Databeers Dub #1 - Cathal Walsh - Statistics in the Big Data World
•
0 likes•193 views
Cathal Walsh is a chair in statistics and health decision science researcher who focuses on model calibration, evidence synthesis, and identifying decision makers' utilities. His work considers issues with big data such as whether conclusions can be drawn from all available data rather than just a sample, and the human decisions and biases that can impact modeling and data analysis. He questions assumptions around data collection and modeling in order to develop a more rigorous statistical framework.
Report
Share
Report
Share
Download to read offline
Recommended
Regression shrinkage: better answers to causal questions
The document discusses a presentation on regression shrinkage and its implications for causal inference in epidemiological research. The presentation argues that alternative statistical models to logistic regression, such as Firth's correction, are generally "better" as they reduce bias. Firth's correction shrinks estimated coefficients towards less extreme values, reducing finite sample bias compared to maximum likelihood estimation. Simulations show that Firth's correction reduces bias in estimated odds ratios from around 25% to approximately 3%.
Is it causal, is it prediction or is it neither?
This document discusses the differences between explanatory models, predictive models, and descriptive models. Explanatory models aim to understand causal relationships by examining regression coefficients and testing theories. Predictive models focus on predicting future observations without considering causality, and addressing overfitting. Descriptive models simply capture data structures. While the goals differ, the document notes problems like generalizability and model misspecification are common challenges. It provides examples of epidemiological and medical prediction models and emphasizes the need for external validation of predictive performance.
Development and evaluation of prediction models: pitfalls and solutions (Part...
Slides for the statistics in practice session for the Biometrisches Kolloquium (organized by the Deutsche Region der Internationalen Biometrischen Gesellschaft), 16 March 2021.
Part I from Maarten van Smeden: https://www.slideshare.net/MaartenvanSmeden/development-and-evaluation-of-prediction-models-pitfalls-and-solutions
Databeers Dub #1 - Krithika Ram - Customer Journey Analytics
This document discusses how adopting customer journey analytics can help improve the customer experience. It suggests companies need to move from providing information only when requested, to proactively sending the right content at the right time based on a deep understanding of customers. It provides an example of how one retail bank mapped customers' entire lifecycles, analyzed satisfaction data, re-designed fraud processes using new data streams, and improved customer satisfaction scores and reduced fraud costs.
The Journey to Exceptional Customer Experience
This Loras College Business Analytics Symposium breakout session presented by Cathy Carlson and Bruce Barchus of Vizability LLC described the journey on the way to exceptional customer experience. What does that have to do with analytics? Everything. Data-driven decisions are critical to optimizing the way your organization delights (or not) your customers. Challenging your organization to be great on an end-to-end basis is never ending. Welcome to the journey.
Participants received a guide for getting real world results, including a list of tools to help along the way.
Optimizing the Customer Journey
Modern businesses spend significant resources analyzing and optimizing the customer journey to understand how customers interact with their brand. However, customer journeys are complex as customers can interact across channels in unpredictable ways. The document discusses the need to view customer journeys from both a macro and micro level. It also identifies some key gaps brands should consider when evaluating customer journeys, such as visibility, interaction intelligence, sales conversion, and the overall experience. The document argues that technology is important to enable brands to actively shape journeys in real-time by surfacing the next best actions based on customer context and operationalizing real-time decisions through advanced analytics.
Customer Managed Journeys: Journey Analytics
Thunderhead argues that businesses should shift their focus from managing the customer journey to understanding the customer-managed journey. A customer's journey consists of interactions they choose across multiple channels and cannot be controlled by a business. However, businesses can gain insight into the actual customer journey by comparing it to journey maps and using data from real interactions to analyze customer behavior and take real-time action. This allows businesses to improve the customer experience at each stage of their personal journey.
Recommended
Regression shrinkage: better answers to causal questions
The document discusses a presentation on regression shrinkage and its implications for causal inference in epidemiological research. The presentation argues that alternative statistical models to logistic regression, such as Firth's correction, are generally "better" as they reduce bias. Firth's correction shrinks estimated coefficients towards less extreme values, reducing finite sample bias compared to maximum likelihood estimation. Simulations show that Firth's correction reduces bias in estimated odds ratios from around 25% to approximately 3%.
Is it causal, is it prediction or is it neither?
This document discusses the differences between explanatory models, predictive models, and descriptive models. Explanatory models aim to understand causal relationships by examining regression coefficients and testing theories. Predictive models focus on predicting future observations without considering causality, and addressing overfitting. Descriptive models simply capture data structures. While the goals differ, the document notes problems like generalizability and model misspecification are common challenges. It provides examples of epidemiological and medical prediction models and emphasizes the need for external validation of predictive performance.
Development and evaluation of prediction models: pitfalls and solutions (Part...
Slides for the statistics in practice session for the Biometrisches Kolloquium (organized by the Deutsche Region der Internationalen Biometrischen Gesellschaft), 16 March 2021.
