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W2 d4
This document discusses predictive analysis and the challenges, statistics, assumptions, and relevance to managers. It introduces the author and defines predictive analysis as using past data and statistical methods to predict the future. The main challenges are lack of good data and unique identifiers. Regression analysis is commonly used to correlate attributes and predict likelihoods. The key assumption is that the future will be like the past, but this can be invalidated over time if assumptions become irrelevant. Predictive analytics allows managers to proactively address opportunities and losses.
3 ways to spot a Bad Statistic
There are 3 questions to ask when evaluating statistics: 1) Can you see the uncertainty in the data? Many visualizations overstate certainty. 2) Can you see yourself represented in the data? Visualizations should make sense to many people. 3) How was the data collected? Government statistics are often more reliable than private statistics because of how the data is collected. Being able to evaluate statistics helps produce better decision making.
Becoming a measurement ninja
This document discusses becoming a measurement ninja and provides a framework for measurement, analysis, insight, strategy, tactics, execution, and review. It emphasizes the importance of collecting good, compatible data and using basic tools like visualization, derivatives, and moving averages for analysis. Insights involve techniques like reverse engineering, journaling, and induction to understand why things are happening. Strategy determines what comes next based on goals and methods within environmental constraints. Tactics and execution implement the strategy, and review feeds back into the measurement process. The presentation encourages attendees to use a discount code for online marketing resources.
Ma cd8 goldstein-mq-august 2020
A poll of 1038 likely Democratic primary voters in Massachusetts' 8th Congressional District found:
- Stephen Lynch has a 46% favorable rating compared to Robbie Goldstein's 36% favorable rating, with Lynch leading 39-32 if the primary was held.
- Among undecided voters, support for a progressive Democrat was higher than for a moderate, and support for a pro-choice candidate was much higher than a pro-life candidate.
- A majority of undecided voters would also support a candidate who backs Medicare for All over one who opposes it.
- The poll had a 3.04 point margin of error and was conducted August 8-9 via phone and text with respondents' gender and age demographics
What people cant capture
Data analytics and personality tests have limitations in predicting job performance, as statistics can never provide certainty. While past performance is often the strongest predictor of future behavior, performance is best evaluated by people familiar with an individual. Relying too heavily on metrics can undermine an organization over the long term by neglecting human factors. Algorithms also have inherent biases that can unfairly discriminate.
Understanding How Emergency Managers Evaluate Crowdsourced Data: A Trust Gam...
Presentation of Kathleen Moore, Andrea H. Tapia and Christopher Griffin on the topic "Understanding How Emergency Managers Evaluate Crowdsourced Data: A Trust Game-Based Approach" at ISCRAM2013
Communicating data
This document discusses the importance of communicating data effectively. It provides examples showing that without proper communication, valuable data can remain hidden or underutilized. Specifically, it notes that (1) scientist Gregor Mendel's discoveries about genetics were not widely adopted due to poor communication, and (2) relationship researchers John and Julie Gottman were able to have much greater impact by effectively disseminating their findings beyond just publishing. The document emphasizes that compiling data is useless without proper communication and that managers especially need to become better "consumers of data" who can understand and apply quantitative analysis.
Big Data & The Role Analytics Can Play In Our Organizations
The document discusses big data and data mining tools. It provides an agenda for a session that will explore big data tools and demonstrate their use in identifying patterns and predicting risks. The session aims to show how these analytics can help organizations identify areas of elevated risk and help plan mitigation strategies. A live demo is presented analyzing patterns in Facebook likes to predict attributes about users. The document concludes that big data tools are now widely available and can provide organizations a competitive advantage through predictive analytics.
Recommended
W2 d4
This document discusses predictive analysis and the challenges, statistics, assumptions, and relevance to managers. It introduces the author and defines predictive analysis as using past data and statistical methods to predict the future. The main challenges are lack of good data and unique identifiers. Regression analysis is commonly used to correlate attributes and predict likelihoods. The key assumption is that the future will be like the past, but this can be invalidated over time if assumptions become irrelevant. Predictive analytics allows managers to proactively address opportunities and losses.
