�ݺ�ߣshows by User: yaelgarten

�ݺ�ߣshows by User: yaelgarten / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: yaelgarten / Sat, 18 Mar 2017 06:08:45 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: yaelgarten Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at LinkedIn /slideshow/building-a-healthy-data-ecosystem-around-kafka-and-hadoop-lessons-learned-at-linkedin/73274021 strata2017sjc-shirshankaandyael-finalprapproved-170318060845
2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047 Description: So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it. As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop. Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness.]]>
2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047 Description: So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it. As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop. Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness.]]> Sat, 18 Mar 2017 06:08:45 GMT /slideshow/building-a-healthy-data-ecosystem-around-kafka-and-hadoop-lessons-learned-at-linkedin/73274021 yaelgarten@slideshare.net(yaelgarten) Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at LinkedIn yaelgarten 2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047 Description: So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it. As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop. Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/strata2017sjc-shirshankaandyael-finalprapproved-170318060845-thumbnail.jpg?width=120&height=120&fit=bounds" /> 2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047 Description: So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it. As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop. Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness.

Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at LinkedIn from Yael Garten

]]> 2827 3 https://cdn.slidesharecdn.com/ss_thumbnails/strata2017sjc-shirshankaandyael-finalprapproved-170318060845-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 Architecting for change: LinkedIn's new data ecosystem /slideshow/architecting-for-change-linkedins-new-data-ecosystem/66615770 strata2016nyc-shirshankaandyael-publish-160930191025-161001010239
2016 StrataHadoop NYC conference talk. http://conferences.oreilly.com/strata/hadoop-big-data-ny/public/schedule/detail/52182 Abstract: Last year, LinkedIn embarked on an ambitious mission to completely revamp the mobile experience for its members. This would mean a completely new mobile application, reimagined user experiences, and new interaction concepts. As the team evaluated the impact of this big rewrite on the data analytics ecosystem, they observed a few problems. Over the past few years, LinkedIn has become extremely good at incrementally changing the site one mini-feature at a time, often in conjunction with hundreds of other incremental changes. LinkedIn’s experimentation platform ensures that it is always monitoring a wide gamut of impacted metrics with every change before rolling fully forward. However, when it comes to rolling out a big change like this, different challenges crop up. You have to rollout the entire application all at once; the new experience means that you have no baseline on new metrics; and existing metrics may see double digit changes just because of the new experience or because the metric’s logic is no longer accurate—the challenge is in figuring out which is which. Shirshanka Das and Yael Garten describe how LinkedIn redesigned its data analytics ecosystem in the face of a significant product rewrite, covering the infrastructure changes that enable LinkedIn to roll out future product innovations with minimal downstream impact. Shirshanka and Yael explore the motivations and the building blocks for this reimagined data analytics ecosystem, the technical details of LinkedIn’s new client-side tracking infrastructure, its unified reporting platform, and its data virtualization layer on top of Hadoop and share lessons learned from data producers and consumers that are participating in this governance model. Along the way, they offer some anecdotal evidence during the rollout that validated some of their decisions and are also shaping the future roadmap of these efforts.]]>
2016 StrataHadoop NYC conference talk. http://conferences.oreilly.com/strata/hadoop-big-data-ny/public/schedule/detail/52182 Abstract: Last year, LinkedIn embarked on an ambitious mission to completely revamp the mobile experience for its members. This would mean a completely new mobile application, reimagined user experiences, and new interaction concepts. As the team evaluated the impact of this big rewrite on the data analytics ecosystem, they observed a few problems. Over the past few years, LinkedIn has become extremely good at incrementally changing the site one mini-feature at a time, often in conjunction with hundreds of other incremental changes. LinkedIn’s experimentation platform ensures that it is always monitoring a wide gamut of impacted metrics with every change before rolling fully forward. However, when it comes to rolling out a big change like this, different challenges crop up. You have to rollout the entire application all at once; the new experience means that you have no baseline on new metrics; and existing metrics may see double digit changes just because of