際際滷shows by User: eugeneyan / http://www.slideshare.net/images/logo.gif 際際滷shows by User: eugeneyan / Tue, 13 Jul 2021 20:14:04 GMT 際際滷Share feed for 際際滷shows by User: eugeneyan System design for recommendations and search /slideshow/system-design-for-recommendations-and-search/249721696 systemdesignforrecommendationsandsearch-210713201404
Shared at San Francisco Big Analytics Meetup (2021-07-13)]]>
Shared at San Francisco Big Analytics Meetup (2021-07-13)]]> Tue, 13 Jul 2021 20:14:04 GMT /slideshow/system-design-for-recommendations-and-search/249721696 eugeneyan@slideshare.net(eugeneyan) System design for recommendations and search eugeneyan Shared at San Francisco Big Analytics Meetup (2021-07-13) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/systemdesignforrecommendationsandsearch-210713201404-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Shared at San Francisco Big Analytics Meetup (2021-07-13)
System design for recommendations and search from Eugene Yan Ziyou
]]> 492 0 https://cdn.slidesharecdn.com/ss_thumbnails/systemdesignforrecommendationsandsearch-210713201404-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 Recommender Systems: Beyond the user-item matrix /slideshow/recommender-systems-beyond-the-useritem-matrix/220336826 recsysslides-200114153600
Recommendation systems. They're a pretty old topic that started way back in the 1990s. A meetup on it sounds like it'll be boring... if we only talked about the standard user-item matrix collaborative filtering on big data systems. Thankfully, for this meetup, we'll be sharing on how we can adopt some more recent techniques to recommend products, including social media graphs (and random walks), sequences (and NLP), and PyTorch. The sharing will cover everything starting from data acquisition and preparation, implementation of multiple techniques, and result comparisons. Some familiarity with Python and PyTorch would be useful; minimal math required.]]>
Recommendation systems. They're a pretty old topic that started way back in the 1990s. A meetup on it sounds like it'll be boring... if we only talked about the standard user-item matrix collaborative filtering on big data systems. Thankfully, for this meetup, we'll be sharing on how we can adopt some more recent techniques to recommend products, including social media graphs (and random walks), sequences (and NLP), and PyTorch. The sharing will cover everything starting from data acquisition and preparation, implementation of multiple techniques, and result comparisons. Some familiarity with Python and PyTorch would be useful; minimal math required.]]> Tue, 14 Jan 2020 15:36:00 GMT /slideshow/recommender-systems-beyond-the-useritem-matrix/220336826 eugeneyan@slideshare.net(eugeneyan) Recommender Systems: Beyond the user-item matrix eugeneyan Recommendation systems. They're a pretty old topic that started way back in the 1990s. A meetup on it sounds like it'll be boring... if we only talked about the standard user-item matrix collaborative filtering on big data systems. Thankfully, for this meetup, we'll be sharing on how we can adopt some more recent techniques to recommend products, including social media graphs (and random walks), sequences (and NLP), and PyTorch. The sharing will cover everything starting from data acquisition and preparation, implementation of multiple techniques, and result comparisons. Some familiarity with Python and PyTorch would be useful; minimal math required. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/recsysslides-200114153600-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Recommendation systems. They&#39;re a pretty old topic that started way back in the 1990s. A meetup on it sounds like it&#39;ll be boring... if we only talked about the standard user-item matrix collaborative filtering on big data systems. Thankfully, for this meetup, we&#39;ll be sharing on how we can adopt some more recent techniques to recommend products, including social media graphs (and random walks), sequences (and NLP), and PyTorch. The sharing will cover everything starting from data acquisition and preparation, implementation of multiple techniques, and result comparisons. Some familiarity with Python and PyTorch would be useful; minimal math required.
