Este documento presenta un cronograma y presupuesto para una propuesta de investigación sobre la relación entre la educación y el trabajo. El cronograma detalla las actividades a realizar cada mes, que incluyen la revisión bibliográfica, diseño de la muestra, recolección de datos, análisis e informe final. El presupuesto estima los gastos en materiales, trabajo de campo, publicación e imprevistos, para un total de 300 balboas.
Knowledge Graphs are Worthless, Knowledge Graph Use Cases are PricelessEnterprise Knowledge
At Knowledge Graph Forum 2022, Lulit Tesfaye and Sara Nash, Senior Consultant discuss the importance of establishing valuable and actionable use cases for knowledge graph efforts. The discussion draws on lessons learned from several knowledge graph development efforts to define how to diagnose a bad use case and outlined their impact on initiatives - including strained relationships with stakeholders, time spent reworking priorities, and team turnover. They also share guidance on how to navigate these scenarios and provide a checklist to assess a strong use case.
The Cloud Playbook showcases how Booz Allen’s Cloud Analytics Reference Architecture can be utilized to build technology infrastructures that can withstand the weight of massive data sets - and deliver the deep insights organizations need to drive innovation.
Obtenga las instrucciones necesarias sobre arquitectura para crear sus aplicaciones de big data y aproveche al máximo la infraestructura de nube de AWS.
Welcome to my post on ‘Architecting Modern Data Platforms’, here I will be discussing how to design cutting edge data analytics platforms which meet the ever-evolving data & analytics needs for the business.
https://www.ankitrathi.com
Data-Ed Slides: Best Practices in Data Stewardship (Technical)DATAVERSITY
In order to find value in your organization's data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is: captured right the first time, validated through automated means, and integrated into business processes. Whether its data profiling or in depth root cause analysis, data stewards can be counted on to ensure the organization's mission critical data is reliable. In this webinar we will approach this framework, and punctuate important facets of a data steward’s role.
Learning Objectives:
- Understand the business need for a data governance framework
- Learn why embedded data quality principles are an important part of system/process design
- Identify opportunities to help drive your organization to a data driven culture
Knowledge Graphs are Worthless, Knowledge Graph Use Cases are PricelessEnterprise Knowledge
At Knowledge Graph Forum 2022, Lulit Tesfaye and Sara Nash, Senior Consultant discuss the importance of establishing valuable and actionable use cases for knowledge graph efforts. The discussion draws on lessons learned from several knowledge graph development efforts to define how to diagnose a bad use case and outlined their impact on initiatives - including strained relationships with stakeholders, time spent reworking priorities, and team turnover. They also share guidance on how to navigate these scenarios and provide a checklist to assess a strong use case.
The Cloud Playbook showcases how Booz Allen’s Cloud Analytics Reference Architecture can be utilized to build technology infrastructures that can withstand the weight of massive data sets - and deliver the deep insights organizations need to drive innovation.
Obtenga las instrucciones necesarias sobre arquitectura para crear sus aplicaciones de big data y aproveche al máximo la infraestructura de nube de AWS.
Welcome to my post on ‘Architecting Modern Data Platforms’, here I will be discussing how to design cutting edge data analytics platforms which meet the ever-evolving data & analytics needs for the business.
https://www.ankitrathi.com
Data-Ed Slides: Best Practices in Data Stewardship (Technical)DATAVERSITY
In order to find value in your organization's data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is: captured right the first time, validated through automated means, and integrated into business processes. Whether its data profiling or in depth root cause analysis, data stewards can be counted on to ensure the organization's mission critical data is reliable. In this webinar we will approach this framework, and punctuate important facets of a data steward’s role.
Learning Objectives:
- Understand the business need for a data governance framework
- Learn why embedded data quality principles are an important part of system/process design
- Identify opportunities to help drive your organization to a data driven culture
Saama Presents Is your Big Data Solution Ready for StreamingSaama
Amit Gulwadi and Karim Damji presented at Panagora's IoT in Clinical Trials Summit in Boston in November 2018. Using the right analytic solution that can incorporate your unstructured IoT data provides tremendous benefits including faster time to commercialization and better business and patient outcomes.
Presentación de Rufino Flores, dictada en la mesa redonda "Gestión del conocimiento en el sector público: de información a innovación para entregar valor a la sociedad", el día 9 de julio de 2015, en la Pontificia Universidad Católica del Perú.
Manifiesto ágil y principios para la gestión de proyectosOpen Source Pyme
El manifiesto para el desarrollo de software ágil en el 2001 ahora es parte de la gestión de proyectos lean y ágil del siglo XXI, notas del taller de Telefonica España
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Slides for the talk at AI in Production meetup:
https://www.meetup.com/LearnDataScience/events/255723555/
Abstract: Demystifying Data Engineering
With recent progress in the fields of big data analytics and machine learning, Data Engineering is an emerging discipline which is not well-defined and often poorly understood.
