{"id":44232,"date":"2025-11-25T09:05:43","date_gmt":"2025-11-25T09:05:43","guid":{"rendered":""},"modified":"2025-11-25T09:05:43","modified_gmt":"2025-11-25T09:05:43","slug":"e-book-big-data-machine-learning-y-data-science-en-python-echt-44232","status":"publish","type":"product","link":"https:\/\/www.casadelectores.com\/index.php\/producto\/e-book-big-data-machine-learning-y-data-science-en-python-echt-44232\/","title":{"rendered":"E-BOOK – BIG DATA, MACHINE LEARNING Y DATA SCIENCE EN PYTHON Echt"},"content":{"rendered":"

CAP\u00cdTULO 1. INTRODUCCI\u00d3N A BIG DATA1.1 INTRODUCCI\u00d3N1.2 DEFINICI\u00d3N DE BIG DATA1.3 TIPOS DE DATOS1.4 CARACTER\u00cdSTICAS DE BIG DATA1.5 DESAF\u00cdOS DE BIG DATA1.6 TECNOLOG\u00cdAS PARA BIG DATA1.7 PERFILES BIG DATA1.7.1 DIRECCI\u00d3N DE DATOS(CHIEF DATA OFFICER-CDO)1.7.2 CIENT\u00cdFICO DE DATOS(SCIENTIST)1.7.3 ANALISTA DE DATOS(DATA ANALYST)1.7.4 INGENIERIO DE DATOS(DATA ENGINEER)1.7.5 ARQUITECTO DE DATOS(DATA ARCHITECT)1.7.6 GESTOR DE DATOS(DATA MANAGER)1.7.7 CIUDADANO CIENT\u00cdFICO DE DATOS(CITIZEN DATA SCIENTIST)1.7.8 ADMINISTRADOR DE DATOS( DATA STEWARD)1.7.9 TABLA COMPARATIVA1.8 BIG DATA ANALYTICSCAP\u00cdTULO 2. ARQUITECTURAS BIG DATA2.1 INTRODUCCI\u00d3N2.2 ACTORES PRINCIPALES EN UNA ARQUITECTURA BIG DATA2.2.1 SISTEMA DE ORQUESTACI\u00d3N2.2.2 PROVEEDOR DE DATOS2.2.3 PROVEEDOR DE APLICACIONES BIG DATA2.2.4 PROVEEDOR DE INFRAESTRUCTURA BIG DATA2.2.5 CONSUMIDOR DE DATOS2.2.6 CAPA DE SEGURIDAD Y PRIVACIDAD2.2.7 CAPA DE GESTI\u00d3N.2.3 TIPOS DE ARQUITECTURAS2.3.1 PROCESAMIENTO BATCH2.3.2 PROCESAMIENTO STREAMING2.3.3 PROCESAMIENTO MAPREDUCE2.4 ARQUITECTURA LAMBDA.2.5 ARQUITECTURA KAPPA2.6 APACHE KAFKA2.7 ARQUITECTURA POR CAPAS2.8 CASOS DE USO DE ARQUITECTURAS BIG DATA2.8.1 AUTOM\u00d3VILES EN UN MUNDO DE STREAMING2.8.2 CONSTRUYENDO UN SISTEMA DE LINAJE DE DATOS2.8.3 WOLFRAM LANGUAGE2.9 BIG DATA LANDSCAPE2.10 HERRAMIENTA PARA EL AN\u00c1LISIS DE DATOS MASIVOS2.11 CONCLUSIONESCAP\u00cdTULO 3. BASES DE DATOS PARA BIG DATA3.1 INTRODUCCI\u00d3N3.2 COMPARACI\u00d3N SQL VS NOSQL3.3 BASES DE DATOS NOSQL3.4 VENTAJAS DE LAS BASES DE DATOS NOSQL3.5 TIPOS DE BASES DE DATOS NOSQL3.6 IMPLANTANDO