3 ways to spot a Bad Statistic
There are 3 questions to ask when evaluating statistics: 1) Can you see the uncertainty in the data? Many visualizations overstate certainty. 2) Can you see yourself represented in the data? Visualizations should make sense to many people. 3) How was the data collected? Government statistics are often more reliable than private statistics because of how the data is collected. Being able to evaluate statistics helps produce better decision making.
Becoming a measurement ninja
This document discusses becoming a measurement ninja and provides a framework for measurement, analysis, insight, strategy, tactics, execution, and review. It emphasizes the importance of collecting good, compatible data and using basic tools like visualization, derivatives, and moving averages for analysis. Insights involve techniques like reverse engineering, journaling, and induction to understand why things are happening. Strategy determines what comes next based on goals and methods within environmental constraints. Tactics and execution implement the strategy, and review feeds back into the measurement process. The presentation encourages attendees to use a discount code for online marketing resources.
Ma cd8 goldstein-mq-august 2020
A poll of 1038 likely Democratic primary voters in Massachusetts' 8th Congressional District found:
- Stephen Lynch has a 46% favorable rating compared to Robbie Goldstein's 36% favorable rating, with Lynch leading 39-32 if the primary was held.
- Among undecided voters, support for a progressive Democrat was higher than for a moderate, and support for a pro-choice candidate was much higher than a pro-life candidate.
- A majority of undecided voters would also support a candidate who backs Medicare for All over one who opposes it.
- The poll had a 3.04 point margin of error and was conducted August 8-9 via phone and text with respondents' gender and age demographics
What people cant capture
Data analytics and personality tests have limitations in predicting job performance, as statistics can never provide certainty. While past performance is often the strongest predictor of future behavior, performance is best evaluated by people familiar with an individual. Relying too heavily on metrics can undermine an organization over the long term by neglecting human factors. Algorithms also have inherent biases that can unfairly discriminate.
Understanding How Emergency Managers Evaluate Crowdsourced Data: A Trust Gam...
Presentation of Kathleen Moore, Andrea H. Tapia and Christopher Griffin on the topic "Understanding How Emergency Managers Evaluate Crowdsourced Data: A Trust Game-Based Approach" at ISCRAM2013
Communicating data
This document discusses the importance of communicating data effectively. It provides examples showing that without proper communication, valuable data can remain hidden or underutilized. Specifically, it notes that (1) scientist Gregor Mendel's discoveries about genetics were not widely adopted due to poor communication, and (2) relationship researchers John and Julie Gottman were able to have much greater impact by effectively disseminating their findings beyond just publishing. The document emphasizes that compiling data is useless without proper communication and that managers especially need to become better "consumers of data" who can understand and apply quantitative analysis.
Big Data & The Role Analytics Can Play In Our Organizations
The document discusses big data and data mining tools. It provides an agenda for a session that will explore big data tools and demonstrate their use in identifying patterns and predicting risks. The session aims to show how these analytics can help organizations identify areas of elevated risk and help plan mitigation strategies. A live demo is presented analyzing patterns in Facebook likes to predict attributes about users. The document concludes that big data tools are now widely available and can provide organizations a competitive advantage through predictive analytics.
Top 25 Big Data Interview Questions and Answers
Preparing for Big Data interview? Here are the top 25 Big Data interview questions and answers to crack the interview!
How tech startups can leverage data analytics and visualization
This session focuses on Lean analytics and data visualization utilizing Pirate Metrics Model for tech startups.
BIG DATA.pdf
Big data is a collection of extremely large data sets that are too large to be processed with traditional data processing applications. It is characterized by high volume, velocity, and variety of structured, semi-structured, and unstructured data. Big data allows companies to gather customer information, anticipate demands, and identify potential problems by analyzing vast amounts of data from various sources. It provides value by converting large volumes of raw data into useful insights and information for businesses.
Fairness and Transparency: Algorithmic Explainability, some Legal and Ethical...
Fairness and Transparency: Algorithmic Explainability, some Legal and Ethical...Patrick Van Renterghem
In this presentation, Nazanin Gifani discussed some of the ethical and legal issues of automated decision making, including algorithmic fairness, transparency and explainability. The big question here is: can AI help us to make fairer decisions ?Business analysis1.9 - business side
This document outlines the key steps and analyses involved in developing a business case as a business analyst. It includes sections on feasibility studies, stakeholder analysis, requirements gathering, prioritization, development planning, testing, and deployment. Methodologies covered include PEST analysis, SWOT analysis, Porter's Five Forces, gap analysis, MOSCOW prioritization, and the use of user stories and use cases. The role of the business analyst in justifying the business case and translating requirements between teams is also discussed.
