The best stats you've ever seen
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An assignment for an internship on Data Analytics with Managerial Applications under Professor Sameer Mathur, IIM Lucknow
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Analysis of "A Predictive Analytics Primer" by Tom Davenport
This document provides an overview of predictive analytics. It explains that predictive analysis uses past data to predict future outcomes. It emphasizes that the quality of the underlying data is crucial, as poor or unrepresentative data can negatively impact predictive models. The document also notes that assumptions used in models are important and can become invalid over time as behaviors change. It concludes by highlighting some key questions managers should ask analysts to better understand the limitations and validity of predictive analytics results.
Resume Data Analyst
Srijon Sarkar is a 4th year CSE student at Heritage Institute of Technology with a 9.23 GPA. His objective is to become a data analyst and he has skills in programming languages like Python, R, and Excel for data analytics. He has taken several online courses in Python, machine learning, and data science and completed projects analyzing bike share data, medical appointments, A/B tests, and Twitter data. His hobbies include cars and sports.
Analysis of "Data is Worthless if You Don’t Communicate It" by Thomas H. Dave...
There is a growing need for businesspeople who can analyze and make decisions based on data. While data is being generated in large quantities, it is often not used efficiently or effectively communicated. Properly analyzing, visualizing, and communicating data is important. Presenting data in a way that convinces others and gives meaning to the analysis is key. A simple framework for communicating includes outlining the business problem, measurement approach, available data, hypothesis, solution, and impact.
Steve Shellman - Past Projects of Strategic Analysis Enterprises, Inc.
Steve Shellman is the president and chief scientist of Strategic Analysis Enterprises, a company that provides statistical analysis and forecasting to government and corporate clients. Using social science theories and natural language processing, the company can predict future events like uprisings with over 90% accuracy. One notable past project was ICEWS, which was developed for DARPA to predict the likelihood of global violence using three components: iTrace analyzes news reports, iSent acquires data from blogs to replace polling, and iCast determines the probability of insurgencies.
Week3 day5slide
Statistical analysis can provide managers with useful insights if done properly. It allows companies to understand market trends from representative consumer data, avoiding reliance on assumptions. Statistics also enable quality control by measuring production processes to minimize variations and ensure consistency, reducing waste and warranty costs. However, statistics must be interpreted carefully as results can be influenced by flawed data or misused to push certain conclusions. The full context must be considered and statistical significance does not necessarily equal practical importance.
Five Tier - Drag and Drop Automated Summary Reporting Example
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Organizational Planing Tools Presentation
This document presents an overview of various organizational planning tools including a business plan for a proposed airline called Puddle Jumpers Airlines. The business plan outlines the management experience, proposed service area, and importance to stakeholders. Additional tools summarized are the 5 Whys technique, cost-benefit analysis, force-field analysis, six thinking hats, fishbone diagrams, decision trees, and a bibliography of references.
BIG DATA is DEAD | Marc Weimer-Hablitzel, Etventure | DN18
The document argues that "Big Data is Dead" and provides evidence for this claim. It outlines three signs that Big Data is failing: (1) most Big Data projects fail due to issues with data quality, lack of data, and lack of talent; (2) the focus should be on use cases and business impact rather than technology; (3) iterative testing and a customer-centric approach are more important than just collecting and storing large amounts of data. The key is to start small, test early, and iterate fast based on customer needs rather than what data is available. Impact should come before collecting large datasets.
Recommended
Analysis of "A Predictive Analytics Primer" by Tom Davenport
This document provides an overview of predictive analytics. It explains that predictive analysis uses past data to predict future outcomes. It emphasizes that the quality of the underlying data is crucial, as poor or unrepresentative data can negatively impact predictive models. The document also notes that assumptions used in models are important and can become invalid over time as behaviors change. It concludes by highlighting some key questions managers should ask analysts to better understand the limitations and validity of predictive analytics results.
Resume Data Analyst
Srijon Sarkar is a 4th year CSE student at Heritage Institute of Technology with a 9.23 GPA. His objective is to become a data analyst and he has skills in programming languages like Python, R, and Excel for data analytics. He has taken several online courses in Python, machine learning, and data science and completed projects analyzing bike share data, medical appointments, A/B tests, and Twitter data. His hobbies include cars and sports.
