This is a talk I gave at Strata NYC 2011 about the contributions of applied economists to data science teams and how their analytical approach can differ from that of computer scientists (machine learning) and statisticians.
There Is A 90% Probability That Your Son Is Pregnant: Predicting The Future ...Health Catalyst
Predictive: Relating to or having the effect of predicting an event or result. Analytics: The systematic computational analysis of data or statistics. Together they make up one of the most popular topics in healthcare today. But predictive analytics is a means to an ends, and there is little good in predicting an event or result without a strategy for acting upon that event, when it happens. If, as the Robert Wood Johnson Foundation recently published, 80% of healthcare determinants fall outside of the healthcare delivery system as we traditionally define it, should we focus our predictive analytics on the traditional 20% of traditional healthcare delivery, or broaden our focus to the 80% that includes social and economic factors, physical environment, and lifestyle behaviors? What if our predictive models reveal to us that the highest risk variable to a patient’s length of life and quality of life is their economic status? Can an accountable care organization and patient centered medical home realistically do anything to reduce that risk, in reaction to the predictive model? Given the current availability and type of data in the healthcare ecosystem, and our organizational ability or inability to realistically intervene, where should we focus our predictive and interventional risk management strategies? There is enormous potential value in the application of predictive analytics to healthcare, but, in contrast to predicting the weather, credit risk, consumer purchasing habits, or college dropout rates, the data collection, and social and ethical complexities of applying predictive analytics in healthcare are significantly higher. This session will explore some of the less technical, more human interest and philosophical issues, associated with predictive analytics in healthcare, including the speaker’s experience prior to healthcare, in the US Air Force, National Security Agency, and manufacturing.
There Is A 90% Probability That Your Son Is Pregnant: Predicting The Future ...Health Catalyst
Predictive: Relating to or having the effect of predicting an event or result. Analytics: The systematic computational analysis of data or statistics. Together they make up one of the most popular topics in healthcare today. But predictive analytics is a means to an ends, and there is little good in predicting an event or result without a strategy for acting upon that event, when it happens. If, as the Robert Wood Johnson Foundation recently published, 80% of healthcare determinants fall outside of the healthcare delivery system as we traditionally define it, should we focus our predictive analytics on the traditional 20% of traditional healthcare delivery, or broaden our focus to the 80% that includes social and economic factors, physical environment, and lifestyle behaviors? What if our predictive models reveal to us that the highest risk variable to a patient’s length of life and quality of life is their economic status? Can an accountable care organization and patient centered medical home realistically do anything to reduce that risk, in reaction to the predictive model? Given the current availability and type of data in the healthcare ecosystem, and our organizational ability or inability to realistically intervene, where should we focus our predictive and interventional risk management strategies? There is enormous potential value in the application of predictive analytics to healthcare, but, in contrast to predicting the weather, credit risk, consumer purchasing habits, or college dropout rates, the data collection, and social and ethical complexities of applying predictive analytics in healthcare are significantly higher. This session will explore some of the less technical, more human interest and philosophical issues, associated with predictive analytics in healthcare, including the speaker’s experience prior to healthcare, in the US Air Force, National Security Agency, and manufacturing.
Data: Past, Present, and Future (Lecture 1, Spring 2018)chris wiggins
Slides from Lecture 1 of "Data: Past, Present, and Future",
Jan 17 2018.
New class on how data is impacting our professional, political, and personal realities. Taught by Profs Matt Jones and Chris Wiggins
Running head IDENTITY THEFT1IDENTITY THEFT 4Identit.docxwlynn1
Running head: IDENTITY THEFT
1
IDENTITY THEFT
4
Identity Theft
(Students name)
(Professors name)
(Course title)
(Date of submission)
Although there have been high rates of cases of the identity theft very little amount of information is known about the people who indulge in this type of crime. This paper has been researched to provide some information on the people who engage in this type of crime. To be able to accomplish this, various people have been evaluated and evaluated on their views regarding identity theft. The individuals who were interviewed have received sentencing and are serving their time in prison. The outcome has indicated that identity theft includes different people which include the low-level and the high-level people. The motivating factor which was singled out from the assessment is that the people engaging in identity theft were driven by the quick need of cash. They were able to use a different kind of techniques to be able to have access to the information which they were able to convert it to cash. For example, they were able to buy information, steal the information, or even being able to access it from those individual people who own the specific information (Andringa et al., 2018).
