Category Archives: Data Science
Wolters Kluwer shows how data is rewriting the future of audit – Business Wire
NEW YORK--(BUSINESS WIRE)--Colleen Knuff, Vice President of Product Management for Audit at Wolters Kluwer Tax & Accounting North America, will be leading an Education Lab session at the AICPA Engage Conference in Las Vegas, Nevada. In her session Increase Your Value as a Client Advisor, Colleen will explain why the AICPA emphasizes Audit Data Analytics (ADA) in reporting requirements, and how ADA tools create new possibilities for auditors to add value as advisors to their clients.
Additionally, on Tuesday, June 7, Knuff will be presenting a session Rewriting the Book on Audit during which attendees will learn how audit work is trending toward data-driven audits, or cloud-enabled data integration. She will explain the technologies behind Wolters Kluwers evolving data-driven audit and show how data and automation can be incorporated into the audit process to reduce risk, add value, and complete audits more quickly.
We provide diagnostics and coaching in our audit approach to guide auditors through the work they need to perform. Doing so creates a much more optimized and intelligent workflow that yields the profitable audit and satisfied client that every firm is looking for, says Knuff. Currently, you provide the audit opinion on financial statements, but wouldnt clients be so much happier if you also came back to them with data visualizations to say, Heres how you compare to your peers in your industry, or heres an analysis weve done over several years, along with where we think your business is going.
ADA tools like Wolters Kluwers TeamMate Analytics provide auditors with robust analytics without needing highly specialized technical training or data science expertise, making it possible to analyze even large amounts of data efficiently and effectively. Colleen will present key concepts during her presentation on Monday during the Education Lab session.
The biggest complaint I get from our auditors is that sample sizes are too big. But the reality is if that is what your risk assessment is producing, then you have to approach it in a different way. So there's a lot of savings with TeamMate Analytics because it digs into those transactions that fall outside the curve or the spectrum of normal, says Christopher ONeal, CPA and Partner at Roedl Management Inc.
Wolters Kluwers suite of cloud-based audit solutions is constructed with this same emphasis on data and cloud-enabled automation. With CCH Axcess Engagement (currently in an early adopter phase) and CCH Axcess Knowledge Coach (Wolters Kluwers proprietary risk-based audit methodology solution) serving as the foundation, additional tools like TeamMate Analytics round out the suite for fully integrated data and data analytics throughout the audit process.
Wolters Kluwer is innovating and investing in ideas like data-driven audit, which will help automate time consuming and highly manual tasks that are performed in almost every engagement. We are incorporating data analytics and harnessing those analytics to perform preliminary analysis on our clients, spot patterns and identify trends. Thats part of our Data-Driven Approach that were very, very excited about, says Knuff.
Visit booth 927 during the 2022 AICPA Engage Conference to learn more about the award-winning portfolio of tax, accounting, and audit solutions from Wolters Kluwer.
About Wolters Kluwer
Wolters Kluwer (WKL) is a global leader in professional information, software solutions, and services for the healthcare; tax and accounting; governance, risk and compliance; and legal and regulatory sectors. We help our customers make critical decisions every day by providing expert solutions that combine deep domain knowledge with advanced technology and services.
Wolters Kluwer reported 2021 annual revenues of 4.8 billion. The group serves customers in over 180 countries, maintains operations in over 40 countries, and employs approximately 19,800 people worldwide. The company is headquartered in Alphen aan den Rijn, the Netherlands.
Wolters Kluwer shares are listed on Euronext Amsterdam (WKL) and are included in the AEX and Euronext 100 indices. Wolters Kluwer has a sponsored Level 1 American Depositary Receipt (ADR) program. The ADRs are traded on the over-the-counter market in the U.S. (WTKWY).
For more information, visit http://www.wolterskluwer.com, follow us on Twitter, Facebook, LinkedIn, and YouTube.
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Wolters Kluwer shows how data is rewriting the future of audit - Business Wire
Modern immigrants’ children have climbed the economic ladder as fast as the Ellis Island generation – Princeton University
Long before Leah Boustan was a professor of economics at Princeton, she was a Princeton undergraduate putting the final touches on her senior thesis.
Working alongside her advisor, longtime professor Henry Hank Farber, Boustan published a 100-page research project that compared outcomes for students who dropped out of high school in the early 1960s with those who dropped out decades later.
"I cant remember the exact moment I decided to become an economic historian, but I remember telling Hank I was really interested in comparisons of cohorts over time," Boustan said. "That interest is the basis for a lot of my work even today."
Twenty-two years after graduating from Princeton, Boustan has published a book that uses troves of data and the latest innovations in data science to examine an issue Boustan considers "one of the most fraught issues in U.S. politics" both today and in the past: immigration.