Book Summary : Everybody Lies
This book discusses how analyzing large amounts of internet search data and digital traces can provide insights into human behavior and psyche. It explains that people will confess private things to Google searches that they wouldn't tell others. The data gives a view into trends of what people want from text, images, and videos. While surveys often receive dishonest answers, digital data may be closer to what people really want or feel as they search for information online.
Big data hype (and reality) by gregory piatetsky shapiro
This document summarizes an analysis of big data hype and reality by Gregory Piatetsky-Shapiro. It discusses how while big data offers unprecedented insights into consumer behavior, human behavior remains unpredictable and inconsistent. Three key findings are discussed: 1) Netflix's algorithm to predict movie ratings improved by less than 0.1 stars after 3 years of work, showing the limits of predicting human tastes. 2) The biggest effects of big data will be creating new areas like search and social media, not radical improvements in prediction. 3) While big data can enhance predictions, managers should not expect it to make human behavior fully predictable and should continue relying on human judgment.
SAS Presentation
This document summarizes a data set from speed dating events and the analysis conducted on it to build a predictive model for matching users. It describes the data, which comes from 8,378 speed dating observations between 2002-2004. Pre-processing steps are outlined to clean and prepare the data. Exploratory analysis finds an overall 16.5% match rate. Various models are tested and a decision tree after data replacement is found to best predict matches with an 80.2% true positive rate. The predictive model results are described to show how likely users are to say yes based on their ratings and attributes. The conclusions discuss using this model to build a dating application with profiles, suggestions, ratings, and chat.
Speed Data Set
Using data from twenty-one speed dating events to create a new dating app, we can connect two individuals based on their interest and preferences thus expediting the dating process. The app will direct the user to rate other users’ profiles based on not only the user’s image but also how much he/she likes the other user based on their profile information. The profiles will include demographic information, shared Interests, and other attributes such as fun factor, attractiveness, etc. After evaluating each user’s preferences and rating, the app will suggest partners who have similar interests and matching preferences.
School customer service presentation
This document provides guidance on designing effective customer satisfaction surveys. It discusses the importance of understanding customer needs, creating surveys of an appropriate length and sample size, ensuring anonymity, and analyzing results collaboratively. The intended audience is a school that is working to improve customer service and seeks to use surveys to identify gaps and track progress. Templates and examples of surveys are provided to help attendees adapt a survey for their own organization.
Four Short Foibles of Organizational Data
Anyone that works with data downstream in an organization has seen things go...wrong, while upstream managers and business leaders are being held accountable. Whether it's a failure in process, or something technically goes wrong, working with data is not always easy. What happened? How can we prevent it from happening again? What's next?
This talk, given at the Portland Data Science Group on October 27, 2016, uncovers 4 common foibles of working with organizational data.
A predictive analytics primer
This document provides an overview of predictive analytics and highlights some key considerations. It defines predictive analytics as using past data to predict the future. The most common barrier to predictive analytics is a lack of good data. All predictive models are based on assumptions about the future being like the past; these assumptions can become invalid over time if key variables change or the model is based on outdated data. Managers should understand the assumptions behind any predictive analysis and monitor whether conditions could make the assumptions invalid.
How Big Data identifies early indicators of Mental Stress
The document discusses how big data and analytics can be used to identify early indicators of mental stress. It outlines the 5 V's of big data: volume, variety, velocity, veracity, and value. A case study is presented on using uncommon insights from behavioral data sources like wearable biometrics and online behaviors to better understand users. Challenges to leveraging big data insights at scale are also examined, such as security, data quality, analytic skills and costs. The potential for data-driven personalized marketing and experiences is explored.
How to use data to make a hit TV show
1) The document discusses how data analysis can be used to create hit TV shows by studying user activities and experiences while watching shows.
2) It provides examples of how Amazon and Netflix used data collected from free pilot episodes and viewership trends to inform their decisions about which shows to develop into full series.