Part I from Maarten van Smeden: https://www.slideshare.net/MaartenvanSmeden/development-and-evaluation-of-prediction-models-pitfalls-and-solutions
Databeers Dub #1 - Krithika Ram - Customer Journey Analytics
This document discusses how adopting customer journey analytics can help improve the customer experience. It suggests companies need to move from providing information only when requested, to proactively sending the right content at the right time based on a deep understanding of customers. It provides an example of how one retail bank mapped customers' entire lifecycles, analyzed satisfaction data, re-designed fraud processes using new data streams, and improved customer satisfaction scores and reduced fraud costs.
The Journey to Exceptional Customer Experience
This Loras College Business Analytics Symposium breakout session presented by Cathy Carlson and Bruce Barchus of Vizability LLC described the journey on the way to exceptional customer experience. What does that have to do with analytics? Everything. Data-driven decisions are critical to optimizing the way your organization delights (or not) your customers. Challenging your organization to be great on an end-to-end basis is never ending. Welcome to the journey.
Participants received a guide for getting real world results, including a list of tools to help along the way.
Optimizing the Customer Journey
Modern businesses spend significant resources analyzing and optimizing the customer journey to understand how customers interact with their brand. However, customer journeys are complex as customers can interact across channels in unpredictable ways. The document discusses the need to view customer journeys from both a macro and micro level. It also identifies some key gaps brands should consider when evaluating customer journeys, such as visibility, interaction intelligence, sales conversion, and the overall experience. The document argues that technology is important to enable brands to actively shape journeys in real-time by surfacing the next best actions based on customer context and operationalizing real-time decisions through advanced analytics.
Customer Managed Journeys: Journey Analytics
Thunderhead argues that businesses should shift their focus from managing the customer journey to understanding the customer-managed journey. A customer's journey consists of interactions they choose across multiple channels and cannot be controlled by a business. However, businesses can gain insight into the actual customer journey by comparing it to journey maps and using data from real interactions to analyze customer behavior and take real-time action. This allows businesses to improve the customer experience at each stage of their personal journey.
Data analytics and analysis trends in 2015 - Webinar
This document provides an overview of data analytics trends for 2015-2016 presented by James Graham, a principal consultant. It discusses the differences between big data and small data, as well as tools used for analyzing each. For big data, descriptive, predictive, and prescriptive analytics tools are covered. Five trends for 2015-2016 involving smart systems, deeper customer insights, data democratization, sensor-driven data growth, and HR analytics are also summarized. For small data, Pareto analysis, sensitivity analysis, and linear programming tools are briefly described. The presentation concludes with time for questions.
Introducing Journey Analytics
IBM introduces Journey Analytics, a solution to visualize the customer journey across channels and gain insights. It allows collaboration to design customer experiences. Journey Analytics uses interactions and audiences from multiple solutions and partners to provide a holistic view. This helps improve business outcomes by increasing loyalty through exceptional customer experiences. The solution is designed for marketing personas to better understand, engage and retain customers.
Logpickr Customer Journey Analytics
The document discusses Logpickr, a customer experience analytics platform that uses process mining, big data, and artificial intelligence techniques to analyze customer journeys across multiple touchpoints and channels. It visualizes the customer journey to provide insights into satisfaction, pain points, and opportunities for improvement. Logpickr helps companies understand customer behavior on digital and retail channels, identify issues with their website, customer service, or logistics. The platform's unique approach automatically rebuilds customer journeys and processes from operational data to improve customer experience.
Customer Journey Analyses: Requirements and Choices Digital Analytics Day 2014
This document discusses customer journey analysis, including:
- Defining customer journeys as the end-to-end steps a customer takes to purchase, inquire about, or experience a company.
- Requirements for analyzing customer journeys like needing to track multiple touchpoints over long periods of time, differentiate touchpoint types, and have high-quality data.
- Tools that can perform customer journey analysis, including web analytics tools, customer journey tools, and data warehouse platforms.
- Conclusions that organizations should evaluate their ability to meet requirements before selecting a tool in order to optimize budgets, campaigns, and personalization based on customer journey insights.
Understand Your Customer Buying Journey with Big Data
Imagine being able to use insights about your customer acquisition journey to design campaigns that improve conversion rates. What if you could identify points of failure along the customer acquisition path or during product usage?
Today more than ever the role of the marketing and product executives have become data-driven. Business executives who leverage data to understand prospect and customer behavior have gained an edge over their peers. Big Data analytics is the key to unlocking insights from your customer behavior data and empowers you to combine and analyze all of your customer interaction data to drive customer acquisition and loyalty.
Understanding Customer Buying Journey with Big Data
Big Data enables you to combine the vast amount of customer behavior data being generated from mobile, web, social media, transaction systems, Ads and turn them into new insights that drive customer acquisition and retention.