Recommender Systems: Beyond the user-item matrix from Eugene Yan Ziyou
]]> 2021 3 https://cdn.slidesharecdn.com/ss_thumbnails/recsysslides-200114153600-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 Predicting Hospital Bills at Pre-admission /slideshow/predicting-hospital-bills-at-preadmission-180494667/180494667 ucaredataxsharing20190918ey-191009225118
Healthcare expenditure is set to rise over the coming years. Cost will undoubtedly influence patients decision-making when it comes to diagnosis and treatment. For healthcare providers, providing up-front cost estimates improves patient experience, making patients more willing to return (if required) in the future. For patients, having accurate pre-admission estimates allow for informed decisions and adequate preparation, reducing payment challenges after treatment. Ultimately, this case is a first step towards (i) standardization of healthcare cost estimation and (ii) price transparency to build trust between healthcare providers, payers, and patients. ]]>
Healthcare expenditure is set to rise over the coming years. Cost will undoubtedly influence patients decision-making when it comes to diagnosis and treatment. For healthcare providers, providing up-front cost estimates improves patient experience, making patients more willing to return (if required) in the future. For patients, having accurate pre-admission estimates allow for informed decisions and adequate preparation, reducing payment challenges after treatment. Ultimately, this case is a first step towards (i) standardization of healthcare cost estimation and (ii) price transparency to build trust between healthcare providers, payers, and patients. ]]> Wed, 09 Oct 2019 22:51:18 GMT /slideshow/predicting-hospital-bills-at-preadmission-180494667/180494667 eugeneyan@slideshare.net(eugeneyan) Predicting Hospital Bills at Pre-admission eugeneyan Healthcare expenditure is set to rise over the coming years. Cost will undoubtedly influence patients decision-making when it comes to diagnosis and treatment. For healthcare providers, providing up-front cost estimates improves patient experience, making patients more willing to return (if required) in the future. For patients, having accurate pre-admission estimates allow for informed decisions and adequate preparation, reducing payment challenges after treatment. Ultimately, this case is a first step towards (i) standardization of healthcare cost estimation and (ii) price transparency to build trust between healthcare providers, payers, and patients. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ucaredataxsharing20190918ey-191009225118-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Healthcare expenditure is set to rise over the coming years. Cost will undoubtedly influence patients decision-making when it comes to diagnosis and treatment. For healthcare providers, providing up-front cost estimates improves patient experience, making patients more willing to return (if required) in the future. For patients, having accurate pre-admission estimates allow for informed decisions and adequate preparation, reducing payment challenges after treatment. Ultimately, this case is a first step towards (i) standardization of healthcare cost estimation and (ii) price transparency to build trust between healthcare providers, payers, and patients.
Predicting Hospital Bills at Pre-admission from Eugene Yan Ziyou
]]> 2270 2 https://cdn.slidesharecdn.com/ss_thumbnails/ucaredataxsharing20190918ey-191009225118-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 OLX Group Prod Tech 2019 Keynote: Asia's Tech Giants /slideshow/olx-group-prod-tech-2019-keynote-asias-tech-giants-179173586/179173586 keynotev2-191004191940
- Scaling across multiple properties while centralising capabilities - How to decide what to centralise / decentralise? - Alibaba & Grab: How do they scale across multiple commerce sites? - SuperApps in China and Southeast Asia - Why / why not go the SuperApp approach? - WeChat & Grab: SuperApps of Asia - Case Study: Alibabas playbook for integrating acquisitions (Lazada and Daraz) - What were the key tactics and priorities? - Lessons learnt]]>
- Scaling across multiple properties while centralising capabilities - How to decide what to centralise / decentralise? - Alibaba & Grab: How do they scale across multiple commerce sites? - SuperApps in China and Southeast Asia - Why / why not go the SuperApp approach? - WeChat & Grab: SuperApps of Asia - Case Study: Alibabas playbook for integrating acquisitions (Lazada and Daraz) - What were the key tactics and priorities? - Lessons learnt]]> Fri, 04 Oct 2019 19:19:40 GMT /slideshow/olx-group-prod-tech-2019-keynote-asias-tech-giants-179173586/179173586 eugeneyan@slideshare.net(eugeneyan) OLX Group Prod Tech 2019 Keynote: Asia's Tech Giants eugeneyan - Scaling across multiple properties while centralising capabilities - How to decide what to centralise / decentralise? - Alibaba & Grab: How do they scale across multiple commerce sites? - SuperApps in China and Southeast Asia - Why / why not go the SuperApp approach? - WeChat & Grab: SuperApps of Asia - Case Study: Alibabas playbook for integrating acquisitions (Lazada and Daraz) - What were the key tactics and priorities? - Lessons learnt <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/keynotev2-191004191940-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> - Scaling across multiple properties while centralising capabilities - How to decide what to centralise / decentralise? - Alibaba &amp; Grab: How do they scale across multiple commerce sites? - SuperApps in China and Southeast Asia - Why / why not go the SuperApp approach? - WeChat &amp; Grab: SuperApps of Asia - Case Study: Alibabas playbook for integrating acquisitions (Lazada and Daraz) - What were the key tactics and priorities? - Lessons learnt