Through the development of different skills in computer science and computer technology; for example the computer system skills, the fraudsters on identity theft were able to accomplish their mission with success. Through the findings of this paper, it can be recommended that having well tested situational crime prevention methods can be very effective through the process of trying to reduce the identity theft through the process of trying to increase the employed efforts. However, also through the findings in this paper, this method may become ineffective at some point, due to the fact that new way can be discovered by the crime offenders. Having an assessment from the crime offenders and basing that information, this sample has been developed which is purposed at trying to do away with the excuses which may result to the few cases of having identity theft.
In the United States of America, identity theft has been able to grab the attention of the country as it has sort to become a very common economic an computer crime. Through the statistical analysis which have previously been conducted, there are many cases of people who have been complaining and have gone ahead to report the crimes. Although so many cases have been filed regarding the identity theft in a computer with the police, not much has been done for the purposes of trying to identify the how this fraud can be controlled with more effective computer methods. For this reason, I have come up with a research that is meant for the examination of those people who have engaged in identity theft to try and understand their own perspective why they do that. The main goal and purpose if provide information on how the process identity theft is conducted and how those people th.
Lab Write-Up Rubric Lab Write-Ups are worth 10 points tota.docxsmile790243
Lab Write-Up Rubric
Lab Write-Ups are worth 10 points total and are due at the beginning of lab each week.
Content (7 points)
Key issues and questions from the prompt are identified and answered
Incorporates a critical level of thinking
Reflects on course materials (i.e. readings, lectures, lab, etc.) and utilizes factually correct
information in order to provide a quality response
Grammar (2 points)
Proper sentence structure is used and sentences make sense/flow together
Provides clear, concise comments formatted in an easy to read style
Free of grammatical or spelling errors
Format (1 point)
Must be typed!
Minimum of 1 page in length, but does not exceed 3 pages
1 inch margins, 12 point font, double spaced
Only student name appears at the top
NOVEMBER 9, 2016
Why 2016 election polls missed their mark
BY ANDREW MERCER, CLAUDIA DEANE AND KYLEY MCGEENEY30 COMMENTS
The results of Tuesday’s presidential election came as a surprise to nearly everyone who
had been following the national and state election polling, which consistently projected
Hillary Clinton as defeating Donald Trump. Relying largely on opinion polls, election
forecasters put Clinton’s chance of winning at anywhere from 70% to as high as 99%,
and pegged her as the heavy favorite to win a number of states such as Pennsylvania
and Wisconsin that in the end were taken by Trump.
How could the polls have been so wrong about the state of the election?
There is a great deal of speculation but no clear answers as to the cause of the
disconnect, but there is one point of agreement: Across the board, polls underestimated
Trump’s level of support. With few exceptions, the final round of public polling showed
Clinton with a lead of 1 to 7 percentage points in the national popular vote. State-level
polling was more variable, but there were few instances where polls overstated Trump’s
support.
The fact that so many forecasts were off-target was particularly notable given the
increasingly wide variety of methodologies being tested and reported via the
mainstream media and other channels. The traditional telephone polls of recent decades
are now joined by increasing numbers of high profile, online probability and
nonprobability sample surveys, as well as prediction markets, all of which showed
similar errors.
Pollsters don’t have a clear diagnosis yet for the misfires, and it will likely be some time
before we know for sure what happened. There are, however, several possible
explanations for the misstep that many in the polling community will be talking about in
upcoming weeks.
One likely culprit is what pollsters refer to as nonresponse bias. This occurs when certain
kinds of people systematically do not respond to surveys despite equal opportunity
outreach to all parts of the electorate. We know that some groups – including the less
educated voters who were a key demographic f ...
What we see may not always be the reality and what we
presume as real may not be our observation always. In a democratic
set-up, this has often emerged as a reality. Democracies had always been subjected to criticism but it is astonishing to note how the
interplay of corrupt vision and changing social attitudes playing a
havoc in our democratic systems. This paper broadly investigates
the voting behavior and attitudes in response to sophisticated
tempting actions by political parties to pull voters. This research
demonstrates that higher the level of temptation combined with
many socio-economic perils leads to higher biasness towards
them. Participatory research, interviews, journals, publications,
and observation and media reporting have been studied, analyzed,
and scrutinized to discover how different poor and illiterate people
vote. Findings and results attribute a greater role of education,
financial liberty, backwardness, and awareness to political reality
in determining voting behavior.
