Simplify your analytics strategy
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The document summarizes two key insights from an article on simplifying analytics strategies: 1. There are methods to simplify complex analytics strategies, such as accelerating data processing, delegating work to analytics technologies like business intelligence and data visualization, data discovery, analytics applications, and machine learning. 2. Companies can take either a hypothesis-based or discovery-based approach to solve business problems depending on how much is known about the problem and potential solutions. A hypothesis-based approach is best for known problems with known solutions, while discovery-based is best for known problems with unknown solutions.
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Simplify your analytics strategy
This document describes a data analytics internship under Prof. Sameer Mathur at IIM Lucknow. The internship involves developing an analytics strategy to drive healthcare transformation. Key responsibilities include analyzing articles to identify important insights and explaining their relevance to managers in India. Two important insights from the article are discussed. The first is ways to simplify an analytics strategy, such as accelerating data and delegating work to technologies like business intelligence and machine learning. The second insight describes two approaches - hypothesis-based and discovery-based - for solving business problems depending on the nature and known solutions. The insights are relevant to managers in India as they can help uncover patterns, rely on business intelligence, and use analytics to handle inventories.
Simplify your analytics strategy
The document provides recommendations for simplifying an analytics strategy in 3 key steps:
1) Accelerate data processing to enable fast insights and outcomes.
2) Delegate analytical work to technologies like business intelligence, data discovery, analytics applications, and machine learning to uncover patterns and insights.
3) Companies can take either a hypothesis-based or discovery-based approach depending on whether the business problem is known or unknown, with the goal of deriving insights to inform decision-making.
Simplify your analytics strategy
The document provides steps for companies to simplify their analytics strategy. It recommends companies accelerate data, use next-gen BI and data visualization, perform data discovery, leverage analytics applications, and adopt machine learning. Companies can take either a hypothesis-based approach for known problems or discovery-based for unknown solutions. The key is to focus on high-value problems and place action behind uncovered insights.
Analysis of 'simplify your analytics strategy'
The document summarizes insights from an article on simplifying analytics strategies. It discusses two main insights:
1) Steps to simplify analytics strategies including accelerating data through data platforms, next-gen business intelligence to visualize data, using data discovery techniques, analytics applications, and machine learning.
2) Two approaches to pave the path to analytics insight with an outcome-driven mindset: a hypothesis-based approach for known problems and a discovery-based approach for unknown solutions.
The document then discusses how these insights are relevant for managers in India, noting that some businesses are challenged by analytics complexity and it's important to focus on deriving insights from important data that add value for customers, stakeholders, and employees.
Simplify Your Analytics Strategy
The document provides steps to simplify analytics strategies for businesses. It recommends accelerating data through hybrid technologies, using next-gen business intelligence and data visualization, conducting data discovery, leveraging analytics applications, and adopting machine learning. It also advises taking either a hypothesis-based approach for known problems or discovery-based approach for unknown solutions to generate insights and outcomes. The goal is to make analytics easier to use and deliver tangible business benefits.
Simplify your analytics strategy
Analysis on the Harvard Business Review article Simplify your analytics strategy by by Narendra Mulani.
Simplify you analytics strategy
1. The document discusses simplifying a company's analytics strategy to enable insight-driven decisions that add value. It recommends accelerating data, using business intelligence and data visualization tools, data discovery, analytics applications, and machine learning.
2. It notes that the path to data-driven insights is unique for each dataset as business goals, technologies, data types, and sources are constantly changing. Companies need a new analytics approach each time to maximize benefits from data.
3. Two approaches are outlined: using existing solutions for known problems like customer segmentation; and discovery-based approaches to find predictive patterns for unknown problems. The goal is to uncover insights and then make data-driven decisions.
E-commerce online review for detecting influencing factors users perception
To date, online shopping using e-commerce services becomes a trend. The emergence of e-commerce truly helps people to shop more effectively and efficiently. However, there are still some problems encountered in e-commerce, especially from the user perspective. This research aims to explore user review data, particularly on factors that influence user perception of e-commerce applications, classify, and identify potential solutions to finding problems in e-commerce applications. Data is grabbed using web scraping techniques and classified using proper machine learning, i.e., support vector machine (SVM). Text associations and fishbone analysis are performed based on the classified user review data. The results of this study show that the user satisfaction problem can be captured. Furthermore, various services that should be provided as a potential solution to experienced customers' problems or application users' perception problems can be generated. A detailed discussion of these findings is available in this article.