Analysis of "Data is Worthless if You Don’t Communicate It" by Thomas H. Dave...
There is a growing need for businesspeople who can analyze and make decisions based on data. While data is being generated in large quantities, it is often not used efficiently or effectively communicated. Properly analyzing, visualizing, and communicating data is important. Presenting data in a way that convinces others and gives meaning to the analysis is key. A simple framework for communicating includes outlining the business problem, measurement approach, available data, hypothesis, solution, and impact.
Steve Shellman - Past Projects of Strategic Analysis Enterprises, Inc.
Steve Shellman is the president and chief scientist of Strategic Analysis Enterprises, a company that provides statistical analysis and forecasting to government and corporate clients. Using social science theories and natural language processing, the company can predict future events like uprisings with over 90% accuracy. One notable past project was ICEWS, which was developed for DARPA to predict the likelihood of global violence using three components: iTrace analyzes news reports, iSent acquires data from blogs to replace polling, and iCast determines the probability of insurgencies.
Week3 day5slide
Statistical analysis can provide managers with useful insights if done properly. It allows companies to understand market trends from representative consumer data, avoiding reliance on assumptions. Statistics also enable quality control by measuring production processes to minimize variations and ensure consistency, reducing waste and warranty costs. However, statistics must be interpreted carefully as results can be influenced by flawed data or misused to push certain conclusions. The full context must be considered and statistical significance does not necessarily equal practical importance.
Five Tier - Drag and Drop Automated Summary Reporting Example
Automate reporting across all channels from inbound to outbound including paid, earned, owned, online and offline channels - only with Five Tier.
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This document presents an overview of various organizational planning tools including a business plan for a proposed airline called Puddle Jumpers Airlines. The business plan outlines the management experience, proposed service area, and importance to stakeholders. Additional tools summarized are the 5 Whys technique, cost-benefit analysis, force-field analysis, six thinking hats, fishbone diagrams, decision trees, and a bibliography of references.
BIG DATA is DEAD | Marc Weimer-Hablitzel, Etventure | DN18
The document argues that "Big Data is Dead" and provides evidence for this claim. It outlines three signs that Big Data is failing: (1) most Big Data projects fail due to issues with data quality, lack of data, and lack of talent; (2) the focus should be on use cases and business impact rather than technology; (3) iterative testing and a customer-centric approach are more important than just collecting and storing large amounts of data. The key is to start small, test early, and iterate fast based on customer needs rather than what data is available. Impact should come before collecting large datasets.
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The problem of hiring the data scientist and how difficult it sometimes is to hire the right data scientist and what steps a manager can take to overcome this
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.
The future of jobs
The document discusses a McKinsey report on the future of jobs and skills in light of advances in cognitive computing, AI, and machine learning. The report sought to answer questions about whether there will be enough work, which jobs will thrive or decline, and the implications for skills and wages. The document notes both good news and not-so-good news from the report. It recommends cultivating foresight, taking stock of industry changes, anticipating implications, and developing agility and resilience to navigate turbulent times. The rest of the document discusses anticipating possible futures by examining how different stages of the data and analytic lifecycles may change and the opportunities and skills needed.
Statistics vs machine learning: which is more powerful
Here is the best ever comparison between statistics and machine learning. Lets know which one is more powerful between these two.
Too Large To Fail: Large Samples and False Discoveries
Slides from Galit Shmueli's talk at the closing panel "Too Much Data + Too Much Statistics = Too Many Errors?" at the 2014 Israel Statistical Association symposium.
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https://generalassemb.ly/education/talk-data-to-me-learning-resources-books-for-data-analysts-and-scientists
The event featured 4 speakers (myself included), all of which were practitioners working in the field of data analytics. Each speaker was asked to share a little about their career path, and to share learning resources for others looking to break into the field.
Leland Lockhart - SXSW Intro to Data Science
Dr. Leland Lockhart gave an introductory presentation on data science. He defined data science as the application of scientific techniques to extract useful information from data, involving interdisciplinary problem solving. Data scientists solve problems using predictive modeling and work with data through primary phases including cleaning, modeling, and visualization. Their daily work involves cleaning and integrating messy data, evaluating counts and metrics, creating models and visualizations, troubleshooting issues, and fixing broken things as data and code evolve. Resources for getting started in data science include books, online courses, and open-source software like Python and R.