Why UBI is Necessary to Restore Trust and Save DemocracyScott Santens
I created this slide deck for a presentation about the immediate need for unconditional universal basic income in order to restore people's trust in government and save democracy from falling to autocracy.
scottsantens.com
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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Data: Past, Present, and Future (Lecture 1, Spring 2018)chris wiggins
Slides from Lecture 1 of "Data: Past, Present, and Future",
Jan 17 2018.
New class on how data is impacting our professional, political, and personal realities. Taught by Profs Matt Jones and Chris Wiggins
Running head IDENTITY THEFT1IDENTITY THEFT 4Identit.docxwlynn1
Running head: IDENTITY THEFT
1
IDENTITY THEFT
4
Identity Theft
(Students name)
(Professors name)
(Course title)
(Date of submission)
Although there have been high rates of cases of the identity theft very little amount of information is known about the people who indulge in this type of crime. This paper has been researched to provide some information on the people who engage in this type of crime. To be able to accomplish this, various people have been evaluated and evaluated on their views regarding identity theft. The individuals who were interviewed have received sentencing and are serving their time in prison. The outcome has indicated that identity theft includes different people which include the low-level and the high-level people. The motivating factor which was singled out from the assessment is that the people engaging in identity theft were driven by the quick need of cash. They were able to use a different kind of techniques to be able to have access to the information which they were able to convert it to cash. For example, they were able to buy information, steal the information, or even being able to access it from those individual people who own the specific information (Andringa et al., 2018).
Through the development of different skills in computer science and computer technology; for example the computer system skills, the fraudsters on identity theft were able to accomplish their mission with success. Through the findings of this paper, it can be recommended that having well tested situational crime prevention methods can be very effective through the process of trying to reduce the identity theft through the process of trying to increase the employed efforts. However, also through the findings in this paper, this method may become ineffective at some point, due to the fact that new way can be discovered by the crime offenders. Having an assessment from the crime offenders and basing that information, this sample has been developed which is purposed at trying to do away with the excuses which may result to the few cases of having identity theft.
In the United States of America, identity theft has been able to grab the attention of the country as it has sort to become a very common economic an computer crime. Through the statistical analysis which have previously been conducted, there are many cases of people who have been complaining and have gone ahead to report the crimes. Although so many cases have been filed regarding the identity theft in a computer with the police, not much has been done for the purposes of trying to identify the how this fraud can be controlled with more effective computer methods. For this reason, I have come up with a research that is meant for the examination of those people who have engaged in identity theft to try and understand their own perspective why they do that. The main goal and purpose if provide information on how the process identity theft is conducted and how those people th.
Lab Write-Up Rubric Lab Write-Ups are worth 10 points tota.docxsmile790243
Lab Write-Up Rubric
Lab Write-Ups are worth 10 points total and are due at the beginning of lab each week.
Content (7 points)
Key issues and questions from the prompt are identified and answered
Incorporates a critical level of thinking
Reflects on course materials (i.e. readings, lectures, lab, etc.) and utilizes factually correct
information in order to provide a quality response
Grammar (2 points)
Proper sentence structure is used and sentences make sense/flow together
Provides clear, concise comments formatted in an easy to read style
Free of grammatical or spelling errors
Format (1 point)
Must be typed!
Minimum of 1 page in length, but does not exceed 3 pages
1 inch margins, 12 point font, double spaced
Only student name appears at the top
NOVEMBER 9, 2016
Why 2016 election polls missed their mark
BY ANDREW MERCER, CLAUDIA DEANE AND KYLEY MCGEENEY30 COMMENTS
The results of Tuesday’s presidential election came as a surprise to nearly everyone who
had been following the national and state election polling, which consistently projected
Hillary Clinton as defeating Donald Trump. Relying largely on opinion polls, election
forecasters put Clinton’s chance of winning at anywhere from 70% to as high as 99%,
and pegged her as the heavy favorite to win a number of states such as Pennsylvania
and Wisconsin that in the end were taken by Trump.
How could the polls have been so wrong about the state of the election?
