ºÝºÝߣshows by User: xuxoramos / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: xuxoramos / Fri, 26 Apr 2019 17:38:49 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: xuxoramos Formando Equipos de Ciencia de Datos https://es.slideshare.net/slideshow/formando-equipos-de-ciencia-de-datos/142378972 formandoequiposdatascience-190426173849
Un overview del talento disponible, del talento que queremos y no tenemos, del talento que deberíamos tener, costos aproximados y formas de organizarlo dentro de nuestras empresas.]]>
Un overview del talento disponible, del talento que queremos y no tenemos, del talento que deberíamos tener, costos aproximados y formas de organizarlo dentro de nuestras empresas.]]> Fri, 26 Apr 2019 17:38:49 GMT https://es.slideshare.net/slideshow/formando-equipos-de-ciencia-de-datos/142378972 xuxoramos@slideshare.net(xuxoramos) Formando Equipos de Ciencia de Datos xuxoramos Un overview del talento disponible, del talento que queremos y no tenemos, del talento que deberíamos tener, costos aproximados y formas de organizarlo dentro de nuestras empresas. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/formandoequiposdatascience-190426173849-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Un overview del talento disponible, del talento que queremos y no tenemos, del talento que deberíamos tener, costos aproximados y formas de organizarlo dentro de nuestras empresas.
from Jesus Ramos
]]> 494 2 https://cdn.slidesharecdn.com/ss_thumbnails/formandoequiposdatascience-190426173849-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Practical Machine Ethics @ SXSW2019 /slideshow/practical-machine-ethics-sxsw2019-136202881/136202881 practicalmachineethics-190313204127
Tallk given at #SXSW2019 in the Intelligent Future track as part of the Interactive Festival. We explain 3 frameworks for MachineEthics and how they affect the supervised and unsupervised methods, and the data engineering discipline.]]>
Tallk given at #SXSW2019 in the Intelligent Future track as part of the Interactive Festival. We explain 3 frameworks for MachineEthics and how they affect the supervised and unsupervised methods, and the data engineering discipline.]]> Wed, 13 Mar 2019 20:41:27 GMT /slideshow/practical-machine-ethics-sxsw2019-136202881/136202881 xuxoramos@slideshare.net(xuxoramos) Practical Machine Ethics @ SXSW2019 xuxoramos Tallk given at #SXSW2019 in the Intelligent Future track as part of the Interactive Festival. We explain 3 frameworks for MachineEthics and how they affect the supervised and unsupervised methods, and the data engineering discipline. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/practicalmachineethics-190313204127-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Tallk given at #SXSW2019 in the Intelligent Future track as part of the Interactive Festival. We explain 3 frameworks for MachineEthics and how they affect the supervised and unsupervised methods, and the data engineering discipline.
Practical Machine Ethics @ SXSW2019 from Jesus Ramos
]]> 884 6 https://cdn.slidesharecdn.com/ss_thumbnails/practicalmachineethics-190313204127-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Historias de Ciencia de Datos desde la Trinchera https://es.slideshare.net/slideshow/historias-de-ciencia-de-datos-desde-la-trinchera/97228754 thedatapubepicqueen-180516004507
Historias buenas, malas y feas de Ciencia de Datos, de México y el mundo. Debilidades y fuerzas del talento Mexicano. ]]>
Historias buenas, malas y feas de Ciencia de Datos, de México y el mundo. Debilidades y fuerzas del talento Mexicano. ]]> Wed, 16 May 2018 00:45:07 GMT https://es.slideshare.net/slideshow/historias-de-ciencia-de-datos-desde-la-trinchera/97228754 xuxoramos@slideshare.net(xuxoramos) Historias de Ciencia de Datos desde la Trinchera xuxoramos Historias buenas, malas y feas de Ciencia de Datos, de México y el mundo. Debilidades y fuerzas del talento Mexicano. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thedatapubepicqueen-180516004507-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Historias buenas, malas y feas de Ciencia de Datos, de México y el mundo. Debilidades y fuerzas del talento Mexicano.
