Data Science Resources : Différence entre versions

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=General Books=
 
* Free books on data science http://www.learndatasci.com/free-data-science-books
 
* Free books on data science http://www.learndatasci.com/free-data-science-books
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* Many online books on various data science topics on https://bookdown.org/
  
 
=Code=
 
=Code=
 
* Data science using Python https://github.com/jakevdp/PythonDataScienceHandbook (see [[Programming_Resources|Programming resources]] for Python and other languages)
 
* Data science using Python https://github.com/jakevdp/PythonDataScienceHandbook (see [[Programming_Resources|Programming resources]] for Python and other languages)
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* [https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks Gallery of Jupyter Notebooks on programming, data science, etc.]
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* Python data science handbook https://jakevdp.github.io/PythonDataScienceHandbook/
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** [https://github.com/jupyter/jupyter/wiki Gallery of Jupyter Notebooks on programming, data science, etc.]
 
* Examples and tutorials (Jupyter notebooks) for the transportation data management course CIV8760 (in French) https://github.com/nsaunier/CIV8760/
 
* Examples and tutorials (Jupyter notebooks) for the transportation data management course CIV8760 (in French) https://github.com/nsaunier/CIV8760/
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* PolyIT GitHub https://github.com/nsaunier/TransportDataEngineering
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=Data Management=
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* [http://www.datacarpentry.org/lessons/ Data carpentry]
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* A Quick Guide to Organizing Computational Biology Projects https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000424
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* Données de recherche https://guides.biblio.polymtl.ca/donneesrecherche [[OpenScience|open science ou science ouverte]], incluant les politiques de gestion des données de recherche
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=Numerical Methods=
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* Python Programming And Numerical Methods: A Guide For Engineers And Scientists¶ https://pythonnumericalmethods.studentorg.berkeley.edu/notebooks/Index.html
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=Statistics=
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* Statistical Thinking for the 21st Century https://statsthinking21.org/
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* Learning Statistics with R https://learningstatisticswithr.com/
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* Answering questions with data https://crumplab.com/statistics/
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* Carnegie Mellon University free online courses: [https://oli.cmu.edu/courses/probability-statistics-open-free/ Probability & Statistics] [https://oli.cmu.edu/courses/statistical-reasoning-copy/  Statistical Reasoning]
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* Scientific Approaches to Transportation Research http://onlinepubs.trb.org/Onlinepubs/nchrp/cd-22/start.htm
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* Understanding and Communicating Multimodal Transportation Data http://web.cecs.pdx.edu/~monserec/t.data/
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* (French) resources
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** Cours MTH2302C: Probabilités et statistique, Denis Marcotte http://cours.polymtl.ca/geo/marcotte/mth2302c.html
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** Notes et ebooks de Ricco Rakotomalala https://cours-machine-learning.blogspot.com/
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** Cours du master économétrie et statistique appliquée de l'Université d’Orléans https://www.univ-orleans.fr/deg/masters/ESA/CH/churlin_E.htm#_Universit%C3%A9_d%27Orl%C3%A9ans,_Master_Econom
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** Explication et interprétation des modèles de choix discrets https://mate-shs.cnrs.fr/actions/tutomate/tuto35-regression-logistique-deauvieau/
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* Software
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** R, Python (scipy, statsmodels)
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** [http://gretl.sourceforge.net/ Gretl (econometrics)]
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=Artificial Intelligence=
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* CS188 Intro to AI http://ai.berkeley.edu
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* Finnish MOOC https://buildingai.elementsofai.com/
  
 
=Machine Learning=
 
=Machine Learning=
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* list of machine learning books http://matpalm.com/blog/cool_machine_learning_books/
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* List of machine learning books http://matpalm.com/blog/cool_machine_learning_books/
 
** Pattern Recognition and Machine Learning by Christopher Bishop free at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
 
** Pattern Recognition and Machine Learning by Christopher Bishop free at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
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* Neural Networks: Zero to Hero https://karpathy.ai/zero-to-hero.html https://github.com/karpathy/nn-zero-to-hero (NanoGPT https://github.com/karpathy/nanoGPT)
 