Leah Boustan as a Princeton undergraduate with her father, Harlan Platt
Photo courtesy of Leah Boustan
Written with her longtime collaborator Ran Abramitzky of Stanford University, Streets of Gold: America's Untold Story of Immigrant Success introduces the public to more than a decade of her rigorous, empirical research on the personal and society-wide impacts of immigration.
Weaving together personal family stories including their own with insights from the data, Boustan and Abramitzky tell an uplifting story about the promise of immigration. One finding Boustan found particularly surprising is how well children of immigrants have done economically, both today and in the past.
The fact that children of immigrants who came from poor families in the 1980s moved up the economic ladder at the same pace as children of the Ellis Island generation that floored me," said Boustan.
One hundred years ago, Italy a major sending country of immigrants to the U.S. had about half the GDP per capita of the United States. Once in America, however, the sons of Italian immigrants rose up. Those who grew up in the 25th percentile of income distribution in the late 1800s earned enough as adults to be near the 60th percentile.
Today, children of immigrants from Nicaragua, which has about one-tenth the GDP per capita of the United States, see similar rates of economic mobility.
"Theres no reason that has to be true but it turned out to be," Boustan said. "It's something really remarkable we're able to see because of the data."
That data and the methodologies Boustan and Abramitzky developed to make use of it deserve almost as much attention as the findings.
In addition to working with and linking modern data like IRS tax records and birth certificate files, a partnership with the genealogy website Ancestry.com made it possible for Boustan and Abramitzky to automate searches and follow millions of families over more than 100 years of Census data. From there, they worked with audio recordings of historical interviews and congressional speeches, using machine-learning tools to analyze these texts and glean big-data insights.
The rigor of the research is one reason it's so groundbreaking, and a tradition Boustan can trace to her days as a Princeton undergraduate.
As a high school debate student interested in public policy, Boustan applied early decision to Princeton with the aim of declaring a concentration at the School of Public and International Affairs (SPIA). But her time learning from Professor Farber changed her mind.
"I took economics classes in order to major in SPIA, she said. One of those classes was ECO 313: Applied Econometrics with Hank Farber, where we used real data sets to answer questions. I fell in love with that class."
Boustan told Farber she wanted to spend a summer working in Washington, but he persuaded her to stay in Princeton instead to learn more about data analysis and to see how building expertise in a discipline like economics could help her produce the kind of policy-relevant work that legislators really need.
"So thats what I did, and I never really looked back," she said.
Boustan declared Economics as her concentration at Princeton and started spending her free time in the computer lab of the Industrial Relations (IR) Section a group widely known for training and supporting some of the most famous labor economists and empiricists in the field, including 2021 Nobel Laureates and Princeton alumni David Card and Joshua Angrist.
From there, she went on to earn a Ph.D. from Harvard. After several years teaching at the University of California Los Angeles, Boustan returned to Princeton in 2017.
As Boustan hits the road to talk about her new book, she's able to marvel at how things have come full circle. When Farber signed on as Boustan's undergraduate senior thesis advisor, he was the director of the IR Section. Last year, Boustan herself was given the title, an honor she doesn't take for granted.
The IR Section is a true intellectual community, she said. The faculty sit right beside the graduate students almost like in a lab and work closely together. And the research coming out of the Section is always connected to the real world, from minimum wage to unemployment to the immigration work that I have been doing.
For Farber, who says Boustan was one of the best undergraduate students he had the pleasure of teaching in his 30 years at Princeton, having Boustan as a colleague has been a source of some pride.
Farber also noted how Boustan, no longer the student, has herself excelled in role of advisor. Leah has really played a key role in guiding the IR Section not only on the research side, but on the teaching side as well, he said.
In addition to committing her time as an advisor to dozens of undergraduate and graduate students, Boustan recently took on the task of teaching Princetons Principles of Microeconomics course a popular class for undergraduates across a wide range of majors.
I taught that class for many years myself, said Farber. It was wonderful to see Leah make it her own and take it in a whole new direction. The reaction from students this year was very positive.
Boustan says her research and her role as an economic historian give her hope for the future of immigration policy.
Sometimes we feel so stuck. We feel polarized. Congress cant pass legislation. On immigration, weve been at a stalemate for 50 years. But you look at history, and you see weve had wild change. It reminds me Im living in one small moment in history.I think economic history helps us recognize the possibility of scope for change.
Specifically, she hopes much-needed policy change will come for the Dreamers, undocumented immigrants who came to the United States as children. "Our research shows the most optimistic vision of what the children of immigrants can achieve," she said.
Because it can take 30 to 40 years to follow children into the labor market, her research on modern immigration focuses on children born in the 1980s. These children lived in households who benefited from immigration amnesty programs during the Reagan administration. Boustan worries that studies of more recent immigrant arrivals many of whom are undocumented without any path to citizenship could produce less optimistic findings.