Beeline Analytics Workshop
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This panel of analytics experts explored how data driven analytics is defined by using insight drawn from a comprehensive and continuous assembly and evaluation of relevant information to aid in a decision or plan of action.
With a more continuous and dynamic collection of data organically prepares a company to proactively meet requirements before they become emergencies.
Learn how data can be harnessed to create actionable insight
Quantitative Information Architecture
Quantitative methods are applicable for IA thinking be it for hypothesis generation, instrumentation, data collection and analysis of information at scales never before possible with insights that are comparable over time, generalizable and extensible.
Quantitative skills can allow IAs to interpret and analyze others’ designs and research more readily, as well as combine methods and models for meta-analysis to help IAs move from description to prediction in designing and developing future interfaces and architectures.
This presentation will review why you should use quantitative methods and discuss both foundational and emerging ideas that are applicable for content analysis, behavioral modeling, social media usage, informetrics and other IA-related issues.
http://donturn.com or http://twitter.com/donturn
Modelling for decisions
The document discusses modeling techniques for business decision making. It introduces Simon Raper and his expertise in areas like statistics, simulation, machine learning and coding. The document then uses an example of determining a marketing budget to illustrate how to build a simple model quickly using available data and common sense assumptions. It discusses exploring the model dynamics through simulation, uncertainty through Monte Carlo simulation, and updating assumptions based on new data using Bayesian inference. The key is building a useful model that directly informs the decision, without unnecessary complexity or assumptions.
Data Analysis: Putting Data Capital to Work
This short deck explains the work of a modern data analyst. Putting data capital to work in the business enables decision quality & velocity, operating speed and growth
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Data Interoperability for Learning Analytics and Lifelong Learning
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WPIPosterPresentation24x36
The document describes a study that tested whether feedback could help everyday people better interpret data visualizations. Participants were asked to compare sections of pie charts and bar charts and estimate percentages. Some participants received feedback on their estimates of pie charts. Those who received feedback improved at interpreting pie charts over the course of the experiment compared to those who did not receive feedback, suggesting feedback can help develop data literacy skills.
Impact of ai on workplace
The document discusses how artificial intelligence will impact the future workplace. It provides examples of technologies like Humanyze employee badges and Hitachi's happiness meter that collect employee data through sensors to provide insights into team interactions, productivity, and satisfaction. It also discusses Workday's talent retention tool that predicts employee turnover using past and real-time data points. While AI can optimize workplace layouts, communications, and engagement, it also poses risks like privacy concerns if data is misused or creates an Orwellian work environment under authoritarian regimes.
Statistically Significant Quotes To Remember
This document contains a collection of quotes related to statistics and data. Some key quotes emphasize that while data and information are important, they must be used carefully and combined with human intelligence, judgement, and insight. Other quotes note that statistics can be flexible and misleading if not interpreted carefully, and that collecting quality data over long periods of time is important for analysis. The overall message is that statistics are a useful tool but have limitations, and human discernment is still needed.
The Data Errors we Make by Sean Taylor at Big Data Spain 2017
Where statistical errors come from, how they cause us to make bad decisions, and what to do about it.
https://www.bigdataspain.org/2017/talk/the-data-errors-we-make
Big Data Spain 2017
16th - 17th November Kinépolis Madrid
Actionable Machine Learning
This document discusses using machine learning to optimize future outcomes rather than just predict them. It explains that randomized studies are needed to accurately predict the effects of actions, but sometimes this is not possible with observational data alone. The document proposes techniques like transfer learning, common support analysis, and generative adversarial networks to help evaluate strategies without randomized trials by expanding the available data.
The best stats you've ever seen by hans rosling
Hans Rosling is a medical doctor and statistician who co-founded the Gapminder Foundation. He taught a class on global development where students had preconceived notions about statistics that failed to predict realities. Rosling argued there is a pressing need to make publicly available data more accessible by moving it out of databases and into searchable, visual formats. This allows people to more easily understand and use data to identify patterns and insights, helping to address common myths. Managers should analyze their company's non-confidential data and transform it into logical infographics to encourage employees to gather new insights.