* [http://profs.polymtl.ca/jagoulet/Site/Goulet_web_page_BOOK.html Probabilistic Machine Learning for Civil Engineers, James Goulet]
 
* [http://profs.polymtl.ca/jagoulet/Site/Goulet_web_page_BOOK.html Probabilistic Machine Learning for Civil Engineers, James Goulet]
 
* [https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discovery/ Hey, T. Tansley, S. & Tolle, K. (Eds.) The Fourth Paradigm: Data-Intensive Scientific Discovery Microsoft Research, 2009]
 
* [https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discovery/ Hey, T. Tansley, S. & Tolle, K. (Eds.) The Fourth Paradigm: Data-Intensive Scientific Discovery Microsoft Research, 2009]
 
* [http://www.kdnuggets.com/ Site KDnuggets]
 
* [http://www.kdnuggets.com/ Site KDnuggets]
 
* (French) reference book: Cornuéjols, A.; Miclet, L. & Kodratoff, Y. Apprentissage Artificiel Eyrolles, 2002
 
* (French) reference book: Cornuéjols, A.; Miclet, L. & Kodratoff, Y. Apprentissage Artificiel Eyrolles, 2002
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* (French) [http://eric.univ-lyon2.fr/~ricco/cours/supports_data_mining.html Notes pour les cours de data mining de Ricco Rakotomalala]
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* (French) [https://cours-machine-learning.blogspot.com/ Notes pour les cours de data mining de Ricco Rakotomalala]
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** [https://tanagra-machine-learning.blogspot.com Tanagra by the same author]
 
* MOOC by IVADO on deep learning https://cours.edulib.org/courses/course-v1:IVADO+IA-101+P2018/
 
* MOOC by IVADO on deep learning https://cours.edulib.org/courses/course-v1:IVADO+IA-101+P2018/
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* Software
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** [http://www.cs.waikato.ac.nz/ml/weka/ Weka]
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=Data Visualization=
 
=Data Visualization=
 
* Tufte, E. R. The Visual Display of Quantitative Information Graphics Press, 1983
 
* Tufte, E. R. The Visual Display of Quantitative Information Graphics Press, 1983
 
* Blog: [https://flowingdata.com/ Flowing data], [https://www.reddit.com/r/dataisbeautiful/ Data Is Beautiful (Reddit)]
 
* Blog: [https://flowingdata.com/ Flowing data], [https://www.reddit.com/r/dataisbeautiful/ Data Is Beautiful (Reddit)]
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* https://datavizcatalogue.com
 
* Articles: Wikipedia [https://en.wikipedia.org/wiki/Diagram diagrams] and [https://en.wikipedia.org/wiki/Chart Charts], [http://queue.acm.org/detail.cfm?id=1805128 ACM paper], [https://www.economist.com/node/15557455 The Economist]
 
* Articles: Wikipedia [https://en.wikipedia.org/wiki/Diagram diagrams] and [https://en.wikipedia.org/wiki/Chart Charts], [http://queue.acm.org/detail.cfm?id=1805128 ACM paper], [https://www.economist.com/node/15557455 The Economist]
 
* Labs:
 
* Labs:
 
** [http://hcil.umd.edu/ Human-Computer Interaction Lab] and [https://www.cattlab.umd.edu/ Center for Advanced Transportation Technology Laboratory], University of Maryland
 
** [http://hcil.umd.edu/ Human-Computer Interaction Lab] and [https://www.cattlab.umd.edu/ Center for Advanced Transportation Technology Laboratory], University of Maryland
 
** TRB [https://www.teachamerica.com/viz/viz2006.html 5th], [http://teachamerica.com/VIZ11/index.html 6th], [https://teachamerica.com/VIZ13/index.html 7th], [http://viz17.businesscatalyst.com/videos.html 8th] and [http://www.cvent.com/events/9th-international-visualization-in-transportation-symposium-a-better-view/event-summary-aa788f12e69f4c5c83936e5c800b1152.aspx 9th] International Visualization in Transportation Symposium and Workshop
 
