From ae47bb431a4c5c48c4758fb0ed8025e07013e5c5 Mon Sep 17 00:00:00 2001 From: Angel Mendez Date: Fri, 15 Oct 2021 00:13:45 -0500 Subject: [PATCH 1/3] feat: Add file to translate --- .../translations/README.es.md | 142 ++++++++++++++++++ 1 file changed, 142 insertions(+) diff --git a/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md b/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md index e69de29b..098a389d 100644 --- a/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md +++ b/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md @@ -0,0 +1,142 @@ +# Data Science in the Real World + +| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) | +| :--------------------------------------------------------------------------------------------------------------: | +| Data Science In The Real World - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +We're almost at the end of this learning journey! + +We started with definitions of data science and ethics, explored various tools & techniques for data analysis and visualization, reviewed the data science lifecycle, and looked at scaling and automating data science workflows with cloud computing services. So, you're probably wondering: _"How exactly do I map all these learnings to real-world contexts?"_ + +In this lesson, we'll explore real-world applications of data science across industry and dive into specific examples in the research, digital humanities, and sustainability, contexts. We'll look at student project opportunities and conclude with useful resources to help you continue your learning journey! +## Pre-Lecture Quiz + +[Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/38) +## Data Science + Industry + +Thanks to the democratization of AI, developers are now finding it easier to design and integrate AI-driven decision-making and data-driven insights into user experiences and development workflows. Here are a few examples of how data science is "applied" to real-world applications across the industry: + + * [Google Flu Trends](https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/) used data science to correlate search terms with flu trends. While the approach had flaws, it raised awareness of the possibilities (and challenges) of data-driven healthcare predictions. + + * [UPS Routing Predictions](https://www.technologyreview.com/2018/11/21/139000/how-ups-uses-ai-to-outsmart-bad-weather/) - explains how UPS uses data science and machine learning to predict optimal routes for delivery, taking into account weather conditions, traffic patterns, delivery deadlines and more. + + * [NYC Taxicab Route Visualization](http://chriswhong.github.io/nyctaxi/) - data gathered using [Freedom Of Information Laws](https://chriswhong.com/open-data/foil_nyc_taxi/) helped visualize a day in the life of NYC cabs, helping us understand how they navigate the busy city, the money they make, and the duration of trips over each 24-hour period. + + * [Uber Data Science Workbench](https://eng.uber.com/dsw/) - uses data (on pickup & dropoff locations, trip duration, preferred routes etc.) gathered from millions of uber trips *daily* to build a data analytics tool to help with pricing, safety, fraud detection and navigation decisions. + + * [Sports Analytics](https://towardsdatascience.com/scope-of-analytics-in-sports-world-37ed09c39860) - focuses on _predictive analytics_ (team and player analysis - think [Moneyball](https://datasciencedegree.wisconsin.edu/blog/moneyball-proves-importance-big-data-big-ideas/) - and fan management) and _data visualization_ (team & fan dashboards, games etc.) with applications like talent scouting, sports gambling and inventory/venue management. + + * [Data Science in Banking](https://data-flair.training/blogs/data-science-in-banking/) - highlights the value of data science in the finance industry with applications ranging from risk modeling and fraud detction, to customer segmentation, real-time prediction and recommender systems. Predictive analytics also drive critical measures like [credit scores](https://dzone.com/articles/using-big-data-and-predictive-analytics-for-credit). + + * [Data Science in Healthcare](https://data-flair.training/blogs/data-science-in-healthcare/) - highlights applications like medical imaging (e.g., MRI, X-Ray, CT-Scan), genomics (DNA sequencing), drug development (risk assessment, success prediction), predictive analytics (patient care & supply logistics), disease tracking & prevention etc. + +![Data Science Applications in The Real World](./images/data-science-applications.png) Image Credit: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) + +The figure shows other domains and examples for applying data science techniques. Want to explore other applications? Check out the [Review & Self Study](?id=review-amp-self-study) section below. + +## Data Science + Research + +| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Research.png) | +| :---------------------------------------------------------------------------------------------------------------: | +| Data Science & Research - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +While real-world applications often focus on industry use cases at scale, _research_ applications and projects can be useful from two perspectives: + +* _innovation opportunities_ - explore rapid prototyping of advanced concepts and testing of user experiences for next-generation applications. +* _deployment challenges_ - investigate potential harms or unintended consequences of data science technologies in real-world contexts. + +For students, these research projects can provide both learning and collaboration opportunities that can improve your understanding of the topic, and broaden your awareness and engagement with relevant people or teams working in areas of interest. So what do research projects look like and how can they make an impact? + +Let's look at one example - the [MIT Gender Shades Study](http://gendershades.org/overview.html) from Joy Buolamwini (MIT Media Labs) with a [signature research paper](http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf) co-authored with Timnit Gebru (then at Microsoft Research) that focused on + + * **What:** The objective of the research project was to _evaluate bias present in automated facial analysis algorithms and datasets_ based on gender and skin type. + * **Why:** Facial analysis is used in areas like law enforcement, airport security, hiring systems and more - contexts where inaccurate classifications (e.g., due to bias) can cause potential economic and social harms to affected individuals or groups. Understanding (and eliminating or mitigating) biases