"Im worried about the next generation and what I'll find when we write Streets of Gold 2.0," she said. "Theres lots of promise for children of immigrants if they and their parents have some pathway into the formal labor market. I think its urgent to pass DACA as legislation and really return to the idea of comprehensive immigration reform."
Readers interested in learning more can read about five immigration myths dispelled in "Streets of Gold." This Thursday, June 9, at 8:30 p.m. ET, Professor Boustan will answer questions about her research in a Twitter Spaces event with Joey Politano, author of the Apricitas Economics blog. Join the event.
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How to Find Residuals in Regression Analysis – Built In
Regression models, both single and multivariate, are the backbone of many types of machine learning. Using the structure you specify, these tools create equations that match the modeled data set as closely as possible. Regression algorithms create the optimal equation by minimizing the error between the results predicted by the model and the provided data.
That said, no regression model will ever be perfect (and if your model does appear to be nearly perfect I recommend you check for overfitting). There will always be a difference between the values predicted by a regression model and the actual data. Those differences will change dramatically as you change the structure of the model, which is where residuals come into play.
The residual for a specific data point is the difference between the value predicted by the regression and the observed value for that data point. Calculating the residual provides a valuable clue into how well your model fits the data set. To calculate residuals we need to find the difference between the calculated value for the independent variable and the observed value for the independent variable.
The residual for a specific data point is the difference between the value predicted by the regression and the observed value for that data point. Calculating the residual provides a valuable clue into how well your model fits the data set. A poorly fit regression model will yield residuals for some data points that are very large, which indicates the model is not capturing a trend in the data set. A well-fit regression model will yield small residuals for all data points.
Lets talk about how to calculate residuals.
In order to calculate residuals we first need a data set for the example. We can create a fairly trivial data set using Pythons Pandas, NumPy and scikit-learn packages. You can use the following code to create a data set thats essentially y = x with some noise added to each point.
That code performs the following steps:
We can now use that data frame as our sample data set.
Want More Data Science Tutorials? We Got You.How to Use Float in Python (With Sample Code!)
The Dependent variable is our x data series, and the Independent variable is our y. Now we need a model which predicts y as a function of x. We can do that using scikit-learns linear regression model with the following code.
That code works as follows:
If the model perfectly matches the data set, then the values in the Calculated column will match the values in the Dependent column. We can plot the data to see if it does or not.
nope.
We could have seen that coming because we used a first-order linear regression model to match a data set with known noise in it. In other words, we know that this model would have perfectly fit y = x, but the variation we added in each data point made everyy a bit different from the corresponding x. Instead of perfection, we see gaps between the Regression line and the Data points. Those gaps are called the residuals. See the following plot which highlights the residual for the point at x = 4.
To calculate the residuals we need to find the difference between the calculated value for the independent variable and the observed value for the independent variable. In other words, we need to calculate the difference between the Calculated and Independent columns in our data frame. We can do so with the following code:
We can now plot the residuals to see how they vary across the data set. Heres an example of a plotted output:
Notice how some of the residuals are greater than zero and others are less than zero. This will always be the case! Since linear regression reduces the total error between the data and the model to zero the result must contain some errors less than zero to balance out the errors that are greater than zero.
You can also see how some of the errors are larger than others. Several of the residuals are in the +0.25 0.5 range, while others have an absolute value in the range of 0.75 1. These are the signs that you look for to ensure a model is well fit to the data. If theres a dramatic difference, such as a single point or a clustered group of points with a much larger residual, you know that your model has an issue. For example, if the residual at x = 4 was -5 that would be a clear sign of an issue. Note that a residual that large would probably indicate an outlier in the data set, and you should consider removing the point using interquartile range (IQR) methods.
More on IQR? Coming Right Up.How to Find Outliers With IQR Using Python
To highlight the argument that residuals can demonstrate a poor model fit, lets consider a second data set. To create the new data set I made two changes. The changed lines of code are as follows:
The first change increased the length of the data frame index to 100. This created a data set with 100 points, instead of the prior 10. The second change made the Dependent variable be a function of the Independent variable squared, creating a parabolic data set. Performing the same linear regression as before (not a single letter of code changed) and plotting the data presents the following:
Since this is just an example meant to demonstrate the point, we can already tell that the regression doesnt fit the data well. Theres an obvious curve to the data, but the regression is a single straight line. The regression under-predicts at the low and high ends, and over-predicts in the middle. We also know its going to be a poor fit because its a first-order linear regression on a parabolic data set.
That said, this visualization effectively demonstrates how examining the residuals can show a model with a poor fit. Consider the following plot, which I generated using the exact same code as the prior residuals plot.
Can you see the trend in the residuals? The residuals are very negative when the X Data is both low and high, indicating that the model is under-predicting the data at those points. The residuals are also positive when the X Data is around the midpoint, indicating the model is over-predicting the data in that range. Clearly the model is the wrong shape and, since the residuals curve only shows one inflection point, we can reasonably guess that we need to increase the order of the model by one (to two).