** TRB [https://www.teachamerica.com/viz/viz2006.html 5th], [http://teachamerica.com/VIZ11/index.html 6th], [https://teachamerica.com/VIZ13/index.html 7th], [http://viz17.businesscatalyst.com/videos.html 8th] and [http://www.cvent.com/events/9th-international-visualization-in-transportation-symposium-a-better-view/event-summary-aa788f12e69f4c5c83936e5c800b1152.aspx 9th] International Visualization in Transportation Symposium and Workshop
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** Professorts [http://www.professeurs.polymtl.ca/thomas.hurtut/\#dataviz Thomas Hurtut (Polytechnique)], [http://perso.telecom-paristech.fr/~elc/ Éric Lecolinet (Télécom ParisTech)]
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** Professors [http://www.professeurs.polymtl.ca/thomas.hurtut/\#dataviz Thomas Hurtut (Polytechnique)], [http://perso.telecom-paristech.fr/~elc/ Éric Lecolinet (Télécom ParisTech)]
 
* Courses  
 
* Courses  
 
** [https://www.graphics.stanford.edu/courses/cs448b-04-winter/ Data Visualization (CS448b)], Stanford
 
** [https://www.graphics.stanford.edu/courses/cs448b-04-winter/ Data Visualization (CS448b)], Stanford
 
** [https://www.cs171.org/ CS117], Hanspeter Pfister, Harvard
 
** [https://www.cs171.org/ CS117], Hanspeter Pfister, Harvard
 
* Videos: [https://www.youtube.com/watch?v=AdSZJzb-aX8 The Art of Data Visualization | PBS Digital Studios], [https://www.youtube.com/watch?v=-xS7QJhVbcM Harvard i-lab | Data Visualization for Non-Programmers], [https://www.youtube.com/watch?v=aT4JvF7sglg Mike Bostock (D3js) - Keynote], [https://www.youtube.com/watch?v=R-oiKt7bUU8 Designing Data Visualizations with Noah Iliinsky]
 
* Videos: [https://www.youtube.com/watch?v=AdSZJzb-aX8 The Art of Data Visualization | PBS Digital Studios], [https://www.youtube.com/watch?v=-xS7QJhVbcM Harvard i-lab | Data Visualization for Non-Programmers], [https://www.youtube.com/watch?v=aT4JvF7sglg Mike Bostock (D3js) - Keynote], [https://www.youtube.com/watch?v=R-oiKt7bUU8 Designing Data Visualizations with Noah Iliinsky]
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* Libraries
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* Libraries / tools
 
** Python: [https://matplotlib.org/ matplotlib], [https://seaborn.pydata.org/ seaborn]
 
** Python: [https://matplotlib.org/ matplotlib], [https://seaborn.pydata.org/ seaborn]
 
** R: [https://ggplot2.tidyverse.org/ ggplot2]
 
** R: [https://ggplot2.tidyverse.org/ ggplot2]
 
** Javascript: [https://d3js.org/ D3.js]
 
** Javascript: [https://d3js.org/ D3.js]
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** Old: [http://www.gnuplot.info gnuplot]
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=Time Series=
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* Forecasting: Principles and Practice (2nd ed) https://otexts.com/fpp2/
  
 
=Spatial Data=
 
=Spatial Data=
 
* Introduction to Geospatial Concepts https://datacarpentry.org/organization-geospatial/
 
* Introduction to Geospatial Concepts https://datacarpentry.org/organization-geospatial/
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* A Gentle Introduction to GIS https://docs.qgis.org/3.10/en/docs/gentle_gis_introduction/
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* QGIS documentation: [https://docs.qgis.org/latest/fr/docs/index.html français], [https://docs.qgis.org/latest/en/docs/index.html english]
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* QGIS how-to: [https://www.qgistutorials.com/en/docs/3/creating_heatmaps.html heatmaps], [https://www.giscourse.com/how-to-add-openstreetmap-basemaps-in-qgis-3-0/|add OSM layer]
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** A Gentle Introduction to GIS https://docs.qgis.org/latest/en/docs/gentle_gis_introduction/index.html
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** QGIS how-to: [https://www.qgistutorials.com/en/docs/3/creating_heatmaps.html heatmaps], [https://www.giscourse.com/how-to-add-openstreetmap-basemaps-in-qgis-3-0/|add OSM layer]
 