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Simplify your analytics strategy
This document describes a data analytics internship under Prof. Sameer Mathur at IIM Lucknow. The internship involves developing an analytics strategy to drive healthcare transformation. Key responsibilities include analyzing articles to identify important insights and explaining their relevance to managers in India. Two important insights from the article are discussed. The first is ways to simplify an analytics strategy, such as accelerating data and delegating work to technologies like business intelligence and machine learning. The second insight describes two approaches - hypothesis-based and discovery-based - for solving business problems depending on the nature and known solutions. The insights are relevant to managers in India as they can help uncover patterns, rely on business intelligence, and use analytics to handle inventories.
Simplify your analytics strategy
The document provides recommendations for simplifying an analytics strategy in 3 key steps:
1) Accelerate data processing to enable fast insights and outcomes.
2) Delegate analytical work to technologies like business intelligence, data discovery, analytics applications, and machine learning to uncover patterns and insights.
3) Companies can take either a hypothesis-based or discovery-based approach depending on whether the business problem is known or unknown, with the goal of deriving insights to inform decision-making.
Simplify your analytics strategy
The document provides steps for companies to simplify their analytics strategy. It recommends companies accelerate data, use next-gen BI and data visualization, perform data discovery, leverage analytics applications, and adopt machine learning. Companies can take either a hypothesis-based approach for known problems or discovery-based for unknown solutions. The key is to focus on high-value problems and place action behind uncovered insights.
Analysis of 'simplify your analytics strategy'
The document summarizes insights from an article on simplifying analytics strategies. It discusses two main insights:
1) Steps to simplify analytics strategies including accelerating data through data platforms, next-gen business intelligence to visualize data, using data discovery techniques, analytics applications, and machine learning.
2) Two approaches to pave the path to analytics insight with an outcome-driven mindset: a hypothesis-based approach for known problems and a discovery-based approach for unknown solutions.
The document then discusses how these insights are relevant for managers in India, noting that some businesses are challenged by analytics complexity and it's important to focus on deriving insights from important data that add value for customers, stakeholders, and employees.
Simplify Your Analytics Strategy
The document provides steps to simplify analytics strategies for businesses. It recommends accelerating data through hybrid technologies, using next-gen business intelligence and data visualization, conducting data discovery, leveraging analytics applications, and adopting machine learning. It also advises taking either a hypothesis-based approach for known problems or discovery-based approach for unknown solutions to generate insights and outcomes. The goal is to make analytics easier to use and deliver tangible business benefits.
Simplify your analytics strategy
Analysis on the Harvard Business Review article Simplify your analytics strategy by by Narendra Mulani.
Simplify you analytics strategy
1. The document discusses simplifying a company's analytics strategy to enable insight-driven decisions that add value. It recommends accelerating data, using business intelligence and data visualization tools, data discovery, analytics applications, and machine learning.
2. It notes that the path to data-driven insights is unique for each dataset as business goals, technologies, data types, and sources are constantly changing. Companies need a new analytics approach each time to maximize benefits from data.
3. Two approaches are outlined: using existing solutions for known problems like customer segmentation; and discovery-based approaches to find predictive patterns for unknown problems. The goal is to uncover insights and then make data-driven decisions.
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2) It reviews several machine learning algorithms that have been used for prediction, including random forest, logistic regression, naive bayes, and support vector machines.
3) Deep learning techniques are also discussed, including the use of convolutional neural networks to reveal hidden patterns in customer data and predict purchases and churn.