If we repeat the process using a second-order regression, we obtain the following residuals plot.
The only discernible pattern here is that the residuals increase as the X Data increases. Since the Dependent data includes noise, which is a function of the X Data, we expect that to happen. What we dont see are very large residuals, or indications of a different shape to the data set. This means we now have a model that fits the data set well.
And with that, youre all set to start evaluating the performance of your machine learning models by calculating and plotting residuals!
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City of Bloomington Partners with Google.org to Improve Access to Government Services – City of Bloomington
Bloomington, Ind.Mayor John Hamilton announced today that the City of Bloomington will receive pro bono support from a team of Google.org Fellows to deploy CiviForm, a tool to simplify and centralize online applications for government assistance programs. CiviForm is an open-source tool developed originally by the City of Seattle with support from Google.org to make applying for government programs easier and faster.
A team of 12 Google employees will work full-time with the City of Bloomington for six months as part of a Google.org Fellowship, providing pro bono technical expertise to nonprofits and civic entities. The City of Bloomington ITS Department (Information & Technology Services) will lead this initiative in partnership with Parks & Recreation.
The CiviForm pilot program will focus on improving the application processes for public benefits programs like the Parks Foundation Youth Scholarship program and the ITS Surplus Computer Request process. After the Fellowship ends, City staff can continue using CiviForm further to improve online access to other City services.
This partnership can help our residents access and apply for City programs. It can help City departments review applicants in an equitable and consistent manner. Thats a win-win, said Mayor Hamilton, and good local democracy into action.
Google and the City of Bloomington share a commitment to creating opportunity for everyone, said Rob Biederman, Director of External Affairs for Google. By bringing together the best of Googles tech expertise with the Citys knowledge of the communitys needs, we hope to simplify the benefits application process for Bloomington residents.
During a project coordinating site visit last week, at the direction of the City, Google researchers conducted user interviews with residents to gain a better understanding of customer needs and experiences related to program access and online applications. This input will help the City improve its online experience for customers.
PROJECT SUMMARY
The City of Bloomington views CiviForm (initially built through a Google.org Fellowship with the City of Seattle) as a means of supporting the Citys goal of providing sustainable, resilient, and equitable economic opportunity for all City residents by enabling residents to apply for City services.
Many Bloomington residents have limited awareness of City programs and must navigate complicated enrollment steps to apply -- some of which are still offline. Google.org Fellows will collaborate across City Departments to deploy CiviForm to enable low-income residents to enter their information once to apply to many programs securely and efficiently.
The City of Bloomingtons goals using CiviForm:
ABOUT GOOGLE.ORG FELLOWSHIP PROGRAM
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XSOLIS Supports Customers with Hospital at Home Legislative Advocacy and New Solutions – PR Newswire
Hospital Inpatient Services Modernization Act and Week of Action on June 6-10highlight evolving data insight needs for providers and payers
NASHVILLE, Tenn., June 6, 2022 /PRNewswire/ -- XSOLIS, the artificial intelligence (AI) technology company creating a more efficient healthcare system, announced today its support of the Hospital Inpatient Services Modernization Act, as it expands its CORTEXsolutions to support its customers with Hospital at Home programs. XSOLIS joins nearly 130 healthcare providers, payer organizations and technology companies in signing a letterto support the bipartisan legislation, which would extend Centers for Medicare & Medicaid Services' (CMS) waiver flexibilities for two years from the end of the COVID-19 Public Health Emergency. Spearheaded by the Advanced Care at Home Coalition, the organization has also designated June 6-10 as Hospital at Home Week of Action.
"COVID-19 accelerated the delivery of healthcare in non-traditional care settings. XSOLIS supports expansion of the CMS waiver and is committed to meeting our provider and payer customers where they need our solutions most, eliminating waste and ensuring appropriate care settings," said Joan Butters, XSOLIS co-founder and CEO. "Many goals of the Hospital at Home strategies directly align with our proven capabilities to reduce per capita healthcare costs while improving the care experience and outcomes. We look forward to exploring these and other solutions to best serve our customers' evolving needs."
While studies have shown that Hospital at Home programs have higher patient satisfaction, lower readmission rates, and reduced costs, efficiently identifying appropriate patients for care at home has been recognized as a challenge. In response to customer requests for Hospital at Home solutions, XSOLIS will target its innovation capabilities to the problem.
"The Hospital at Home movement presents a great opportunity to collaborate with our clients to understand their hospital at home operations, while furthering our mission to reduce friction, improve efficiency and re-center healthcare toward the patient, not the process," said Butters.
To learn more about XSOLIS' and CORTEX's capabilities, visit http://www.xsolis.com.
