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[null,"1.","Record Nr.",null,"UNINA9910286440903321"]
[null,null,"Autore",null,"Pardo María del Carmen"]
[null,null,"Titolo",null,"Federalización e innovación educativa en México / María del Carmen Pardo (coordinadora)"]
[null,null,"Pubbl/distr/stampa",null,"El Colegio de México, 1999"]
[{"$nulls":4},"Mexico : , : El Colegio de Mexico, Centro de Estudios Internacionales, , 1999"]
[{"$nulls":4},"©1999"]
[null,null,"ISBN",null,"607-628-651-2"]
[{"$nulls":4},"968-12-0945-1"]
[null,null,"Edizione",null,"[Primera edición.]"]
[null,null,"Descrizione fisica",null,"1 online resource (578 p.)"]
[null,null,"Disciplina",null,"379.72"]
[null,null,"Soggetti",null,"Política educativa - Mexico"]
[{"$nulls":4},"Educació - Mexico"]
[null,null,"Lingua di pubblicazione",null,"Spagnolo"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","En este libro se recogen estudios que permiten entender el proceso de descentralización de los servicios de educación básica transferidos por el gobierno federal a los gobiernos estatales a partir del Acuerdo Nacional para la Modernización de la Educación Básica de mayo de 1992. Estos estudios están divididos en dos grandes apartados: los referidos a aspectos generales del proceso de la descentralización educativa en los que resaltan tanto los antencedentes históricos de ese cambio, como el papel de actores fundamentales en dicho proceso, la Secretaría de Educación Pública (SEP) y el Sindicato Nacional de Trabajadores de la Educación (SNTE), así como los llamados estudios de caso que incorporan un análisis detallado de las formas tanto administrativas como pedagógicas ensayadas en cinco estados de la República para recibir y gestionar los servicios educativos transferidos. Aguascalientes, Guanajuato, Chihuahua, Nuevo León y Oaxaca fueron los estados seleccionados para observar la transferencia de los servicios de educación básica, subrayando de manera destacada la oportunidad que tendrían para generar innovaciones en el campo educativo, a partir del momento en que se convirtieran en responsabilidad de los respectivos gobiernos estatales. En el análisis de los cinco estados se incorporaron elementos que permitieron hacer un "]
[{"$nulls":4},"ejercicio de comparación y llegar a conclusiones preliminares, puesto que se trata de un proceso en gestación."]
[null,"2.","Record Nr.",null,"UNINA9910162857403321"]
[null,null,"Autore",null,"Moses Alan M."]
[null,null,"Titolo",null,"Statistical modeling and machine learning for molecular biology / / Alan Moses, University of Toronto, Canada"]
[null,null,"Pubbl/distr/stampa",null,"Boca Raton : , : CRC Press, , [2017]"]
[{"$nulls":4},"©2017"]
[null,null,"ISBN",null,"1-4822-5862-5"]
[{"$nulls":4},"1-315-37226-6"]
[{"$nulls":4},"1-4822-5860-9"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource"]
[null,null,"Collana",null,"Chapman & Hall/CRC mathematical and computational biology series"]
[null,null,"Disciplina",null,"572.8"]
[null,null,"Soggetti",null,"Molecular biology - Statistical methods"]
[{"$nulls":4},"Molecular biology - Data processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"section 1. Overview -- section 2. Clustering -- section 3. Regression -- section 4. Classification."]
[null,null,null,"Sommario/riassunto","Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics."]