Data Science: A Mindset for Productivity
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Data Science: A Mindset for Productivity Keynote at 2015 Ronin Labs West Coast CTO Summit https://www.eventjoy.com/e/west-coast-cto-summit-2015 Abstract Data science isn't just about using a collection of technologies and algorithms. Data science requires a mindset that solves problems at a higher level of abstraction. How do we model utility when we think about optimization? How do we decide which hypotheses to test? How do we allocate our scarce resources to make progress? There are no silver bullets. But I'll share what I've learned from a variety of contexts over the course of my work at Endeca, Google, and LinkedIn; and I hope you'll leave this talk with some practical wisdom you can apply to your next data science project.
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My Three Ex’s: A Data Science Approach for Applied Machine Learning
My Three Ex’s: A Data Science Approach for Applied Machine Learning
Daniel Tunkelang (LinkedIn)
Presented at QCon San Francisco 2014 in the Applied Machine Learning and Data Science track
https://qconsf.com/presentation/my-three-ex%E2%80%99s-data-science-approach-applied-machine-learning
Abstract
This talk is about applying machine learning to solve problems.
It’s not a talk about machine learning — or at least not about the theory of machine learning. Theoretical machine learning requires a deep understanding of computer science and statistics. It’s one of the most studied areas of computer science, and advances in theoretical machine learning give us hope of solving the world’s “AI-hard” problems.
Applied machine learning is more grounded but no less important. We are surrounded by opportunities to apply classifiers, learn rules, compute similarity, and assemble clusters. We don’t need to develop new algorithms for any of these problems — our textbooks and open-source libraries have done that hard work for us.
But algorithms are not enough. Applying machine learning to solve problems requires a data science mindset that transcends the algorithmic details.
In this talk, I’ll communicate the data science mindset by describing my three ex’s: express, explain, and experiment. These three activities are the pillars of a successful strategy for applying machine learning to solve problems. Whether you’re a machine learning novice or expert, I hope you’ll leave this talk with some practical wisdom you can apply to your next project.
Search as Communication: Lessons from a Personal Journey
Search as Communication: Lessons from a Personal Journey
by Daniel Tunkelang (Head of Query Understanding, LinkedIn)
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When I tell people I spent a decade studying computer science at MIT and CMU, most assume that I focused my studies in information retrieval — after all, I’ve spent most of my professional life working on search.
But that’s not how it happened. I learned about information extraction as a summer intern at IBM Research, where I worked on visual query reformulation. I learned how search engines work by building one at Endeca. It was only after I’d hacked my way through the problem for a few years that I started to catch up on the rich scholarly literature of the past few decades.
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Presented at KDD 2023 Workshop on E-Commerce and Natural Language Processing (ECNLP 2023).
Helping Searchers Satisfice through Query Understanding
Behavioral economics transformed how we think about human decision making, rejecting expected utility maximization for the real world of heuristics, biases, and satisficing. In this talk, I'll argue that our thinking about search engines needs a similar transformation. I will compare the Probability Ranking Principle to expected utility maximization and offer ways that AI can help searchers satisfice through query understanding.
This was an invited talk given at the 2023 Walmart AI Summit.
Speaker Bio
Daniel Tunkelang is an independent consultant specializing in search, machine learning / AI, and data science. He completed undergraduate and master's degrees in Computer Science and Math at MIT and a PhD in computer science at CMU. He was a founding employee and chief scientist of Endeca, a search pioneer that Oracle acquired in 2011. He then led engineering and data science teams at Google and LinkedIn. He has written a book on Faceted Search, and he blogs on Medium about search-related topics — particularly query understanding. He has worked with numerous tech companies, retailers, and others, including Algolia, Apple, Canva, Coupang, eBay, Etsy, Flipkart, Home Depot, Oracle, Pinterest, Salesforce, Target, Yelp, and Zoom.
MMM, Search!
MMM, Search!
An opinionated discussion of search metrics, models, and methods. Presented to the Wikimedia Foundation on April 27, 2020.
About the Speaker
Daniel Tunkelang is an independent consultant specializing in search, discovery, machine learning / AI, and data science.