In this talk, we aim to explain Data Engineering, its role in Data Science, the difference between a Data Scientist and a Data Engineer, the role of a Data Engineer and common concepts as well as commonly misunderstood ones found in Data Engineering. Toward the end of the talk, we will examine a typical Data Analytics system architecture.
Data Catalogues - Architecting for Collaboration & Self-ServiceDATAVERSITY
The interest in Data Catalogs is growing as more business & technical users are looking to gain insight from data using a self-service approach. Architectural techniques for Data Provisioning and Metadata Cataloging have evolved to cater to these new audiences and ways of working. This webinar provides concrete methods of architecting your Self-service BI & Analytics environment to foster collaboration while at the same time maintaining Data Quality and reducing risk.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2OUz6dt.
Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealing with data streaming and warehousing. Filmed at qconsf.com.
Chris Riccomini works as a Software Engineer at WePay.
SF Big Analytics 2020-07-28
Anecdotal history of Data Lake and various popular implementation framework. Why certain tradeoff was made to solve the problems, such as cloud storage, incremental processing, streaming and batch unification, mutable table, ...
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This presentation will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
Saama Presents Is your Big Data Solution Ready for StreamingSaama
Amit Gulwadi and Karim Damji presented at Panagora's IoT in Clinical Trials Summit in Boston in November 2018. Using the right analytic solution that can incorporate your unstructured IoT data provides tremendous benefits including faster time to commercialization and better business and patient outcomes.
Presentación de Rufino Flores, dictada en la mesa redonda "Gestión del conocimiento en el sector público: de información a innovación para entregar valor a la sociedad", el día 9 de julio de 2015, en la Pontificia Universidad Católica del Perú.
Manifiesto ágil y principios para la gestión de proyectosOpen Source Pyme
El manifiesto para el desarrollo de software ágil en el 2001 ahora es parte de la gestión de proyectos lean y ágil del siglo XXI, notas del taller de Telefonica España
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Slides for the talk at AI in Production meetup:
https://www.meetup.com/LearnDataScience/events/255723555/
Abstract: Demystifying Data Engineering
With recent progress in the fields of big data analytics and machine learning, Data Engineering is an emerging discipline which is not well-defined and often poorly understood.
In this talk, we aim to explain Data Engineering, its role in Data Science, the difference between a Data Scientist and a Data Engineer, the role of a Data Engineer and common concepts as well as commonly misunderstood ones found in Data Engineering. Toward the end of the talk, we will examine a typical Data Analytics system architecture.
Data Catalogues - Architecting for Collaboration & Self-ServiceDATAVERSITY
The interest in Data Catalogs is growing as more business & technical users are looking to gain insight from data using a self-service approach. Architectural techniques for Data Provisioning and Metadata Cataloging have evolved to cater to these new audiences and ways of working. This webinar provides concrete methods of architecting your Self-service BI & Analytics environment to foster collaboration while at the same time maintaining Data Quality and reducing risk.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2OUz6dt.
Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealing with data streaming and warehousing. Filmed at qconsf.com.
Chris Riccomini works as a Software Engineer at WePay.
SF Big Analytics 2020-07-28
Anecdotal history of Data Lake and various popular implementation framework. Why certain tradeoff was made to solve the problems, such as cloud storage, incremental processing, streaming and batch unification, mutable table, ...
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This presentation will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
1. CRONOGRAMA DE TRABAJO Y
PRESUPUESTO PARA PROPUESTA
DE INVESTIGACIÓN
Brechas entre la relación Educación y
trabajo
2. Cronograma de Trabajo
Propuesta de Investigación
Actividades a realizar Mayo Junio Julio Agosto Septiembre Octubre
Revisión de la bibliografía existente sobre el X
tema de investigación
Determinación de la población, diseño y X
selección de la muestra
Elaboración final del instrumento para la X
recolección de información
Presentación de la propuesta de investigación X
para su aprobación
Aprobación de la propuesta X
Recolección de la información X
Análisis de la información X
Distribución de los datos obtenidos para el X
análisis de las diferentes secciones que
contendrá el informe final
Redacción del informe final X
Corrección del informe final X
Impresión y empastado del informe final X
Entrega del informe final X
3. Presupuesto
No. Categorías de los Gastos Cantidades
1. Compra de materiales a utilizar 50.00
(papel y tinta para imprimir)
2. Costo de trabajo de campo 50.00
(transporte y alimentación)
3. Costo de publicación 150.00
4. Imprevistos 50.00
Total B/. 300.00