OLX Group Prod Tech 2019 Keynote: Asia's Tech Giants from Eugene Yan Ziyou
]]> 1046 4 https://cdn.slidesharecdn.com/ss_thumbnails/keynotev2-191004191940-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 Challenges and Impact at Lazada (Big Data and Analytics Innovation Summit Singapore 2018) /slideshow/data-science-challenges-and-impact-at-lazada-big-data-and-analytics-innovation-summit-singapore-2018/95477873 lzdchallengesandimpact-180430100147
Sharing about how Lazada overcame our challenges with scaling and having a proper data culture at the Big Data and Analytics Innovation Summit Singapore 2018]]>
Sharing about how Lazada overcame our challenges with scaling and having a proper data culture at the Big Data and Analytics Innovation Summit Singapore 2018]]> Mon, 30 Apr 2018 10:01:46 GMT /slideshow/data-science-challenges-and-impact-at-lazada-big-data-and-analytics-innovation-summit-singapore-2018/95477873 eugeneyan@slideshare.net(eugeneyan) Data Science Challenges and Impact at Lazada (Big Data and Analytics Innovation Summit Singapore 2018) eugeneyan Sharing about how Lazada overcame our challenges with scaling and having a proper data culture at the Big Data and Analytics Innovation Summit Singapore 2018 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lzdchallengesandimpact-180430100147-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Sharing about how Lazada overcame our challenges with scaling and having a proper data culture at the Big Data and Analytics Innovation Summit Singapore 2018
Data Science Challenges and Impact at Lazada (Big Data and Analytics Innovation Summit Singapore 2018) from Eugene Yan Ziyou
]]> 3029 4 https://cdn.slidesharecdn.com/ss_thumbnails/lzdchallengesandimpact-180430100147-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 INSEAD Sharing on Lazada Data Science and my Journey /eugeneyan/insead-sharing-on-lazada-data-science-and-my-journey inseadsharing-180430084336
Sharing about how Lazada applies data science to improve customer and seller experience, and my personal journey to my current role in Lazada as Data Science Lead, VP]]>
Sharing about how Lazada applies data science to improve customer and seller experience, and my personal journey to my current role in Lazada as Data Science Lead, VP]]> Mon, 30 Apr 2018 08:43:36 GMT /eugeneyan/insead-sharing-on-lazada-data-science-and-my-journey eugeneyan@slideshare.net(eugeneyan) INSEAD Sharing on Lazada Data Science and my Journey eugeneyan Sharing about how Lazada applies data science to improve customer and seller experience, and my personal journey to my current role in Lazada as Data Science Lead, VP <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/inseadsharing-180430084336-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Sharing about how Lazada applies data science to improve customer and seller experience, and my personal journey to my current role in Lazada as Data Science Lead, VP
INSEAD Sharing on Lazada Data Science and my Journey from Eugene Yan Ziyou
]]> 2655 4 https://cdn.slidesharecdn.com/ss_thumbnails/inseadsharing-180430084336-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 SMU BIA Sharing on Data Science /eugeneyan/smu-bia-sharing-on-data-science smusharing20170823-171014002540
Sharing at SMU's Business Intelligence and Analytics Club on Data Analytics and how to pick it up.]]>
Sharing at SMU's Business Intelligence and Analytics Club on Data Analytics and how to pick it up.]]> Sat, 14 Oct 2017 00:25:40 GMT /eugeneyan/smu-bia-sharing-on-data-science eugeneyan@slideshare.net(eugeneyan) SMU BIA Sharing on Data Science eugeneyan Sharing at SMU's Business Intelligence and Analytics Club on Data Analytics and how to pick it up. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/smusharing20170823-171014002540-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Sharing at SMU&#39;s Business Intelligence and Analytics Club on Data Analytics and how to pick it up.
SMU BIA Sharing on Data Science from Eugene Yan Ziyou
]]> 1822 3 https://cdn.slidesharecdn.com/ss_thumbnails/smusharing20170823-171014002540-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 Culture at Lazada Data Science /slideshow/culture-at-lazada-data-science-80794046/80794046 dsculturedeckv2-171014002105
Here are the values and culture Lazada Data Science lives by daily to fulfil our mission of using data to serve our buyers, sellers, and Lazadians. If this appeals to you, reach out to me!]]>
Here are the values and culture Lazada Data Science lives by daily to fulfil our mission of using data to serve our buyers, sellers, and Lazadians. If this appeals to you, reach out to me!]]> Sat, 14 Oct 2017 00:21:05 GMT /slideshow/culture-at-lazada-data-science-80794046/80794046 eugeneyan@slideshare.net(eugeneyan) Culture at Lazada Data Science eugeneyan Here are the values and culture Lazada Data Science lives by daily to fulfil our mission of using data to serve our buyers, sellers, and Lazadians. If this appeals to you, reach out to me! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dsculturedeckv2-171014002105-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Here are the values and culture Lazada Data Science lives by daily to fulfil our mission of using data to serve our buyers, sellers, and Lazadians. If this appeals to you, reach out to me!