the new experience or because the metric’s logic is no longer accurate—the challenge is in figuring out which is which. Shirshanka Das and Yael Garten describe how LinkedIn redesigned its data analytics ecosystem in the face of a significant product rewrite, covering the infrastructure changes that enable LinkedIn to roll out future product innovations with minimal downstream impact. Shirshanka and Yael explore the motivations and the building blocks for this reimagined data analytics ecosystem, the technical details of LinkedIn’s new client-side tracking infrastructure, its unified reporting platform, and its data virtualization layer on top of Hadoop and share lessons learned from data producers and consumers that are participating in this governance model. Along the way, they offer some anecdotal evidence during the rollout that validated some of their decisions and are also shaping the future roadmap of these efforts.]]> Sat, 01 Oct 2016 01:02:39 GMT /slideshow/architecting-for-change-linkedins-new-data-ecosystem/66615770 yaelgarten@slideshare.net(yaelgarten) Architecting for change: LinkedIn's new data ecosystem yaelgarten 2016 StrataHadoop NYC conference talk. http://conferences.oreilly.com/strata/hadoop-big-data-ny/public/schedule/detail/52182 Abstract: Last year, LinkedIn embarked on an ambitious mission to completely revamp the mobile experience for its members. This would mean a completely new mobile application, reimagined user experiences, and new interaction concepts. As the team evaluated the impact of this big rewrite on the data analytics ecosystem, they observed a few problems. Over the past few years, LinkedIn has become extremely good at incrementally changing the site one mini-feature at a time, often in conjunction with hundreds of other incremental changes. LinkedIn’s experimentation platform ensures that it is always monitoring a wide gamut of impacted metrics with every change before rolling fully forward. However, when it comes to rolling out a big change like this, different challenges crop up. You have to rollout the entire application all at once; the new experience means that you have no baseline on new metrics; and existing metrics may see double digit changes just because of the new experience or because the metric’s logic is no longer accurate—the challenge is in figuring out which is which. Shirshanka Das and Yael Garten describe how LinkedIn redesigned its data analytics ecosystem in the face of a significant product rewrite, covering the infrastructure changes that enable LinkedIn to roll out future product innovations with minimal downstream impact. Shirshanka and Yael explore the motivations and the building blocks for this reimagined data analytics ecosystem, the technical details of LinkedIn’s new client-side tracking infrastructure, its unified reporting platform, and its data virtualization layer on top of Hadoop and share lessons learned from data producers and consumers that are participating in this governance model. Along the way, they offer some anecdotal evidence during the rollout that validated some of their decisions and are also shaping the future roadmap of these efforts. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/strata2016nyc-shirshankaandyael-publish-160930191025-161001010239-thumbnail.jpg?width=120&height=120&fit=bounds" /> 2016 StrataHadoop NYC conference talk. http://conferences.oreilly.com/strata/hadoop-big-data-ny/public/schedule/detail/52182 Abstract: Last year, LinkedIn embarked on an ambitious mission to completely revamp the mobile experience for its members. This would mean a completely new mobile application, reimagined user experiences, and new interaction concepts. As the team evaluated the impact of this big rewrite on the data analytics ecosystem, they observed a few problems. Over the past few years, LinkedIn has become extremely good at incrementally changing the site one mini-feature at a time, often in conjunction with hundreds of other incremental changes. LinkedIn’s experimentation platform ensures that it is always monitoring a wide gamut of impacted metrics with every change before rolling fully forward. However, when it comes to rolling out a big change like this, different challenges crop up. You have to rollout the entire application all at once; the new experience means that you have no baseline on new metrics; and existing metrics may see double digit changes just because of the new experience or because the metric’s logic is no longer accurate—the challenge is in figuring out which is which. Shirshanka Das and Yael Garten describe how LinkedIn redesigned its data analytics ecosystem in the face of a significant product rewrite, covering the infrastructure changes that enable LinkedIn to roll out future product innovations with minimal downstream impact. Shirshanka and Yael explore the motivations and the building blocks for this reimagined data analytics ecosystem, the technical details of LinkedIn’s new client-side tracking infrastructure, its unified reporting platform, and its data virtualization layer on top of Hadoop and share lessons learned from data producers and consumers that are participating in this governance model. Along the way, they offer some anecdotal evidence during the rollout that validated some of their decisions and are also shaping the future roadmap of these efforts.

