LEADER 05632nam 22007333u 450 001 9910132334903321 005 20240404170240.0 010 $a9781118691786 010 $a1118691784 035 $a(CKB)3710000000111791 035 $a(EBL)1687540 035 $a(FR-PaCSA)88944254 035 $a(MiAaPQ)EBC1687540 035 $a(FRCYB88944254)88944254 035 $a(EXLCZ)993710000000111791 100 $a20140519d2014|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBig data, data mining, and machine learning $evalue creation for business leaders and practitioners 205 $a1st ed. 210 1$aHoboken :$cWiley,$d2014 215 $a1 online resource (289 pages) 225 1 $aWiley and SAS business series 225 1 $aTHEi Wiley ebooks 300 $aDescription based upon print version of record. 311 08$a9781118618042 311 08$a1118618041 327 $aBig Data, Data Mining, and Machine Learning; Contents; Forward; Preface; Acknowledgments; Introduction; Big Data Timeline; Why This Topic Is Relevant Now; Is Big Data a Fad?; Where Using Big Data Makes a Big Difference; Technical Issue; Work Flow Productivity; The Complexities When Data Gets Large; Part One The Computing Environment; Chapter 1 Hardware; Storage (Disk); Central Processing Unit; Graphical Processing Unit; Memory; Network; Chapter 2 Distributed Systems; Database Computing; File System Computing; Considerations; Chapter 3 Analytical Tools; Weka; Java and JVM Languages; R; Python 327 $aSASPart Two Turning Data into Business Value; Chapter 4 Predictive Modeling; A Methodology for Building Models; sEMMA; sEMMA for the Big Data Era; Binary Classification; Multilevel Classification; Interval Prediction; Assessment of Predictive Models; Classification; Receiver Operating Characteristic; Lift; Gain; Akaike's Information Criterion; Bayesian Information Criterion; Kolmogorov‐Smirnov; Chapter 5 Common Predictive Modeling Techniques; RFM; Regression; Basic Example of Ordinary Least Squares; Assumptions of Regression Models; Additional Regression Techniques 327 $aApplications in the Big Data EraGeneralized Linear Models; Example of a Probit GLM; Applications in the Big Data Era; Neural Networks; Basic Example of Neural Networks; Decision and Regression Trees; Support Vector Machines; Bayesian Methods Network Classification; Naive Bayes Network; Parameter Learning; Learning a Bayesian Network; Inference in Bayesian Networks; Scoring for Supervised Learning; Ensemble Methods; Chapter 6 Segmentation; Cluster Analysis; Distance Measures (Metrics); Evaluating Clustering; Number of Clusters; K-means Algorithm; Hierarchical Clustering; Profiling Clusters 327 $aChapter 7 Incremental Response ModelingBuilding the Response Model; Measuring the Incremental Response; Chapter 8 Time Series Data Mining; Reducing Dimensionality; Detecting Patterns; Fraud Detection; New Product Forecasting; Time Series Data Mining in Action: Nike+ FuelBand; Seasonal Analysis; Trend Analysis; Similarity Analysis; Chapter 9 Recommendation Systems; What Are Recommendation Systems?; Where Are They Used?; How Do They Work?; Baseline Model; Low‐Rank Matrix Factorization; Stochastic Gradient Descent; Alternating Least Squares; Restricted Boltzmann Machines; Contrastive Divergence 327 $aAssessing Recommendation QualityRecommendations in Action: SAS Library; Chapter 10 Text Analytics; Information Retrieval; Content Categorization; Text Mining; Text Analytics in Action: Let's Play Jeopardy!; Information Retrieval Steps; Discovering Topics in Jeopardy! Clues; Topics from Clues Having Incorrect or Missing Answers; Discovering New Topics from Clues; Contestant Analysis: Fantasy Jeopardy!; Part Three Success Stories of Putting It All Together; Chapter 11 Case Study of a Large U.S.-Based Financial Services Company; Traditional Marketing Campaign Process 327 $aHigh-Performance Marketing Solution 330 $aWith big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computin 410 0$aWiley and SAS business series 410 0$aTHEi Wiley ebooks. 