from Jesus Ramos
]]> 416 3 https://cdn.slidesharecdn.com/ss_thumbnails/thedatapubepicqueen-180516004507-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Inferencia Estadística para Periodistas https://es.slideshare.net/slideshow/inferencia-estadstica-para-periodistas/89823660 inferenciaestadisticaperiodistas1-180306181852
Para lograr una labor periodística más ética, justa y veraz, es importante incorporar elementos del método científico a la práctica. Las pruebas de hipótesis son la herramienta base en el método científico, y pensar en las aseveraciones y evaluarlas con esta técnica resultará en una nota más blindada y difícil de debatir.]]>
Para lograr una labor periodística más ética, justa y veraz, es importante incorporar elementos del método científico a la práctica. Las pruebas de hipótesis son la herramienta base en el método científico, y pensar en las aseveraciones y evaluarlas con esta técnica resultará en una nota más blindada y difícil de debatir.]]> Tue, 06 Mar 2018 18:18:52 GMT https://es.slideshare.net/slideshow/inferencia-estadstica-para-periodistas/89823660 xuxoramos@slideshare.net(xuxoramos) Inferencia Estadística para Periodistas xuxoramos Para lograr una labor periodística más ética, justa y veraz, es importante incorporar elementos del método científico a la práctica. Las pruebas de hipótesis son la herramienta base en el método científico, y pensar en las aseveraciones y evaluarlas con esta técnica resultará en una nota más blindada y difícil de debatir. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/inferenciaestadisticaperiodistas1-180306181852-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Para lograr una labor periodística más ética, justa y veraz, es importante incorporar elementos del método científico a la práctica. Las pruebas de hipótesis son la herramienta base en el método científico, y pensar en las aseveraciones y evaluarlas con esta técnica resultará en una nota más blindada y difícil de debatir.
from Jesus Ramos
]]> 459 2 https://cdn.slidesharecdn.com/ss_thumbnails/inferenciaestadisticaperiodistas1-180306181852-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data Quality for Data Science Projects /slideshow/data-quality-for-data-science-projects-82992066/82992066 charladama1-171129174319
(Spanish) Data Modelling takes companies to Data Quality, and Data Quality takes them to truthful analytics and unbiased Machine Learning. This talk establishes and explains this relationship, and where in materializes within the data architecture of a firm.]]>
(Spanish) Data Modelling takes companies to Data Quality, and Data Quality takes them to truthful analytics and unbiased Machine Learning. This talk establishes and explains this relationship, and where in materializes within the data architecture of a firm.]]> Wed, 29 Nov 2017 17:43:18 GMT /slideshow/data-quality-for-data-science-projects-82992066/82992066 xuxoramos@slideshare.net(xuxoramos) Data Quality for Data Science Projects xuxoramos (Spanish) Data Modelling takes companies to Data Quality, and Data Quality takes them to truthful analytics and unbiased Machine Learning. This talk establishes and explains this relationship, and where in materializes within the data architecture of a firm. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/charladama1-171129174319-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> (Spanish) Data Modelling takes companies to Data Quality, and Data Quality takes them to truthful analytics and unbiased Machine Learning. This talk establishes and explains this relationship, and where in materializes within the data architecture of a firm.