Culture at Lazada Data Science from Eugene Yan Ziyou
]]> 3023 5 https://cdn.slidesharecdn.com/ss_thumbnails/dsculturedeckv2-171014002105-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 Competition Improves Performance: Only when Competition Form matches Goal Orientation /slideshow/competition-improves-performance-only-when-competition-form-matches-goal-orientation/70609805 oraldefensepresentation-170103003331
SMU Social Science Thesis Oral Defense]]>
SMU Social Science Thesis Oral Defense]]> Tue, 03 Jan 2017 00:33:31 GMT /slideshow/competition-improves-performance-only-when-competition-form-matches-goal-orientation/70609805 eugeneyan@slideshare.net(eugeneyan) Competition Improves Performance: Only when Competition Form matches Goal Orientation eugeneyan SMU Social Science Thesis Oral Defense <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/oraldefensepresentation-170103003331-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> SMU Social Science Thesis Oral Defense
Competition Improves Performance: Only when Competition Form matches Goal Orientation from Eugene Yan Ziyou
]]> 1135 7 https://cdn.slidesharecdn.com/ss_thumbnails/oraldefensepresentation-170103003331-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 Lazada ranks products to improve customer experience and conversion /slideshow/how-lazada-ranks-products-to-improve-customer-experience-and-conversion/69916965 howlzdranksfull-161207144254
際際滷s from sharing at Strata + Hadoop Singapore 2016 (http://conferences.oreilly.com/strata/hadoop-big-data-sg/public/schedule/detail/54542) Ecommerce has enabled retailers to make all of their products available to consumers and consumers to access niche products not found in brick-and-mortar stores. This growth provides consumers with unparalleled choice. Nonetheless, the sheer number of products brings with it the challenge of helping users find relevant products with ease. Lazada has tens of millions of products on its platform, and this number grows by approximately one million monthly. Lazadas challenge: How can we help users easily discover good quality products they will like? How can we ensure product selection remains fresh and constantly updated? One way to do this is through the ranking of products. Via ranking, Lazada helps customers easily find products that will delight them by ensuring these products appear in the first few pages. Ill share how Lazada ranks products on our website. (Note: Google how amazon ranks products for some industry background) Topics include how we: * Develop methodology (and tricks) to solve not-so-well-defined problems * Collect and store user-behavior data from our website and app * Clean and prepare the data (e.g., handling outliers) * Discover and create features useful features * Build models to improve customer experience and meet business objectives * Measure and test outcomes on our website * Built this end-to-end on our Hadoop infrastructure, with tools including Kafka and Spark]]>
際際滷s from sharing at Strata + Hadoop Singapore 2016 (http://conferences.oreilly.com/strata/hadoop-big-data-sg/public/schedule/detail/54542) Ecommerce has enabled retailers to make all of their products available to consumers and consumers to access niche products not found in brick-and-mortar stores. This growth provides consumers with unparalleled choice. Nonetheless, the sheer number of products brings with it the challenge of helping users find relevant products with ease. Lazada has tens of millions of products on its platform, and this number grows by approximately one million monthly. Lazadas challenge: How can we help users easily discover good quality products they will like? How can we ensure product selection remains fresh and constantly updated? One way to do this is through the ranking of products. Via ranking, Lazada helps customers easily find products that will delight them by ensuring these products appear in the first few pages. Ill share how Lazada ranks products on our website. (Note: Google how amazon ranks products for some industry background) Topics include how we: * Develop methodology (and tricks) to solve not-so-well-defined problems * Collect and store user-behavior data from our website and app * Clean and prepare the data (e.g., handling outliers) * Discover and create features useful features * Build models to improve customer experience and meet business objectives * Measure and test outcomes on our website * Built this end-to-end on our Hadoop infrastructure, with tools including Kafka and Spark]]> Wed, 07 Dec 2016 14:42:54 GMT /slideshow/how-lazada-ranks-products-to-improve-customer-experience-and-conversion/69916965 eugeneyan@slideshare.net(eugeneyan) How Lazada ranks products to improve customer experience and conversion eugeneyan 際際滷s from sharing at Strata + Hadoop Singapore 2016 (http://conferences.oreilly.com/strata/hadoop-big-data-sg/public/schedule/detail/54542) Ecommerce has enabled retailers to make all of their products available to consumers and consumers to access niche products not found in brick-and-mortar stores. This growth provides consumers with unparalleled choice. Nonetheless, the sheer number of products brings with it the challenge of helping users find relevant products with