There is a great deal of speculation but no clear answers as to the cause of the
disconnect, but there is one point of agreement: Across the board, polls underestimated
Trump’s level of support. With few exceptions, the final round of public polling showed
Clinton with a lead of 1 to 7 percentage points in the national popular vote. State-level
polling was more variable, but there were few instances where polls overstated Trump’s
support.
The fact that so many forecasts were off-target was particularly notable given the
increasingly wide variety of methodologies being tested and reported via the
mainstream media and other channels. The traditional telephone polls of recent decades
are now joined by increasing numbers of high profile, online probability and
nonprobability sample surveys, as well as prediction markets, all of which showed
similar errors.
Pollsters don’t have a clear diagnosis yet for the misfires, and it will likely be some time
before we know for sure what happened. There are, however, several possible
explanations for the misstep that many in the polling community will be talking about in
upcoming weeks.
One likely culprit is what pollsters refer to as nonresponse bias. This occurs when certain
kinds of people systematically do not respond to surveys despite equal opportunity
outreach to all parts of the electorate. We know that some groups – including the less
educated voters who were a key demographic f ...
What we see may not always be the reality and what we
presume as real may not be our observation always. In a democratic
set-up, this has often emerged as a reality. Democracies had always been subjected to criticism but it is astonishing to note how the
interplay of corrupt vision and changing social attitudes playing a
havoc in our democratic systems. This paper broadly investigates
the voting behavior and attitudes in response to sophisticated
tempting actions by political parties to pull voters. This research
demonstrates that higher the level of temptation combined with
many socio-economic perils leads to higher biasness towards
them. Participatory research, interviews, journals, publications,
and observation and media reporting have been studied, analyzed,
and scrutinized to discover how different poor and illiterate people
vote. Findings and results attribute a greater role of education,
financial liberty, backwardness, and awareness to political reality
in determining voting behavior.
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Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
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- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
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Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
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Elevating Tactical DDD Patterns Through Object Calisthenics
Data Science from the Perspective of an Applied Economist
1. Data Science from the Perspective of an Applied Economist Scott Nicholson – @scootrous
2. This Talk A 30 minute Applied Economics PhD Will make you a better data scientist Exhibits the value-add of econometrician on a data science team
3. Recent Research by Economists Why Do Mothers Breastfeed Girls Less than Boys? Evidence and Implications for Child Health in India Family Violence and Football: The Effect of Unexpected Emotional Cues on Violent Behavior Does Terrorism Work? Racial Discrimination Among NBA Referees The Effects of Lottery Prizes on Winners and Their Neighbors: Evidence from the Dutch Postcode Lottery
4. What Makes an Applied Economist? Intuition Methods Curiosity about human decision-making Attention to underlying mechanisms
5. If you care about prediction, think like a computer scientist. If you care about causality, think like an economist.
6. Gradations of Identifying Causal Relationships Randomized controlled experiments Natural experiments Regression discontinuity Panel data econometrics Instrumental variables
8. Natural Experiment How does having been a child soldier in Uganda affect lifetime earnings and likelihood of voting?
9. Natural Experiment How does a 100 point decrease in SAT score affect likelihood of entering a ‘top’ school?
10. Regression Discontinuity Does voting increase the likelihood of voting in the next election? Turnout rate in 2004 election Just eligible to vote in 2000 election Just NOT eligible to vote in 2000 election
11. Regression Discontinuity Does being a prisoner in a maximum security prison increase the likelihood of prisoner misconduct?