NOSQL3.7 BASES DE DATOS DOCUMENTALES3.7.1 CASOS DE USO BASES DE DATOS DOCUMENTALES3.7.2 MONGODB3.7.3 INDEXACI\u00d3N EN MONGODB3.7.4 REPLICACI\u00d3N EN MONGODB3.7.5 USO DE MONGODB DESDE PYTHON3.7.6 COUCHDB3.7.7 ARQUITECTURA DE COUCHDB3.8 BASES DE DATOS ORIENTADAS A COLUMNAS3.8.1 APACHE CASSANDRA3.8.2 CONSISTENCIA EN APACHE CASSANDRA3.8.3 CASOS DE USO3.9 BASES DE DATOS CLAVE-VALOR(KEY-VALUE)3.9.1 REDIS3.10 BASES DE DATOS ORIENTADAS A GRAFOS3.10.1 CASOS DE USO BASES DATOS DE GRAFOS3.10.2 NEO4J3.11 TEOREMA CAP3.12 CONCLUSIONES NOSQLCAP\u00cdTULO 4. INTRODUCCI\u00d3N A LA CIENCIA DE DATOS YMACHINE LEARNING4.1 DEFINICI\u00d3N DE CIENCIA DE DATOS4.2 DEFINICIONES DE APRENDIZAJE Y MACHINE LEARNING4.3 SISTEMAS EXPERTOS4.4 MINER\u00cdA DE DATOS ( DATA MINING)4.4.1 INTEGRACI\u00d3N Y RECOPILACI\u00d3N DE INFORMACI\u00d3N4.4.2 SELECCI\u00d3N, LIMPIEZA Y TRANSFORMACI\u00d3N DE DATOS4.4.3 T\u00c9CNICAS DE MINER\u00cdA DE DATOS4.5 INTRODUCCI\u00d3N AL APRENDIZAJE AUTOM\u00c1TICO4.6 TIPOS DE APRENDIZAJE AUTOM\u00c1TICO.4.7 APRENDIZAJE SUPERVISADO VS NO SUPERVISADO4.7.1 APRENDIZAJE SUPERVISADO:CLASIFICACI\u00d3N Y REGRESI\u00d3N4.7.2 \u00c1RBOLES DE DECISI\u00d3N4.7.3 ALGORITMO K-NEAREST NEIGHBOR4.7.4 APRENDIZAJE NO SUPERVISADO4.8 T\u00c9CNICAS DE MACHINE LEARNING4.9 PROBLEMA DEL SOBREENTRENAMIENTO4.9.1 C\u00d3MO EVITAR EL SOBREENTRENAMIENTO4.10 FASES PARA ABORDAR UN PROBLEMA DE ML4.10.1 PASOS PARA CONSTRUIR UN MODELO DE ML4.10.2 EVALUACI\u00d3N DE MODELOSCAP\u00cdTULO 5. TRATAMIENTO DE DATOS CON PYTHON5.1 JUPYTER NOTEBOOK5.2 MERCURY5.3 NUMPY5.4 SCIPY5.5 PANDAS5.5.1 ESTRUCTURAS DE DATOS EN PANDAS5.5.2 SERIES5.5.3 DATAFRAMES5.5.4 LECTURA DE UN FICHERO CSV CON PANDAS5.5.5 ALTERNATIVAS A PANDAS.5.6 LECTURA DE UN FICHERO JSON5.7 LECTURA Y ESCRITURA EN FORMATO PICKLECAP\u00cdTULO 6. SCIKIT-LEARN COMO LIBRER\u00cdA DE MACHINE LEARNING6.1 INTRODUCCI\u00d3N A SCIKIT-LEARN6.2 DATASETS EN SCIKIT-LEARN6.3 CARGANDO CONJUNTOS DE DATOS EN SCIKIT-LEARN.6.3.1 CONJUNTOS DE DATOS GENERADOS DE FORMA ALEATORIA6.4 DIVIDIR DATOS DE ENTRENAMIENTO Y TEST.6.5 APRENDIZAJE AUTOM\u00c1TICO CON SCIKIT-LEARN.6.5.1 ESTABLECER UNA METODOLOG\u00cdA DE EVALUACI\u00d3N6.6 REGRESI\u00d3N