ease. Lazada has tens of millions of products on its platform, and this number grows by approximately one million monthly. Lazadas challenge: How can we help users easily discover good quality products they will like? How can we ensure product selection remains fresh and constantly updated? One way to do this is through the ranking of products. Via ranking, Lazada helps customers easily find products that will delight them by ensuring these products appear in the first few pages. Ill share how Lazada ranks products on our website. (Note: Google how amazon ranks products for some industry background) Topics include how we: * Develop methodology (and tricks) to solve not-so-well-defined problems * Collect and store user-behavior data from our website and app * Clean and prepare the data (e.g., handling outliers) * Discover and create features useful features * Build models to improve customer experience and meet business objectives * Measure and test outcomes on our website * Built this end-to-end on our Hadoop infrastructure, with tools including Kafka and Spark <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howlzdranksfull-161207144254-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 際際滷s from sharing at Strata + Hadoop Singapore 2016 (http://conferences.oreilly.com/strata/hadoop-big-data-sg/public/schedule/detail/54542) Ecommerce has enabled retailers to make all of their products available to consumers and consumers to access niche products not found in brick-and-mortar stores. This growth provides consumers with unparalleled choice. Nonetheless, the sheer number of products brings with it the challenge of helping users find relevant products with ease. Lazada has tens of millions of products on its platform, and this number grows by approximately one million monthly. Lazadas challenge: How can we help users easily discover good quality products they will like? How can we ensure product selection remains fresh and constantly updated? One way to do this is through the ranking of products. Via ranking, Lazada helps customers easily find products that will delight them by ensuring these products appear in the first few pages. Ill share how Lazada ranks products on our website. (Note: Google how amazon ranks products for some industry background) Topics include how we: * Develop methodology (and tricks) to solve not-so-well-defined problems * Collect and store user-behavior data from our website and app * Clean and prepare the data (e.g., handling outliers) * Discover and create features useful features * Build models to improve customer experience and meet business objectives * Measure and test outcomes on our website * Built this end-to-end on our Hadoop infrastructure, with tools including Kafka and Spark
How Lazada ranks products to improve customer experience and conversion from Eugene Yan Ziyou
]]> 14473 11 https://cdn.slidesharecdn.com/ss_thumbnails/howlzdranksfull-161207144254-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 Sharing about my data science journey and what I do at Lazada /slideshow/sharing-about-my-data-science-journey-and-what-i-do-at-lazada/60631113 smusharing2016-04-04-160407224144
Was invited to share with the SMU Masters of IT in Business students on (i) how I got to my current position as a data scientist and (ii) what I do in my current position. Includes suggested areas to focus on (e.g., distributed systems and processing) and how to gain more experience (e.g., volunteering). I also go through the problems that we solve at Lazada using machine learning and a high level architecture of how we do it.]]>
Was invited to share with the SMU Masters of IT in Business students on (i) how I got to my current position as a data scientist and (ii) what I do in my current position. Includes suggested areas to focus on (e.g., distributed systems and processing) and how to gain more experience (e.g., volunteering). I also go through the problems that we solve at Lazada using machine learning and a high level architecture of how we do it.]]> Thu, 07 Apr 2016 22:41:44 GMT /slideshow/sharing-about-my-data-science-journey-and-what-i-do-at-lazada/60631113 eugeneyan@slideshare.net(eugeneyan) Sharing about my data science journey and what I do at Lazada eugeneyan Was invited to share with the SMU Masters of IT in Business students on (i) how I got to my current position as a data scientist and (ii) what I do in my current position. Includes suggested areas to focus on (e.g., distributed systems and processing) and how to gain more experience (e.g., volunteering). I also go through the problems that we solve at Lazada using machine learning and a high level architecture of how we do it. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/smusharing2016-04-04-160407224144-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Was invited to share with the SMU Masters of IT in Business students on (i) how I got to my current position as a data scientist and (ii) what I do in my current position. Includes suggested areas to focus on (e.g., distributed systems and processing) and how to gain more experience (e.g., volunteering). I also go through the problems that we solve at Lazada using machine learning and a high level architecture of how we do it.