is key to fairness in usage. + * **How:** Researchers recongized that existing benchmarks used predominantly lighter-skinned subjects, and curated a new data set (1000+ images) that was _more balanced_ by gender and skin type. The data set was used to evaluate the accuracy of three gender classification products (from Microsoft, IBM & Face++). + +Results showed that though overall classification accuracy was good, there was a noticeable difference in error rates between various subgroups - with **misgendering** being higher for females or persons with darker skin types, indicative of bias. + +**Key Outcomes:** Raised awareness that data science needs more _representative datasets_ (balanced subgroups) and more _inclusive teams_ (diverse backgrounds) to recognize and eliminate or mitigate such biases earlier in AI solutions. Research efforts like this are also instrumental in many organizations defining principles and practices for _responsible AI_ to improve fairness across their AI products and processes. + + +**Want to learn about relevant research efforts in Microsoft?** + +* Check out [Microsoft Research Projects](https://www.microsoft.com/research/research-area/artificial-intelligence/?facet%5Btax%5D%5Bmsr-research-area%5D%5B%5D=13556&facet%5Btax%5D%5Bmsr-content-type%5D%5B%5D=msr-project) on Artificial Intelligence. +* Explore student projects from [Microsoft Research Data Science Summer School](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/). +* Check out the [Fairlearn](https://fairlearn.org/) project and [Responsible AI](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6) initiatives. + + + +## Data Science + Humanities + +| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Humanities.png) | +| :---------------------------------------------------------------------------------------------------------------: | +| Data Science & Digital Humanities - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + + +Digital Humanities [has been defined](https://digitalhumanities.stanford.edu/about-dh-stanford) as "a collection of practices and approaches combining computational methods with humanistic inquiry". [Stanford projects](https://digitalhumanities.stanford.edu/projects) like _"rebooting history"_ and _"poetic thinking"_ illustrate the linkage between [Digital Humanities and Data Science](https://digitalhumanities.stanford.edu/digital-humanities-and-data-science) - emphasizing techniques like network analysis, information visualization, spatial and text analysis that can help us revisit historical and literary data sets to derive new insights and perspective. + +*Want to explore and extend a project in this space?* + +Check out ["Emily Dickinson and the Meter of Mood"](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671) - a great example from [Jen Looper](https://twitter.com/jenlooper) that asks how we can use data science to revisit familiar poetry and re-evaluate its meaning and the contributions of its author in new contexts. For instance, _can we predict the season in which a poem was authored by analyzing its tone or sentiment_ - and what does this tell us about the author's state of mind over the relevant period? + +To answer that question, we follow the steps of our data science lifecycle: + * [`Data Acquisition`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#acquiring-the-dataset) - to collect a relevant dataset for analysis. Options including using an API ( e.g., [Poetry DB API](https://poetrydb.org/index.html)) or scraping web pages (e.g., [Project Gutenberg](https://www.gutenberg.org/files/12242/12242-h/12242-h.htm)) using tools like [Scrapy](https://scrapy.org/). + * [`Data Cleaning`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#clean-the-data) - explains how text can be formatted, sanitized and simplified using basic tools like Visual Studio Code and Microsoft Excel. + * [`Data Analysis`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#working-with-the-data-in-a-notebook) - explains how we can now import the dataset into "Notebooks" for analysis using Python packages (like pandas, numpy and matplotlib) to organize and visualize the data. + * [`Sentiment Analysis`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#sentiment-analysis-using-cognitive-services) - explains how we can integrate cloud services like Text Analytics, using low-code tools like [Power Automate](https://flow.microsoft.com/en-us/) for automated data processing workflows. + +Using this workflow, we can explore the seasonal impacts on the sentiment of the poems, and help us fashion our own perspectives on the author. Try it out yourself - then extend the notebook to ask other questions or visualize the data in new ways! + +> You can use some of the tools in the [Digital Humanities toolkit](https://github.com/Digital-Humanities-Toolkit) to pursue these avenues of inquiry + + +## Data Science + Sustainability + +| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Sustainability.png) | +| :---------------------------------------------------------------------------------------------------------------: | +| Data Science & Sustainability - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +The [2030 Agenda For Sustainable Development](https://sdgs.un.org/2030agenda) - adopted by all United Nations members in 2015 - identifies 17 goals including ones that focus on **Protecting the Planet** from degradation and the impact of climate change. The [Microsoft Sustainability](https://www.microsoft.com/en-us/sustainability) initiative supports these goals by exploring ways in which technology solutions can support and build more sustainable futures with a [focus on 4 goals](https://dev.to/azure/a-visual-guide-to-sustainable-software-engineering-53hh) - being carbon negative, water positive, zero waste, and bio-diverse by 2030. + +Tackling these challenges in a scalable and timely manner requires cloud-scale thinking - and large scale data. The [Planetary Computer](https://planetarycomputer.microsoft.com/) initiative provides 4 components to help data scientists and developers in this effort: + + * [Data Catalog](https://planetarycomputer.microsoft.com/catalog) - with petabytes of Earth Systems data (free & Azure-hosted). + * [Planetary API](https://planetarycomputer.microsoft.com/docs/reference/stac/) - to help users search for relevant data across space and time. + * [Hub](https://planetarycomputer.microsoft.com/docs/overview/environment/) - managed environment for scientists to process massive geospatial datasets. + * [Applications](https://planetarycomputer.microsoft.com/applications) - showcase use cases & tools for sustainability insights. + +**The