LEADER 00850nam a2200241 a 4500 001 991003517849707536 008 080328s2006 it 000 0 ita d 020 $a8889473037 035 $ab13708648-39ule_inst 040 $aDip.to SSC$bita 082 0 $a306 100 1 $aHerzfeld, Michael$0446542 245 10$aAntropologia :$bpratica della teoria nella cultura e nella societą /$cMichael Herzfeld 260 $aFirenze :$bSeid,$c2006 300 $aXXII, 433 p. ;$c24 cm 440 0$aCollana di antropologia 650 4$aAntropologia culturale 907 $a.b13708648$b02-04-14$c28-03-08 912 $a991003517849707536 945 $aLE021 SOC25D205$g1$i2021000103988$lle021$o-$pE38.00$q-$rn$sm $t0$u0$v0$w0$x0$y.i14726968$z16-04-08 996 $aAntropologia$91229306 997 $aUNISALENTO 998 $ale021$b28-03-08$cm$da $e-$fita$git $h0$i0 LEADER 02996nam 2200529 450 001 9910821331603321 005 20220819004647.0 010 $a0-8218-8164-7 010 $a0-8218-4649-3 035 $a(CKB)3240000000070010 035 $a(EBL)3113324 035 $a(SSID)ssj0000629297 035 $a(PQKBManifestationID)11393257 035 $a(PQKBTitleCode)TC0000629297 035 $a(PQKBWorkID)10719224 035 $a(PQKB)10290852 035 $a(MiAaPQ)EBC3113324 035 $a(RPAM)15514853 035 $a(PPN)197108121 035 $a(EXLCZ)993240000000070010 100 $a20081107h20092009 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aErgodic theory $eProbability and Ergodic Theory Workshops, February 15-18, 2007, February 14-17, 2008, University of North Carolina, Chapel Hill /$fIdris Assani, editor 210 1$aProvidence, Rhode Island :$cAmerican Mathematical Society,$d[2009] 210 4$d©2009 215 $a1 online resource (171 p.) 225 1 $aContemporary mathematics,$v485$x0271-4132 300 $aDescription based upon print version of record. 320 $aIncludes bibliographical references. 327 $aContents -- Preface -- Injectivity of the Dubins-Freedman construction of random distributions -- A maximal inequality for the tail of the bilinear Hardy-Littlewood function -- Almost sure convergence of weighted sums of independent random variables -- Recurrence, ergodicity and invariant measures for cocycles over a rotation -- 1. Invariant measures, regularity of a cocycle -- 2. Growth of the ergodic sums over a rotation, application to recurrence -- 3. Examples of ergodic BV Rd-cocycles -- 4. Examples of non-regular cocycles -- 5. Appendix : A Diophantine property for (I?±, I?²) -- References -- Examples of recurrent or transient stationary walks in Rd over a rotation of T2 -- 1. A sufficient condition of recurrence for stationary walks -- 2. Series with small denominators -- 3. Growth in norm ll ll2 of the ergodic sums and recurrence -- 4. An example of transient cocycle -- References -- A short proof of the unique ergodicity of horicyclic flows -- A-periodic order via dynamical systems: Diffraction for sets of finite local complexity -- Laws of iterated logarithm for weighted sums of iid random variables -- Homeomorphic Bernoulli trial measures and ergodic theory -- Distinguishing transformations by averaging methods -- Some open problems. 410 0$aContemporary mathematics,$v485$x0271-4132 606 $aErgodic theory$vCongresses 615 0$aErgodic theory 676 $a515/.48 702 $aAssani$b Idris 712 12$aChapel Hill Ergodic Theory Workshop$f(2008 :$eUniversity of North Carolina, Chapel Hill), 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910821331603321 996 $aErgodic theory$980545 997 $aUNINA LEADER 03937nam 22005895 450 001 9910427050203321 005 20260810132835.0 010 $a1-4842-5829-0 024 7 $a10.1007/978-1-4842-5829-3 035 $a(CKB)4100000011505216 035 $a(DE-He213)978-1-4842-5829-3 035 $a(MiAaPQ)EBC6371569 035 $a(CaSebORM)9781484258293 035 $a(PPN)252511611 035 $a(EXLCZ)994100000011505216 100 $a20201012d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Analytics in Power BI with R and Python $eIngesting, Transforming, Visualizing /$fby Ryan Wade 205 $a1st ed. 2020. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2020. 215 $a1 online resource (XLVI, 391 p. 84 illus.) 300 $aIncludes index. 311 08$a1-4842-5828-2 327 $aPart I. Creating Custom Data Visualizations using R -- 1. The Grammar of Graphics -- 2. Creating R custom visuals in Power BI using ggplot2 -- Part II. Ingesting Data into the Power BI Data Model using R and Python -- 3. Reading CSV Files -- 4. Reading Excel Files -- 5. Reading SQL Server Data -- 6. Reading Data into the Power BI Data Model via an API -- Part III. Transforming Data using R and Python.-7. Advanced String Manipulation and Pattern Matching -- 8. Calculated Columns using R and Python -- Part IV. Machine Learning & AI in Power BI using R and Python -- 9. Applying Machine Learning and AI to your Power BI Data Models -- 10. Productionizing Data Science Models and Data Wrangling Scripts. . 330 $aThis easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard to do, if not impossible, using native Power BI tools without Power BI Premium capacity. For example, you will learn to score Power BI data using custom data science models, including powerful models from Microsoft Cognitive Services. The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration, but become easier through the application of data wrangling features that languages such as R and Python support. If you are a BI developer, business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from beingjust a business intelligence tool into an advanced data analytics tool, then this is the book to help you to do that. You will: Create advanced data visualizations through R using the ggplot2 package Ingest data using R and Python to overcome the limitations of Power Query Apply machine learning models to your data using R and Python Incorporate advanced AI in Power BI via Microsoft Cognitive Services, IBM Watson, and pre-trained models in SQL Server Machine Learning Services Perform string manipulations not otherwise possible in Power BI using R and Python. 606 $aMicrosoft software 606 $aMicrosoft .NET Framework 606 $aQuantitative research 606 $aBig data 606 $aMicrosoft 606 $aData Analysis and Big Data 606 $aBig Data 615 0$aMicrosoft software. 615 0$aMicrosoft .NET Framework. 615 0$aQuantitative research. 615 0$aBig data. 615 14$aMicrosoft. 615 24$aData Analysis and Big Data. 615 24$aBig Data. 676 $a001.4226028566 700 $aWade$b Ryan$0859869 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910427050203321 996 $aAdvanced analytics in power BI with R and Python$91918799 997 $aUNINA