PPT-What is fairness anyway? Interdisciplinary concepts and data science

Interdisciplinary concepts and data science Bettina Berendt KU Leuven Publications and materials on privacy privacy education discrimination and ethics at httpspeoplecskuleuvenbebettinaberendt. Definition and Taxonomy Subgroup Presentation. September 30, 2013. Nancy Grady, SAIC . Natasha . Balac, SDSC. Eugene Lister, R2AD. Overview. Objectives. Approach. Big Data Component Definitions. Data Science Component Definitions. Denis M Medeiros, PhD, RD. Dean, UMKC School of Graduate Studies. Why Interdisciplinary Approaches?. Problems facing society tend to be interdisciplinary. Effective solutions to complex issues require understanding of how disciplines impact one another. Zhenhua . Guo. , Geoffrey Fox, Mo Zhou. Outline. Introduction. Data Locality and Fairness. Experiments. Conclusions. MapReduce Execution Overview. 3. Google File System. Read input data. Data locality. : . a . Cliff . Dahm. Legacy. Nicholas G. Aumen. . Southeast Region, . U.S. Geological Survey, . Davie. , . FL. Zhenhua . Guo. , Geoffrey Fox, Mo Zhou. Outline. Introduction. Data Locality and Fairness. Experiments. Conclusions. MapReduce Execution Overview. 3. Google File System. Read input data. Data locality. Ann Tyler, Associate Dean, College of Health and Human Services. Interim Chair, Department of Speech, Language and Hearing Sciences . Western Michigan University. CAPCSD, April 22, 2016. New Orleans, LA. What is DESE?. If your science and engineering is not data enabled…. …you’re not doing it right.. http://. drewconway.com. /. zia. /2013/3/26/the-data-science-. venn. -diagram. Big Data in Agriculture (Today). Transdisciplinary. . Curriculum Design. What is “Interdisciplinary Learning”?. Interdisciplinary Learning: . A . knowledge view and curriculum approach that consciously applies methodology and language from more than one discipline to examine a central theme, topic, issue, problem, or work.. 3. )– . Melding Mechanisms, Models, & Minds. Richard A. Duschl . The Pennsylvania State University. Building Capacity for State Science Education – September 30, 2011. . Crosscutting Concepts. Ann Tyler, Associate Dean, College of Health and Human Services. Interim Chair, Department of Speech, Language and Hearing Sciences . Western Michigan University. CAPCSD, April 22, 2016. New Orleans, LA. John Callister & Marty Murtagh. NCIIA Conference . San Jose, California. March 21, 2014. Marty Murtagh. Material Sci.. Tobias Hanrath. Chemical Eng.. Rhett Weiss. Johnson School. John Callister. Mechanical Eng.. Transdisciplinary. . Curriculum Design. What is “Interdisciplinary Learning”?. Interdisciplinary Learning: . A . knowledge view and curriculum approach that consciously applies methodology and language from more than one discipline to examine a central theme, topic, issue, problem, or work.. -------INTERDISCIPLINARY CORE COMPLETE THIS SECTION 38 48 CREDITS BIOL 11000 11100 or 12100 13100 135007-8 cr CHM 11500 11600 or 12500 126008-10 cr C S 158001590017700 or 18000 3-4 cr EAS 10000 1090 Cet ouvrage s?adresse 224 tous ceux qui cherchent 224 tirer parti de l?233norme potentiel des 171technologies Big Data187, qu?ils soient data scientists, DSI, chefs de projets ou sp233cialistes m233tier. Le Big Data s?est impos233 comme une innovation majeure pour toutes les entreprises qui cherchent 224 construire un avantage concurrentiel gr226ce 224 l?exploitation de leurs donn233es clients, fournisseurs, produits, processus, machines, etc. Mais quelle solution technique choisir? Quelles comp233tences m233tier d233velopper au sein de la DSI? Ce livre est un guide pour comprendre les enjeux d?un projet Big Data, en appr233hender les concepts sous-jacents (en particulier le Machine Learning) et acqu233rir les comp233tences n233cessaires 224 la mise en place d?un data lab. Il combine la pr233sentation - De notions th233oriques (traitement statistique des donn233es, calcul distribu233...) - Des outils les plus r233pandus (233cosyst232me Hadoop, Storm...) - D?exemples d?applications - D?une organisation typique d?un projet de data science. Cette deuxi232me 233dition est compl233t233e et enrichie par des mises 224 jour sur les r233seaux de neurones et sur le Deep Learning ainsi que sur Spark.