Planetary Computer Project is currently in preview (as of Sep 2021)** - here's how you can get started contributing to sustainability solutions using data science. + +* [Request access](https://planetarycomputer.microsoft.com/account/request) to start exploration and connect with peers. +* [Explore documentation](https://planetarycomputer.microsoft.com/docs/overview/about) to understand supported datasets and APIs. +* Explore applications like [Ecosystem Monitoring](https://analytics-lab.org/ecosystemmonitoring/) for inspiration on application ideas. + +Think about how you can use data visualization to expose or amplify relevant insights into areas like climate change and deforestation. Or think about how insights can be used to create new user experiences that motivate behavioral changes for more sustainable living. + +## Data Science + Students + +We've talked about real-world applications in industry and research, and explored data science application examples in digital humanities and sustainability. So how can you build your skills and share your expertise as data science beginners? + +Here are some examples of data science student projects to inspire you. + + * [MSR Data Science Summer School](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/#!projects) with GitHub [projects](https://github.com/msr-ds3) exploring topics like: + - [Racial Bias in Police Use of Force](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2019-replicating-an-empirical-analysis-of-racial-differences-in-police-use-of-force/) | [Github](https://github.com/msr-ds3/stop-question-frisk) + - [Reliability of NYC Subway System](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2018-exploring-the-reliability-of-the-nyc-subway-system/) | [Github](https://github.com/msr-ds3/nyctransit) + * [Digitizing Material Culture: Exploring socio-economic distributions in Sirkap](https://claremont.maps.arcgis.com/apps/Cascade/index.html?appid=bdf2aef0f45a4674ba41cd373fa23afc)- from [Ornella Altunyan](https://twitter.com/ornelladotcom) and team at Claremont, using using [ArcGIS StoryMaps](https://storymaps.arcgis.com/). + +## 馃殌 Challenge + +Search for articles that recommend data science projects that are beginner friendly - like [these 50 topic areas](https://www.upgrad.com/blog/data-science-project-ideas-topics-beginners/) or [these 21 project ideas](https://www.intellspot.com/data-science-project-ideas) or [these 16 projects with source code](https://data-flair.training/blogs/data-science-project-ideas/) that you can deconstruct and remix. And don't forget to blog about your learning journeys and share your insights with all of us. +## Post-Lecture Quiz + +[Post-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/39) +## Review & Self Study + +Want to explore more use cases? Here are a few relevant articles: + * [17 Data Science Applications and Examples](https://builtin.com/data-science/data-science-applications-examples) - Jul 2021 + * [11 Breathtaking Data Science Applications in Real World](https://myblindbird.com/data-science-applications-real-world/) - May 2021 + * [Data Science In The Real World](https://towardsdatascience.com/data-science-in-the-real-world/home) - Article Collection + * Data Science In: [Education](https://data-flair.training/blogs/data-science-in-education/), [Agriculture](https://data-flair.training/blogs/data-science-in-agriculture/), [Finance](https://data-flair.training/blogs/data-science-in-finance/), [Movies](https://data-flair.training/blogs/data-science-at-movies/) & more. +## Assignment + +[Explore A Planetary Computer Dataset](assignment.md) From e3a9312591ae90a9fd607d591552a2ac62883be1 Mon Sep 17 00:00:00 2001 From: Angel Mendez Date: Sat, 16 Oct 2021 01:19:49 -0500 Subject: [PATCH 2/3] feat: (translation): Translate module 6 - 20 real world examples * translate to spanish 20 real world examples README file --- .../translations/README.es.md | 170 +++++++++--------- 1 file changed, 86 insertions(+), 84 deletions(-) diff --git a/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md b/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md index 098a389d..231495d1 100644 --- a/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md +++ b/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md @@ -1,142 +1,144 @@ -# Data Science in the Real World +# Ciencia de Datos en el mundo real -| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) | +| ![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/20-DataScience-RealWorld.png) | | :--------------------------------------------------------------------------------------------------------------: | -| Data Science In The Real World - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | +| Ciencia de Datos en el mundo real - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | -We're almost at the end of this learning journey! +隆Estamos casi al final de esta aventura de aprendizaje! -We started with definitions of data science and ethics, explored various tools & techniques for data analysis and visualization, reviewed the data science lifecycle, and looked at scaling and automating data science workflows with cloud computing services. So, you're probably wondering: _"How exactly do I map all these learnings to real-world contexts?"_ +Empezamos con las definiciones de ciencia de datos y 茅tica, se exploraron diveras herramientas y t茅cnicas para el an谩lisis y visualizaci贸n de datos, se revis贸 el ciclo de vida de los datos, y se busc贸 escalar y automatizar flujos de trabajo de ciencia de datos con servicios de c贸mputo en la nube. Por lo que te preguntar谩s: _"驴C贸mo relaciono todo este aprendizaje con el mundo real?"_ -In this lesson, we'll explore real-world applications of data science across industry and dive into specific examples in the research, digital humanities, and sustainability, contexts. We'll look at student project opportunities and conclude with useful resources to help you continue your learning journey! -## Pre-Lecture Quiz +En esta lecci贸n, exploraremos la aplicaci贸n de la ciencia de datos en el mundo real en la industria y profundizaremos en ejemplos espec铆ficos en la investigaci贸n, humanidades digitales y sustentabilidad. Analizaremos oportunidades de proyectos para estudiantes y concluiremos con recursos 煤tiles que te ayuden en tu aventura de aprendizaje. +## Examen previo a la lecci贸n -[Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/38) -## Data Science + Industry +[Examen previo a la lecci贸n](https://red-water-0103e7a0f.azurestaticapps.net/quiz/38) +## Ciencia de Datos + Industria -Thanks to the democratization of AI, developers are now finding it easier to design and