To learn about the Hospital Inpatient Services Modernization Act, visit https://www.achcoalition.org/ahcah-legislation-introduced-in-house-and-senate-and-over-110-organizations-sign-on-in-letter-urging-passage/.
To learn more about educational opportunities that are part of the Hospital at Home Week of Action, visit https://extendhospitalathome.com/schedule/.
About XSOLIS
XSOLIS is a platform, data science and solutions innovator serving health plans, hospitals and payer organizations nationwide to create a more efficient healthcare system. Through its purpose-built solutions and industry-leading AI, XSOLIS breaks down healthcare silos to accelerate data-driven decision making and collaboration across a connected network of providers and payers. CORTEX, its AI-driven technology platform, is the first and only solution to use real-time predictive analytics to continuously assign an objective medical necessity score and assess the anticipated level of care for every patient. CORTEX eliminates waste through the science of data using automation, transparency and objective insights to ensure appropriate care settings, enabling more efficiency across the healthcare system. XSOLIS is headquartered in Nashville, Tennessee. For more information, visit http://www.xsolis.com.
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XSOLIS Supports Customers with Hospital at Home Legislative Advocacy and New Solutions - PR Newswire
ELEVAI LABS, INC., IS PLEASED TO ANNOUCE CO-FOUNDER AND CEO, DR. JORDAN PLEWS, INVITED TO SPEAK AS LEADING KOL ON STEMCELL EXOSOMES AT THE 2022 BEAUTY…
"THE KOL MEETING FOR KOL'S"
DAVIS, Calif., June 6, 2022 /PRNewswire/ -- ELEVAI LABS, INC., ("ELEVAI" or the "Company") a science-based, data-driven regenerative aesthetic skincare company is pleased to announce that Co-Founder and CEO Dr. Jordan Plews has been selected to speak at the world-renowned Beauty Through Sciences Symposium as a Stem Cell Exosome Key Opinion Leader (KOL).
About the Beauty Through Science (BTS) Congress
The BTS Congress (Beauty Through Science) is one of the largest aesthetic medicine symposiums in Europe. The Beauty Through Science 2022 Congress is held June 8-11 in Stockholm, Sweden. BTS is a boutique meeting, covering both surgical and non-surgical aesthetics. Esteemed KOL speakers come from all over the world to share the latest insights and advancements. For more information and sign up visit: https://www.beautythroughscience.com/bts-stockholm-home
Dr. Jordan Plews stated: Innovation in Stem Cell Exosome Research has come a long way in the last 5-10 years. I'm honored to be invited to speak at this year's BTS Congress on the science and the opportunity for human stem cell derived exosomes in regenerative skin care applications. I look forward to meeting many other talented scientists and medical aesthetic professionals that share a passion for next generation regenerative technologies.
About ELEVAI LABS, INC.
ELEVAI LABS, INC. is a medical aesthetic biotechnology company developing cutting-edge regenerative skin care applications. The company solves the unmet needs in the regenerative aesthetics space through a combination of cutting-edge science and next-generation consumer applications. Elevai Labs develops state-of-the-art topical aesthetic and medical-grade skincare for the physician-dispensed market, with a focus on leveraging stem cell exosome technology. Learn more about Elevai Labs at http://www.elevaiskincare.com.
For further information: Kendra Ciardiello, Associate Director, [emailprotected], 1-866-794-4940 CO: ELEVAI Labs Inc.
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Why Hiring More Data Scientists Won’t Unlock the ROI of Your AI – InformationWeek
Enterprises have poured billions of dollars into artificial intelligence based on promises around increased automation, personalizing the customer experience at scale, or delivering more accurate predictions to drive revenue or optimize operating costs. As the expectations for these projects have grown, organizations have been hiring more and more data scientists to build ML models. But so far there has been a massive gap between AIs potential and the outcomes, with only about 10% of AI investments yielding significant ROI.
When I was part of the automated trading business for one of the top investment banks a decade ago, we saw that finding patterns in the data and building models (aka, algorithms) was the easier part vs. operationalizing the models. The hard part was quickly deploying the models against live market data, running them efficiently so the compute cost didnt outweigh the investment gains, and then measuring their performance so we could immediately pull the plug on any bad trading algorithms while continuously iterating and improving the best algorithms (generating P&L). This is what I call the last mile of machine learning.
Today, line of business leaders and chief data and analytics officers tell my team how they have reached the point that hiring more data scientists isnt producing business value. Yes, expert data scientists are needed to develop and improve machine learning algorithms. Yet, as we started asking questions to identify the blockers to extracting value from their AI, they quickly realized their bottleneck was actually at the last mile, after the initial model development.
As AI teams moved from development to production, data scientists were being asked to spend more and more time on infrastructure plumbing issues. In addition, they didn't have the tools to troubleshoot models that were in production or answer business questions about model performance, so they were also spending more and more time on ad hoc queries to gather and aggregate production data so they could at least do some basic analysis of the production models. The result was that models were taking days and weeks (or, for large, complex datasets, even months) to get into production, data science teams were flying blind in the production environment, and while the teams were growing they weren't doing the things they were really good at.