He was a founding employee of Endeca, a search pioneer that Oracle acquired. After 10 years at Endeca, he moved to Google, where he led a local search team. He then served as a director of data science and search at LinkedIn.
After leaving LinkedIn in 2015, he became an independent consultant. His clients have included Apple, eBay, Coupang, Etsy, Flipkart, Gartner, Pinterest, Salesforce, and Yelp; as well as some of the largest traditional retailers.
Daniel completed undergraduate and master's degrees in Computer Science and Math at MIT and a Ph.D. in computer science at CMU. He wrote a book on Faceted Search, published by Morgan & Claypool, and he blogs on Medium about search-related topics -- particularly about query understanding. He is also active on Twitter, LinkedIn, and Quora.
Enterprise Intelligence
Enterprise Intelligence: Putting the Pieces Together
http://enterpriserelevance.com/kdd2016/keynote.html
These slides are for a keynote presentation delivered at the Workshop on Enterprise Intelligence, held in conjunction with the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016).
About the author:
Daniel Tunkelang is a data science and engineering executive who has built and led some of the strongest teams in the software industry. He studied computer science and math at MIT and has a PhD in computer science from CMU. He was a founding employee and chief scientist of Endeca, a search pioneer that Oracle acquired for $1.1B. He led a local search team at Google. He was a director of data science and engineering at LinkedIn, and he established their query understanding team. Daniel is a widely recognized writer and speaker. He is frequently invited to speak at academic and industry conferences, particularly in the areas of information retrieval, web science, and data science. He has written the definitive textbook on faceted search (now a standard for ecommerce sites), established an annual symposium on human-computer interaction and information retrieval, and authored 24 US patents. His social media posts have attracted over a million page views. Daniel advises and consults for companies that can benefit strategically from his expertise. His clients range from early-stage startups to "unicorn" technology companies like Etsy and Pinterest. He helps companies make decisions around algorithms, technology, product strategy, hiring, and organizational structure.
Query Understanding: A Manifesto
Query understanding is about focusing less on the results and more on the query. It’s about figuring out what the searcher wants, rather than scoring and ranking results. Once you’ve established this mindset, your approach to search changes: you focus on query performance rather than ranking.
Presented at QConSF 2016: https://qconsf.com/sf2016/presentation/query-understanding-manifesto
Where should you put your data scientists?
I delivered this keynote at the Fast Forward Labs Data Leadership Conference on April 28, 2016. You can find related materials in the following publications:
https://www.oreilly.com/ideas/where-should-you-put-your-data-scientists
http://firstround.com/review/doing-data-science-right-your-most-common-questions-answered/
Better Search Through Query Understanding
Better Search Through Query Understanding
Presented as a Data Talk at Intuit on April 22, 2014
Search is a fundamental problem of our time — we use search engines daily to satisfy a variety of personal and professional information needs. But search engine development still feels stuck in an information retrieval paradigm that focuses on result ranking. In this talk, I’ll advocate an emphasis on query understanding. I’ll talk about how we implement query understanding at LinkedIn, and I’ll present examples from the broader web. Hopefully you’ll come out with a different perspective on search and share my appreciation for how we can improve search through query understanding.
About the Speaker
Daniel Tunkelang leads LinkedIn's efforts around query understanding. Before that, he led LinkedIn's product data science team. He previously led a local search quality team at Google and was a founding employee of Endeca (acquired by Oracle in 2011). He has written a textbook on faceted search, and is a recognized advocate of human-computer interaction and information retrieval (HCIR). He has a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.
Find and be Found: Information Retrieval at LinkedIn
Find and Be Found: Information Retrieval at LinkedIn
SIGIR 2013 Industry Track Presentation
http://sigir2013.ie/industry_track.html
LinkedIn has a unique data collection: the 200M+ members who use LinkedIn are also the most valuable entities in our corpus, which consists of people, companies, jobs, and a rich content ecosystem. Our members use LinkedIn to satisfy a diverse set of navigational and exploratory information needs, which we address by leveraging semi-structured and social content to understanding their query intent and deliver a personalized search experience. In this talk, we will discuss some of the unique challenges we face in building the LinkedIn search platform, the solutions we've developed so far, and the open problems we see ahead of us.