3) The key lessons are that data analysis alone cannot determine the solution - intellectual decisions are also needed to interpret the data and take wise risks that can lead to extraordinary success. Managers should use both data and decision-making skills to solve problems.
What people analytics can’t capture
Analysis on the Harvard Business Review article What people analytics can’t capture by Daniel Goleman .
Data Is Power: How to Harness It by Gabrielle Solomon (Director, Editorial Co...
The document discusses the power of data and some challenges in effectively using data. It notes that data can help better understand audiences, create stronger content, and make smarter business decisions. However, it also notes that data is not always accurate, complete, relevant, or actionable. The document provides tips for ensuring data quality such as checking accuracy, sources, and relevance. It emphasizes telling stories with data and using it to create richer content that users care about.
How to Get More Value from Your Social Data
Creating a meaningful insight is similar to baking a cake, with out the write ingredients, proper recipe, and something bake the batter- you'll struggle to produce something people want to consume. This deck explores at a high level how to work with data at each stage of the process. It is meant for anyone working with data- from savants to noobs.
A/B testing, optimization and results analysis by Mariia Bocheva, ATD'18
The document discusses A/B testing and optimization. It covers why companies do A/B testing, common mistakes in testing like poor data quality and not following statistics properly, and how to properly conduct tests. Key recommendations include testing everything, prioritizing high impact tests, understanding customer problems by analyzing metrics and flows, removing friction from the user experience, and building a data-driven culture.
It's all about big data
The document discusses key topics from IBM's Business Analytics Summit in Toronto in 2013. It outlines the four dimensions of big data: volume, velocity, variety, and veracity. It also discusses challenges organizations face in managing big data and key shifts driving the need for smarter analytics. Additionally, it provides examples of how leading organizations are using analytics to gain insights from data and outperform competitors. Finally, it briefly describes several IBM products for big data analytics.
The challenges of big data, how data capable is your business? DQM Group
The document discusses the challenges of big data and how to measure an organization's data capabilities. It outlines the opportunities big data provides, such as recalculating risk portfolios and analyzing social media data. However, big data also presents challenges like needing a defined data strategy and ensuring talent and technology are aligned. It introduces the data maturity curve to assess an organization's data management practices and provides examples of metrics to measure people, processes, and technology. The document emphasizes having a data strategy, considering third parties and future legislation, and thinking carefully about current capabilities before pursuing big data initiatives.
W2 d3
This document discusses how perceptions and prejudices can influence how data is interpreted. It notes that average data can be irrelevant and dangerous, and that relational data provides a closer interpretation of the actual scenario by accounting for variation. It emphasizes the need for data visualization and publicly available, contextualized data to overcome biases and properly communicate insights from vast databases.
Visualization
The document discusses the importance and benefits of data visualization. It notes that data visualization can help make large amounts of data more understandable by providing visual context and representations. It also suggests that data visualization can help reveal patterns and insights that may not be obvious from raw data alone. Finally, it states that visualization can help managers more easily find and understand relevant data to inform important decisions.
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BIG DATA.pdf
Big data is a collection of extremely large data sets that are too large to be processed with traditional data processing applications. It is characterized by high volume, velocity, and variety of structured, semi-structured, and unstructured data. Big data allows companies to gather customer information, anticipate demands, and identify potential problems by analyzing vast amounts of data from various sources. It provides value by converting large volumes of raw data into useful insights and information for businesses.
Fairness and Transparency: Algorithmic Explainability, some Legal and Ethical...
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This document outlines the key steps and analyses involved in developing a business case as a business analyst. It includes sections on feasibility studies, stakeholder analysis, requirements gathering, prioritization, development planning, testing, and deployment. Methodologies covered include PEST analysis, SWOT analysis, Porter's Five Forces, gap analysis, MOSCOW prioritization, and the use of user stories and use cases. The role of the business analyst in justifying the business case and translating requirements between teams is also discussed.
Book Summary : Everybody Lies
This book discusses how analyzing large amounts of internet search data and digital traces can provide insights into human behavior and psyche. It explains that people will confess private things to Google searches that they wouldn't tell others. The data gives a view into trends of what people want from text, images, and videos. While surveys often receive dishonest answers, digital data may be closer to what people really want or feel as they search for information online.