Data Quality for Data Science Projects from Jesus Ramos
]]> 175 1 https://cdn.slidesharecdn.com/ss_thumbnails/charladama1-171129174319-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Algorithmic Transparency https://es.slideshare.net/slideshow/algorithmic-transparency/77809693 algorithmictransparency1-170712221820
Why algorithmic transparency is important in the wake of new regulations aimed at curbing the increasing number of decisions made by algorithms that are unappealable.]]>
Why algorithmic transparency is important in the wake of new regulations aimed at curbing the increasing number of decisions made by algorithms that are unappealable.]]> Wed, 12 Jul 2017 22:18:20 GMT https://es.slideshare.net/slideshow/algorithmic-transparency/77809693 xuxoramos@slideshare.net(xuxoramos) Algorithmic Transparency xuxoramos Why algorithmic transparency is important in the wake of new regulations aimed at curbing the increasing number of decisions made by algorithms that are unappealable. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/algorithmictransparency1-170712221820-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Why algorithmic transparency is important in the wake of new regulations aimed at curbing the increasing number of decisions made by algorithms that are unappealable.
from Jesus Ramos
]]> 510 3 https://cdn.slidesharecdn.com/ss_thumbnails/algorithmictransparency1-170712221820-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 WTF with Big Data? https://es.slideshare.net/slideshow/wtf-with-big-data/77213166 disruptcity-170623174031
ºÝºÝߣs for Distupt City's session IV. On Big Data, Analytics, Machine Learning and Artificial Intelligence.]]>
ºÝºÝߣs for Distupt City's session IV. On Big Data, Analytics, Machine Learning and Artificial Intelligence.]]> Fri, 23 Jun 2017 17:40:31 GMT https://es.slideshare.net/slideshow/wtf-with-big-data/77213166 xuxoramos@slideshare.net(xuxoramos) WTF with Big Data? xuxoramos ºÝºÝߣs for Distupt City's session IV. On Big Data, Analytics, Machine Learning and Artificial Intelligence. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/disruptcity-170623174031-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ºÝºÝߣs for Distupt City&#39;s session IV. On Big Data, Analytics, Machine Learning and Artificial Intelligence.
from Jesus Ramos
]]> 319 3 https://cdn.slidesharecdn.com/ss_thumbnails/disruptcity-170623174031-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Entrepreneurship with Data, Machine Learning and AI https://es.slideshare.net/slideshow/entrepreneurship-with-data-machine-learning-and-ai/73839037 emprendiendoconml-170329051804
Description of data-based business models and how they can be taken to the next level with Machine Learning and AI.]]>
Description of data-based business models and how they can be taken to the next level with Machine Learning and AI.]]> Wed, 29 Mar 2017 05:18:03 GMT https://es.slideshare.net/slideshow/entrepreneurship-with-data-machine-learning-and-ai/73839037 xuxoramos@slideshare.net(xuxoramos) Entrepreneurship with Data, Machine Learning and AI xuxoramos Description of data-based business models and how they can be taken to the next level with Machine Learning and AI. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/emprendiendoconml-170329051804-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Description of data-based business models and how they can be taken to the next level with Machine Learning and AI.
from Jesus Ramos
]]> 972 2 https://cdn.slidesharecdn.com/ss_thumbnails/emprendiendoconml-170329051804-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Estadistica y Machine Learning para Todos https://es.slideshare.net/slideshow/estadistica-y-machine-learning-para-periodistas/72866212 estadisticaymlparaperiodistas-170306164414
Recorrido rápido de Estadística y Aprendizaje Automático en casos de transparencia, y el panorama Mexicano para estas disciplinas.]]>
Recorrido rápido de Estadística y Aprendizaje Automático en casos de transparencia, y el panorama Mexicano para estas disciplinas.]]> Mon, 06 Mar 2017 16:44:14 GMT https://es.slideshare.net/slideshow/estadistica-y-machine-learning-para-periodistas/72866212 xuxoramos@slideshare.net(xuxoramos) Estadistica y Machine Learning para Todos xuxoramos Recorrido rápido de Estadística y Aprendizaje Automático en casos de transparencia, y el panorama Mexicano para estas disciplinas. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/estadisticaymlparaperiodistas-170306164414-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Recorrido rápido de Estadística y Aprendizaje Automático en casos de transparencia, y el panorama Mexicano para estas disciplinas.