Architecting for change: LinkedIn's new data ecosystem from Yael Garten

]]> 790 3 https://cdn.slidesharecdn.com/ss_thumbnails/strata2016nyc-shirshankaandyael-publish-160930191025-161001010239-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 How to use your data science team: Becoming a data-driven organization /slideshow/how-to-use-your-data-science-team-becoming-a-datadriven-organization/60358780 strata2016santaclarayaelgarten-data101-160401224152
Talk given at Strata Hadoop World conference March 2016. http://conferences.oreilly.com/strata/hadoop-big-data-ca/public/schedule/detail/48305 In this talk we review the culture, process and tools needed for a data driven organization. We review an example of how companies like LinkedIn use data to make business decisions, and then walk through the culture, process, and tools needed to foster this. We review the spectrum of data science used within an organization and explore organizational needs, such as the democratization of data via self-serve data platforms for experimentation, monitoring, and data exploration, as well as the challenges that come with such systems. Participants leave this session with the ability to identify opportunities for data scientists to contribute within their organization and with an understanding of what investments are needed to drive transformation into a data-driven organization.]]>
Talk given at Strata Hadoop World conference March 2016. http://conferences.oreilly.com/strata/hadoop-big-data-ca/public/schedule/detail/48305 In this talk we review the culture, process and tools needed for a data driven organization. We review an example of how companies like LinkedIn use data to make business decisions, and then walk through the culture, process, and tools needed to foster this. We review the spectrum of data science used within an organization and explore organizational needs, such as the democratization of data via self-serve data platforms for experimentation, monitoring, and data exploration, as well as the challenges that come with such systems. Participants leave this session with the ability to identify opportunities for data scientists to contribute within their organization and with an understanding of what investments are needed to drive transformation into a data-driven organization.]]> Fri, 01 Apr 2016 22:41:51 GMT /slideshow/how-to-use-your-data-science-team-becoming-a-datadriven-organization/60358780 yaelgarten@slideshare.net(yaelgarten) How to use your data science team: Becoming a data-driven organization yaelgarten Talk given at Strata Hadoop World conference March 2016. http://conferences.oreilly.com/strata/hadoop-big-data-ca/public/schedule/detail/48305 In this talk we review the culture, process and tools needed for a data driven organization. We review an example of how companies like LinkedIn use data to make business decisions, and then walk through the culture, process, and tools needed to foster this. We review the spectrum of data science used within an organization and explore organizational needs, such as the democratization of data via self-serve data platforms for experimentation, monitoring, and data exploration, as well as the challenges that come with such systems. Participants leave this session with the ability to identify opportunities for data scientists to contribute within their organization and with an understanding of what investments are needed to drive transformation into a data-driven organization. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/strata2016santaclarayaelgarten-data101-160401224152-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk given at Strata Hadoop World conference March 2016. http://conferences.oreilly.com/strata/hadoop-big-data-ca/public/schedule/detail/48305 In this talk we review the culture, process and tools needed for a data driven organization. We review an example of how companies like LinkedIn use data to make business decisions, and then walk through the culture, process, and tools needed to foster this. We review the spectrum of data science used within an organization and explore organizational needs, such as the democratization of data via self-serve data platforms for experimentation, monitoring, and data exploration, as well as the challenges that come with such systems. Participants leave this session with the ability to identify opportunities for data scientists to contribute within their organization and with an understanding of what investments are needed to drive transformation into a data-driven organization.

How to use your data science team: Becoming a data-driven organization from Yael Garten

]]> 743 7 https://cdn.slidesharecdn.com/ss_thumbnails/strata2016santaclarayaelgarten-data101-160401224152-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 A Perspective from the intersection Data Science, Mobility, and Mobile Devices /slideshow/2014-04-stanford-ee392i-yael-garten-for-pdf-33928511/33928511 201404stanfordee392i-yaelgartenforpdf-140425004350-phpapp02
Invited talk at Stanford CSEE392I (Seminar on Trends in Computing and Communications) April 24, 2014. Covered three topics: (1) Data science at LinkedIn. (2) Mobile data science — how is it different, challenges and opportunities. Examples of how data science impacts business and product decisions. (3) Mobile today, and LinkedIn's mobile story.]]>
Invited talk at Stanford CSEE392I (Seminar on Trends in Computing and Communications) April 24, 2014. Covered three topics: (1) Data science at LinkedIn. (2) Mobile data science — how is it different, challenges and opportunities. Examples of how data science impacts business and product decisions. (3) Mobile today, and LinkedIn's mobile story.]]> Fri, 25 Apr 2014 00:43:50 GMT /slideshow/2014-04-stanford-ee392i-yael-garten-for-pdf-33928511/33928511 yaelgarten@slideshare.net(yaelgarten) A Perspective from the intersection Data Science, Mobility, and Mobile Devices yaelgarten Invited talk at Stanford CSEE392I (Seminar on Trends in Computing and Communications) April 24, 2014. Covered three topics: (1) Data science at LinkedIn. (2) Mobile data science — how is it different, challenges and opportunities. Examples of how data science impacts business and product decisions. (3) Mobile today, and LinkedIn's mobile story. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/201404stanfordee392i-yaelgartenforpdf-140425004350-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Invited talk at Stanford CSEE392I (Seminar on Trends in Computing and Communications) April 24, 2014. Covered three topics: (1) Data science at LinkedIn. (2) Mobile data science — how is it different, challenges and opportunities. Examples of how data science impacts business and product decisions. (3) Mobile today, and LinkedIn's mobile story.