Big data hype (and reality) by gregory piatetsky shapiro
This document summarizes an analysis of big data hype and reality by Gregory Piatetsky-Shapiro. It discusses how while big data offers unprecedented insights into consumer behavior, human behavior remains unpredictable and inconsistent. Three key findings are discussed: 1) Netflix's algorithm to predict movie ratings improved by less than 0.1 stars after 3 years of work, showing the limits of predicting human tastes. 2) The biggest effects of big data will be creating new areas like search and social media, not radical improvements in prediction. 3) While big data can enhance predictions, managers should not expect it to make human behavior fully predictable and should continue relying on human judgment.
SAS Presentation
This document summarizes a data set from speed dating events and the analysis conducted on it to build a predictive model for matching users. It describes the data, which comes from 8,378 speed dating observations between 2002-2004. Pre-processing steps are outlined to clean and prepare the data. Exploratory analysis finds an overall 16.5% match rate. Various models are tested and a decision tree after data replacement is found to best predict matches with an 80.2% true positive rate. The predictive model results are described to show how likely users are to say yes based on their ratings and attributes. The conclusions discuss using this model to build a dating application with profiles, suggestions, ratings, and chat.
Speed Data Set
Using data from twenty-one speed dating events to create a new dating app, we can connect two individuals based on their interest and preferences thus expediting the dating process. The app will direct the user to rate other users’ profiles based on not only the user’s image but also how much he/she likes the other user based on their profile information. The profiles will include demographic information, shared Interests, and other attributes such as fun factor, attractiveness, etc. After evaluating each user’s preferences and rating, the app will suggest partners who have similar interests and matching preferences.
School customer service presentation
This document provides guidance on designing effective customer satisfaction surveys. It discusses the importance of understanding customer needs, creating surveys of an appropriate length and sample size, ensuring anonymity, and analyzing results collaboratively. The intended audience is a school that is working to improve customer service and seeks to use surveys to identify gaps and track progress. Templates and examples of surveys are provided to help attendees adapt a survey for their own organization.
Four Short Foibles of Organizational Data
Anyone that works with data downstream in an organization has seen things go...wrong, while upstream managers and business leaders are being held accountable. Whether it's a failure in process, or something technically goes wrong, working with data is not always easy. What happened? How can we prevent it from happening again? What's next?
This talk, given at the Portland Data Science Group on October 27, 2016, uncovers 4 common foibles of working with organizational data.
A predictive analytics primer
This document provides an overview of predictive analytics and highlights some key considerations. It defines predictive analytics as using past data to predict the future. The most common barrier to predictive analytics is a lack of good data. All predictive models are based on assumptions about the future being like the past; these assumptions can become invalid over time if key variables change or the model is based on outdated data. Managers should understand the assumptions behind any predictive analysis and monitor whether conditions could make the assumptions invalid.
How Big Data identifies early indicators of Mental Stress
The document discusses how big data and analytics can be used to identify early indicators of mental stress. It outlines the 5 V's of big data: volume, variety, velocity, veracity, and value. A case study is presented on using uncommon insights from behavioral data sources like wearable biometrics and online behaviors to better understand users. Challenges to leveraging big data insights at scale are also examined, such as security, data quality, analytic skills and costs. The potential for data-driven personalized marketing and experiences is explored.
How to use data to make a hit TV show
1) The document discusses how data analysis can be used to create hit TV shows by studying user activities and experiences while watching shows.
2) It provides examples of how Amazon and Netflix used data collected from free pilot episodes and viewership trends to inform their decisions about which shows to develop into full series.
3) The key lessons are that data analysis alone cannot determine the solution - intellectual decisions are also needed to interpret the data and take wise risks that can lead to extraordinary success. Managers should use both data and decision-making skills to solve problems.
What people analytics can’t capture
Analysis on the Harvard Business Review article What people analytics can’t capture by Daniel Goleman .
Data Is Power: How to Harness It by Gabrielle Solomon (Director, Editorial Co...
The document discusses the power of data and some challenges in effectively using data. It notes that data can help better understand audiences, create stronger content, and make smarter business decisions. However, it also notes that data is not always accurate, complete, relevant, or actionable. The document provides tips for ensuring data quality such as checking accuracy, sources, and relevance. It emphasizes telling stories with data and using it to create richer content that users care about.