606 $aBig data 606 $aCOMPUTERS / Database Management / Data Mining 606 $aData mining 606 $aDatabase management 606 $aInformation technology -- Management 606 $aManagement -- Data processing 606 $aManagement 615 4$aBig data. 615 4$aCOMPUTERS / Database Management / Data Mining. 615 4$aData mining. 615 4$aDatabase management. 615 4$aInformation technology -- Management. 615 4$aManagement -- Data processing. 615 4$aManagement. 676 $a658 676 $a658.05631 676 $a658/.05631 700 $aDean$b Jared$0957980 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910132334903321 996 $aBig data, data mining, and machine learning$92170309 997 $aUNINA LEADER 03950nam 2200697Ia 450 001 9910971754103321 005 20251116205621.0 010 $a9786612437274 010 $a9780309147354 010 $a0309147352 010 $a9781282437272 010 $a1282437275 010 $a9780309143677 010 $a0309143675 035 $a(CKB)2560000000007923 035 $a(EBL)3378559 035 $a(SSID)ssj0000341033 035 $a(PQKBManifestationID)11243869 035 $a(PQKBTitleCode)TC0000341033 035 $a(PQKBWorkID)10390565 035 $a(PQKB)10695895 035 $a(MiAaPQ)EBC3378559 035 $a(Au-PeEL)EBL3378559 035 $a(CaPaEBR)ebr10355557 035 $a(CaONFJC)MIL243727 035 $a(OCoLC)923281052 035 $a(Perlego)4734513 035 $a(BIP)27725326 035 $a(EXLCZ)992560000000007923 100 $a20090930d2009 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aNurturing and sustaining effective programs in science education for grades K-8 $ebuilding a village in California : summary of a convocation /$fSteve Olson, rapporteur ; Jay B. Labov, editor 205 $a1st ed. 210 $aWashington $cNational Academies Press$dc2009 215 $a1 online resource (155 p.) 300 $a"National Academy of Sciences and National Academy of Engineering of the National Academies." 311 08$a9780309143660 311 08$a0309143667 320 $aIncludes bibliographical references (p. 75-78). 327 $a""Front Matter""; ""Contents""; ""Preface""; ""Structure of the Report""; ""Acknowledgments""; ""1 The Challenges Facing California""; ""2 The National Context""; ""3 Science Education in Action""; ""4 Exemplary Programs""; ""5 Fostering Sustainable Programs""; ""6 Rising to the Challenge""; ""References""; ""Appendix A: Convocation Agenda""; ""Appendix B: Convocation Participants""; ""Appendix C: Biographical Sketches of Presenters and Facilitators""; ""Appendix D: Summary of Selected National Academies Reports"" 330 $aK-8 science education in California (as in many other parts of the country) is in a state of crisis. K-8 students in California spend too little time studying science, many of their teachers are not well prepared in the subject, and the support system for science instruction has deteriorated. A proliferation of overly detailed standards and poorly conceived assessments has trivialized science education. And all these problems are likely to intensify: an ongoing fiscal crisis in the state threatens further cutbacks, teacher and administrator layoffs, and less money for professional development. A convocation held on April 29-30, 2009, sought to confront the crisis in California science education, particularly at the kindergarten through eighth grade level. The convocation, summarized in this volume, brought together key stakeholders in the science education system to enable and facilitate an exploration of ways to more effectively, efficiently, and collectively support, sustain, and communicate across the state concerning promising research and practices in K-8 science education and how such programs can be nurtured by communities of stakeholders. 606 $aCurriculum planning$zCalifornia 606 $aScience$xStudy and teaching (Elementary)$zCalifornia 615 0$aCurriculum planning 615 0$aScience$xStudy and teaching (Elementary) 676 $a372.35 700 $aOlson$b Steve$f1956-$0488724 701 $aLabov$b Jay B$g(Jay Brian)$01813377 712 02$aNational Academy of Engineering. 712 02$aNational Academy of Sciences (U.S.) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910971754103321 996 $aNurturing and sustaining effective programs in science education for grades K-8$94366448 997 $aUNINA