LEADER 01088nam0-22003611i-450- 001 990003019400403321 010 $a0-521-38473-7 035 $a000301940 035 $aFED01000301940 035 $a(Aleph)000301940FED01 035 $a000301940 100 $a20000920d1992----km-y0itay50------ba 101 0 $aita 102 $aIT 200 1 $aBiotechnology$eEconomic and Social Aspects$eIssues for Developing Countries$fEdIted by E. J. Da Silva, C. Ratledge and A. Sasson. 210 $aCambridge$cCambridge University Press$cin association with UNESCO$d1992. 215 $aXIII, 388 p.$d23 cm 610 0 $aBiotecnologia e paesi in via di sviluppo$aImpatto economico e sociale 676 $aH/1.111 676 $aH/1.114 676 $aH/1.21 676 $aH/2.227 702 1$aDa Silva,$bEdgar J. 702 1$aRatledge,$bColin 702 1$aSasson,$bAlbert 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003019400403321 952 $aH/0.2 DAS$b11731$fSES 959 $aSES 996 $aBiotechnology$9135463 997 $aUNINA DB $aING01 LEADER 05439nam 22007211 450 001 9910973988803321 005 20230912153559.0 010 $a1-118-83986-2 010 $a1-118-66148-6 035 $a(CKB)3710000000057606 035 $a(EBL)1527439 035 $a(OCoLC)862611724 035 $a(SSID)ssj0001155641 035 $a(PQKBManifestationID)11618739 035 $a(PQKBTitleCode)TC0001155641 035 $a(PQKBWorkID)11187821 035 $a(PQKB)11289045 035 $a(Au-PeEL)EBL1527439 035 $a(CaPaEBR)ebr10799629 035 $a(CaONFJC)MIL576406 035 $a(Au-PeEL)EBL7147356 035 $a(OCoLC)869748048 035 $a(OCoLC)ocn869748048 035 $a(CaSebORM)9781118661468 035 $a(MiAaPQ)EBC1527439 035 $a(EXLCZ)993710000000057606 100 $a20131107h20142014 uy 0 101 0 $aeng 135 $aurunu||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData smart $eusing data science to transform information into insight /$fJohn W. Foreman 205 $a1st ed. 210 1$aIndianapolis :$cWiley,$d[2014] 210 4$d©2014 215 $a1 online resource (434 p.) 300 $aDescription based upon print version of record. 311 08$a1-118-66146-X 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Chapter 1 Everything You Ever Needed to Know about Spreadsheets but Were Too Afraid to Ask; Some Sample Data; Moving Quickly with the Control Button; Copying Formulas and Data Quickly; Formatting Cells; Paste Special Values; Inserting Charts; Locating the Find and Replace Menus; Formulas for Locating and Pulling Values; Using VLOOKUP to Merge Data; Filtering and Sorting; Using PivotTables; Using Array Formulas; Solving Stuff with Solver; OpenSolver: I Wish We Didn't Need This, but We Do; Wrapping Up 327 $aChapter 2 Cluster Analysis Part I: Using K-Means to Segment Your Customer Base Girls Dance with Girls, Boys Scratch Their Elbows; Getting Real: K-Means Clustering Subscribers in E-mail Marketing; Joey Bag O' Donuts Wholesale Wine Emporium; The Initial Dataset; Determining What to Measure; Start with Four Clusters; Euclidean Distance: Measuring Distances as the Crow Flies; Distances and Cluster Assignments for Everybody!; Solving for the Cluster Centers; Making Sense of the Results; Getting the Top Deals by Cluster; The Silhouette: A Good Way to Let Different K Values Duke It Out 327 $aHow about Five Clusters? Solving for Five Clusters; Getting the Top Deals for All Five Clusters; Computing the Silhouette for 5-Means Clustering; K-Medians Clustering and Asymmetric Distance Measurements; Using K-Medians Clustering; Getting a More Appropriate Distance Metric; Putting It All in Excel; The Top Deals for the 5-Medians Clusters; Wrapping Up; Chapter 3 Naive Bayes and the Incredible Lightness of Being an Idiot; When You Name a Product Mandrill, You're Going to Get Some Signal and Some Noise; The World's Fastest Intro to Probability Theory; Totaling Conditional Probabilities 327 $aJoint Probability, the Chain Rule, and Independence What Happens in a Dependent Situation?; Bayes Rule; Using Bayes Rule to Create an AI Model; High-Level Class Probabilities Are Often Assumed to Be Equal; A Couple More Odds and Ends; Let's Get This Excel Party Started; Removing Extraneous Punctuation; Splitting on Spaces; Counting Tokens and Calculating Probabilities; And We Have a Model! Let's Use It; Wrapping Up; Chapter 4 Optimization Modeling: Because That "Fresh Squeezed" Orange Juice Ain't Gonna Blend Itself; Why Should Data Scientists Know Optimization? 