Simplify your analytics strategy
The document discusses simplifying analytics strategies for businesses dealing with big data. It identifies issues companies face in discovering opportunities in their data and achieving desired outcomes. It outlines various analytics technologies that can help including business intelligence, data visualization, data discovery, analytics applications, and machine learning. The key insights are that analytics solutions must provide the right data at the right time and place for users, allow users to test and discover patterns in data, and put analytics power in users' hands. It also notes there is no one-size-fits-all approach and strategies depend on a company's goals, technologies, data types, and culture. The document advocates for a simplified strategy to generate insights that lead to real outcomes through a hybrid technology environment
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Simplify your analytics strategy
This presentation discusses simplifying analytics strategies for businesses. It suggests that while interest in analytics is growing, some businesses are overwhelmed by the complexity. It recommends pursuing a simpler path to uncover insights from data to make informed decisions. Fast data processing can provide fast insights and outcomes. Next-gen business intelligence and data visualization can help decision-makers explore opportunities. Data discovery alongside projects can uncover new patterns. Machine learning can reduce human elements and improve predictions. Each company's analytics journey depends on its unique culture and existing technologies. Companies can take discovery-based or known solution approaches depending on the problem.
Simplify your analytics strategy
Companies should simplify their analytics strategies by focusing on discovering real business opportunities and outcomes for customers, stakeholders, and employees. They can do this by creating a hybrid data environment that enables fast data movement and using techniques like next-gen business intelligence, data discovery, analytics applications, and machine learning to delegate work to analytics technologies. The optimal path depends on a company's goals, culture, and existing technologies, but generally involves either testing known solutions or taking a discovery-based approach to find patterns for known problem areas. The highest value problems should be addressed first using the most appropriate approach.
Simplifying your analytics strategy
The document provides insights on simplifying an analytics strategy. It suggests that companies accelerate data through a hybrid data supply chain to generate fast insights and outcomes. It also recommends delegating analytics work to technologies like business intelligence, data discovery, analytics applications, and machine learning. The document outlines two approaches for solving business problems - a hypothesis-based approach for known problems and a discovery-based approach for problems with unknown solutions. It notes that managers must decide on the nature of problems and choose the right framework to address them.
Simplify your analytics strategy
This document discusses simplifying analytics strategies. It recommends pursuing a simpler path to insights by accelerating data through a hybrid technology environment. This allows for fast delivery of analytics to improve service quality. It also recommends delegating work to analytics technologies like business intelligence and data visualization to more easily uncover patterns. Different analytic techniques like data discovery, applications, and machine learning can further help companies gain insights from their data in a simplified manner. The path to insights is unique for each company based on their goals, data, and technologies.
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Simplify your analytics strategy
While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate.
Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees.
Discovering real business opportunities and achieving desired outcomes can be elusive.
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Businesses are challenged by complex analytics that can generate confusion instead of insights. Companies should simplify their analytics strategy by focusing on the most important data first to uncover real business opportunities and achieve desired outcomes. They can do this by creating a hybrid data environment that enables fast data delivery and real-time analytics, speeding up decision making. Delegating analytics work to technologies like next-gen business intelligence and data visualization can help turn data into useful insights without needing to analyze everything possible.
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The Data Analytics Lifecycle is designed specifically for Big Data problems and data science projects. The lifecycle has six phases, and project work can occur in several phases at once. For most phases in the lifecycle, the movement can be either forward or backward. This iterative depiction of the lifecycle is intended to more closely portray a real project, in which aspects of the project move forward and may return to earlier stages as new information is uncovered and team members learn more about various stages of the project. This enables participants to move iteratively through the process and drive toward operationalizing the project work.
Phase 1—Discovery: In Phase 1, the team learns the business domain, including relevant history such as whether the organization or business unit has attempted similar projects in the past from which they can learn. The team assesses the resources available to support the project in terms of people, technology, time, and data. Important activities in this phase include framing the business problem as an analytics challenge that can be addressed in subsequent phases and formulating initial hypotheses (IHs) to test and begin learning the data.
Phase 2—Data preparation: Phase 2 requires the presence of an analytic sandbox, in which the team can work with data and perform analytics for the duration of the project. The team needs to execute extract, load, and transform (ELT) or extract, transform and load (ETL) to get data into the sandbox. The ELT and ETL are sometimes abbreviated as ETLT. Data should be transformed in the ETLT process so the team can work with it and analyze it. In this phase, the team also needs to familiarize itself with the data thoroughly and take steps to condition the data.