Analysis of “what do you do with all this big data” –ted talk by susan etlinger
The document summarizes key points from a Ted Talk about managing big data. It notes that big data comes from many sources like social media, smartphones, and online activities. While big data can provide insights, it also needs to be interpreted carefully to avoid misinterpretations. Managers need to focus on critical thinking when analyzing big data and consider factors beyond just facts and figures to avoid misleading conclusions. Proper analysis and communication is needed to ensure insights are derived while maintaining public trust in how data is used and interpreted.
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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.
The future of jobs
The document discusses a McKinsey report on the future of jobs and skills in light of advances in cognitive computing, AI, and machine learning. The report sought to answer questions about whether there will be enough work, which jobs will thrive or decline, and the implications for skills and wages. The document notes both good news and not-so-good news from the report. It recommends cultivating foresight, taking stock of industry changes, anticipating implications, and developing agility and resilience to navigate turbulent times. The rest of the document discusses anticipating possible futures by examining how different stages of the data and analytic lifecycles may change and the opportunities and skills needed.
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Slides from Galit Shmueli's talk at the closing panel "Too Much Data + Too Much Statistics = Too Many Errors?" at the 2014 Israel Statistical Association symposium.
My Career in Data Analytics + Learning Resources for Data Scientists and Anal...
These are slides from a talk I gave at a General Assembly event hosted in Singapore on June 6th 2018, titled Talk Data to Me. Here's a link to the event page:
https://generalassemb.ly/education/talk-data-to-me-learning-resources-books-for-data-analysts-and-scientists
The event featured 4 speakers (myself included), all of which were practitioners working in the field of data analytics. Each speaker was asked to share a little about their career path, and to share learning resources for others looking to break into the field.
Leland Lockhart - SXSW Intro to Data Science
Dr. Leland Lockhart gave an introductory presentation on data science. He defined data science as the application of scientific techniques to extract useful information from data, involving interdisciplinary problem solving. Data scientists solve problems using predictive modeling and work with data through primary phases including cleaning, modeling, and visualization. Their daily work involves cleaning and integrating messy data, evaluating counts and metrics, creating models and visualizations, troubleshooting issues, and fixing broken things as data and code evolve. Resources for getting started in data science include books, online courses, and open-source software like Python and R.
Beeline Analytics Workshop
Martin Matula - Beeline, David Pesante - Beeline, Dylan Cotter - Tibco
This panel of analytics experts explored how data driven analytics is defined by using insight drawn from a comprehensive and continuous assembly and evaluation of relevant information to aid in a decision or plan of action.
With a more continuous and dynamic collection of data organically prepares a company to proactively meet requirements before they become emergencies.
Learn how data can be harnessed to create actionable insight
Quantitative Information Architecture
Quantitative methods are applicable for IA thinking be it for hypothesis generation, instrumentation, data collection and analysis of information at scales never before possible with insights that are comparable over time, generalizable and extensible.
Quantitative skills can allow IAs to interpret and analyze others’ designs and research more readily, as well as combine methods and models for meta-analysis to help IAs move from description to prediction in designing and developing future interfaces and architectures.
This presentation will review why you should use quantitative methods and discuss both foundational and emerging ideas that are applicable for content analysis, behavioral modeling, social media usage, informetrics and other IA-related issues.
http://donturn.com or http://twitter.com/donturn
Modelling for decisions
The document discusses modeling techniques for business decision making. It introduces Simon Raper and his expertise in areas like statistics, simulation, machine learning and coding. The document then uses an example of determining a marketing budget to illustrate how to build a simple model quickly using available data and common sense assumptions. It discusses exploring the model dynamics through simulation, uncertainty through Monte Carlo simulation, and updating assumptions based on new data using Bayesian inference. The key is building a useful model that directly informs the decision, without unnecessary complexity or assumptions.
Data Analysis: Putting Data Capital to Work
This short deck explains the work of a modern data analyst. Putting data capital to work in the business enables decision quality & velocity, operating speed and growth
Forecasting6
This document provides an overview of various quantitative forecasting techniques, including moving averages, trend analysis, exponential smoothing, ARIMA models, and econometric models. It describes when each technique is best used, their advantages and disadvantages, and provides examples. The techniques range from simple methods like moving averages to more complex approaches like ARIMA and econometric models, with the key being choosing the right technique based on the characteristics of the data and forecasting needs.