This talk will highlight and showcase how leading edge companies are leveraging big data to:
Combine customer interaction data to understand customer buying journey
Understand high-value customer behavior beyond profile segmentation
Identify the most common path to customer churn
Perform market basket analysis to help with cross-sell and up-sell
Speakers:
Matt Schumpert
Director of Product Management
Matt has been working in the enterprise infrastructure software space for 15 years in various capacities, including product management, sales engineering, sales leadership and strategic alliances. An early employee of Big Data pioneer Datameer, Matt is currently focused on driving the strategic direction and technology strategy for Datameer’s flagship Big Data analytics product. Previously, Matt built the sales engineering organization at Datameer, managed strategic partnerships at Oracle & BEA Systems in the US an Europe, and drove sales of business rules management software at ILOG (now IBM). Matt holds a BS in Computer Science from the University of Virginia.
Understanding User Journey using Google Analytics (360)
Using Google Analytics and Analytics 360 to understand Customer Journey.
Google Analytics User Conference 2016 Sydney and Melbourne
Customer Journey Analytics and Big Data
This document summarizes key insights from a McKinsey presentation on customer journey analytics and big data. It finds that companies are storing large amounts of data but few know how to extract value from it. Analyzing customer journeys rather than individual touchpoints provides more predictive insights into customer satisfaction and churn. Mapping important customer journeys in an industry reveals opportunities to improve the customer experience and reduce costs. The presentation provides an example of a retail bank that identified ways to decrease service costs and improve customer satisfaction by analyzing its customer journey data.
Glymour aaai
The document discusses the importance of causal frameworks and artificial intelligence (AI) for health research. It provides examples of how causal diagrams (DAGs) have been useful in health research by correcting an analytic mistake and evaluating survival bias. The document also presents a simulation study examining potential collider bias in racial disparities in stroke risk between middle and late life. The simulation compares different causal structures and finds bias only in structures where an unmeasured factor influences both mortality and stroke risk.
Explore, Analyze and Present your data
This document discusses the importance of properly analyzing and visualizing data when conducting statistical tests and reporting results. It recommends displaying raw data through dot plots instead of bar graphs to avoid concealing variance. The document discusses how the mean may not always be the best descriptor of data and how providing confidence intervals around measures provides important context about uncertainty. It also emphasizes choosing statistical tests wisely based on the characteristics of the data and justifying choices. Overall, the document stresses the importance of exploring data visually and using appropriate analyses and reporting to avoid drawing incorrect conclusions.
Stats Workshop2010
1. The document introduces statistical analysis and the scientific method, including deductive and inductive reasoning approaches.
2. It discusses experimental design, observational studies, and the importance of gathering sufficient data to draw meaningful conclusions.
3. Guidelines are provided for statistical analysis techniques including summarizing data, forming hypotheses to test, and using common fundamental methods such as t-tests, regression, and analysis of variance.
MAKING SENSE OFSTATISTICSWhat statistics tell you an.docx
This document provides an overview of key concepts for understanding statistics and outlines common pitfalls to avoid when interpreting statistics. It discusses how the questions asked and methods used to collect and analyze data impact the results. Specifically, it notes that statistics are shaped by human choices and judgment, and understanding what and how something was counted is crucial for properly interpreting a statistic. The document also describes common misuses of statistics like citing outliers as the norm, misusing averages, and not providing necessary context for large or small numbers. Overall, it aims to equip readers with questions to ask to critically evaluate statistics and arguments based on numbers.
What's the Science in Data Science? - Skipper Seabold
The gold standard for validating any scientific assumption is to run an experiment. Data science isn’t any different. Unfortunately, it’s not always possible to design the perfect experiment. In this talk, we’ll take a realistic look at measurement using tools from the social sciences to conduct quasi-experiments with observational data.
Mayo O&M slides (4-28-13)
The document discusses how statistical methodology and modeling can influence theories and findings. It notes that all statistical models are imperfect, statistical significance does not equal substantive significance, correlation does not imply causation, and data can be manipulated. Specifically, it warns about issues like cherry picking data, multiple testing inflating false positives, and nominal significance levels differing from actual error rates when accounting for selection effects. The document advocates evaluating statistical models based on how well they capture phenomena of interest and checking their adequacy despite violations of assumptions.
Research by MAGIC
This document provides guidance on key principles for conducting rigorous statistical analysis and research. It discusses the importance of clearly articulating the story being told with the data through use of graphs and tables. Variables of interest, outcomes, and potential confounding factors should be identified. The generalizability and interestingness of results are important to consider. Prospective studies are preferable to retrospective studies, which require consideration of multiple factors to establish credibility. Multiple statistical tests on a single data set require adjustments to avoid inflated false positive rates. Data collection and coding should be done consistently to allow for proper analysis. Overall, the document emphasizes the need for thoughtful statistical methodology to ensure useful and meaningful results.