* SpatiaLite cookbook http://www.gaia-gis.it/gaia-sins/spatialite-cookbook-5/index.html
 
* SpatiaLite cookbook http://www.gaia-gis.it/gaia-sins/spatialite-cookbook-5/index.html
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* Introduction to Python for Geographic Data Analysis https://python-gis-book.readthedocs.io
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* Introduction to GIS Programming https://geog-312.gishub.org
 
* (French) books from EPFL: Systèmes d'Information Géographique [https://www.researchgate.net/publication/320979981_Systemes_d%27Information_Geographique_1 Partie 1] et [https://www.researchgate.net/publication/320980079_Systemes_d%27Information_Geographique_2 Partie 2]
 
* (French) books from EPFL: Systèmes d'Information Géographique [https://www.researchgate.net/publication/320979981_Systemes_d%27Information_Geographique_1 Partie 1] et [https://www.researchgate.net/publication/320980079_Systemes_d%27Information_Geographique_2 Partie 2]
 
* (French) resources, including online courses in https://claroline-connect.univ-st-etienne.fr/web/app.php/resource/open/icap_wiki/224152#/
 
* (French) resources, including online courses in https://claroline-connect.univ-st-etienne.fr/web/app.php/resource/open/icap_wiki/224152#/
  
 
=Spatial Analysis=
 
=Spatial Analysis=
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* [https://paezha.github.io/applied_spatial_statistics/ Applied Spatial Statistics with R], Prof. Antonio Paez
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* [https://ipeagit.github.io/intro_access_book/ Introduction to urban accessibility, a practical guide with R]
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* [https://paezha.github.io/spatial-analysis-r/ An Introduction to Spatial Data Analysis and Statistics: A Course in R], Prof. Antonio Paez
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* [https://mgimond.github.io/Spatial/index.html Intro to GIS and Spatial Analysis]
 
* [https://pysal.org Python Spatial Analysis Library (PySAL)]
 
* [https://pysal.org Python Spatial Analysis Library (PySAL)]
 
* [https://geodacenter.github.io/documentation.html Documentation de GeoDa]
 
* [https://geodacenter.github.io/documentation.html Documentation de GeoDa]
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* [https://www.spatialanalysisonline.com/ Geospatial Analysis - A comprehensive guide]
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=Statistics=
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* [https://spacetimewithr.org Spatio-Temporal Statistics with R]
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* Carnegie Mellon University free online courses: [https://oli.cmu.edu/courses/probability-statistics-open-free/ Probability & Statistics] [https://oli.cmu.edu/courses/statistical-reasoning-copy/  Statistical Reasoning]
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* Scientific Approaches to Transportation Research http://onlinepubs.trb.org/Onlinepubs/nchrp/cd-22/start.htm
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* Understanding and Communicating Multimodal Transportation Data http://web.cecs.pdx.edu/~monserec/t.data/
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* (French) resources
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** Cours MTH2302C: Probabilités et statistique, Denis Marcotte http://cours.polymtl.ca/geo/marcotte/mth2302c.html
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** Notes et ebooks de Ricco Rakotomalala http://eric.univ-lyon2.fr/~ricco/
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** Cours du master économétrie et statistique appliquée de l'Université d’Orléans https://www.univ-orleans.fr/deg/masters/ESA/CH/churlin_E.htm#_Universit%C3%A9_d%27Orl%C3%A9ans,_Master_Econom
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Version actuelle en date du 23 janvier 2025 à 11:53

General Books

Code

Data Management

Numerical Methods

Statistics

Artificial Intelligence

Machine Learning


Data Visualization

Time Series

Spatial Data

Spatial Analysis

Récupérée de « https://www.polymtl.ca/wikitransport/index.php?title=Data_Science_Resources&oldid=956 »