Data scientists excel at turning data into models that help solve business problems and make business decisions. But the expertise and skills required to build great models aren't the same skills needed to push those models in the real world with production-ready code, and then monitor and update on an ongoing basis.
ML engineers are responsible for integrating tools and frameworks together to ensure the data, data engineering pipelines, and key infrastructure are working cohesively to productionize ML models at scale. Adding these engineers to teams helps put the focus back on the model development and management for the data scientists and alleviates some of the pressures in AI teams. But even with the best ML engineers, enterprises face three major problems to scaling AI:
To really take their AI to the next level, todays enterprises need to focus on the people and tools that can productionize ML models at scale. This means shifting attention away from ever-expanding data science teams and taking a close look at where the true bottlenecks lie. Only then will they begin to see the business value they set out to achieve with their ML projects in the first place.
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Why Hiring More Data Scientists Won't Unlock the ROI of Your AI - InformationWeek
Training and Retaining Home-grown Data Scientists | UArizona Health Sciences – The University of Arizona Health Sciences |
Jonathan Lifshitz, PhD, is renowned for his research into traumatic brain injuries. He leads a neurotrauma and social impact research team that advances the national conversation on intimate partner violence-induced traumatic brain injuries and related topics. The success of the teams research projects, and the data obtained for those studies, has resulted in an unexpected problem.
I have a couple of projects that have been mothballed, admitted Dr. Lifshitz, a child health professor at the University of Arizona College of Medicine Phoenix. Ive had to rely on my colleagues goodwill to handle the level of data science these projects require. But sometimes it is hard to find people that have the time to help, even if they want to.
Dr. Lifshitzs experience is not unlike that of many researchers across the university.
We had more people sitting with data ready to be processed than we had people to help them process their data, explained Nirav Merchant, MS, director of the UArizona Data Science Institute and leader of Health Analytics Powerhouse, an initiative of UArizona Health Sciences.
A key part of the Health Analytics Powerhouse initiative is the Data Science Fellows program, which provides training and mentorship to UArizona postdoctoral scientists and doctoral candidates with a health sciences focus.
The goal of the program is workforce development, said Merchant, who is a member of the universitys BIO5 Institute. We want a workforce to be literate in software, data and machine learning so as we grow, we can reduce the strain on researchers.
Data science is more than just dealing with large amounts of data.
If youre working with a lot of data, youre just doing science, Merchant explained. Data science combines data sets and methods that allow you to connect things that are otherwise not easily connected.
For example, a researcher may want to develop a software program to evaluate and train health care providers on their interactions with consenting patients. One way to design the software is by analyzing audio recordings from the patient interactions, Merchant said.
Researchers with medical and clinical expertise could collaborate with speech and hearing scientists, who can help with natural language patterns, but it is unlikely either group will have the technical expertise to work with a software developer to create the program.
Someone trained in data science, however, can bridge these gaps in expertise. A data scientist has the tools to organize and analyze large quantities of data, such as what might be collected over thousands of hours of audio recordings. They can train the researcher how to better collect and organize data on the front end and how to interpret the data that are produced.
A lot of what happens in data science is team science, Merchant said. Expertise is so broad that you cannot expect one person to know everything. Having the diversity of expertise really elevates the kind of science we can do.
With the help of Luisa Rojas, a doctoral candidate in the UArizona Health Sciences Clinical Translational Sciences graduate program, Dr. Lifshitz has found someone capable and eager to get started on his mothballed projects.
Rojas, a native of Colombia, is in the second cohort of data science fellows that began in January. She is quickly gaining expertise and sharing that knowledge with Dr. Lifshitz and fellow researchers. Rojas even created a Slack channel called Data Science to share information she is learning with her peers.
She is inspiring others to embrace data science, Dr. Lifshitz said. Perhaps now we can make strides on the projects that stalled because we didnt have an analyst able or available to do the required work.
Dr. Lifshitz includes Rojas in meetings with his research teams so she can listen and provide relevant input when she sees opportunities to implement data science principles. Rojas is applying data science techniques to her own research, as well. For her translational dissertation, she is conducting research using the fecal microbiome to track the effects of therapeutic drugs for traumatic brain injury.
I am grateful for the fellowship because now I have a different mindset about data science, the need to think about all the tools, and how we apply these to research projects, Rojas said.
The second class of fellows also includes Lydia Jennings, PhD, a postdoctoral researcher in the Department of Community, Environment and Policy at the UArizona Mel and Enid Zuckerman College of Public Health. Dr. Jennings research focuses on data policy and governance of environmental databases in relationship to Indigenous communities.