Introduction to medical statistics
This document provides an introduction to medical statistics. It discusses why statistics are used in medical research, which is to collect, summarize, analyze and draw conclusions from sample data. It then describes different types of data (categorical, numerical) and statistical methods used to describe data, including percentages, mean, median, mode, standard deviation. Methods to test confidence such as confidence intervals and p-values are also introduced. Key concepts like null hypotheses and what confidence intervals say about significance are explained through examples.
assignment of statistics 2.pdf
This document provides an introduction to statistics. It discusses descriptive statistics, which summarize and describe data, versus inferential statistics, which make generalizations about a population based on a sample. Descriptive statistics include measures like percentages, averages, and tables to characterize data. Inferential statistics are used to compare treatment groups and determine whether observed differences could occur by chance or are likely due to the treatments. The document provides examples of statistics encountered in various fields and emphasizes the importance of understanding statistics to evaluate claims critically.
Introduction to Biostatistics.pptx
Biostatistics (also known as biometry) are the development and application of statistical methods to a wide range of topics in biology. It encompasses the design of biological experiments, the collection and analysis of data from those experiments and the interpretation of the results.
Basics of Research and Bias
This document discusses several key aspects of research and statistics:
1. It emphasizes that reliable evidence generally comes from multiple studies and research teams, and the totality of evidence matters most.
2. Statistics are useful for determining whether differences or associations are likely due to chance or represent real effects, but numbers can be easily manipulated.
3. Several types of biases and errors can influence research, decision making, memory, and social judgments. Rigorous methodology is important to produce high quality, accurate research and publications.
Overview of Statistical Concepts
This document provides an overview of an introductory course on statistical concepts at the University of South Florida. It outlines the course objectives, which are to identify the course structure, recap foundational statistics concepts, and identify the programming structure in SAS. The agenda covers topics like data analytics, probability, statistical inference, distributions, and SAS basics. It also discusses key statistical thinking concepts like variation, inference from data, and the relationship between data, information, knowledge and wisdom. Hypothesis testing and its errors and power are explained. Issues with correlated data are also covered.
Introduction to Research Methods in the Social Services 07
This document provides an introduction to different types of statistical data and how to analyze statistics. It discusses nominal and ordinal data, including dichotomous and non-dichotomous variables. Nominal data can be analyzed using mode, while ordinal data can use mode or median. It also discusses how sample sizes of 120+ are needed for good statistical analysis. Analyses often look at p-values to determine how close results are to the null hypothesis. The document includes links to TED talks applying statistics and encourages reflection on applying data analysis skills to personal and professional practice.
Thompson Sampling for Machine Learning - Ruben Mak
PyData Amsterdam 2018
In this talk I hope to give a clear overview of the opportunites for applying Thompson Sampling in machine learning. I will share some technical examples in recent developments (for example Bayesian Neural Networks using Edward) but more importantly I hope to trigger the audience to start thinking strategically about how we want our machine learning models to learn from new data.
More Related Content
Viewers also liked
Data analytics and analysis trends in 2015 - Webinar
This document provides an overview of data analytics trends for 2015-2016 presented by James Graham, a principal consultant. It discusses the differences between big data and small data, as well as tools used for analyzing each. For big data, descriptive, predictive, and prescriptive analytics tools are covered. Five trends for 2015-2016 involving smart systems, deeper customer insights, data democratization, sensor-driven data growth, and HR analytics are also summarized. For small data, Pareto analysis, sensitivity analysis, and linear programming tools are briefly described. The presentation concludes with time for questions.
Introducing Journey Analytics
IBM introduces Journey Analytics, a solution to visualize the customer journey across channels and gain insights. It allows collaboration to design customer experiences. Journey Analytics uses interactions and audiences from multiple solutions and partners to provide a holistic view. This helps improve business outcomes by increasing loyalty through exceptional customer experiences. The solution is designed for marketing personas to better understand, engage and retain customers.
Logpickr Customer Journey Analytics
The document discusses Logpickr, a customer experience analytics platform that uses process mining, big data, and artificial intelligence techniques to analyze customer journeys across multiple touchpoints and channels. It visualizes the customer journey to provide insights into satisfaction, pain points, and opportunities for improvement. Logpickr helps companies understand customer behavior on digital and retail channels, identify issues with their website, customer service, or logistics. The platform's unique approach automatically rebuilds customer journeys and processes from operational data to improve customer experience.
Customer Journey Analyses: Requirements and Choices Digital Analytics Day 2014
This document discusses customer journey analysis, including:
- Defining customer journeys as the end-to-end steps a customer takes to purchase, inquire about, or experience a company.
- Requirements for analyzing customer journeys like needing to track multiple touchpoints over long periods of time, differentiate touchpoint types, and have high-quality data.
- Tools that can perform customer journey analysis, including web analytics tools, customer journey tools, and data warehouse platforms.
- Conclusions that organizations should evaluate their ability to meet requirements before selecting a tool in order to optimize budgets, campaigns, and personalization based on customer journey insights.
Understand Your Customer Buying Journey with Big Data
Imagine being able to use insights about your customer acquisition journey to design campaigns that improve conversion rates. What if you could identify points of failure along the customer acquisition path or during product usage?