How to Get More Value from Your Social Data
Creating a meaningful insight is similar to baking a cake, with out the write ingredients, proper recipe, and something bake the batter- you'll struggle to produce something people want to consume. This deck explores at a high level how to work with data at each stage of the process. It is meant for anyone working with data- from savants to noobs.
A/B testing, optimization and results analysis by Mariia Bocheva, ATD'18
The document discusses A/B testing and optimization. It covers why companies do A/B testing, common mistakes in testing like poor data quality and not following statistics properly, and how to properly conduct tests. Key recommendations include testing everything, prioritizing high impact tests, understanding customer problems by analyzing metrics and flows, removing friction from the user experience, and building a data-driven culture.
It's all about big data
The document discusses key topics from IBM's Business Analytics Summit in Toronto in 2013. It outlines the four dimensions of big data: volume, velocity, variety, and veracity. It also discusses challenges organizations face in managing big data and key shifts driving the need for smarter analytics. Additionally, it provides examples of how leading organizations are using analytics to gain insights from data and outperform competitors. Finally, it briefly describes several IBM products for big data analytics.
The challenges of big data, how data capable is your business? DQM Group
The document discusses the challenges of big data and how to measure an organization's data capabilities. It outlines the opportunities big data provides, such as recalculating risk portfolios and analyzing social media data. However, big data also presents challenges like needing a defined data strategy and ensuring talent and technology are aligned. It introduces the data maturity curve to assess an organization's data management practices and provides examples of metrics to measure people, processes, and technology. The document emphasizes having a data strategy, considering third parties and future legislation, and thinking carefully about current capabilities before pursuing big data initiatives.
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Visualization
The document discusses the importance and benefits of data visualization. It notes that data visualization can help make large amounts of data more understandable by providing visual context and representations. It also suggests that data visualization can help reveal patterns and insights that may not be obvious from raw data alone. Finally, it states that visualization can help managers more easily find and understand relevant data to inform important decisions.
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一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
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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
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6:个人职称评审加20分
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留信网服务项目：
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3、留学人员区块链存储服务
→ 【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。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）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。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【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【微信41543339】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41543339】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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【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。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）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。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【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【微信41543339】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41543339】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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【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。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Chester毕业证书)切斯特大学毕业证如何办理
毕业原版【微信:41543339】【(Chester毕业证书)切斯特大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）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。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【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【微信41543339】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41543339】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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【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。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
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：微信95270640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）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。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的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
State of Artificial intelligence Report 2023
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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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
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Decision making
- 1. Data Analyis in Decision Making
- 2. Hello! I am Vikrant Narayan Data Analytics enthusiast You can find me at @vikrant.m.narayan@gmail.com 2
- 3. “ Decision making can sometimes seem like an inner civil war - Jim Rohn 3
- 4. Is Data enough? ● Events from the past have shown us that, just looking into past data and analysing attributes of data points, do not guaranty success. ● We need to ask ourselves if data and data analysis is enough to guaranty success of the model. 4
- 5. Is Data enough? ● The answer to the question is NO. ● There have been instances where similar data analysis models have been used but the results of the models have been drastically different. 5
- 6. Data Analyis. How does it work?
- 7. Et tu, Google? ● Data analytics was used to study the data about influenza virus and predict future outbreaks. ● The model worked perfectly for a few years before it eventually failed. 7
- 8. Reliability ● If data analysis does not guaranty success, is it okay to rely on them in more serious domains like, say, medicine and law enforcement? ● We find that data is involved in real life decision making, in spite of all these failures. 8
- 10. Pattern Recognition ● Analysing patterns between successful and unsuccessful models reveal that the success of the model depends on the decision making. ● They show that results depend on instinctive decisions and risk management. 10
- 11. Role of Data ● Data analysis is primarily for breaking up the data and understanding the data. ● Data might not be preferred to put back the broken pieces together. 11
- 13. Relevance to managers ● Managers should be able to not just collect data points and analyse them numerically, but also to make decisions and take risks for the data analysis to be succesful. 13
- 15. 15 Thank You!