Shakti Sinha heads LinkedIn's search relevance team, and has been making key contributions to LinkedIn's search products since 2010. He previously worked at Google as both a research intern and a software engineer. He has an MS in Computer Science from Stanford, as well as a BS degree from College of Engineering, Pune.
Daniel Tunkelang leads LinkedIn's efforts around query understanding. Before that, he led LinkedIn's product data science team. He previously led a local search quality team at Google and was a founding employee of Endeca (acquired by Oracle in 2011). He has written a textbook on faceted search, and is a recognized advocate of human-computer interaction and information retrieval (HCIR). He has a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.
Enterprise Search: How do we get there from here?
Enterprise Search: How Do We Get There From Here?
by Daniel Tunkelang (Head of Query Understanding, LinkedIn)
Keynote at 2013 Enterprise Search Summit
We've been tackling the challenges of enterprise and site search for at least 3 decades. We've succeeded to the point that search is the gateway to many of our information repositories. Nonetheless, users of enterprise search systems are frustrated with these systems' shortcomings. We see this frustration in surveys, but, more importantly, most of us experience it personally in our daily work life. We all dream of a world where searching any information repository is as effective as searching the web—perhaps even more so. A world where we find what we're looking for, or quickly determine that it doesn't exist. Is this Utopia possible? If so, how do we get there from here? Or at least somewhere close? In this talk, Tunkelang reviews the track record of enterprise search. He talks about what's worked and what hasn't, especially as compared to web search. Finally, he proposes some paths to bring us closer to our dream.
--
Daniel Tunkelang is Head of Query Understanding at LinkedIn. Educated at MIT and CMU, he has his career working on big data, addressing key challenges in search, data mining, user interfaces, and network analysis. He co-founded enterprise search and business intelligence pioneer Endeca, where he spent a decade as its Chief Scientist. In 2011, Endeca was acquired by Oracle for over $1B. Previous to LinkedIn, he led a team at Google working on local search quality. Daniel has authored fifteen patents, written a textbook on faceted search, and created the annual symposium on human-computer interaction and information retrieval.
Big Data, We Have a Communication Problem
Big Data, We Have a Communication Problem
by Daniel Tunkelang
Presented on April 30, 2013 at the TTI/Vanguard Conference on Ginormous Systems
http://www.ttivanguard.com/conference/2013/ginormous.html
It's a cliché that we live in a world of Big Data. But the bottleneck in understanding data is not computational. Rather, the biggest challenge is designing technical solutions that effectively leverage human cognitive ability. Data analysis systems should augment people's capabilities rather than replace them. This argument is as old as computer science itself: in 1962, Doug Engelbart said that the goal of technology is “the enhancement of human intellect by increasing the capability of a human to approach a complex problem situation.” Algorithms extract signal from raw data, but people fill in the gaps, creating models and evaluating analyses.
Empowering people to understand data is not just a surface problem of building better interfaces and visualizations. We need to interact with data not only after performing computational analysis, but throughout the analysis process in order to improve our models and algorithms. In order to do so, we need tools and processes specifically designed to offer people transparency, guidance, and control.
Human-computer information retrieval has been revolutionizing our approach to information seeking -- no modern search engine limits users to black-box relevance ranking and ten blue links. We need to take similar steps in our analysis of big data, making people the center of the analysis process and developing the technical innovations that enable people to fulfill this role.
How to Interview a Data Scientist
How To Interview a Data Scientist
Daniel Tunkelang
Presented at the O'Reilly Strata 2013 Conference
Video: https://www.youtube.com/watch?v=gUTuESHKbXI
Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy.
At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work.
In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.
Information, Attention, and Trust: A Hierarchy of Needs
Presented by Daniel Tunkelang, LinkedIn Director of Data Science, at Stanford's 2nd annual conference on Computational Social Science (CSS), hosted by Institute for Research in the Social Sciences (IRiSS).