Only a few months into the fellowship, Dr. Jennings is already seeing the benefits of the data science toolsets she is acquiring, which go far beyond organizing data.
I did not learn these skills in my doctoral program, but I really feel like this is the direction research is going, Dr. Jennings said. If youre going to be at the forefront of research, I think it is important to have these skills because they are going to be required for most of the funding opportunities going forward.
A lot of what happens in data science is team science.Nirav Merchant, MS
Dr. Jennings also offers a unique perspective for her peers in the Data Science Fellows program, according to her postdoctoral advisor, Stephanie Russo Carroll, DrPH, MPH, assistant professor of public health policy and management at the Zuckerman College of Public Health.
I think she is contributing more than most students are able to, in terms of thinking about the broader sets of principles that need to affect not only how we behave as data scientists, but how we create cyber infrastructure and how we set policies for interacting with that infrastructure, Dr. Carroll said.
Data science fellows are trained and mentored for one year, during which they meet for formal lecture and lab time twice weekly.
They are expected to spend some of their time in the Bioscience Research Laboratories Data Science Learning Space, where they work with other fellows, assisting on special projects, developing their data science and domain expertise, developing training material, and delivering workshops and webinars. They spend the rest of their time applying the tools and concepts to their laboratories and research projects.
The program already has its first success story. Gustavo de Oliveira Almeida, PhD, coordinator of the University of Arizona Health Sciences Sensor Lab, was among the first cohort of data science fellows. His fellowship focused on how multiple streams of data can be collected in real time to drive action. The experience prepared Dr. Almeida for his new position with the UArizona Sensor Lab, and now he is helping others across campus.
We did not lose this talent, Merchant said. That is our goal with each cohort. We want to find them a home at the university, where they can help multiple people. We are hoping to build a network of people across campus to collectively elevate data science and the best practices for research analysis.
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Bryant announces interdisciplinary School of Health and Behavioral Sciences, extends its high ROI academic programs to healthcare – Bryant University
SMITHFIELD, RI -Answering the call for impact thinkers equipped with critical 21st century skills, Bryants Vision 2030 Strategic Plan is expanding the Universitys academic programs that prepare real-world ready students for outcomes and economic mobility ranked in the nations top 1%.
A new School of Health and Behavioral Sciences with a unique interdisciplinary educationintegrating studies in health sciences, cognitive and behavioral sciences, data analytics and businessaddresses the growing demand for healthcare experts and prepares students for successful careers in data-rich STEM fields.
In establishing its School of Health and Behavioral Sciences, Bryant University is providing a uniquely focused education for future healthcare professionals, said Bryant President Ross Gittell, Ph.D. Bryants new programs at the intersection of health and behavioral sciences, data analytics and business will prepare students for exceptional career opportunities in fields including healthcare analytics, healthcare management and finance, and public and community health."
Data from the Bureau of Labor and Statistics indicates a growth rate of 16% percent for occupations in healthcare until 2030, more than triple the projected average employment growth rate of 5%, with projected earnings well above national and New England averages for college graduates.
Bryant is creating an ecosystem of experiential learning where students will develop the specialized skills and insights to provide employers and society with professionals who can enter the field directly and make immediate impact and contributions.
Bryants School of Health and Behavioral Sciences has a unique perspective on healthcare education that addresses the need for skilled professionals who can integrate knowledge between disciplines. We will prepare the next generation of healthcare professionals by building on our core strengths in business and leadership skills, data analytics and quantitative skills, and cognitive and psychological skills, said Provost and Chief Academic Officer Rupendra Paliwal, Ph.D.Bryant is creating an ecosystem of experiential learning where students will develop the specialized skills and insights to provide employers and society with professionals who can enter the field directly and make immediate impact and contributions.
Bryant has recently commissioned architectural design to establish a dedicated Science wing and expanded lab and classroom space within its landmark Unistructure facility.
"Bryant is poised to provide leaders who are dedicated to improving the health and well-being of others and embrace a collaborative and interdisciplinary approach to solving problems.
With the new School of Health and Behavioral Sciences and its new degrees, Bryant is poised to provide leaders who are dedicated to improving the health and well-being of others and embrace a collaborative and interdisciplinary approach to solving problems, said Kirsten Hokeness, Ph.D., Chair of the Biology Department who will be assuming the role of Director of Bryants School of Health and Behavioral Sciences. Healthcare is a data-intensive industry, she notes. Specialists who can transform data into meaningful insights for numerous and diverse stakeholders are urgently needed and in high demand.
Our new Bachelors degrees in Health Analytics and Exercise and Movement Science are a natural extension of Bryants core strengths."
Our new Bachelors degrees in Health Analytics and Exercise and Movement Science are a natural extension of Bryants core strengths, said Joseph Trunzo, Ph.D., Chair of the Psychology Department and a practicing clinical psychologist who will be assuming the role of Associate Director of the School. Bryants student-centered focus and strong interdisciplinary collaborations between faculty and students across the University empower Bryant graduates to excel. The new School and added majors will provide students with unparalled opportunities.