Today more than ever the role of the marketing and product executives have become data-driven. Business executives who leverage data to understand prospect and customer behavior have gained an edge over their peers. Big Data analytics is the key to unlocking insights from your customer behavior data and empowers you to combine and analyze all of your customer interaction data to drive customer acquisition and loyalty.
Understanding Customer Buying Journey with Big Data
Big Data enables you to combine the vast amount of customer behavior data being generated from mobile, web, social media, transaction systems, Ads and turn them into new insights that drive customer acquisition and retention.
This talk will highlight and showcase how leading edge companies are leveraging big data to:
Combine customer interaction data to understand customer buying journey
Understand high-value customer behavior beyond profile segmentation
Identify the most common path to customer churn
Perform market basket analysis to help with cross-sell and up-sell
Speakers:
Matt Schumpert
Director of Product Management
Matt has been working in the enterprise infrastructure software space for 15 years in various capacities, including product management, sales engineering, sales leadership and strategic alliances. An early employee of Big Data pioneer Datameer, Matt is currently focused on driving the strategic direction and technology strategy for Datameer’s flagship Big Data analytics product. Previously, Matt built the sales engineering organization at Datameer, managed strategic partnerships at Oracle & BEA Systems in the US an Europe, and drove sales of business rules management software at ILOG (now IBM). Matt holds a BS in Computer Science from the University of Virginia.
Understanding User Journey using Google Analytics (360)
Using Google Analytics and Analytics 360 to understand Customer Journey.
Google Analytics User Conference 2016 Sydney and Melbourne
Customer Journey Analytics and Big Data
This document summarizes key insights from a McKinsey presentation on customer journey analytics and big data. It finds that companies are storing large amounts of data but few know how to extract value from it. Analyzing customer journeys rather than individual touchpoints provides more predictive insights into customer satisfaction and churn. Mapping important customer journeys in an industry reveals opportunities to improve the customer experience and reduce costs. The presentation provides an example of a retail bank that identified ways to decrease service costs and improve customer satisfaction by analyzing its customer journey data.
Viewers also liked (8)
Data analytics and analysis trends in 2015 - Webinar
Data analytics and analysis trends in 2015 - Webinar
Customer Journey Analyses: Requirements and Choices Digital Analytics Day 2014
Customer Journey Analyses: Requirements and Choices Digital Analytics Day 2014
Understand Your Customer Buying Journey with Big Data
Understand Your Customer Buying Journey with Big Data
Understanding Customer Buying Journey with Big Data
Understanding Customer Buying Journey with Big Data
Understanding User Journey using Google Analytics (360)
Understanding User Journey using Google Analytics (360)
Similar to Databeers Dub #1 - Cathal Walsh - Statistics in the Big Data World
Glymour aaai
The document discusses the importance of causal frameworks and artificial intelligence (AI) for health research. It provides examples of how causal diagrams (DAGs) have been useful in health research by correcting an analytic mistake and evaluating survival bias. The document also presents a simulation study examining potential collider bias in racial disparities in stroke risk between middle and late life. The simulation compares different causal structures and finds bias only in structures where an unmeasured factor influences both mortality and stroke risk.
Explore, Analyze and Present your data
This document discusses the importance of properly analyzing and visualizing data when conducting statistical tests and reporting results. It recommends displaying raw data through dot plots instead of bar graphs to avoid concealing variance. The document discusses how the mean may not always be the best descriptor of data and how providing confidence intervals around measures provides important context about uncertainty. It also emphasizes choosing statistical tests wisely based on the characteristics of the data and justifying choices. Overall, the document stresses the importance of exploring data visually and using appropriate analyses and reporting to avoid drawing incorrect conclusions.
Stats Workshop2010
1. The document introduces statistical analysis and the scientific method, including deductive and inductive reasoning approaches.
2. It discusses experimental design, observational studies, and the importance of gathering sufficient data to draw meaningful conclusions.
3. Guidelines are provided for statistical analysis techniques including summarizing data, forming hypotheses to test, and using common fundamental methods such as t-tests, regression, and analysis of variance.
MAKING SENSE OFSTATISTICSWhat statistics tell you an.docx
This document provides an overview of key concepts for understanding statistics and outlines common pitfalls to avoid when interpreting statistics. It discusses how the questions asked and methods used to collect and analyze data impact the results. Specifically, it notes that statistics are shaped by human choices and judgment, and understanding what and how something was counted is crucial for properly interpreting a statistic. The document also describes common misuses of statistics like citing outliers as the norm, misusing averages, and not providing necessary context for large or small numbers. Overall, it aims to equip readers with questions to ask to critically evaluate statistics and arguments based on numbers.
What's the Science in Data Science? - Skipper Seabold
The gold standard for validating any scientific assumption is to run an experiment. Data science isn’t any different. Unfortunately, it’s not always possible to design the perfect experiment. In this talk, we’ll take a realistic look at measurement using tools from the social sciences to conduct quasi-experiments with observational data.