Sharing about my data science journey and what I do at Lazada from Eugene Yan Ziyou
]]> 8381 15 https://cdn.slidesharecdn.com/ss_thumbnails/smusharing2016-04-04-160407224144-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 AXA x DSSG Meetup Sharing (Feb 2016) /slideshow/axa-x-dssg-meetup-sharing-feb-2016/58995718 presentationdsmeetupsing160223envoi-160303012906
Philippe MARIE-JEANNE, Head of Global Data Innovation Lab , What's an insurer like AXA doing in the Big Data world?"]]>
Philippe MARIE-JEANNE, Head of Global Data Innovation Lab , What's an insurer like AXA doing in the Big Data world?"]]> Thu, 03 Mar 2016 01:29:05 GMT /slideshow/axa-x-dssg-meetup-sharing-feb-2016/58995718 eugeneyan@slideshare.net(eugeneyan) AXA x DSSG Meetup Sharing (Feb 2016) eugeneyan Philippe MARIE-JEANNE, Head of Global Data Innovation Lab , What's an insurer like AXA doing in the Big Data world?" <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationdsmeetupsing160223envoi-160303012906-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Philippe MARIE-JEANNE, Head of Global Data Innovation Lab , What&#39;s an insurer like AXA doing in the Big Data world?&quot;
AXA x DSSG Meetup Sharing (Feb 2016) from Eugene Yan Ziyou
]]> 3167 9 https://cdn.slidesharecdn.com/ss_thumbnails/presentationdsmeetupsing160223envoi-160303012906-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 Garuda Robotics x DataScience SG Meetup (Sep 2015) /slideshow/garuda-robotics-x-datascience-sg-meetup-sep-2015/53279547 dssgmeetupclean-150928142023-lva1-app6891
What exactly goes on in the commercial drone/UAV industry in Singapore and globally? Behind the hype of consumer selfie drones lies a vast number of interesting commercial applications, where drones become an enabler for enterprises to gain new aerial perspectives of their facilities and estates, to make intelligent decisions incorporating this additional dimension of data. In this presentation, we will look at one such drones-at-work application to reveal some of the behind-the-scene processes and technologies employed. Specifically, we will dive into the precision agriculture domain and share some of the computer vision problems we face, and take a look at various potential solutions to these challenges. ]]>
What exactly goes on in the commercial drone/UAV industry in Singapore and globally? Behind the hype of consumer selfie drones lies a vast number of interesting commercial applications, where drones become an enabler for enterprises to gain new aerial perspectives of their facilities and estates, to make intelligent decisions incorporating this additional dimension of data. In this presentation, we will look at one such drones-at-work application to reveal some of the behind-the-scene processes and technologies employed. Specifically, we will dive into the precision agriculture domain and share some of the computer vision problems we face, and take a look at various potential solutions to these challenges. ]]> Mon, 28 Sep 2015 14:20:23 GMT /slideshow/garuda-robotics-x-datascience-sg-meetup-sep-2015/53279547 eugeneyan@slideshare.net(eugeneyan) Garuda Robotics x DataScience SG Meetup (Sep 2015) eugeneyan What exactly goes on in the commercial drone/UAV industry in Singapore and globally? Behind the hype of consumer selfie drones lies a vast number of interesting commercial applications, where drones become an enabler for enterprises to gain new aerial perspectives of their facilities and estates, to make intelligent decisions incorporating this additional dimension of data. In this presentation, we will look at one such drones-at-work application to reveal some of the behind-the-scene processes and technologies employed. Specifically, we will dive into the precision agriculture domain and share some of the computer vision problems we face, and take a look at various potential solutions to these challenges. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dssgmeetupclean-150928142023-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> What exactly goes on in the commercial drone/UAV industry in Singapore and globally? Behind the hype of consumer selfie drones lies a vast number of interesting commercial applications, where drones become an enabler for enterprises to gain new aerial perspectives of their facilities and estates, to make intelligent decisions incorporating this additional dimension of data. In this presentation, we will look at one such drones-at-work application to reveal some of the behind-the-scene processes and technologies employed. Specifically, we will dive into the precision agriculture domain and share some of the computer vision problems we face, and take a look at various potential solutions to these challenges.