integrate AI-driven decision-making and data-driven insights into user experiences and development workflows. Here are a few examples of how data science is "applied" to real-world applications across the industry: +Gracias a la democratizaci贸n de la AI, los desarrolladores encuentran m谩s f谩cil el dise帽ar e integrar tanto la toma de decisiones dirigidas por AI como el conocimiento pr谩ctico dirigido por datos en experiencias de usuario y desarrollar flujos de trabajo. Aqu铆 algunos ejemplos de c贸mo la ciencia de datos es "aplicada" en aplicaciones del mundo real a trav茅s de la industria: - * [Google Flu Trends](https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/) used data science to correlate search terms with flu trends. While the approach had flaws, it raised awareness of the possibilities (and challenges) of data-driven healthcare predictions. + * [Tendencias de la gripe de Google](https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/) se us贸 ciencia de datos para correlacionar t茅rminos de b煤squeda con tendencias de la gripe. Mientras el enfoque tuvo fallos, este resalt贸 las posibilidades (y retos) de las predicciones de cuidados de la salud dirigidos por datos. - * [UPS Routing Predictions](https://www.technologyreview.com/2018/11/21/139000/how-ups-uses-ai-to-outsmart-bad-weather/) - explains how UPS uses data science and machine learning to predict optimal routes for delivery, taking into account weather conditions, traffic patterns, delivery deadlines and more. + * [Predicciones de enrutamiento de UPS](https://www.technologyreview.com/2018/11/21/139000/how-ups-uses-ai-to-outsmart-bad-weather/) - explica c贸mo UPS usa ciencia de datos y aprendizaje autom谩tico para predecir rutas 贸ptimas para la entrega, tomando en cuenta condiciones clim谩ticas, patrones de tr谩fico, fechas l铆mite de entrega y m谩s. - * [NYC Taxicab Route Visualization](http://chriswhong.github.io/nyctaxi/) - data gathered using [Freedom Of Information Laws](https://chriswhong.com/open-data/foil_nyc_taxi/) helped visualize a day in the life of NYC cabs, helping us understand how they navigate the busy city, the money they make, and the duration of trips over each 24-hour period. + * [Visualizaci贸n de rutas de taxis en la ciudad de Nueva York](http://chriswhong.github.io/nyctaxi/) - se reunieron los datos usando [leyes de libertad de la informaci贸n](https://chriswhong.com/open-data/foil_nyc_taxi/) lo cual ayud贸 a visualizar un d铆a en la vida de los taxis de Nueva York, ayudando a entender como recorren la ajetreada ciudad, cu谩nto dinero ganan, y la duraci贸n de los viajes durante un per铆odo de 24 horas. - * [Uber Data Science Workbench](https://eng.uber.com/dsw/) - uses data (on pickup & dropoff locations, trip duration, preferred routes etc.) gathered from millions of uber trips *daily* to build a data analytics tool to help with pricing, safety, fraud detection and navigation decisions. + * [Banco de trabajo de Ciencia de Datos de Uber](https://eng.uber.com/dsw/) - usa los datos (de ubicaciones de inicio y fin de ruta, duraci贸n del viaje, rutas preferidas, etc.) reunidos de millones de viajes *diarios* en uber para construir una herramienta de anal铆tica de datos para ayudar con los precios, seguridad, detecci贸n de fraude y decisiones de navegaci贸n. - * [Sports Analytics](https://towardsdatascience.com/scope-of-analytics-in-sports-world-37ed09c39860) - focuses on _predictive analytics_ (team and player analysis - think [Moneyball](https://datasciencedegree.wisconsin.edu/blog/moneyball-proves-importance-big-data-big-ideas/) - and fan management) and _data visualization_ (team & fan dashboards, games etc.) with applications like talent scouting, sports gambling and inventory/venue management. + * [Anal铆tica de deportes](https://towardsdatascience.com/scope-of-analytics-in-sports-world-37ed09c39860) - se enfoca en _anal铆tica predictiva_ (an谩lisis de equipo y jugador) - piensa [Moneyball](https://datasciencedegree.wisconsin.edu/blog/moneyball-proves-importance-big-data-big-ideas/) - y gesti贸n de admiradores) y _visualizaci贸n de datos_ (tableros de equipo y admiradores, juegos, etc.) con aplicaciones como b煤squeda de talento, apuestas deportivas y gesti贸n de sedes/inventario. - * [Data Science in Banking](https://data-flair.training/blogs/data-science-in-banking/) - highlights the value of data science in the finance industry with applications ranging from risk modeling and fraud detction, to customer segmentation, real-time prediction and recommender systems. Predictive analytics also drive critical measures like [credit scores](https://dzone.com/articles/using-big-data-and-predictive-analytics-for-credit). +* [Ciencia de Datos en el sector bancario](https://data-flair.training/blogs/data-science-in-banking/) - resalta el valor de la ciencia de datos en la industria financiera con aplicaciones que var铆an desde el modelado de riesgo y detecci贸n de fraudes, a segmentaci贸n de clientes, sistemas de predicci贸n y recomendaci贸n en tiempo real. La anal铆tica predictiva tambi茅n dirige medidas cr铆ticas como [puntaje de cr茅dito](https://dzone.com/articles/using-big-data-and-predictive-analytics-for-credit). - * [Data Science in Healthcare](https://data-flair.training/blogs/data-science-in-healthcare/) - highlights applications like medical imaging (e.g., MRI, X-Ray, CT-Scan), genomics (DNA sequencing), drug development (risk assessment, success prediction), predictive analytics (patient care & supply logistics), disease tracking & prevention etc. + * [Ciencia de Datos en el cuidado de la salud](https://data-flair.training/blogs/data-science-in-healthcare/) - resalta aplicaciones como im谩genes m茅dicas (por ejemplo, resonancias magn茅ticas, rayos X, tomograf铆as computarizadas), gen贸micas (secuencia de ADN), desarrollo de f谩rmacos (evaluaci贸n de riesgos, predicci贸n de 茅xito), an谩lisis predictivos (cuidado del paciente y log铆stica de suministro), seguimiento y prevenci贸n de enfermedades, etc茅tera. -![Data Science Applications in The Real World](./images/data-science-applications.png) Image Credit: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Aplicaciones de la Ciencia de Datos en el mundo real](../images/data-science-applications.png) Image Credit: [Estilos de datos: 6 sorprendentes aplicaciones de la Ciencia de Datos](https://data-flair.training/blogs/data-science-applications/) -The figure shows other domains and examples for applying data science techniques. Want to explore other applications? Check out the [Review & Self Study](?id=review-amp-self-study) section below. +La imagen muestra otros dominios y ejemplos para aplicar t茅cnicas de ciencia de datos. 