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The document discusses simplifying analytics strategies for businesses dealing with big data. It identifies issues companies face in discovering opportunities in their data and achieving desired outcomes. It outlines various analytics technologies that can help including business intelligence, data visualization, data discovery, analytics applications, and machine learning. The key insights are that analytics solutions must provide the right data at the right time and place for users, allow users to test and discover patterns in data, and put analytics power in users' hands. It also notes there is no one-size-fits-all approach and strategies depend on a company's goals, technologies, data types, and culture. The document advocates for a simplified strategy to generate insights that lead to real outcomes through a hybrid technology environment
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Simplify your analytics strategy
This presentation discusses simplifying analytics strategies for businesses. It suggests that while interest in analytics is growing, some businesses are overwhelmed by the complexity. It recommends pursuing a simpler path to uncover insights from data to make informed decisions. Fast data processing can provide fast insights and outcomes. Next-gen business intelligence and data visualization can help decision-makers explore opportunities. Data discovery alongside projects can uncover new patterns. Machine learning can reduce human elements and improve predictions. Each company's analytics journey depends on its unique culture and existing technologies. Companies can take discovery-based or known solution approaches depending on the problem.
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While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate.
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Businesses are challenged by complex analytics that can generate confusion instead of insights. Companies should simplify their analytics strategy by focusing on the most important data first to uncover real business opportunities and achieve desired outcomes. They can do this by creating a hybrid data environment that enables fast data delivery and real-time analytics, speeding up decision making. Delegating analytics work to technologies like next-gen business intelligence and data visualization can help turn data into useful insights without needing to analyze everything possible.
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Phase 1—Discovery: In Phase 1, the team learns the business domain, including relevant history such as whether the organization or business unit has attempted similar projects in the past from which they can learn. The team assesses the resources available to support the project in terms of people, technology, time, and data. Important activities in this phase include framing the business problem as an analytics challenge that can be addressed in subsequent phases and formulating initial hypotheses (IHs) to test and begin learning the data.
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An analysis of the article 'Making advanced analytics work for you' by Dominic Barton and David Court.
W4 d5 - Simplify Your Analytics Strategy
The document discusses how businesses can simplify their analytics strategy to generate insights that lead to real outcomes. It recommends that companies accelerate data through emerging technologies to speed up insight generation and business outcomes. Next-gen business intelligence can help companies improve decision making by presenting data in a visually appealing way to enable data-driven opportunities. The document also discusses how applications and machine learning can simplify advanced analytics to put power in the hands of business users to make data-driven decisions. It emphasizes that each company's path to analytics insight is unique and should have an outcome-driven mindset.
Simplify our analytics strategy
This document discusses simplifying analytics strategies. It recommends pursuing a simpler path to insights by accelerating data through a hybrid data platform and emerging technologies. This allows for fast data, insights, and outcomes. Examples show how next-gen BI and data visualization, data discovery, and machine learning can delegate work to analytics technologies to more easily uncover patterns and opportunities. The document emphasizes that each path to insights is unique and may involve hypothesis-based or discovery-based approaches.
Simple data analytics
The document discusses simplifying analytics strategies. It recommends that companies focus on analyzing what is important for customers, stakeholders, and employees rather than trying to analyze all possible data. It also suggests using next-gen business intelligence, data visualization, data discovery techniques, and machine learning to gain fast insights from data and uncover real business opportunities. The key is recognizing that each path to data-driven insights is unique and placing action behind the insights.