A Perspective from the intersection Data Science, Mobility, and Mobile Devices from Yael Garten

]]> 843 4 https://cdn.slidesharecdn.com/ss_thumbnails/201404stanfordee392i-yaelgartenforpdf-140425004350-phpapp02-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 Data Infused Product Design and Insights at LinkedIn /slideshow/data-infused-product-design-and-insights-at-linkedin/14341438 bostonbigdatainnovationsummit-yaelgarten-120919024817-phpapp02
Presentation from a talk given at Boston Big Data Innovation Summit, September 2012. Summary: The Data Science team at LinkedIn focuses on 3 main goals: (1) providing data-driven business and product insights, (2) creating data products, and (3) extracting interesting insights from our data such as analysis of the economic status of the country or identifying hot companies in a certain geographic region. In this talk I describe how we ensure that our products are data driven -- really data infused at the core -- and share interesting insights we uncover using LinkedIn's rich data. We discuss what makes a good data scientist, and what techniques and technologies LinkedIn data scientists use to convert our rich data into actionable product and business insights, to create data-driven products that truly serve our members. ]]>
Presentation from a talk given at Boston Big Data Innovation Summit, September 2012. Summary: The Data Science team at LinkedIn focuses on 3 main goals: (1) providing data-driven business and product insights, (2) creating data products, and (3) extracting interesting insights from our data such as analysis of the economic status of the country or identifying hot companies in a certain geographic region. In this talk I describe how we ensure that our products are data driven -- really data infused at the core -- and share interesting insights we uncover using LinkedIn's rich data. We discuss what makes a good data scientist, and what techniques and technologies LinkedIn data scientists use to convert our rich data into actionable product and business insights, to create data-driven products that truly serve our members. ]]> Wed, 19 Sep 2012 02:48:15 GMT /slideshow/data-infused-product-design-and-insights-at-linkedin/14341438 yaelgarten@slideshare.net(yaelgarten) Data Infused Product Design and Insights at LinkedIn yaelgarten Presentation from a talk given at Boston Big Data Innovation Summit, September 2012. Summary: The Data Science team at LinkedIn focuses on 3 main goals: (1) providing data-driven business and product insights, (2) creating data products, and (3) extracting interesting insights from our data such as analysis of the economic status of the country or identifying hot companies in a certain geographic region. In this talk I describe how we ensure that our products are data driven -- really data infused at the core -- and share interesting insights we uncover using LinkedIn's rich data. We discuss what makes a good data scientist, and what techniques and technologies LinkedIn data scientists use to convert our rich data into actionable product and business insights, to create data-driven products that truly serve our members. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bostonbigdatainnovationsummit-yaelgarten-120919024817-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation from a talk given at Boston Big Data Innovation Summit, September 2012. Summary: The Data Science team at LinkedIn focuses on 3 main goals: (1) providing data-driven business and product insights, (2) creating data products, and (3) extracting interesting insights from our data such as analysis of the economic status of the country or identifying hot companies in a certain geographic region. In this talk I describe how we ensure that our products are data driven -- really data infused at the core -- and share interesting insights we uncover using LinkedIn's rich data. We discuss what makes a good data scientist, and what techniques and technologies LinkedIn data scientists use to convert our rich data into actionable product and business insights, to create data-driven products that truly serve our members.