Data Interoperability for Learning Analytics and Lifelong Learning
This document discusses the need for data interoperability to enable lifelong learning analytics. It notes that currently most learning analytics focus on understanding and optimizing formal learning environments rather than the learner perspective across multiple contexts. The lack of interoperability between different education systems means data is often stored in incompatible ways, making analysis difficult. The document proposes using open standards like xAPI to better link learning data across systems and support personalized, lifelong learning through interoperable analysis.
AEC 2013 Big Data PG&E
This presentation explores the use of data to evaluate ergonomic risk factors and how PG&E collected this data to help create an algorithm that accurately predicts the risk of ergonomic discomfort.
Data Strategy and xAPI
This document discusses using a data strategy and the Experience API (xAPI) to analyze workforce performance data and improve business results. It argues that learning and development should be data-driven like other business functions. xAPI allows organizations to capture data on employee interactions, activities, and results in a standardized way. The document outlines developing a data strategy, including deciding what interactions and results to track. It also discusses using analytics and intelligence amplification to provide adaptive learning experiences based on employee data and predict future needs.
WPIPosterPresentation24x36
The document describes a study that tested whether feedback could help everyday people better interpret data visualizations. Participants were asked to compare sections of pie charts and bar charts and estimate percentages. Some participants received feedback on their estimates of pie charts. Those who received feedback improved at interpreting pie charts over the course of the experiment compared to those who did not receive feedback, suggesting feedback can help develop data literacy skills.
Impact of ai on workplace
The document discusses how artificial intelligence will impact the future workplace. It provides examples of technologies like Humanyze employee badges and Hitachi's happiness meter that collect employee data through sensors to provide insights into team interactions, productivity, and satisfaction. It also discusses Workday's talent retention tool that predicts employee turnover using past and real-time data points. While AI can optimize workplace layouts, communications, and engagement, it also poses risks like privacy concerns if data is misused or creates an Orwellian work environment under authoritarian regimes.
Statistically Significant Quotes To Remember
This document contains a collection of quotes related to statistics and data. Some key quotes emphasize that while data and information are important, they must be used carefully and combined with human intelligence, judgement, and insight. Other quotes note that statistics can be flexible and misleading if not interpreted carefully, and that collecting quality data over long periods of time is important for analysis. The overall message is that statistics are a useful tool but have limitations, and human discernment is still needed.
The Data Errors we Make by Sean Taylor at Big Data Spain 2017
Where statistical errors come from, how they cause us to make bad decisions, and what to do about it.
https://www.bigdataspain.org/2017/talk/the-data-errors-we-make
Big Data Spain 2017
16th - 17th November Kinépolis Madrid
Actionable Machine Learning
This document discusses using machine learning to optimize future outcomes rather than just predict them. It explains that randomized studies are needed to accurately predict the effects of actions, but sometimes this is not possible with observational data alone. The document proposes techniques like transfer learning, common support analysis, and generative adversarial networks to help evaluate strategies without randomized trials by expanding the available data.
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Information overload! The phrase alone is enough to strike terror into the hardiest of managers; it presages the breakdown of society as we know it and the failure of management to cope with change. The media constantly dissect the forthcoming collapse brought on by TMI ("Too Much Information"), even as they themselves pile up larger and larger dossiers on the subject, and we are frequently informed that it is our own damn fault that we are drowning in data, since we simply can't discriminate between the important stuff and everything else. Hence, the info-tsunami warning signs posted all along what we once so naively called the "information superhighway".
Of course, this is arrant nonsense -- human beings have been suffering from information overload in varying forms since about the time we hit the ground and found ourselves simultaneously running after the antelope and away from the lion. There's no question that the human mind has a limited capacity to process information, but after several million years we've gotten pretty good at figuring out how to handle a lot. The two basic tricks turn out to be distinguishing between short-term and long-term information storage, and "chunking" -- putting things in a limited number of baskets. This isn't primarily a course in the psychology of memory -- it's about information tools and systems -- but in fact the same things that make our information tools and systems work are the same things that have kept us near the antelopes and away from the lions (mostly) for the last million years or so. So we're beginning this course by thinking about information tools, what makes them like and unlike other kinds of tools, how the concept of a socio-technical system (in which social and behavioral functions shape results as much as does the technology itself) helps make sense of what we're facing, and why the technology just might win after all.