Details at https://iriss.stanford.edu/css/conference-agenda-2013
Data By The People, For The People
Data By The People, For The People
Daniel Tunkelang
Director, Data Science at LinkedIn
Invited Talk at the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012)
LinkedIn has a unique data collection: the 175M+ members who use LinkedIn are also the content those same members access using our information retrieval products. LinkedIn members performed over 4 billion professionally-oriented searches in 2011, most of those to find and discover other people. Every LinkedIn search and recommendation is deeply personalized, reflecting the user's current employment, career history, and professional network. In this talk, I will describe some of the challenges and opportunities that arise from working with this unique corpus. I will discuss work we are doing in the areas of relevance, recommendation, and reputation, as well as the ecosystem we have developed to incent people to provide the high-quality semi-structured profiles that make LinkedIn so useful.
Bio:
Daniel Tunkelang leads the data science team at LinkedIn, which analyzes terabytes of data to produce products and insights that serve LinkedIn's members. Prior to LinkedIn, Daniel led a local search quality team at Google. Daniel was a founding employee of faceted search pioneer Endeca (recently acquired by Oracle), where he spent ten years as Chief Scientist. He has authored fourteen patents, written a textbook on faceted search, created the annual workshop on human-computer interaction and information retrieval (HCIR), and participated in the premier research conferences on information retrieval, knowledge management, databases, and data mining (SIGIR, CIKM, SIGMOD, SIAM Data Mining). Daniel holds a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.
Content, Connections, and Context
Content, Connections, and Context
Daniel Tunkelang, LinkedIn
Keynote at Workshop on Recommender Systems and the Social Web
At 6th ACM International Conference on Recommender Systems (RecSys 2012)
Recommender systems for the social web combine three kinds of signals to relate the subject and object of recommendations: content, connections, and context.
Content comes first - we need to understand what we are recommending and to whom we are recommending it in order to decide whether the recommendation is relevant. Connections supply a social dimension, both as inputs to improve relevance and as social proof to explain the recommendations. Finally, context determines where and when a recommendation is appropriate.
I'll talk about how we use these three kinds of signals in LinkedIn's recommender systems, as well as the challenges we see in delivering social recommendations and measuring their relevance.
Scale, Structure, and Semantics
Keynote at 2012 Semantic Technology and Business Conference
Scale, Structure, and Semantics
Daniel Tunkelang, LinkedIn
Science fiction has a mixed track record when it comes to anticipating technological innovations. While Jules Verne fared well with with his predictions of submarine and space technology, artificial intelligence hasn't produced anything like Arthur C. Clarke's HAL 9000.
Instead, we've managed to elicit intelligence from machines through unexpected means. Search engines have achieved remarkable success in organizing the world's information by crawling the web, indexing documents, and exploiting link structure to establish authoritativeness. At LinkedIn, we apply large-scale analytics to terabytes of semistructured data to deliver products and insights that serve our 150M+ members. Semantics emerge when we apply the right analytical techniques to a sufficient quality and quantity of data.
In this talk, I will describe how LinkedIn's huge and rich graph of relationship data that powers the products our users love. I believe that the lessons we have learned apply broadly to other semantic applications. While quantity and quality of data are the key challenges to delivering a semantically rich experience, the key is to create the right ecosystem that incents people to give you good data, which then forms the basis for great data products.
Strata 2012: Humans, Machines, and the Dimensions of Microwork
Presentation from O'Reilly Strata 2012 on Big Data
Humans, Machines, and the Dimensions of Microwork
Daniel Tunkelang (LinkedIn)
Claire Hunsaker (Samasource)
The advent of crowdsourcing has wildly expanded the ways we think of incorporating human judgments into computational workflows. Computer scientists, economists, and sociologists have explored how to effectively and efficiently distribute microwork tasks to crowds and use their work as inputs to create or improve data products. Simultaneously, crowdsourcing providers are exploring the bounds of mechanical QA flows, worker interfaces, and workforce management systems.