16. If you care about prediction, think like a computer scientist. If you care about causality, think like an economist.
17. Sources Blattman, Christoper; Jeannie Annan. 2010. The Consequences of Child Soldiering. The Review of Economics and Statistics, November 2010, 92(4): 882–898 Meredith, Marc. 2009. Persistence in Political Participation. Quarterly Journal of Political Science 4(3): 186-208 Richard A. Berk; Jan de Leeuw. 1999. An Evaluation of California's Inmate Classification System Using a Generalized Regression Discontinuity Design. Journal of the American Statistical Association, Vol. 94, No. 448. (Dec., 1999), pp. 1045-1052 Augenblick, Ned; Scott Nicholson. 2011. Ballot Position, Choice Fatigue, and Voter Behavior. Submitted, under review. http://faculty.haas.berkeley.edu/ned/Choice_Fatigue.pdf Photo credit (cats): Eric Cheng / Lytro
what i want to do...applied econ phd in less than 30 minuteswhat i'm going to talk about is a set of intuition and methodoligies that economists use to answer a certain set of questionsand in the process make you a better data scientist AND understand the contributions economists can make to DS teamsthe type of questions that we're going to talk about is teasing causation from correlationthe typical toolkit of data scientists of machine learning algorithms or fitting statistical models is insufficient for identifying causality from observational dataTypically we use A/B tests to send the right email, find the best UX, make the most $, but what if we can’t run an A/B test?if you can't run an A/B test, what are the options availble to you to get causation out of data?My perspective…about me
Economists are interested in a wide variety of topics where data can inform us of the world through better understanding incentives and individuals’ decision making processes.For applied economists doing these kinds of research, what is in their toolkit?
If you want to predict whether or not someone will vote or what a child’s score on a standardized test will be, think like a CS.To find causal effects of how changes to one variable affect another variable, think like an economist.You need to look for random variations in the data that allow you to identify causal effects, not just the prediction of what school a student will end up in.
Spectrum…Decreasing in confidence of gaining causality
This technique needs no explanation. We are all familiar with controlled experiments either in the lab, an email or a UX on the web. This is the gold standard when you have the ability/time/resources to construct the experiment. What if you only have observational data?What if you only have data from the past and need to disentangle causality from correlation?What if the experiment you want to run is not feasible or unethical?Example: examining the effects of pre-kindergarten classes on student achievement.
Natural experiment: treatment groups were assigned without researcher interventionAnother method for disentangling causality from correlation is to exploit natural variation in the data.Look for random sources of variation that are correlated with the outcome variable but uncorrelated with the explanatory variable (feature)What is the value of an extra 100 points on the SAT? We can follow outcomes of these students to find out.Email outageVoter fatigueServer outages, search results
Regression discontinuity: assignment to treatment/control determined by a threshold that is exogenously decided by external factorsQuestion: How much does voting in one election affect your likelihood of voting in the next election?Problem:Also correlated with age. Older people exhibit higher turnout.Selection issues for why people choose to voteVoting rights are in the constitution! Can’t randomly vary them.What if you turned 18 on the last day eligible voters were able to register for a presidential election. Let’s say 2008 where Obama really inspired a lot of young people. What if your friend turned 18 the day AFTER the final registration date. You were able to vote and your friend wasn’t. Turns out you are 1) more likely to vote in subsequent elections and 2) more likely to have the same party affiliation as who you voted for in that previous election.
QUESTIONDoes being assigned to a high-security prison make a prison more likely to engage in misconduct?PROBLEMMore dangerous prisoners tend to be assigned to higher-security prisonsSOLUTIONClassification score…similarly-dangerous prisoners, but sent to prisons of different security levelsIMPLEMENTInteract classification score with cutoff
Panel data: Following observations over time allows us to control for subject-specific (unobservable) effects Going further away from the gold standard of A/B testing and moving closer to establishing predictive power
The next level of gradations…QUESTIONDo voters tire and not vote on some contests as they move down the ballot?PROBLEMInfeasible to run a RCEContests less salient as you move down the ballotSome precincts may be more likely to just not vote SOLUTIONPanel data: Following observations over time allows us to control for subject-specific (unobservable) effects Plus: natural experiment allows us to observe a contest at different positions on the ballotThis one is actually a combination of panel data & natural experimentVoter fatigue confounded with lower information contests appearing further down the ballotSolutionFor the same state proposition, we observe variation in ballot position across voters in different precincts due to different sets of local offices on ballot. Controlling for some other stuff, we can estimate the causal effect from voter fatigue from moving a contest 1 position further down the ballot.MethodologyFixed and randomeffects estimators
Instrumental variables: For your predictor that is correlated with a confounding factor, find an “instrument” that is correlated with your predictor and dependent variable but not the confounding variableDisentangling causation from correlation really means that we need to deal with the confounding factor that is correlated with both our outcome variable and our explanatory variable. Finding an instrument means to find a variable that is correlated with the explanatory variable
At this slide, wrap it all up. Economists bring a specialized skill set to the table, think about causality before all else. Some skills gap but