驴Quieres explorar otras aplicaciones? Revisa la secci贸n [revisi贸n y auto-estudio](?id=review-amp-self-study) abajo. -## Data Science + Research +## Ciencia de datos + Investigaci贸n -| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Research.png) | +| ![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/20-DataScience-Research.png) | | :---------------------------------------------------------------------------------------------------------------: | -| Data Science & Research - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | +| Ciencia de Datos e Investigaci贸n - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | -While real-world applications often focus on industry use cases at scale, _research_ applications and projects can be useful from two perspectives: +Mientras las aplicaciones del mundo real suelen enfocarse en los casos de uso a escala en la industri, las aplicaciones y proyectos de _investigaci贸n_ son 煤tiles desde dos prespectivas: -* _innovation opportunities_ - explore rapid prototyping of advanced concepts and testing of user experiences for next-generation applications. -* _deployment challenges_ - investigate potential harms or unintended consequences of data science technologies in real-world contexts. +* _oportunidades de inovaci贸n_ - explora el prototipado r谩pido de conceptos avanzados y pruebas de experiencias de usuario para aplicaciones de pr贸xima generaci贸n. +* _desaf铆os de despliegue_ - investiga da帽os potenciales o consecuencias involuntarias de las tecnolog铆as de ciencia de datos el mundo real. -For students, these research projects can provide both learning and collaboration opportunities that can improve your understanding of the topic, and broaden your awareness and engagement with relevant people or teams working in areas of interest. So what do research projects look like and how can they make an impact? +Para los estudiantes, estos proyectos de investigaci贸n pueden proveer tanto aprendizaje como oportunidades de colaboraci贸n que podr铆an mejorar tu entendimiento del tema, y ampliar tu conciencia y compromiso con gente o equipos relevantes en el 谩rea de inter茅s. 驴Entonces, qu茅 te parecen los proyectos de investigaci贸n y c贸mo pueden tener impacto? -Let's look at one example - the [MIT Gender Shades Study](http://gendershades.org/overview.html) from Joy Buolamwini (MIT Media Labs) with a [signature research paper](http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf) co-authored with Timnit Gebru (then at Microsoft Research) that focused on +Veamos un ejemplo - el [estudio de sombras de g茅nero del MIT](http://gendershades.org/overview.html) de Joy Buolamwini (MIT Media Labs) con el[documento de investigaci贸n de firma](http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf) en co-autor铆a con Timnit Gebru (luego en Microsoft Research) se enfoc贸 en: - * **What:** The objective of the research project was to _evaluate bias present in automated facial analysis algorithms and datasets_ based on gender and skin type. - * **Why:** Facial analysis is used in areas like law enforcement, airport security, hiring systems and more - contexts where inaccurate classifications (e.g., due to bias) can cause potential economic and social harms to affected individuals or groups. Understanding (and eliminating or mitigating) biases is key to fairness in usage. - * **How:** Researchers recongized that existing benchmarks used predominantly lighter-skinned subjects, and curated a new data set (1000+ images) that was _more balanced_ by gender and skin type. The data set was used to evaluate the accuracy of three gender classification products (from Microsoft, IBM & Face++). + * **Qu茅:** El objetivo del proyecto de investigaci贸n fue el _evaluar sesgos presentes en los algoritmos de an谩lisis facial automatizado y conjuntos de datos_ basados en el g茅nero y tipo de piel. -Results showed that though overall classification accuracy was good, there was a noticeable difference in error rates between various subgroups - with **misgendering** being higher for females or persons with darker skin types, indicative of bias. + * **Porqu茅:** El an谩lisis facial es usado en 谩rea como cumplimiento de la ley, seguridad aeroportuaria, sistemas de contrataci贸n y m谩s - contextos donde las clasificaciones imprecisas (por ejemplo, debido a sesgos) pueden causar da帽os potenciales econ贸micos y sociales a los individuos o grupos afectados. Entender (y eliminar o mitigar) sesgos es la clave para ser justos en pr谩ctica. -**Key Outcomes:** Raised awareness that data science needs more _representative datasets_ (balanced subgroups) and more _inclusive teams_ (diverse backgrounds) to recognize and eliminate or mitigate such biases earlier in AI solutions. Research efforts like this are also instrumental in many organizations defining principles and practices for _responsible AI_ to improve fairness across their AI products and processes. + * **C贸mo:** Lso investigadores reconocieron que los puntos de referencia existentes usaron predominantemente sujetos de piel m谩s clara, y curaron un nuevo conjunto de datos (m谩s de 1000 im谩genes) que estaban _m谩s equilibradas_ por g茅nero y tipo de piel. El conjunto de datos se us贸 para evaluar la precisi贸n de tres productos de clasificaci贸n de g茅nero (de Microsoft, IBM y Face++). +Los resultados mostraton que aunque la precisi贸n de clasificaci贸n general era buena, hab铆a una notable diferencia en las tasas de error entre distintos subgrupos - con la **mala clasificaci贸n de g茅nero** siendo m谩s alta para mujeres o personas con tipos de piel m谩s oscuros, indicativo de un sesgo. -**Want to learn about relevant research efforts in Microsoft?** +**Resultados clave:** Hicieron evidente que la ciencia de datos necesita m谩s _conjuntos de datos representativos_ (subgrupos equilibrados) y m谩s _equipos incluyentes_ (distintos antecedentes) para reconocer y eliminar o mitigar esos sesgos antes en soluciones de AI. los esfuerzos de investigaci贸n como este tambi茅n son instrumentales en muchas organizaciones definiendo principios y pr谩ticas para una _AI responsable_ para mejorar la justicia a trav茅s de los productos y procesos de AI. -* Check out [Microsoft Research Projects](https://www.microsoft.com/research/research-area/artificial-intelligence/?facet%5Btax%5D%5Bmsr-research-area%5D%5B%5D=13556&facet%5Btax%5D%5Bmsr-content-type%5D%5B%5D=msr-project) on Artificial Intelligence. -* Explore student projects from [Microsoft Research Data Science Summer School](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/). -* Check out the [Fairlearn](https://fairlearn.org/) project and [Responsible AI](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6) initiatives. +**驴quieres aprender acerca de esfuerzos relevantes de investigaci贸n en Microsoft?