But what tasks should be performed by humans rather than algorithms? And what makes a set of human judgments robust? Quantity? Consensus? Quality or trustworthiness of the workers? Moreover, the robustness of judgments depends not only on the workers, but on the task design. Effective crowdsourcing is a cooperative endeavor.
In this talk, we will analyze various dimensions of microwork that characterize applications, tasks, and crowds. Drawing on our experience at companies that have pioneered the use of microwork (Samasource) and data science (LinkedIn), we will offer practical advice to help you design crowdsourcing workflows to meet your data product needs.
Recommendations as a Conversation with the User
These slides are from a tutorial at the 5th ACM International Conference on Recommender Systems (RecSys 2011).
Recommender systems aim to provide users with products or content that satisfy the users' stated or inferred needs. The primary evaluation measures for recommender systems emphasize either the perceived relevance of the recommendations or the actions associated with those recommendations (e.g., purchases or clicks). Unfortunately, this transactional emphasis neglects how users interact with recommendations in the context of information seeking tasks. The effectiveness of this interaction determines the user's experience beyond a single transaction. This tutorial explores the role of recommendations as part of a conversation between the user and an information seeking system. The tutorial does not require any special background in interfaces or usability, and will focus on practical techniques to make recommender systems most effective for users.
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Daniel Tunkelang (LinkedIn)
LinkedIn operates the world's largest professional network on the Internet with more than 100 million members in over 200 countries. In order to connect its users to the people, opportunities, and content
that best advance their careers, LinkedIn has developed a variety of algorithms that surface relevant content, offer personalized recommendations, and establish topic-sensitive reputation -- all at a
massive scale. In this talk, I will discuss some of the most challenging technical problems we face at LinkedIn, and the approaches we are taking to address them.
Note: This talk was presented at the Carnegie Mellon University School of Computer Science Intelligence Seminar on September 20, 2011. As of May 2013, LinkedIn has over 225 million members.
The War on Attention Poverty: Measuring Twitter Authority
The War on Attention Poverty: Measuring Twitter Authority
As social networks like Facebook and Twitter have grown in popularity, we've had ample opportunity to appreciate Herb Simon's admonition that "a wealth of information creates a poverty of attention". Since there is no way we can hope to follow all of the information being shared by our social networks, we need some filtering or ranking mechanism.
A broad class of approaches involves determining which authors are the most authoritative or influential. There are already a variety of proposed authority measures, as well as research on their effectiveness. In this talk, I will review the various attempts that have been made to measure Twitter authority. In particular, I will discuss the work on TunkRank, a measure inspired by PageRank that explicitly models attention scarcity.