Let's start with a little historical review. Amy Blair has recently done a very intriguing summary of just why information overload isn't something that we, or still less our kids, dreamed up -- people have been drowning in data for ages regardless of the tools at their disposal:
Blair, A. (2010) Information Overload, Then and Now. The Chronicle of Higher Education Review. November 28. Retrieved November 15, 2010 from http://chronicle.com/article/Information-Overload-Then-and/125479/?sid=cr&utm_source=cr&utm_medium=en
We thought we had it all nailed down when the information theorists came up with their typology distinguishing between "data" (raw stuff), "information" (cooked stuff), and "knowledge" (cooked stuff that we've eaten). Thi.
How to start thinking like a data scientist
Data scientists spend most of their time preparing data by getting it into the right format, augmenting it, and checking for missing information. This is an ongoing process that is time-intensive. They also face challenges in applying domain expertise to solve problems and refining data for high-quality analytics. Valuable skills for data scientists include expertise in databases, software engineering, machine learning, statistics, and being inquisitive. For managers in India, data science is the fastest growing field and companies are looking to strengthen their data teams and hire people skilled in tools like R, Python, and Hadoop.
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Using a Big Data approach to adapt Market Strategies
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This document discusses how companies can use big data to adapt their market strategies in a volatile, uncertain, complex, and ambiguous (VUCA) world. It explains that big data allows for predictive, descriptive, and discovery analytics that can help companies anticipate issues, understand consequences, and identify opportunities. However, companies need to have an adaptable structure and decentralized data architecture to effectively leverage big data insights. Doing so will help companies better plan for alternative realities, manage risks, and foster change to remain competitive in a constantly changing environment.Why should you_love_statistics
This presentation analyses the beautiful TED Talk of Alan Smith on "Why should you love statistics". Gathering the insights and employing those insights is the major task of this presentation.
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Please accept this assignment 25 pages minimum double space courie.docx
Please accept this assignment 25 pages minimum double space courie.docx
Using a Big Data approach to adapt Market Strategies
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This document outlines how managers can start thinking like data scientists through a simple exercise. It recommends asking a question, defining relevant data to answer it, collecting that data, creating visualizations and summary statistics, and developing insights. This helps managers understand the importance of data analysis for effective decision making and gaining competitive advantages. The insights are relevant for Indian managers as it encourages incorporating data-driven thinking in their daily work for better outcomes.
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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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一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【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(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】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176555708】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176555708】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！
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】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176555708】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176555708】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(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！
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.
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
毕业原版【微信:41543339】【(牛布毕业证书)牛津布鲁克斯大学毕业证】【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！
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
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
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
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Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
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The best stats you've ever seen
- 1. ATEDTalk on THE BEST STATSYOU'VE EVER SEEN - by Hans Rosling
- 2. classroom experiment, Hans Rosling discovered something. Quoting him -
- 3. Yes, Data Studies can give funny results at times. It is not ignorance, but, preconceived ideas. There is a real need to communicate data using proper tools to ensure clarity. Rosling then used a software to study various aspects of the world Statistically, some of which are – (next slide please)
- 5. List the two most (important/interesting/ informative) insights from this TALK ?
- 6. The first insight is - ●Sophisticated software, design tools and technology can give us a very clear understanding about the socio-economic aspects of the world and how these factors are changing with variables like time. ● We need proper databases for the purpose
- 7. GapMinder – Rosling's non-profit venture
- 8. ●Errors will be minimal if the difference between the measure of the data elements is bigger than the weakness of the data. This helps us in application of big data analysis in several cases. ● It can be misleading to use average data when statistically analyzing the world because of the many differences among the countries.
- 9. Why and How are these insights relevant to a manager in India?
- 10. Indian Managers can conduct similar data studies state-wise, region-wise to gain more information about the diverse market, consumer preferences, etc. Using similar tools will help increase understanding and clarity about changing patterns.
- 12. The importance of right data and access to reliable data sources is evident here. Entire projects could be corrupted if correct values of measurement are not taken. This would lead to poor decisions in implementation of strategies. Making available public-funded statistics can also be considered.
- 14. Managers and their companies therefore, must invest in technology which helps analyze economies of countries. Running these analyses on a global level will help expand their market to international levels.
- 16. - PARUL VERMA