** +* Revisa los [proyectos de investigaci贸n de Microsoft](https://www.microsoft.com/research/research-area/artificial-intelligence/?facet%5Btax%5D%5Bmsr-research-area%5D%5B%5D=13556&facet%5Btax%5D%5Bmsr-content-type%5D%5B%5D=msr-project) en Inteligencia Artificial. +* Explorar proyectos de estudiantes de la [escuela de verano de investigaci贸n en Ciencia de Datos de Microsoft](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/). +* Revisa el proyecto [Fairlearn](https://fairlearn.org/) e iniciativas de [AI responsable](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6). -## Data Science + Humanities -| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Humanities.png) | +## Ciencia de Datos + Humanidades + +| ![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/20-DataScience-Humanities.png) | | :---------------------------------------------------------------------------------------------------------------: | -| Data Science & Digital Humanities - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | +| Ciencia de Datos & Humanidades Digitales - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | -Digital Humanities [has been defined](https://digitalhumanities.stanford.edu/about-dh-stanford) as "a collection of practices and approaches combining computational methods with humanistic inquiry". [Stanford projects](https://digitalhumanities.stanford.edu/projects) like _"rebooting history"_ and _"poetic thinking"_ illustrate the linkage between [Digital Humanities and Data Science](https://digitalhumanities.stanford.edu/digital-humanities-and-data-science) - emphasizing techniques like network analysis, information visualization, spatial and text analysis that can help us revisit historical and literary data sets to derive new insights and perspective. +Las Humanidades Digitales [han sido definidas](https://digitalhumanities.stanford.edu/about-dh-stanford) como "una coleci贸n de pr谩cticas y enfoques que combinan m茅todos computacionales con investigaci贸n human铆stica". Los [proyectos de Stanford](https://digitalhumanities.stanford.edu/projects) como _"reiniciando la historia"_ y _"pensamiento po茅tico"_ ilustran el v铆culo entre [las Humanidades Digitales y Ciencia de Datos](https://digitalhumanities.stanford.edu/digital-humanities-and-data-science) - enfatizando t茅cnicas como el an谩lisis de red, visualizaci贸n de la informaci贸n, an谩lisis espacial y de texto que nos ayudan a revisitar datos hist贸ricos y literarios para derivar en nuevos conocimientos y perspectivas. -*Want to explore and extend a project in this space?* +*驴Quieres explorar y extender un proyecto en este espacio?* -Check out ["Emily Dickinson and the Meter of Mood"](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671) - a great example from [Jen Looper](https://twitter.com/jenlooper) that asks how we can use data science to revisit familiar poetry and re-evaluate its meaning and the contributions of its author in new contexts. For instance, _can we predict the season in which a poem was authored by analyzing its tone or sentiment_ - and what does this tell us about the author's state of mind over the relevant period? +Revisa ["Emily Dickinson y el medidor de estado de 谩nimo"](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671) - un gran ejemplo de [Jen Looper](https://twitter.com/jenlooper) que planteacomo podemos usar la ciencia de datos para revisitar poes铆a familiar y re-evaluar su significado y las contribuciones de su autor en nuevos contextos. Por ejemplo, _驴podemos predecir la estaci贸n en la cual fue creado un poema realizando un an谩lisis en su tono o sentimiento_? y 驴qu茅 nos dices esto acerca del estado mental del autor en ese per铆odo en particular? -To answer that question, we follow the steps of our data science lifecycle: - * [`Data Acquisition`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#acquiring-the-dataset) - to collect a relevant dataset for analysis. Options including using an API ( e.g., [Poetry DB API](https://poetrydb.org/index.html)) or scraping web pages (e.g., [Project Gutenberg](https://www.gutenberg.org/files/12242/12242-h/12242-h.htm)) using tools like [Scrapy](https://scrapy.org/). - * [`Data Cleaning`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#clean-the-data) - explains how text can be formatted, sanitized and simplified using basic tools like Visual Studio Code and Microsoft Excel. - * [`Data Analysis`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#working-with-the-data-in-a-notebook) - explains how we can now import the dataset into "Notebooks" for analysis using Python packages (like pandas, numpy and matplotlib) to organize and visualize the data. - * [`Sentiment Analysis`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#sentiment-analysis-using-cognitive-services) - explains how we can integrate cloud services like Text Analytics, using low-code tools like [Power Automate](https://flow.microsoft.com/en-us/) for automated data processing workflows. +Para responder a esa pregunta, seguiremos los pasos de nuestro ciclo de vida de ciencia de datos: + * [`Adquisici贸n de datos`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#acquiring-the-dataset) - para recolectar un conjunto de datos relevante para el an谩lisis. Las opciones incluyen el uso de un API (por ejemplo, [Poetry DB API](https://poetrydb.org/index.html)) o realizar raspado de p谩ginas web (por ejemplo, [Proyecto Gutenberg](https://www.gutenberg.org/files/12242/12242-h/12242-h.htm)) usando herramientas como [Scrapy](https://scrapy.org/). + * [`Limpieza de datos`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#clean-the-data) - explica como se puede dar formato al texto, la sanitizaci贸n y simplificaci贸n usando herramientas b谩sicas como Visual Studio Code y Microsoft Excel. + * [`An谩lisis de datos`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#working-with-the-data-in-a-notebook) - explica como podemos importar los conjuntos de trabajo en "Notebooks" para an谩lisis usando paquetes de Python (como pandas, numpy y matplotlib) para organizar y visualizar los datos. + * [`An谩lisis de sentimiento`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#sentiment-analysis-using-cognitive-services) - explica como podemos integrar servicios en la nube como Text Analytics, usando herramientas de low-code tools como [Power Automate](https://flow.microsoft.com/en-us/) para flujos de trabajo de procesamiento de datos automatizados. -Using this workflow, we can explore the seasonal impacts on the sentiment of the poems, and help us fashion our own perspectives on the author. Try it out yourself - then extend the notebook to ask other questions or visualize the data in new ways! +Usando este flujo de trabajo, podemos explorar los impactos estacionales en el sentimiento de los poemas, y nos ayuda a formar nuestras propias perspectivas del autor. 