Garuda Robotics x DataScience SG Meetup (Sep 2015) from Eugene Yan Ziyou
]]> 1288 4 https://cdn.slidesharecdn.com/ss_thumbnails/dssgmeetupclean-150928142023-lva1-app6891-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 DataKind SG sharing of our first DataDive /slideshow/datakind-sg-sharing-of-our-first-datadive/50882733 dksgdatalearn20150723-150724095132-lva1-app6891
DataKind SG sharing on our first DataDive with Humanitarian Organization for Migration Economics (HOME) and Earth Hour. Know of other non-profits we can help? Reach out to singapore@datakind.org or drop me a note =)]]>
DataKind SG sharing on our first DataDive with Humanitarian Organization for Migration Economics (HOME) and Earth Hour. Know of other non-profits we can help? Reach out to singapore@datakind.org or drop me a note =)]]> Fri, 24 Jul 2015 09:51:32 GMT /slideshow/datakind-sg-sharing-of-our-first-datadive/50882733 eugeneyan@slideshare.net(eugeneyan) DataKind SG sharing of our first DataDive eugeneyan DataKind SG sharing on our first DataDive with Humanitarian Organization for Migration Economics (HOME) and Earth Hour. Know of other non-profits we can help? Reach out to singapore@datakind.org or drop me a note =) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dksgdatalearn20150723-150724095132-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> DataKind SG sharing on our first DataDive with Humanitarian Organization for Migration Economics (HOME) and Earth Hour. Know of other non-profits we can help? Reach out to singapore@datakind.org or drop me a note =)
DataKind SG sharing of our first DataDive from Eugene Yan Ziyou
]]> 5007 4 https://cdn.slidesharecdn.com/ss_thumbnails/dksgdatalearn20150723-150724095132-lva1-app6891-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 Social network analysis and growth recommendations for DataScience SG community /slideshow/datascience-sg-meetup/49632278 datasciencesg-150620133832-lva1-app6892
Sharing by Michael Ng and Arun Elangovan at DataScience SG on 20 Jun 2015]]>
Sharing by Michael Ng and Arun Elangovan at DataScience SG on 20 Jun 2015]]> Sat, 20 Jun 2015 13:38:32 GMT /slideshow/datascience-sg-meetup/49632278 eugeneyan@slideshare.net(eugeneyan) Social network analysis and growth recommendations for DataScience SG community eugeneyan Sharing by Michael Ng and Arun Elangovan at DataScience SG on 20 Jun 2015 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datasciencesg-150620133832-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Sharing by Michael Ng and Arun Elangovan at DataScience SG on 20 Jun 2015
Social network analysis and growth recommendations for DataScience SG community from Eugene Yan Ziyou
]]> 1284 1 https://cdn.slidesharecdn.com/ss_thumbnails/datasciencesg-150620133832-lva1-app6892-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 Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learnt /slideshow/kaggle-otto-challenge-how-we-achieved-85th-out-of-3845-and-what-we/49486013 sharingonkaggleottoproductclassificationchallenge-150617020529-lva1-app6892
Our team achieved 85th position out of 3,514 at the very popular Kaggle Otto Product Classification Challenge. Here's an overview of how we did it, as well as some techniques we learnt from fellow Kagglers during and after the competition.]]>
Our team achieved 85th position out of 3,514 at the very popular Kaggle Otto Product Classification Challenge. Here's an overview of how we did it, as well as some techniques we learnt from fellow Kagglers during and after the competition.]]> Wed, 17 Jun 2015 02:05:29 GMT /slideshow/kaggle-otto-challenge-how-we-achieved-85th-out-of-3845-and-what-we/49486013 eugeneyan@slideshare.net(eugeneyan) Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learnt eugeneyan Our team achieved 85th position out of 3,514 at the very popular Kaggle Otto Product Classification Challenge. Here's an overview of how we did it, as well as some techniques we learnt from fellow Kagglers during and after the competition. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sharingonkaggleottoproductclassificationchallenge-150617020529-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Our team achieved 85th position out of 3,514 at the very popular Kaggle Otto Product Classification Challenge. Here&#39;s an overview of how we did it, as well as some techniques we learnt from fellow Kagglers during and after the competition.