Mayo O&M slides (4-28-13)
The document discusses how statistical methodology and modeling can influence theories and findings. It notes that all statistical models are imperfect, statistical significance does not equal substantive significance, correlation does not imply causation, and data can be manipulated. Specifically, it warns about issues like cherry picking data, multiple testing inflating false positives, and nominal significance levels differing from actual error rates when accounting for selection effects. The document advocates evaluating statistical models based on how well they capture phenomena of interest and checking their adequacy despite violations of assumptions.
Research by MAGIC
This document provides guidance on key principles for conducting rigorous statistical analysis and research. It discusses the importance of clearly articulating the story being told with the data through use of graphs and tables. Variables of interest, outcomes, and potential confounding factors should be identified. The generalizability and interestingness of results are important to consider. Prospective studies are preferable to retrospective studies, which require consideration of multiple factors to establish credibility. Multiple statistical tests on a single data set require adjustments to avoid inflated false positive rates. Data collection and coding should be done consistently to allow for proper analysis. Overall, the document emphasizes the need for thoughtful statistical methodology to ensure useful and meaningful results.
Introduction to medical statistics
This document provides an introduction to medical statistics. It discusses why statistics are used in medical research, which is to collect, summarize, analyze and draw conclusions from sample data. It then describes different types of data (categorical, numerical) and statistical methods used to describe data, including percentages, mean, median, mode, standard deviation. Methods to test confidence such as confidence intervals and p-values are also introduced. Key concepts like null hypotheses and what confidence intervals say about significance are explained through examples.
assignment of statistics 2.pdf
This document provides an introduction to statistics. It discusses descriptive statistics, which summarize and describe data, versus inferential statistics, which make generalizations about a population based on a sample. Descriptive statistics include measures like percentages, averages, and tables to characterize data. Inferential statistics are used to compare treatment groups and determine whether observed differences could occur by chance or are likely due to the treatments. The document provides examples of statistics encountered in various fields and emphasizes the importance of understanding statistics to evaluate claims critically.
Introduction to Biostatistics.pptx
Biostatistics (also known as biometry) are the development and application of statistical methods to a wide range of topics in biology. It encompasses the design of biological experiments, the collection and analysis of data from those experiments and the interpretation of the results.
Basics of Research and Bias
This document discusses several key aspects of research and statistics:
1. It emphasizes that reliable evidence generally comes from multiple studies and research teams, and the totality of evidence matters most.
2. Statistics are useful for determining whether differences or associations are likely due to chance or represent real effects, but numbers can be easily manipulated.
3. Several types of biases and errors can influence research, decision making, memory, and social judgments. Rigorous methodology is important to produce high quality, accurate research and publications.
Overview of Statistical Concepts
This document provides an overview of an introductory course on statistical concepts at the University of South Florida. It outlines the course objectives, which are to identify the course structure, recap foundational statistics concepts, and identify the programming structure in SAS. The agenda covers topics like data analytics, probability, statistical inference, distributions, and SAS basics. It also discusses key statistical thinking concepts like variation, inference from data, and the relationship between data, information, knowledge and wisdom. Hypothesis testing and its errors and power are explained. Issues with correlated data are also covered.
Introduction to Research Methods in the Social Services 07
This document provides an introduction to different types of statistical data and how to analyze statistics. It discusses nominal and ordinal data, including dichotomous and non-dichotomous variables. Nominal data can be analyzed using mode, while ordinal data can use mode or median. It also discusses how sample sizes of 120+ are needed for good statistical analysis. Analyses often look at p-values to determine how close results are to the null hypothesis. The document includes links to TED talks applying statistics and encourages reflection on applying data analysis skills to personal and professional practice.
Thompson Sampling for Machine Learning - Ruben Mak
PyData Amsterdam 2018
In this talk I hope to give a clear overview of the opportunites for applying Thompson Sampling in machine learning. I will share some technical examples in recent developments (for example Bayesian Neural Networks using Edward) but more importantly I hope to trigger the audience to start thinking strategically about how we want our machine learning models to learn from new data.
June - Simulation for Health Economics Analysis
In this workshop Ron will discuss the benefits of discrete-event simulation for Health Economic investigations that are conducive for decision-making by payers and providers, and talk through a "Real-World" application example.
Today more than ever governments and health providers are under extreme pressure to reduce costs while maintaining patient quality of life. Accuracy of information presented to them is critical for acceptance. This workshop will justify the use of discrete-event simulation by health economists as a means to provide accurate assessments of value for medical products or services.
Dr. RM Pandey -Importance of Biostatistics in Biomedical Research.pptx
The document discusses the importance of biostatistics in biomedical research. It defines clinical research and outlines common issues and questions in biomedical research such as diagnosis, risk factors, and treatment effectiveness. It emphasizes that clinical expertise alone may not improve patient outcomes and that research should be aimed at improving patient lives. The document stresses that all studies should begin with a well-defined research question and overview types of study designs used in clinical research such as observational and interventional studies. It discusses key concepts in research including variables, biases, confounding, and validity and reliability of results.