隆Prueba esto t煤 mismo - luego extiende el notebook para preguntar otras cuestiones o visualizar los datos de nuevas formas! -> You can use some of the tools in the [Digital Humanities toolkit](https://github.com/Digital-Humanities-Toolkit) to pursue these avenues of inquiry +> Puedes usar algunas de las herramientas en la [caja de herramientas de Humanidades Digitales](https://github.com/Digital-Humanities-Toolkit) para seguir estas v铆as de investigaci贸n. -## Data Science + Sustainability +## Ciencia de Datos + Sustentabilidad -| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Sustainability.png) | +| ![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/20-DataScience-Sustainability.png) | | :---------------------------------------------------------------------------------------------------------------: | -| Data Science & Sustainability - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | +| Ciencia de Datos y Sustentabilidad - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | + +La [agenda de 2030 para el desarrollo sostenible](https://sdgs.un.org/2030agenda) - adoptada por todos los miembros de las Naciones Unidas en 2015 - identifica 17 metas incluyendo algunas que se enfocan en la **protecci贸n del planeta** de la degradaci贸n y el impacto del cambio clim谩tico. La iniciativa de [sustentabilidad de Microsoft](https://www.microsoft.com/en-us/sustainability) soporta estas metas explorando formas en las cuales las soluciones tecnol贸gicas pueden soportar y construir futuros m谩s sostenibles con un [enfoque en 4 metas](https://dev.to/azure/a-visual-guide-to-sustainable-software-engineering-53hh) - siendo negativas al carbono, positivas al agua, cero desperdicio y biodiversas para el 2030. + +Abordar estos desaf铆os de forma escalable y oportuna requiere pensamiento a escala de la nuber y datos en gran escala. La iniciativa de [Computadora Planetaria](https://planetarycomputer.microsoft.com/) provee 4 componentes que ayudan a los cient铆ficos de datos y desarrolladores en este esfuerzo: + + * [Cat谩logo de datos](https://planetarycomputer.microsoft.com/catalog) - con petabytes de datos de los sistemas de la tierra (gratuitos y hospedados en Azure). + * [API Planetaria](https://planetarycomputer.microsoft.com/docs/reference/stac/) - para ayudar a los usuarios a buscar datos relevantes a trav茅s del espacio y tiempo. + * [Hub](https://planetarycomputer.microsoft.com/docs/overview/environment/) - entorno gestionado por cient铆ficos par el proceso de conjuntos de datos geoespaciales masivos. + * [Aplicaciones](https://planetarycomputer.microsoft.com/applications) - exhibe casos de uso y herramientas para conocimientos pr谩cticos sostenibles. -The [2030 Agenda For Sustainable Development](https://sdgs.un.org/2030agenda) - adopted by all United Nations members in 2015 - identifies 17 goals including ones that focus on **Protecting the Planet** from degradation and the impact of climate change. The [Microsoft Sustainability](https://www.microsoft.com/en-us/sustainability) initiative supports these goals by exploring ways in which technology solutions can support and build more sustainable futures with a [focus on 4 goals](https://dev.to/azure/a-visual-guide-to-sustainable-software-engineering-53hh) - being carbon negative, water positive, zero waste, and bio-diverse by 2030. +**El proyecto de Computadora Planetaria est谩 actualmente en progreso (a Septiembre de 2021)** - as铆 es como puedes iniciarte en la contribuci贸n a soluciones sostenibles usando ciencia de datos. -Tackling these challenges in a scalable and timely manner requires cloud-scale thinking - and large scale data. The [Planetary Computer](https://planetarycomputer.microsoft.com/) initiative provides 4 components to help data scientists and developers in this effort: - - * [Data Catalog](https://planetarycomputer.microsoft.com/catalog) - with petabytes of Earth Systems data (free & Azure-hosted). - * [Planetary API](https://planetarycomputer.microsoft.com/docs/reference/stac/) - to help users search for relevant data across space and time. - * [Hub](https://planetarycomputer.microsoft.com/docs/overview/environment/) - managed environment for scientists to process massive geospatial datasets. - * [Applications](https://planetarycomputer.microsoft.com/applications) - showcase use cases & tools for sustainability insights. +* [Solicita acceso](https://planetarycomputer.microsoft.com/account/request) para iniciar la exploraci贸n y conecta con compa帽eros. +* [Explora la documentaci贸n](https://planetarycomputer.microsoft.com/docs/overview/about) para entender los conjuntos de datos y APIs soportados. +* Explora aplicaciones como [Monitoreo del ecosistema](https://analytics-lab.org/ecosystemmonitoring/) en b煤squeda de inspiraci贸n en ideas de aplicaci贸n. -**The Planetary Computer Project is currently in preview (as of Sep 2021)** - here's how you can get started contributing to sustainability solutions using data science. +Piensa en c贸mo puedes usar la visualizaci贸n de datos para exponer o amplificar los conocimientos en 谩reas como el cambio clim谩tico y deforestaci贸n. O piensa en como pueden ser usados los conocimientos para crear nuevas experiencias de usuario para motivar cambios en comportamiento para una vida m谩s sostenible. -* [Request access](https://planetarycomputer.microsoft.com/account/request) to start exploration and connect with peers. -* [Explore documentation](https://planetarycomputer.microsoft.com/docs/overview/about) to understand supported datasets and APIs. -* Explore applications like [Ecosystem Monitoring](https://analytics-lab.org/ecosystemmonitoring/) for inspiration on application ideas. - -Think about how you can use data visualization to expose or amplify relevant insights into areas like climate change and deforestation. Or think about how insights can be used to create new user experiences that motivate behavioral changes for more sustainable living. +## Ciencia de Datos + Estudiantes -## Data Science + Students +Hemos hablado acerca de aplicaciones en el mundo real en la industria y la investigaci贸n y explorado ejemplos de aplicaci贸n de la ciencia de datos en las humanidades digitales y sostenibilidad. Entonces, 驴c贸mo puedes construir tus habilidades y compartir tu experienca como principiantes en la ciencia de datos? -We've talked about real-world applications in industry and research, and explored data science application examples in