327 $aStarting with a Simple Trade-Off Representing the Problem as a Polytope; Solving by Sliding the Level Set; The Simplex Method: Rooting around the Corners; Working in Excel; There's a Monster at the End of This Chapter; Fresh from the Grove to Your Glass...with a Pit Stop Through a Blending Model; You Use a Blending Model; Let's Start with Some Specs; Coming Back to Consistency; Putting the Data into Excel; Setting Up the Problem in Solver; Lowering Your Standards; Dead Squirrel Removal: The Minimax Formulation; If-Then and the "Big M" Constraint 327 $aMultiplying Variables: Cranking Up the Volume to 11 330 $aData Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the ""data scientist,"" to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. 517 3 $aUsing data science to transform information into insight 606 $aData mining 606 $aWeb sites$xDesign 606 $aWeb usage mining 615 0$aData mining. 615 0$aWeb sites$xDesign. 615 0$aWeb usage mining. 676 $a006.312 700 $aForeman$b John W.$091588 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910973988803321 996 $aData smart$94435666 997 $aUNINA LEADER 04136nam 22005175 450 001 9910337611403321 005 20200702035507.0 010 $a3-030-04465-3 024 7 $a10.1007/978-3-030-04465-7 035 $a(CKB)4100000007881156 035 $a(MiAaPQ)EBC5754952 035 $a(DE-He213)978-3-030-04465-7 035 $a(PPN)235670855 035 $a(EXLCZ)994100000007881156 100 $a20190408d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAddressing the Climate in Modern Age's Construction History $eBetween Architecture and Building Services Engineering /$fedited by Carlo Manfredi 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (208 pages) 311 $a3-030-04464-5 327 $aChapter1. - Indoor Climate, Technological Tools and Design Awareness: An Introduction -- Chapter2. - Heating Verona in the Nineteenth Century. From the fireplace to the Hot Water Systems -- Chapter 3 -- Comfort And Conservation In The Munich?s Alte Pinakothek -- Chapter 4 -- Central Heating Systems In France Through Several Examples -- Chapter 5 -- Camillo Boito and the School buildings indoor climate in the unified Italy (1870 ? 1890) -- Chapter 6 -- Tradition And Science: The Evolution Of Environmental Architecture From 16th To 19 Th Century -- Chapter 7 -- Applying Actor-Network-Theory To The History Of Indoor Climate Control Technology -- Chapter 8 -- Asserting Adequacy: The Crescendo Of Nineteenth Century Voices That Demanded Brightness For The Modern World -- Chapter 9 -- What Were The Real Functions Of Houses Of Parliament?s Ventilation Towers. 330 $aThis book sheds light on environmental control in buildings from the 17th century onwards. Even before building services became a hallmark of buildings, in order to address increasing sanitary and comfort needs, pioneering experiences had contributed to improve design skills of professionals. After long being determined by passive features, indoor climate became influenced by installations and plants, representing the most significant shift of paradigm in the modern age?s construction history. This change was not without consequences, and the book presents contributions showing the deep connection between architectural design, comfort requirements and environmental awareness throughout the 19th century. Taking into account the differences between different European countries, the book is a valuable resource for architects, designers and heritage professionals who are interested in environmental design, enabling them to develop a deeper knowledge of heritage in order to address to climate demands, particularly going towards a future in which energy savings and fuel consumption reduction will dictate our behaviour. It includes contributions by leading international experts: Melanie Bauernfeind, Marco Cofani, Lino Vittorio Bozzetto, Emmanuelle Gallo, Alberto Grimoldi, Dean Hawkes, Angelo Giuseppe Landi, Mattias Legnér, Oriel Prizeman, and Henrik Schoenefeldt. 606 $aBuilding construction 606 $aTechnology?History 606 $aEnergy consumption 606 $aBuilding Physics, HVAC$3https://scigraph.springernature.com/ontologies/product-market-codes/T23080 606 $aHistory of Technology$3https://scigraph.springernature.com/ontologies/product-market-codes/T29000 606 $aEnergy Efficiency$3https://scigraph.springernature.com/ontologies/product-market-codes/118000 615 0$aBuilding construction. 615 0$aTechnology?History. 615 0$aEnergy consumption. 615 14$aBuilding Physics, HVAC. 615 24$aHistory of Technology. 615 24$aEnergy Efficiency. 676 $a696 676 $a697.009 702 $aManfredi$b Carlo$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910337611403321 996 $aAddressing the Climate in Modern Age's Construction History$92199672 997 $aUNINA