Measurement in social science: quantitative and qualitative approaches
Measurement in social science: quantitative and qualitative approachesUniversity of Amsterdam and University College London
This document discusses approaches to measurement in social science, including quantitative and qualitative methods. It addresses conceptual issues around measuring abstract concepts and outlines methodological precepts. Two examples are provided: measuring socioeconomic status and age. While age is relatively easy to measure, socioeconomic status involves more controversy over indicators. The document advocates first using qualitative methods on small scales to observe practices before measuring on a large scale. It also argues that larger samples and more precise measurements are not always better, and qualitative research can provide important contextual interpretation to complement quantitative data.02 young vpi lecture 2014
This document summarizes issues with observational studies replicating claims about human health. It notes that while randomized clinical trials replicate over 80% of the time, observational studies only replicate 10-20% of the time. The document discusses how data staging, lack of analysis protocols, multiple testing, multiple modeling, and uncorrected bias can lead observational studies to produce essentially all positive results. It argues that funding agencies and journal editors need to implement management solutions like requiring data and analysis protocols to be posted publicly to improve the reliability of claims from observational studies.
Unit III - Statistical Process Control (SPC)
The seven tools of quality – Statistical Fundamentals – Measures of central Tendency and Dispersion, Population and Sample, Normal Curve, Control Charts for variables Xbar and R chart and attributes P, nP, C, and u charts, Industrial Examples, Process capability, Concept of six sigma – New seven Management tools.
Surviving statistics lecture 1
A presentation for medical students and residents in Jos, Nigeria. Brief orientation to a course in basic statistics.
Similar to Databeers Dub #1 - Cathal Walsh - Statistics in the Big Data World (20)
MAKING SENSE OFSTATISTICSWhat statistics tell you an.docx
MAKING SENSE OFSTATISTICSWhat statistics tell you an.docx
What's the Science in Data Science? - Skipper Seabold
What's the Science in Data Science? - Skipper Seabold
Introduction to Research Methods in the Social Services 07
Introduction to Research Methods in the Social Services 07
Thompson Sampling for Machine Learning - Ruben Mak
Thompson Sampling for Machine Learning - Ruben Mak
Dr. RM Pandey -Importance of Biostatistics in Biomedical Research.pptx
Dr. RM Pandey -Importance of Biostatistics in Biomedical Research.pptx
Measurement in social science: quantitative and qualitative approaches
Measurement in social science: quantitative and qualitative approaches
More from Databeers Dublin
Databeers Dub #6 - Colm Clancy - Numerical Weather Prediction at Met Eireann
This document discusses numerical weather prediction at Met Éireann. It describes Met Éireann's HARMONIE-AROME model which runs on a 1000 x 900 x 65 grid with 2.5km horizontal resolution and produces forecasts out to 54 hours. The model runs 4 times daily and produces around 350GB of data per run. An ensemble system is coming in 2020 which will have 11 members and 36-hour forecasts running twice daily, archiving around 150TB annually. It also describes post-processing of temperatures for Met.ie and freely available climate reanalysis data from the MÉRA project spanning 1981-2017 consisting of over 900TB of data.
Databeers Dub #6 - Alberto Caimo - Investigating the local dependencies of ne...
This document discusses using Bayesian methods to model and analyze network data, specifically whole-brain networks across the lifespan. It summarizes using exponential random graph models (ERGMs) to probabilistically describe network structure and generatively explain how links are formed. It then discusses challenges in Bayesian computation for ERGMs and introduces the Bergm package for simulation-based computation. Results show that whole-brain network structures remain stable across adulthood but that damage to hub nodes has stronger effects in people over 70 years old. The document concludes by noting various applications of Bayesian ERGM modeling through the Bergm package.
Databeers Dub #4 - Barry Smyth - Running with Data
The document discusses how data can help runners improve their performance by analyzing a large marathon dataset containing over 3 million race records. Some key insights from the data include that women and men benefit from different pacing strategies, many runners hit a wall around the 30km mark, and fast starts often lead to slow finishes. The data is also used to predict personal best times for individual runners based on their past race performance and recommend training plans to achieve those predicted times.
Databeers Dub #3 - Dave Tierney - Banking on you
Banks are changing to keep up with faster, more digital and open banking. They are focusing on customers, analytics, and innovation to remain competitive. Analytics allows banks to get to know individual customers better and provide personalized services, even for millions of customers, by understanding customer data behind the scenes and using that knowledge to offer financial advice and assistance to customers up front.
Databeers Dub #3 - Diarmuid Ryan - Data Den
This document contains snippets of text from various sources on a variety of topics including music, language, driving accidents, health accidents, religion, atheism, weather, hurricanes, climate change and bringing people together. The text discusses how roundabouts reduce driving accidents, how religiosity may impact health outcomes, hurricanes causing billions in damage in the US, and how climate change skepticism threatens research.
