how many square meters of heat-absorbing surfaces (asphalt, concrete, roofs, bare soil) exist for every square meter of tree canopy.
🔹 Spatial Inequality: While mature western neighborhoods maintain ratios between 1.5 and 4, eastern and peripheral zones reach values from 10 to over 50.
🔹 Evidence-Based Planning: It moves the discussion from generic averages to identifying exact spatial boundaries where microclimatic mitigation is most needed.Note: The map uses a logarithmic scale.
#Calgary #YYC #DataScience #GIS #RStats #RemoteSensing #UrbanHeat #DataViz #SpatialAnalysis #MachineLearning #GreennessOfCalgary #FOSSGIS #Sentinel1 #Sentinel2
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learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
https://thierrymoudiki.github.io/blog/2024/07/08/r/learningmachine-docs -
learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
https://thierrymoudiki.github.io/blog/2024/07/08/r/learningmachine-docs -
learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
https://thierrymoudiki.github.io/blog/2024/07/08/r/learningmachine-docs -
learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
https://thierrymoudiki.github.io/blog/2024/07/08/r/learningmachine-docs -
learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
https://thierrymoudiki.github.io/blog/2024/07/08/r/learningmachine-docs -
Juno 4 in one minute: an entire Python project, a local Flask web server in
the built-in browser, Jupyter notebooks, and a live REPL — running natively
on an iPad, fully offline.AI assistance is strictly bring-your-own-key; your code never touches our
servers (there aren't any).▶ Full demo: https://www.youtube.com/watch?v=GMor7FRp4nw
📱 App Store: https://apps.apple.com/app/id1462586500
What's new in Juno 4: https://juno.sh/blog/juno-4/ -
Juno 4 in one minute: an entire Python project, a local Flask web server in
the built-in browser, Jupyter notebooks, and a live REPL — running natively
on an iPad, fully offline.AI assistance is strictly bring-your-own-key; your code never touches our
servers (there aren't any).▶ Full demo: https://www.youtube.com/watch?v=GMor7FRp4nw
📱 App Store: https://apps.apple.com/app/id1462586500
What's new in Juno 4: https://juno.sh/blog/juno-4/ -
Juno 4 in one minute: an entire Python project, a local Flask web server in
the built-in browser, Jupyter notebooks, and a live REPL — running natively
on an iPad, fully offline.AI assistance is strictly bring-your-own-key; your code never touches our
servers (there aren't any).▶ Full demo: https://www.youtube.com/watch?v=GMor7FRp4nw
📱 App Store: https://apps.apple.com/app/id1462586500
What's new in Juno 4: https://juno.sh/blog/juno-4/ -
Juno 4 in one minute: an entire Python project, a local Flask web server in
the built-in browser, Jupyter notebooks, and a live REPL — running natively
on an iPad, fully offline.AI assistance is strictly bring-your-own-key; your code never touches our
servers (there aren't any).▶ Full demo: https://www.youtube.com/watch?v=GMor7FRp4nw
📱 App Store: https://apps.apple.com/app/id1462586500
What's new in Juno 4: https://juno.sh/blog/juno-4/ -
Juno 4 in one minute: an entire Python project, a local Flask web server in
the built-in browser, Jupyter notebooks, and a live REPL — running natively
on an iPad, fully offline.AI assistance is strictly bring-your-own-key; your code never touches our
servers (there aren't any).▶ Full demo: https://www.youtube.com/watch?v=GMor7FRp4nw
📱 App Store: https://apps.apple.com/app/id1462586500
What's new in Juno 4: https://juno.sh/blog/juno-4/ -
Presenting Lightweight Transfer Learning for Financial Forecasting (Risk 2026)
https://thierrymoudiki.github.io/blog/2026/02/04/r/Presenting-Transfer-Learning -
Presenting Lightweight Transfer Learning for Financial Forecasting (Risk 2026)
https://thierrymoudiki.github.io/blog/2026/02/04/r/Presenting-Transfer-Learning -
Presenting Lightweight Transfer Learning for Financial Forecasting (Risk 2026)
https://thierrymoudiki.github.io/blog/2026/02/04/r/Presenting-Transfer-Learning