More from Daniel Tunkelang (20)
Helping Searchers Satisfice through Query Understanding
Helping Searchers Satisfice through Query Understanding
Find and be Found: Information Retrieval at LinkedIn
Find and be Found: Information Retrieval at LinkedIn
Information, Attention, and Trust: A Hierarchy of Needs
Information, Attention, and Trust: A Hierarchy of Needs
Strata 2012: Humans, Machines, and the Dimensions of Microwork
Strata 2012: Humans, Machines, and the Dimensions of Microwork
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
The War on Attention Poverty: Measuring Twitter Authority
The War on Attention Poverty: Measuring Twitter Authority
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2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。有一次山娃坐在门口写作业写着写着竟伏在桌上睡着了迷迷糊糊中山娃似乎听到了父亲的脚步声当他晃晃悠悠站起来时才诧然发现一位衣衫破旧的妇女挎着一只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃母
一比一原版(TWU毕业证)西三一大学毕业证成绩单
TWU毕业证【微信95270640】西三一大学没毕业>办理西三一大学毕业证成绩单【微信TWU】TWU毕业证成绩单TWU学历证书TWU文凭《TWU毕业套号文凭网认证西三一大学毕业证成绩单》《哪里买西三一大学毕业证文凭TWU成绩学校快递邮寄信封》《开版西三一大学文凭》TWU留信认证本科硕士学历认证
[留学文凭学历认证(留信认证使馆认证)西三一大学毕业证成绩单毕业证证书大学Offer请假条成绩单语言证书国际回国人员证明高仿教育部认证申请学校等一切高仿或者真实可查认证服务。
多年留学服务公司,拥有海外样板无数能完美1:1还原海外各国大学degreeDiplomaTranscripts等毕业材料。海外大学毕业材料都有哪些工艺呢？工艺难度主要由：烫金.钢印.底纹.水印.防伪光标.热敏防伪等等组成。而且我们每天都在更新海外文凭的样板以求所有同学都能享受到完美的品质服务。
国外毕业证学位证成绩单办理方法：
1客户提供办理西三一大学西三一大学本科学位证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — 我们是挂科和未毕业同学们的福音我们是实体公司精益求精的工艺！ — — — -
一真实留信认证的作用(私企外企荣誉的见证):
1：该专业认证可证明留学生真实留学身份同时对留学生所学专业等级给予评定。
2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。
3：凡是获得留信网入网的信息将会逐步更新到个人身份内将在公安部网内查询个人身份证信息后同步读取人才网入库信息。
4：个人职称评审加20分个人信誉贷款加10分。
5：在国家人才网主办的全国网络招聘大会中纳入资料供国家500强等高端企业选择人才。广州火车东站的那一刻山娃感受到了一种从未有过的激动和震撼太美了可爱的广州父亲的城山娃惊喜得几乎叫出声来山娃觉得父亲太伟大了居然能单匹马地跑到这么远这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到拉
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Empowering Data Analytics Ecosystem.pptx
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
一比一原版(QU毕业证)皇后大学毕业证成绩单
QU毕业证【微信95270640】办理皇后大学毕业证原版一模一样、QU毕业证制作【Q微信95270640】《皇后大学毕业证购买流程》《QU成绩单制作》皇后大学毕业证书QU毕业证文凭皇后大学
本科毕业证书,学历学位认证如何办理【留学国外学位学历认证、毕业证、成绩单、大学Offer、雅思托福代考、语言证书、学生卡、高仿教育部认证等一切高仿或者真实可查认证服务】代办国外（海外）英国、加拿大、美国、新西兰、澳大利亚、新西兰等国外各大学毕业证、文凭学历证书、成绩单、学历学位认证真实可查。
1:1完美还原海外各大学毕业材料上的工艺：水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪。
可办理以下真实皇后大学存档留学生信息存档认证：
1皇后大学真实留信网认证（网上可查永久存档无风险百分百成功入库）；
2真实教育部认证（留服）等一切高仿或者真实可查认证服务（暂时不可办理）；
3购买英美真实学籍（不用正常就读直接出学历）；
4英美一年硕士保毕业证项目（保录取学校挂名不用正常就读保毕业）
留学本科/硕士毕业证书成绩单制作流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询皇后大学皇后大学硕士毕业证成绩单）；
2开始安排制作皇后大学毕业证成绩单电子图；
3皇后大学毕业证成绩单电子版做好以后发送给您确认；
4皇后大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5皇后大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — — — — — — — — 《文凭顾问Q/微：95270640》很感动很无奈房东的儿子小伍笨手笨脚的不会说普通话满口粤语态度十分傲慢一副盛气凌人的样子山娃试图接近他跟他交友与城里人交友但他俩就好像是两个世界里的人根本拢不到一块儿不知不觉山娃倒跟周围出租屋里的几个小伙伴成了好朋友因为他们也是从乡下进城过暑假的小学生快乐的日子总是过得飞快山娃尚未完全认清那几位小朋友时他们却一个接一个地回家了山娃这时才恍然发现二个月的暑假已转到了尽头他的城市生活也将划上一个不很圆义
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
RUG毕业证【微信95270640】办文凭{格罗宁根大学毕业证}Q微Q微信95270640RUG毕业证书成绩单/学历认证RUG Diploma未毕业、挂科怎么办？+QQ微信：Q微信95270640-大学Offer（申请大学）、成绩单（申请考研）、语言证书、在读证明、使馆公证、办真实留信网认证、真实大使馆认证、学历认证
办理国外格罗宁根大学毕业证书 #成绩单改成绩 #教育部学历学位认证 #毕业证认证 #留服认证 #使馆认证（留学回国人员证明） #（证）等
真实教育部认证教育部存档中国教育部留学服务中心认证（即教育部留服认证）网站100%可查.