Databeers Dub #3 - Chiara Leva - The ironies of automation
This document discusses the ironies of automation and opportunities for human-machine interactions. It notes that while automation has benefits, it can also dull human senses, making operators less able to notice problems. The key is designing systems to accommodate and support human factors rather than substitute for them. It proposes using adaptive automation and data models to better understand humans and build close feedback loops between user actions and system effects. The goal is to leverage the best of human intuition and machine data processing to improve overall performance.
Databeers Dub #2 - James Gleeson - Mathematical modelling of cascades on netw...
This document discusses mathematical modeling of cascades on networks. It describes modeling techniques like branching processes that can be used to model how information spreads through social networks. Several examples are provided of modeling hashtag cascades on Twitter, the spreading of apps on Facebook, and retweet cascades from an Irish marriage referendum. The document lists collaborators from various universities and companies that contribute to this research.
Databeers Dub #2 - Brian Mac Namee - Deep Learning, A Different Kind of Machi...
The document discusses the differences and relationships between artificial intelligence, machine learning, and deep learning. It notes that while these fields overlap, there are some subtle differences. Machine learning is a type of artificial intelligence that allows systems to automatically learn and improve from experience, while deep learning is a subset of machine learning that utilizes neural networks with many layers to learn representations of data. The document argues that the definitions of these terms may change over time as the fields continue to evolve.
Databeers Dub #2 - Tim Willoughby - Mobility challenges in An Garda Siochana
This document discusses how mobile technology can help improve policing by moving away from paper-based reporting systems to digital systems accessible from any device. It recommends creating new mobile-friendly processes, designing systems around information needs rather than specific devices, and building flexible architectures. A case study is presented of an eVetting portal that has been accessed from over 150 countries, demonstrating how mobile technology can provide relevant information from multiple systems to officers in the field.
More from Databeers Dublin (9)
Databeers Dub #6 - Colm Clancy - Numerical Weather Prediction at Met Eireann
Databeers Dub #6 - Colm Clancy - Numerical Weather Prediction at Met Eireann
Databeers Dub #6 - Alberto Caimo - Investigating the local dependencies of ne...
Databeers Dub #6 - Alberto Caimo - Investigating the local dependencies of ne...
Databeers Dub #4 - Barry Smyth - Running with Data
Databeers Dub #4 - Barry Smyth - Running with Data
Databeers Dub #3 - Chiara Leva - The ironies of automation
Databeers Dub #3 - Chiara Leva - The ironies of automation
Databeers Dub #2 - James Gleeson - Mathematical modelling of cascades on netw...
Databeers Dub #2 - James Gleeson - Mathematical modelling of cascades on netw...
Databeers Dub #2 - Brian Mac Namee - Deep Learning, A Different Kind of Machi...
Databeers Dub #2 - Brian Mac Namee - Deep Learning, A Different Kind of Machi...
Databeers Dub #2 - Tim Willoughby - Mobility challenges in An Garda Siochana
Databeers Dub #2 - Tim Willoughby - Mobility challenges in An Garda Siochana
Recently uploaded
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
原版制作【微信:41543339】【(Unimelb毕业证书)墨尔本大学毕业证】【微信:41543339】《成绩单、外壳、雅思、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进口机器一比一制作，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！
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
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.
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
学校原件一模一样【微信：741003700 】《(unimelb毕业证书)墨尔本大学毕业证》【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
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）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，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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
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
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.
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【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(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！
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【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
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.
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
Recently uploaded (20)
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Global Situational Awareness of A.I. and where its headed
Global Situational Awareness of A.I. and where its headed
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
Databeers Dub #1 - Cathal Walsh - Statistics in the Big Data World
- 1. Cathal Walsh Chair in Statistics HRB Research Leader Health Decision Science Model Calibration Identify Utility of Decision Makers Evidence Synthesis BIG DATA ?
- 2. Real Decisions! “Just because they haven’t seen it there, doesn’t mean it isn’t present.” BIG DATA = = small data Model Free N=“All”
- 3. N=“All”? Others, however,especially pattern detection problems …, cannot be based on a sample. Moreover, in some cases one has the entire data set available (in the numerical examples above,all of the Wal- Mart transactions for a year will be available,not merely a sample of them). (Hand et al, 2000.) Who did the counting? What did they count, and why? How did they go about it? Statistics as construction (Best 2005)
- 4. Model Free? Human Decisions: Probability of Death S(T=5 | M,X, Diet) vs S(T=5 | M,X, No Diet) Boiled Model Syndrome Glimcher (2004)
- 5. Bias? (Meng 2016) Variability of Measure Sample Size Random Sampling? I = indicator variable for Sample
- 6. Framework revisited: Science / Industry Computing Maths / Stats
- 7. Prediction? Classification … Health Analytics