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Analysis of 'making advanced analytics work for you'
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一比一原版(uob毕业证书)伯明翰大学毕业证如何办理
原版一模一样【微信：6496090 】【(uob毕业证书)伯明翰大学毕业证成绩单】【微信：6496090 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。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，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
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三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
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◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
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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
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办理(uob毕业证书)伯明翰大学毕业证【微信：6496090 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uob毕业证书)伯明翰大学毕业证【微信：6496090 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uob毕业证书)伯明翰大学毕业证【微信：6496090 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
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Telemetry Solution for Gaming (AWS Summit'24)
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
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一比一原版南昆士兰大学毕业证如何办理
原版一模一样【微信：741003700 】【南昆士兰大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。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，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
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、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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
办理南昆士兰大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理南昆士兰大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理南昆士兰大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理南昆士兰大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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原版一模一样【微信：741003700 】【渥太华大学毕业证（uottawa毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。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，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
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、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
一比一原版加拿大麦吉尔大学毕业证（mcgill毕业证书）如何办理
原版一模一样【微信：741003700 】【加拿大麦吉尔大学毕业证（mcgill毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。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，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
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、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
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一比一原版莱斯大学毕业证（rice毕业证）如何办理
原版一模一样【微信：741003700 】【莱斯大学毕业证（rice毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。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，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
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、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理莱斯大学毕业证（rice毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
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Simplify your analytics strategy
- 1. Data Analytics Internship under Prof. Sameer Mathur, Associate Professor of Marketing, IIM Lucknow Submitted by, Shivaprakash.B, NIT-Trichy
- 2. WEEK 4 DAY-5- Task 3-b Q1) List the two most (important / interesting / informative) insights from this article? Q2) Why and how are these insights relevant to a manager in India?
- 3. Q1) List the two most (important / interesting / informative) insights from this article?
- 4. Insight 1- Ways to simplify the analytics strategy • Businesses are challenged by the complexity and confusion generated by analytics. • Finding real time opportunities and achieving desired outcomes can be elusive. So, there are methods available simplify this analytics strategy.
- 5. • So, there are methods available simplify this analytics strategy: 1. Accelerate the data: o Fast data leads to fast insights and outcomes. A hybrid technology environment enables businesses to move, manage and mobilize the data. o Real-time delivery of analytics speeds up the execution velocity and improves the service quality of an organization.
- 6. 2. Delegating the work to the analytics technologies: There are 4 ways of delegating the work. They are: (a) Next-Gen Business Intelligence (BI) and data visualization: o BI gives life to data and analytics by optimizing the decision making.
- 8. o This is carried out by turning the organization’s data into an asset by having the right data, at the right time and place and displayed in the right visual form. (b) Data discovery: o By using data discovery techniques, companies can test their data to uncover data patterns that aren’t clearly evident.
- 9. o When more insights and patterns are discovered, more opportunities to drive value for the business can be found. (c) Analytics Applications: o Applications can simplify advanced analytics by putting the power of analytics elegantly into the hands of the business user to make data-driven business decisions. o They can also be industry-specific, flexible, and tailored to meet the needs of the individual users across organizations.
- 11. (d) Machine learning and cognitive computing: o With an influx of big data, and advances in processing power, data science and cognitive technology, software intelligence is helping machines make even better- informed decisions. o Machine learning can be used to target customers effectively in sales platforms.
- 12. Insight 2- The 2 types of approaches to solve a business problem • Each path to a data insight is different and unique. Also, another main component of a company’s analytics journey depends on the company’s culture itself.
- 13. • While determining which problem to address, companies should first focus on the one that can offer the highest value, then it can choose a hypothesis-based or discovery-based approach based on the degree of institutional knowledge it has to solve that kind of problem. • Once the insights are uncovered, the next step is for the business, of course, to make the data-driven decisions that place action behind the data.
- 14. • Companies can take two approaches depending on the nature of the business problem. They are: 1. Hypothesis-based approach: o For a known problem with a known solution, the company could take a hypothesis-based approach by starting with the outcome and test the solution with a control group and then scale broadly across the customer base.
- 15. 2. Discovery-based approach: o For a known problem with an unknown solution, companies should go with a discovery-based approach to look for patterns in the data to land up on interesting correlations.
- 16. Q2) Why and how are these insights relevant to a manager in India?
- 17. MANAGERIAL RELEVANCE • Managers should insist their company executives to make a hypothesis/ discovery-based approach to solve business problems. • They should make intellectual decisions by relying on Business Intelligence.
- 18. • Managers should be able to uncover the data patterns using data discovery methods. • Advanced analytics can help managers handle their inventories.