Data Infused Product Design and Insights at LinkedIn from Yael Garten

]]> 1951 6 https://cdn.slidesharecdn.com/ss_thumbnails/bostonbigdatainnovationsummit-yaelgarten-120919024817-phpapp02-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 Science at LinkedIn - Data-Driven Products & Insights /yaelgarten/data-science-at-linkedin-datadriven-products-insights bigboulderlinkedindatasciencetalk-yaelgartensharenonotes-120709041044-phpapp02
Talk given at Big Boulder conference hosted by Gnip in Boulder, Colorodo on June 21, 2012. This talk provides an intro to Data Science at LinkedIn, and highlights the type of roles a Data Science team can play at a data-driven company. We use data (1) to create products that truly serve our members, (2) to derive insights, and (3) to generate wisdom which enables us to take the products and company to the next level. LinkedIn's data on 160+ million professionals' careers and networks provides a fascinating playground for data scientists to discover data insights about career trends, the social web and the economy. ]]>
Talk given at Big Boulder conference hosted by Gnip in Boulder, Colorodo on June 21, 2012. This talk provides an intro to Data Science at LinkedIn, and highlights the type of roles a Data Science team can play at a data-driven company. We use data (1) to create products that truly serve our members, (2) to derive insights, and (3) to generate wisdom which enables us to take the products and company to the next level. LinkedIn's data on 160+ million professionals' careers and networks provides a fascinating playground for data scientists to discover data insights about career trends, the social web and the economy. ]]> Mon, 09 Jul 2012 04:10:43 GMT /yaelgarten/data-science-at-linkedin-datadriven-products-insights yaelgarten@slideshare.net(yaelgarten) Data Science at LinkedIn - Data-Driven Products & Insights yaelgarten Talk given at Big Boulder conference hosted by Gnip in Boulder, Colorodo on June 21, 2012. This talk provides an intro to Data Science at LinkedIn, and highlights the type of roles a Data Science team can play at a data-driven company. We use data (1) to create products that truly serve our members, (2) to derive insights, and (3) to generate wisdom which enables us to take the products and company to the next level. LinkedIn's data on 160+ million professionals' careers and networks provides a fascinating playground for data scientists to discover data insights about career trends, the social web and the economy. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigboulderlinkedindatasciencetalk-yaelgartensharenonotes-120709041044-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk given at Big Boulder conference hosted by Gnip in Boulder, Colorodo on June 21, 2012. This talk provides an intro to Data Science at LinkedIn, and highlights the type of roles a Data Science team can play at a data-driven company. We use data (1) to create products that truly serve our members, (2) to derive insights, and (3) to generate wisdom which enables us to take the products and company to the next level. LinkedIn's data on 160+ million professionals' careers and networks provides a fascinating playground for data scientists to discover data insights about career trends, the social web and the economy.

Data Science at LinkedIn - Data-Driven Products & Insights from Yael Garten

]]> 1454 5 https://cdn.slidesharecdn.com/ss_thumbnails/bigboulderlinkedindatasciencetalk-yaelgartensharenonotes-120709041044-phpapp02-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 https://cdn.slidesharecdn.com/profile-photo-yaelgarten-48x48.jpg?cb=1523366320 I love interacting with people and data, and have a passion for effecting organizational efficiency and ensuring an inclusive, productive, happy culture. I am an energetic, passionate biomedical informatician by training, and thrive on working in cross-functional teams. Seasoned data science leader with a track record in building and growing high-performing teams. I love meeting people, leading new initiatives, working alongside of motivated colleagues, developing teams and mentoring team members. I enjoy applying my technical background to new areas solving challenging and important problems, and am passionate about the use of data to impact business decisions and product design and inn... https://cdn.slidesharecdn.com/ss_thumbnails/strata2017sjc-shirshankaandyael-finalprapproved-170318060845-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/building-a-healthy-data-ecosystem-around-kafka-and-hadoop-lessons-learned-at-linkedin/73274021 Building a healthy dat... https://cdn.slidesharecdn.com/ss_thumbnails/strata2016nyc-shirshankaandyael-publish-160930191025-161001010239-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/architecting-for-change-linkedins-new-data-ecosystem/66615770 Architecting for chang... https://cdn.slidesharecdn.com/ss_thumbnails/strata2016santaclarayaelgarten-data101-160401224152-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/how-to-use-your-data-science-team-becoming-a-datadriven-organization/60358780 How to use your data s...