from Jesus Ramos
]]> 3308 4 https://cdn.slidesharecdn.com/ss_thumbnails/estadisticaymlparaperiodistas-170306164414-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Mexican Landscape of DS & AI /xuxoramos/mexican-landscape-of-ds-ai wheretogofromhere-2-170219194807
This deck lays out the landscape for AI & DS in the Mexican Market. From MOOCs to national academic programs, and from our weaknesses as a country, to how we can tackle them.]]>
This deck lays out the landscape for AI & DS in the Mexican Market. From MOOCs to national academic programs, and from our weaknesses as a country, to how we can tackle them.]]> Sun, 19 Feb 2017 19:48:07 GMT /xuxoramos/mexican-landscape-of-ds-ai xuxoramos@slideshare.net(xuxoramos) Mexican Landscape of DS & AI xuxoramos This deck lays out the landscape for AI & DS in the Mexican Market. From MOOCs to national academic programs, and from our weaknesses as a country, to how we can tackle them. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wheretogofromhere-2-170219194807-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This deck lays out the landscape for AI &amp; DS in the Mexican Market. From MOOCs to national academic programs, and from our weaknesses as a country, to how we can tackle them.
Mexican Landscape of DS & AI from Jesus Ramos
]]> 919 4 https://cdn.slidesharecdn.com/ss_thumbnails/wheretogofromhere-2-170219194807-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Machine Learning For Organizations https://es.slideshare.net/slideshow/machine-learning-for-organizations/65719805 xuxoramossgnext-160906030056
(Spanish) An overview of machine/statistical learning, what it is, what's it for, and some firms that are using it to drive up revenue and create new products.]]>
(Spanish) An overview of machine/statistical learning, what it is, what's it for, and some firms that are using it to drive up revenue and create new products.]]> Tue, 06 Sep 2016 03:00:56 GMT https://es.slideshare.net/slideshow/machine-learning-for-organizations/65719805 xuxoramos@slideshare.net(xuxoramos) Machine Learning For Organizations xuxoramos (Spanish) An overview of machine/statistical learning, what it is, what's it for, and some firms that are using it to drive up revenue and create new products. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/xuxoramossgnext-160906030056-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> (Spanish) An overview of machine/statistical learning, what it is, what&#39;s it for, and some firms that are using it to drive up revenue and create new products.
from Jesus Ramos
]]> 1701 2 https://cdn.slidesharecdn.com/ss_thumbnails/xuxoramossgnext-160906030056-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Wonderful Wacky Wide World of Data Analysis Applications /xuxoramos/wonderful-wacky-wide-world-of-data-analysis-applications wideworldofdataanalysis-160525080425
A survey ranging from the tried and true to the unclassifiably experimental]]>
A survey ranging from the tried and true to the unclassifiably experimental]]> Wed, 25 May 2016 08:04:24 GMT /xuxoramos/wonderful-wacky-wide-world-of-data-analysis-applications xuxoramos@slideshare.net(xuxoramos) Wonderful Wacky Wide World of Data Analysis Applications xuxoramos A survey ranging from the tried and true to the unclassifiably experimental <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wideworldofdataanalysis-160525080425-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A survey ranging from the tried and true to the unclassifiably experimental
Wonderful Wacky Wide World of Data Analysis Applications from Jesus Ramos
]]> 878 6 https://cdn.slidesharecdn.com/ss_thumbnails/wideworldofdataanalysis-160525080425-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Big Data, Big Flops: The gag reel of algorithms /slideshow/big-data-big-flops-the-gag-reel-of-algorithms/59756368 3-jesusramosbigdatabigflops-160319060003
The gag reel of algorithms. When programmers go into statistical learning without learning statistics, dangerous things can happen.]]>
The gag reel of algorithms. When programmers go into statistical learning without learning statistics, dangerous things can happen.]]> Sat, 19 Mar 2016 06:00:03 GMT /slideshow/big-data-big-flops-the-gag-reel-of-algorithms/59756368 xuxoramos@slideshare.net(xuxoramos) Big Data, Big Flops: The gag reel of algorithms xuxoramos The gag reel of algorithms. When programmers go into statistical learning without learning statistics, dangerous things can happen. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3-jesusramosbigdatabigflops-160319060003-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The gag reel of algorithms. When programmers go into statistical learning without learning statistics, dangerous things can happen.