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learnt from Eugene Yan Ziyou
]]> 16388 6 https://cdn.slidesharecdn.com/ss_thumbnails/sharingonkaggleottoproductclassificationchallenge-150617020529-lva1-app6892-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 Nielsen x DataScience SG Meetup (Apr 2015) /slideshow/nielsen-x-datascience-sg-meetup-apr-2015/47331003 datasciencesgmeetup0420released-150423080057-conversion-gate02
Here's a summarised version of the slides shared by Nielsen at the DataScience SG meetup on 20 Apr 2015. Thanks to our generous speakers for sharing on their data science endeavours =D]]>
Here's a summarised version of the slides shared by Nielsen at the DataScience SG meetup on 20 Apr 2015. Thanks to our generous speakers for sharing on their data science endeavours =D]]> Thu, 23 Apr 2015 08:00:57 GMT /slideshow/nielsen-x-datascience-sg-meetup-apr-2015/47331003 eugeneyan@slideshare.net(eugeneyan) Nielsen x DataScience SG Meetup (Apr 2015) eugeneyan Here's a summarised version of the slides shared by Nielsen at the DataScience SG meetup on 20 Apr 2015. Thanks to our generous speakers for sharing on their data science endeavours =D <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datasciencesgmeetup0420released-150423080057-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Here&#39;s a summarised version of the slides shared by Nielsen at the DataScience SG meetup on 20 Apr 2015. Thanks to our generous speakers for sharing on their data science endeavours =D
Nielsen x DataScience SG Meetup (Apr 2015) from Eugene Yan Ziyou
]]> 1762 4 https://cdn.slidesharecdn.com/ss_thumbnails/datasciencesgmeetup0420released-150423080057-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 Statistical inference: Statistical Power, ANOVA, and Post Hoc tests /slideshow/statistical-inference-4/43774701 statisticalinference4-150122041344-conversion-gate01
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 4 (statistical power, ANOVA, and post hoc tests). The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference]]>
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 4 (statistical power, ANOVA, and post hoc tests). The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference]]> Thu, 22 Jan 2015 04:13:44 GMT /slideshow/statistical-inference-4/43774701 eugeneyan@slideshare.net(eugeneyan) Statistical inference: Statistical Power, ANOVA, and Post Hoc tests eugeneyan This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 4 (statistical power, ANOVA, and post hoc tests). The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/statisticalinference4-150122041344-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This deck was used in the IDA facilitation of the John Hopkins&#39; Data Science Specialization course for Statistical Inference. It covers the topics in week 4 (statistical power, ANOVA, and post hoc tests). The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference
Statistical inference: Statistical Power, ANOVA, and Post Hoc tests from Eugene Yan Ziyou
]]> 7132 16 https://cdn.slidesharecdn.com/ss_thumbnails/statisticalinference4-150122041344-conversion-gate01-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 Statistical inference: Hypothesis Testing and t-tests /slideshow/statistical-inference-3/43545054 statisticalinference3-150115063005-conversion-gate01
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 3 (hypothesis testing and t tests). The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference]]>
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 3 (hypothesis testing and t tests). The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference]]> Thu, 15 Jan 2015 06:30:05 GMT /slideshow/statistical-inference-3/43545054 eugeneyan@slideshare.net(eugeneyan) Statistical inference: Hypothesis Testing and t-tests eugeneyan This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 3 (hypothesis testing and t tests). The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/statisticalinference3-150115063005-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This deck was used in the IDA facilitation of the John Hopkins&#39; Data Science Specialization course for Statistical Inference. It covers the topics in week 3 (hypothesis testing and t tests). The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference
Statistical inference: Hypothesis Testing and t-tests from Eugene Yan Ziyou
]]> 6277 10 https://cdn.slidesharecdn.com/ss_thumbnails/statisticalinference3-150115063005-conversion-gate01-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 Statistical inference: Probability and Distribution /slideshow/statistical-inference-probability-and-distribution/43302075 statisticalinference1and2-150107193328-conversion-gate02
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 1 (probability) and week 2 (distribution).]]>
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 1 (probability) and week 2 (distribution).]]> Wed, 07 Jan 2015 19:33:28 GMT /slideshow/statistical-inference-probability-and-distribution/43302075 eugeneyan@slideshare.net(eugeneyan) Statistical inference: Probability and Distribution eugeneyan This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 1 (probability) and week 2 (distribution). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/statisticalinference1and2-150107193328-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This deck was used in the IDA facilitation of the John Hopkins&#39; Data Science Specialization course for Statistical Inference. It covers the topics in week 1 (probability) and week 2 (distribution).
Statistical inference: Probability and Distribution from Eugene Yan Ziyou
]]> 5345 9 https://cdn.slidesharecdn.com/ss_thumbnails/statisticalinference1and2-150107193328-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-eugeneyan-48x48.jpg?cb=1658119374 Eugene is a hands-on lead data scientist with a record of planning and executing data strategy, deploying multiple data and ML systems globally, and delivering measurable value (millions in revenue, customer acquisition). As DS manager, he's scaled and mentored teams through hypergrowth and is effective in levelling up junior-mid level members. He collaborates with business, engineering, product, etc. to execute data roadmaps at scale. Focuses on end-to-end: (i) defining scope, (ii) data acquisition, analysis, preparation, (iii) ML, (iv) production. eugeneyan.com https://cdn.slidesharecdn.com/ss_thumbnails/systemdesignforrecommendationsandsearch-210713201404-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/system-design-for-recommendations-and-search/249721696 System design for reco... https://cdn.slidesharecdn.com/ss_thumbnails/recsysslides-200114153600-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/recommender-systems-beyond-the-useritem-matrix/220336826 Recommender Systems: B... https://cdn.slidesharecdn.com/ss_thumbnails/ucaredataxsharing20190918ey-191009225118-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/predicting-hospital-bills-at-preadmission-180494667/180494667 Predicting Hospital Bi...