Bryant Universitys School of Health and Behavioral Sciences builds on the success of its School of Health Sciences, first established in 2014, and its fully-accredited Master of Science in Physician Assistant Studies (MSPAS) program and Center for Health and Behavioral Sciences. The new School unites undergraduate and graduate programs and complements the Universitys College of Business and College of Arts and Sciences. It also unifies new majors in some of the worlds fastest-growing fields, Healthcare Analytics and Exercise and Movement Science, with areas of strength in Bryants current Health Sciences, Biology and Psychology majors.
New majors in Healthcare Analytics and Exercise Science
Bryants Bachelor of Science in Healthcare Analytics degree is the first of its kind in Rhode Island.The interdisciplinary degree integrates health sciences, statistics, math and data science with behavioral science and is designed to provide students with real-world, specialized skills for careers in fields such as biomedical research and public health. At Bryant, Healthcare Analytics majors will have the flexibility to minor in a complementary discipline they select from either the College or Business or the College of Arts and Sciences.
The Bachelor of Science in Exercise and Movement Science draws upon the strength of the Universitys highly competitive NCAA Division I athletics program to prepare graduates forcertification as a Strength and Conditioning Specialist (CSCS) andsurging career opportunitiesin a wide array of fitness and athletic settings. The program develops the foundation to understand and analyze human movement, incorporating an appreciation of the overlapping influences from anatomical, physiological, psychological and neurological factors. Two tracks of study are available: the Applied Exercise and Coaching track, designed for students who plan to enter the workforce directly after graduation, and the Healthcare Provider Prep track, designed for those who plan to pursue graduate study in a healthcare or medical field. All students will select a complementary minor in the College of Business and complete an internship to gain real-world experience in a setting that aligns with their career goals.
About Bryant University
For nearly 160 years, Bryant University has been at the forefront of delivering an exceptional education that anticipates the future and prepares students to be innovative leaders of character in a changing world. The University delivers a uniquely integrated academic and student life experience with nationally recognized academic programs at the intersection of business, STEM fields and the liberal arts. Located on a 428-acre contemporary campus in Smithfield, R.I., Bryantis recognized as a top 1% national leader in student education outcomes and ROI and regularly receives high rankings fromU.S. News and World Report, Money, Bloomberg Businessweek, Wall Street Journal, College Factual and Barrons. Visit http://www.bryant.edu.
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Saving Furry Pets: Data Science Students to the Rescue – Newsroom | University of St. Thomas – University of St. Thomas Newsroom
As animal rescues work to recover from the pandemic, Secondhand Hounds in Minnetonka has a secret weapon: three data science graduate students from the University of St. Thomas.
When the pandemic hit in March 2020, thousands of families went searching for a new furry friend. Unfortunately, many of those pets are now being returned. As their human owners return to in-person work, pets may experience separation anxiety, making it more difficult for the owner to care for its needs.
Looking to cut down on surrenders and help keep pets in their loving homes, Secondhand Hounds turned to St. Thomas.
Working as part of a graduate data science capstone project, students were tasked with finding ways to collect and analyze Secondhand Hounds data from local surrenders.
When someone applies online to surrender an animal, theyre giving us a lot of information, said Maggie Schmitz, director of marketing for Secondhand Hounds. Prior to the St. Thomas project, we didnt have a super automated way of looking at this data.
St. Thomas graduate students Nanda, Sumit Panjwani 22 MS and Ola Sanusi 23 MS got to work, relying on lessons learned in the classroom and extra mentorship from local data engineering firm phData.
We started this project to better help them be able to preempt surrendering of pets by their owners, Sanusi said. If they can analyze that data theyll find out factoids that make people want to surrender their pets, which they can now try to mitigate and reduce surrender applications.
With a manual entry system now transformed by the students use of cloud technologies, Secondhand Hounds hopes to put the information it will glean from each surrender application toward new initiatives.
Most people who come to us would love another option than surrender, Carrie Openshaw, program director for Secondhand Hounds, said. If given another opportunity they would take a resource and go a different route.
Options include additional owner training or even therapy, training or medication for the pet to help calm anxiety.
The experience is considered a win-win from all sides. Secondhand Hounds is walking away with overhauled systems, while St. Thomas graduate students have enjoyed a walk in the real world.
"It was as life-changing for us as it was for them," Nanda said.
Were always looking for projects in the real world where students can apply whatever they have learned in class and go and solve a real-world problem, said Manjeet Rege, professor and chair of graduate programs in software and data science.
And the journey is far from over. Secondhand Hounds hopes to apply its new data collection system to other areas of its operation, while St. Thomas hopes to share the project more widely, so that other rescues might benefit in similar ways.
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