真实使馆认证（即留学人员回国证明）使馆存档可通过大使馆查询确认.
留信网认证国家专业人才认证中心颁发入库证书留信网永久存档可查.
格罗宁根大学格罗宁根大学毕业证学历书毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金跟学校原版100%相同.
国际留学归国服务中心：实体公司注册经营行业标杆精益求精！
国外毕业证学位证成绩单办理流程：
1客户提供办理格罗宁根大学格罗宁根大学毕业证学历书信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作格罗宁根大学毕业证成绩单电子图；
3格罗宁根大学毕业证成绩单电子版做好以后发送给您确认；
4格罗宁根大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5格罗宁根大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快递邮寄格罗宁根大学格罗宁根大学毕业证学历书）。也得开灯开风扇山娃不想浪费电总将小方桌搁在门口看书写作业有一次山娃坐在门口写作业写着写着竟伏在桌上睡着了迷迷糊糊中山娃似乎听到了父亲的脚步声当他晃晃悠悠站起来时才诧然发现一位衣衫破旧的妇女挎着一只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死受
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
原版定制【Q微信:741003700】《(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书》【Q微信:741003700】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【Q微信741003700】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信741003700】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Object oriented system analysis and design
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
一般的なロボットポリシーモデルであり、トランスフォーマーベースの拡散ポリシーとして実装されているOctoについて紹介しています。
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptx
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Data Science: A Mindset for Productivity
- 1. Data Science: A Mindset for Productivity Daniel Tunkelang @dtunkelang
- 2. tl;dr The most important part of data science is picking the right problem and figuring out how to frame it.
- 3. We’re all technologists, right?
- 4. But nobody knows everything.* Class HashMap<K,V> java.lang.Object java.util.AbstractMap<K,V> java.util.HashMap<K,V> Type Parameters: K - the type of keys maintained by this map V - the type of mapped values All Implemented Interfaces: Serializable, Cloneable, Map<K,V> *Except Jeff Dean.
- 5. Math and computer science matter…
- 6. But you have to solve the right problem.
- 7. Stay friends with your exes. explain express experiment
- 8. Data science is a mindset. Explain Iterate using explainable models. Express Model your utility and inputs. Experiment Optimize for speed of learning.
- 9. Explain
- 10. With apologies to the little prince.
- 11. Deep learning is the new black.
- 12. But accuracy isn’t everything.
- 13. The importance of being explainable. • Algorithms can protect you from overfitting, but they can’t protect you from the biases you introduce. • Introspection into your models and features makes it easier for you and others to debug them. • Especially if you don’t completely trust your objective function or representativeness of your training data.
- 14. Linear models? Decision trees? • Linear regression and decision trees favor explainability over accuracy, compared to more sophisticated models. • But size matters. If you have too many features or too deep a decision tree, you lose explainability. • You can always upgrade to a more sophisticated model when you trust your objective function and training data. • Build a machine learning model is an iterative process. Optimize for the speed of your own learning.
- 15. Express
- 16. Machine learning for dummies. • Define objective function. • Collect training data. • Build models. • Profit!
- 17. You only improve what you measure. Clicks? Actions? Outcomes?
- 18. Sometimes accuracy is complicated.
- 19. What’s your error function?
- 21. Experiment
- 22. How to find your prince. You have to kiss a lot of frogs to find one prince. So how can you find your prince faster? By finding more frogs and kissing them faster and faster. -- Mike Moran
- 23. Think like an economist. Yesterday Experiments are expensive, choose hypotheses wisely. Today Experiments are cheap, do as many as you can!
- 24. But don’t forget you’re a scientist.
- 25. Optimize for the speed of learning.
- 26. Test one variable at a time. • Autocomplete • Entity Tagging • Vertical Intent • # of Suggestions • Suggestion Order • Language • Query Construction • Ranking Model
- 27. tl;dr The most important part of data science is picking the right problem and figuring out how to frame it.