LINEAL6.6.1 IMPLEMENTACI\u00d3N DE REGRESI\u00d3N LINEAL6.6.2 PREDECIR EL VALOR DEL ALQUILER DE LAS VIVIENDAS6.7 ALGORITMO DE REGRESI\u00d3N LOG\u00cdSTICA6.7.1 VALIDACI\u00d3N CRUZADA EN SCIKIT-LEARN6.7.2 OBTENER LA MATRIZ DE CONFUSI\u00d3N6.8 INTRODUCCI\u00d3N A LOS \u00c1RBOLES DE DECISI\u00d3N6.8.1 ALGORITMO DE \u00c1RBOLES DE DECISI\u00c7ON EN SCIKIT-LEARN6.9 SVM COMO ALGORITMO DE M\u00c1QUINAS DE VECTORES DE SOPORTE6.9.1 ALGORITMO DE SUPPORT VECTOR MACHINE EN SCIKIT-LEARN6.9.2 OPTIMIZANDO PAR\u00c1METROS CON GRIDSEARCHCV6.10 KNN COMO ALGORITMO DE CLASIFICACI\u00d3N SUPERVISADA6.10.1 IMPLEMENTACI\u00d3N DE KNEIGHBORSCLASSIFIER6.10.2 RADIUSNEIGHBORSCLASSIFIER6.11 CLUSTERING Y APRENDIZAJE NO SUPERVISADO6.12 EXTRACCI\u00d3N DE CARACTER\u00cdSTICASCAP\u00cdTULO 7. REDES NEURONALES ARTIFICIALES7.1 INTRODUCCI\u00d3N7.2 PERCEPTR\u00d3N SIMPLE7.3 PERCEPTR\u00d3N MULTICAPA7.4 RED NEURONAL RECURRENTE7.5 RED NEURONAL CONVOLUCIONAL(CNN)7.6 REDES NEURONALES CON TENSOR FLOW7.6.1 ALGORITMO DE BACKPROPAGATION7.6.2 PLAYGROUND TENSOR FLOW7.6.3 INTRODUCCI\u00d3N A TENSOR FLOW7.6.4 FUNCIONAMIENTO DE TENSOR FLOW7.7 USO DE LA LIBRER\u00cdA KERAS EN DEEP LEARNING7.8 USO DE GOOGLE COLAB.7.9 REDES NEURONALES CON SKLEARN7.10 TABLA COMPARATIVACAP\u00cdTULO 8. PLATAFORMA HADOOP8.1 INTRODUCCI\u00d3N8.2 HERRAMIENTAS8.3 SERVICIOS Y HERRAMIENTAS DEL ECOSISTEMA HADOOP8.4 HADOOP DISTRIBUTED FILE SYSTEM (HDFS)8.5 HADOOP MAPREDUCE8.6 INTRODUCCI\u00d3N A MAPREDUCE8.7 DISTRIBUCIONES HADOOP8.7.1 CLOUDERA8.8 CONCLUSIONESCAP\u00cdTULO 9. PROCESAMIENTO DISTRIBU\u00cdDO CON APACHE SPARK9.1 INTRODUCCI\u00d3N9.2 INTRODUCCI\u00d3N AL PROCESAMIENTO DISTRIBU\u00cdDO9.3 INTRODUCCI\u00d3N A APACHE SPARK9.4 ECOSISTEMA DE APACHE SPARK9.5 VENTAJAS DE APACHE SPARK9.6 ARQUITECTURA DE APACHE SPARK9.7 RDD (RESILIENT DISTRIBUTED DATASETS)9.8 SPARK CON SCALA9.9 SPARK PARA CIENT\u00cdFICO DE DATOSCAP\u00cdTULO 10. PYSPARK COMO LIBRER\u00cdA DE PROCESAMIENTO DISTRIBU\u00cdDO10.1 INSTALACI\u00d3N DE APACHE SPARK10.2 INTRODUCCI\u00d3N A DOCKER10.3 INSTALAR Y EJECUTAR PYSPARK CON DOCKER10.4 API DE SPARK EN PYTHON10.5 INTRODUCCI\u00d3N A PYSPARK10.6 MAPREDUCE A PYSPARK10.7 TRABAJANDO