digital humanities and sustainability. So how can you build your skills and share your expertise as data science beginners? +Aqu铆 tienes algunos ejemplos de proyectos de estudiantes de ciencia de datos para inspirarte. -Here are some examples of data science student projects to inspire you. +* [Escuela de verano de ciencia de datos MSR](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/#!projects) en [proyectos](https://github.com/msr-ds3) de Github explora temas como: + - [Sesgo racial en el uso de la fuerza policial](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2019-replicating-an-empirical-analysis-of-racial-differences-in-police-use-of-force/) | [Github](https://github.com/msr-ds3/stop-question-frisk) + - [Fiabilidad del sistema de transporte Metro de la Ciudad de Nueva York](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2018-exploring-the-reliability-of-the-nyc-subway-system/) | [Github](https://github.com/msr-ds3/nyctransit) + * [Digitalizaci贸n de la Cultura Material: explora las distribuciones socio-econ贸micas en Sirkap](https://claremont.maps.arcgis.com/apps/Cascade/index.html?appid=bdf2aef0f45a4674ba41cd373fa23afc)- por [Ornella Altunyan](https://twitter.com/ornelladotcom) y el equipo en Claremont, usando [ArcGIS StoryMaps](https://storymaps.arcgis.com/). - * [MSR Data Science Summer School](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/#!projects) with GitHub [projects](https://github.com/msr-ds3) exploring topics like: - - [Racial Bias in Police Use of Force](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2019-replicating-an-empirical-analysis-of-racial-differences-in-police-use-of-force/) | [Github](https://github.com/msr-ds3/stop-question-frisk) - - [Reliability of NYC Subway System](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2018-exploring-the-reliability-of-the-nyc-subway-system/) | [Github](https://github.com/msr-ds3/nyctransit) - * [Digitizing Material Culture: Exploring socio-economic distributions in Sirkap](https://claremont.maps.arcgis.com/apps/Cascade/index.html?appid=bdf2aef0f45a4674ba41cd373fa23afc)- from [Ornella Altunyan](https://twitter.com/ornelladotcom) and team at Claremont, using using [ArcGIS StoryMaps](https://storymaps.arcgis.com/). +## 馃殌 Desaf铆o -## 馃殌 Challenge +Busca art铆culos que recomienden proyectos de ciencia de datos que son amigables para principiantes - como [茅stas 50 temas de 谩rea](https://www.upgrad.com/blog/data-science-project-ideas-topics-beginners/) o [estas 21 ideas de proyecto](https://www.intellspot.com/data-science-project-ideas) o [estos 16 proyectos con c贸digo fuente](https://data-flair.training/blogs/data-science-project-ideas/) que puedes deconstruir y remezclar. Y no olvides crear un blog acerca de tu viaje de aprendizaje y comparte tus conocimientos con todos nosotros. -Search for articles that recommend data science projects that are beginner friendly - like [these 50 topic areas](https://www.upgrad.com/blog/data-science-project-ideas-topics-beginners/) or [these 21 project ideas](https://www.intellspot.com/data-science-project-ideas) or [these 16 projects with source code](https://data-flair.training/blogs/data-science-project-ideas/) that you can deconstruct and remix. And don't forget to blog about your learning journeys and share your insights with all of us. -## Post-Lecture Quiz +## Examen posterior a la lecci贸n -[Post-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/39) -## Review & Self Study +[Examen posterior a la lecci贸n](https://red-water-0103e7a0f.azurestaticapps.net/quiz/39) +## Revisi贸n y auto-estudio -Want to explore more use cases? Here are a few relevant articles: - * [17 Data Science Applications and Examples](https://builtin.com/data-science/data-science-applications-examples) - Jul 2021 - * [11 Breathtaking Data Science Applications in Real World](https://myblindbird.com/data-science-applications-real-world/) - May 2021 - * [Data Science In The Real World](https://towardsdatascience.com/data-science-in-the-real-world/home) - Article Collection - * Data Science In: [Education](https://data-flair.training/blogs/data-science-in-education/), [Agriculture](https://data-flair.training/blogs/data-science-in-agriculture/), [Finance](https://data-flair.training/blogs/data-science-in-finance/), [Movies](https://data-flair.training/blogs/data-science-at-movies/) & more. -## Assignment +驴Quieres explorar m谩s casos de uso? Aqu铆 hay algunos art铆culos relevantes: + * [17 aplicaciones de Ciencia de Datos y ejemplos](https://builtin.com/data-science/data-science-applications-examples) - Julio de 2021 + * [11 proyectos de Ciencia de Datos sorprendentes en el mundo real](https://myblindbird.com/data-science-applications-real-world/) - Mayo de 2021 + * [Ciencia de Datos en el mundo real](https://towardsdatascience.com/data-science-in-the-real-world/home) - colecci贸n de art铆culos + * Ciencia de Datos en la [Educaci贸n](https://data-flair.training/blogs/data-science-in-education/), [Agricultura](https://data-flair.training/blogs/data-science-in-agriculture/), [Finanzas](https://data-flair.training/blogs/data-science-in-finance/), [Pel铆culas](https://data-flair.training/blogs/data-science-at-movies/) y m谩s. +## Asignaci贸n -[Explore A Planetary Computer Dataset](assignment.md) +[Explora un conjunto de datos de la Computadora Planetaria](../assignment.md) From 1c4e3a836aaadeb44d7ecdd999c059a8108cdebb Mon Sep 17 00:00:00 2001 From: Angel Mendez Date: Sat, 16 Oct 2021 01:32:18 -0500 Subject: [PATCH 3/3] fix: Solve issue with broken link inner reference --- .../20-Real-World-Examples/translations/README.es.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md b/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md index 231495d1..f708850a 100644 --- a/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md +++ b/6-Data-Science-In-Wild/20-Real-World-Examples/translations/README.es.md @@ -32,7 +32,7 @@ Gracias a la democratizaci贸n de la AI, los desarrolladores encuentran m谩s f谩c ![Aplicaciones de la Ciencia de Datos en el mundo real](../images/data-science-applications.png) Image Credit: [Estilos de datos: 6 sorprendentes aplicaciones de la Ciencia de Datos](https://data-flair.training/blogs/data-science-applications/) -La imagen muestra otros dominios y ejemplos para aplicar t茅cnicas de ciencia de datos. 驴Quieres explorar otras aplicaciones? Revisa la secci贸n [revisi贸n y auto-estudio](?id=review-amp-self-study) abajo. +La imagen muestra otros dominios y ejemplos para aplicar t茅cnicas de ciencia de datos. 驴Quieres explorar otras aplicaciones? Revisa la secci贸n [revisi贸n y auto-estudio](#revisi%C3%B3n-y-auto-estudio) abajo. ## Ciencia de datos + Investigaci贸n