Big Data, Big Flops: The gag reel of algorithms from Jesus Ramos
]]> 1398 4 https://cdn.slidesharecdn.com/ss_thumbnails/3-jesusramosbigdatabigflops-160319060003-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Big Data, Big Disappointment (@TheDataPub) https://es.slideshare.net/xuxoramos/big-data-big-disappointment-thedatapub bigdatabigdisappointmentdatapub-151221205248
Versión corta de charla original. Creada para el meetup de @TheDataPub el 15 de Diciembre de 2015.]]>
Versión corta de charla original. Creada para el meetup de @TheDataPub el 15 de Diciembre de 2015.]]> Mon, 21 Dec 2015 20:52:48 GMT https://es.slideshare.net/xuxoramos/big-data-big-disappointment-thedatapub xuxoramos@slideshare.net(xuxoramos) Big Data, Big Disappointment (@TheDataPub) xuxoramos Versión corta de charla original. Creada para el meetup de @TheDataPub el 15 de Diciembre de 2015. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdatabigdisappointmentdatapub-151221205248-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Versión corta de charla original. Creada para el meetup de @TheDataPub el 15 de Diciembre de 2015.
from Jesus Ramos
]]> 2597 54 https://cdn.slidesharecdn.com/ss_thumbnails/bigdatabigdisappointmentdatapub-151221205248-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Big Data, Big Disappointment /slideshow/big-data-big-disappointment/44581714 bigdatabigdisappointment-150212003803-conversion-gate02
A diagnosis and prescription (sort of) for (somewhat) successful analytics efforts in medium to large firms in Mexico.]]>
A diagnosis and prescription (sort of) for (somewhat) successful analytics efforts in medium to large firms in Mexico.]]> Thu, 12 Feb 2015 00:38:02 GMT /slideshow/big-data-big-disappointment/44581714 xuxoramos@slideshare.net(xuxoramos) Big Data, Big Disappointment xuxoramos A diagnosis and prescription (sort of) for (somewhat) successful analytics efforts in medium to large firms in Mexico. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdatabigdisappointment-150212003803-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A diagnosis and prescription (sort of) for (somewhat) successful analytics efforts in medium to large firms in Mexico.
Big Data, Big Disappointment from Jesus Ramos
]]> 4707 9 https://cdn.slidesharecdn.com/ss_thumbnails/bigdatabigdisappointment-150212003803-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-xuxoramos-48x48.jpg?cb=1650597751 Highly skilled Data and Product Manager with 11 years' of experience in the financial services industry. Specialized in 'taming' data to put it at the service of business strategy. - 5 year's experience evolving software products that help businesses capitalize on data. - 10 years' experience building and leading small, driven, highly specialized teams of data professionals. - "Product ownership" approach to software that enables engagement between clients, business environment and software specialists. - Able to build strong partnerships with customers, users and stakeholders. - Management style focused on 'get highly talented people, and get out of their way'. Specializations: - Trans... www.datank.ai https://cdn.slidesharecdn.com/ss_thumbnails/formandoequiposdatascience-190426173849-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/formando-equipos-de-ciencia-de-datos/142378972 Formando Equipos de Ci... https://cdn.slidesharecdn.com/ss_thumbnails/practicalmachineethics-190313204127-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/practical-machine-ethics-sxsw2019-136202881/136202881 Practical Machine Ethi... https://cdn.slidesharecdn.com/ss_thumbnails/thedatapubepicqueen-180516004507-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/historias-de-ciencia-de-datos-desde-la-trinchera/97228754 Historias de Ciencia d...