CON SPARK SQL Y DATAFRAMES10.8 SPARK STREAMINGCAP\u00cdTULO 11. ENTORNOS DE EJECUCI\u00d3N SPARK11.1 INTRODUCCI\u00d3N11.2 FINDSPARK11.3 DATABRICKS:INTRODUCCI\u00d3N A SPARK EN LA NUBE11.4 APACHE ZEPPELINCAP\u00cdTULO 12. MLLIB COMO M\u00d3DULO DE MACHINE LEARNING12.1 INTRODUCCI\u00d3N.12.2 REGRESI\u00d3N LINEAL CON PYSPARK12.3 CLUSTERING CON PYSPARK12.4 CLASIFICACI\u00d3N MENSAJES SPAM CON PYSPARKCAP\u00cdTULO 13. SISTEMAS DE RECOMENDACI\u00d3N13.1 INTRODUCCI\u00d3N.13.2 TIPOS DE SISTEMAS DE RECOMENDACI\u00d3N13.4 FILTRADO COLABORATIVOMATERIAL ADICIONAL \u00ab,<\/p>\n","protected":false},"excerpt":{"rendered":"

CAP\u00cdTULO 1. INTRODUCCI\u00d3N A BIG DATA1.1 INTRODUCCI\u00d3N1.2 DEFINICI\u00d3N DE BIG DATA1.3 TIPOS DE DATOS1.4 CARACTER\u00cdSTICAS DE BIG DATA1.5 DESAF\u00cdOS DE BIG DATA1.6 TECNOLOG\u00cdAS PARA BIG DATA1.7 PERFILES BIG DATA1.7.1 DIRECCI\u00d3N DE DATOS(CHIEF DATA OFFICER-CDO)1.7.2 CIENT\u00cdFICO DE DATOS(SCIENTIST)1.7.3 ANALISTA DE DATOS(DATA ANALYST)1.7.4 INGENIERIO DE DATOS(DATA ENGINEER)1.7.5 ARQUITECTO DE DATOS(DATA ARCHITECT)1.7.6 GESTOR DE DATOS(DATA MANAGER)1.7.7 CIUDADANO CIENT\u00cdFICO… E-BOOK – BIG DATA, MACHINE LEARNING Y DATA SCIENCE EN PYTHON Echt<\/span><\/a><\/p>\n","protected":false},"featured_media":44332,"template":"","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","_themeisle_gutenberg_block_has_review":false},"product_brand":[],"product_cat":[26],"product_tag":[],"class_list":["post-44232","product","type-product","status-publish","has-post-thumbnail","product_cat-publicaciones-de-libros","first","instock","sale","shipping-taxable","purchasable","product-type-simple"],"yoast_head":"\nE-BOOK - BIG DATA, MACHINE LEARNING Y DATA SCIENCE EN PYTHON Echt - casadelectores<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.casadelectores.com\/index.php\/producto\/e-book-big-data-machine-learning-y-data-science-en-python-echt-44232\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"E-BOOK - BIG DATA, MACHINE LEARNING Y DATA SCIENCE EN PYTHON Echt - casadelectores\" \/>\n<meta property=\"og:description\" content=\"CAP\u00cdTULO 1. 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