Category Archives: Data Science
ARM plans upgrades as it marks 30 years of collecting atmospheric data – EurekAlert
As the Department of Energys Atmospheric Radiation Measurement user facility marks 30 years of collecting continuous measurements of the Earths atmosphere this year, the ARM Data Center at Oak Ridge National Laboratory is shepherding changes to its operations to make the treasure trove of data more easily accessible and useful to scientists studying Earths climate around the world.
The observations, comprising more than 3.3 petabytes of data so thus far, start as raw data from more than 460 instruments worldwide. Observational measurements include daily records of temperature, wind speed, humidity, cloud cover, atmospheric particles called aerosols and dozens of other atmospheric processes that are critically important to weather and climate.
The team at the ARM Data Center refine the data so they are more useful to researchers and ensure their quality. In some cases, experts use these processed data to create higher-end data products that sharpen high-resolution models.
In the past 30 years, the multi-laboratory ARM facility has amassed more than 11,000 data products. Thats the capacity of about 50,000 smartphones, at 64 gigabytes per phone. With that much data on hand, ARM is taking steps over the next decade to upgrade its field measurements, data analytics, data-model interoperability and data services. Upgrades and aspirations are outlined in a 31-page Decadal Vision document, released last year.
ARM Data Services Manager Giri Prakash said that when he started at ORNL in 2002, ARM had about 16 terabytes of stored observational data.
I looked at that as big data, he said.
By 2010, the total was 200 terabytes. In 2016, ARM reached one petabyte of data.
Collecting those first 16 terabytes took nearly 10 years. Today, ARM, a DOE Office of Science user facility supported by nine national laboratories, collects that much data about every six days. Its data trove is growing at a rate of one petabyte a year.
Prakash credits this meteoric rise to more complex data, more sophisticated instruments, more high-resolution measurements (mostly from radars), more field campaigns and more high-resolution models.
Rethinking data management
How should all these data be handled?
We had to completely rethink our approach to data management and re-design much of it from the ground up, said Prakash. We need end-to-end data services competence to streamline and automate more of the data process. We refreshed almost 70 data-processing tools and workflows in the last four years.
That effort has brought recognition. Since 2020, the ARM Data Center has been recognized as a CoreTrustSeal repository, was named a DOE Office of Science PuRe (Public Reusable Research) Data Resource and earned membership in the World Data System.
All these important professional recognitions require a rigorous review process.
ARM is special, said Prakash, who represents the United States on the International Science Councils Committee on Data. We have an operationally robust and mature data service, which allows us to process quality data and distribute them to users.
ARM measurements, free to researchers worldwide, flow continuously from field instruments at six fixed and mobile observatories. The instruments operate in climate-critical regions across the world.
Jim Mather, ARM technical director at Pacific Northwest National Laboratory, said that as part of the Decadal Vision, increasingly complex ARM data will get a boost from emerging data management practices, hardware and software, which are increasingly sophisticated.
Data services, as the name suggests, said Mather, is in direct service to enable data analysis.
That service includes different kinds of ARM assets, he said, including physical infrastructure, software tools, and new policies and frameworks for software development.
Meanwhile, adds Prakash, ARM employs FAIR guidelines for its data management and stewardship. FAIR stands for Findability, Accessibility, Interoperability and Reuse. Following FAIR principles helps ensure that data are findable and useful for repeatable research as scientists increasingly rely on data digitization and artificial intelligence.
One step in ARMs decadal makeover will be to improve its operational and research computing infrastructure. Greater computing, memory and storage assets will make it easier to couple high-volume data sets from scanning radars, for instance with high-resolution models. More computing power and new software tools will also support machine learning and other techniques required by big-data science.
The ARM Data Center already supports the user facilitys computational and data-access needs. But the data center is being expanded to strengthen its present mix of high-performance and cloud computing resources by providing seamless access to data and computing.
Mather laid out the challenge: ARM has more than 2,500 active datastreams rolling in from its hundreds of instruments. Processing bottlenecks are possible when you add the pressure of those datastreams to the challenge of managing petabytes of information. In all, volumes like that could mean it is harder to make science advances with ARM data.
To get around that, in the realm of computing hardware, said Mather, ARM will provide more powerful computation services for data processed and stored at the ARM Data Center.
The need continues to grow
Some of that ramped-up computing power came online in the last few years to support a new ARM modeling framework, where large-eddy simulations, or LES, require a lot of computational horsepower.
So far, the LES ARM Symbiotic Simulation and Observation, or LASSO, activity has created a large library of simulations informed by ARM data. These exhaustively screened and streamlined data bundles, to atmospheric researchers, are proxies of the atmosphere. For example, they make it easier to test the accuracy of climate models.
Conceived in 2015, LASSO first focused on shallow cumulus clouds. Now, data bundles are being developed for a deep-convection scenario. Some of those data were made available through a beta release in May 2022.
Still, the need continues to grow for more computing power, said Mather. Looking ahead, we need to continually assess the magnitude and nature of the computing need.
ARM has a new Cumulus high-performance computing cluster at the Oak Ridge Leadership Computing Facility, which provides more than 16,000 processing cores to ARM users. The average laptop has four to six cores.
As needed, ARM users can apply for more computing power at other DOE facilities, such as the National Energy Research Scientific Computing Center. Access to external cloud computing resources is also available through DOE.
Prakash envisions a menu of user-friendly tools, including Jupyter Notebook, available to ARM users to work with ARM data. The tools are designed for users to transition from a laptop or workstation while they access petabytes of ARM data at a time.
Prakash said, Our aim is to provide ARM data, wherever the computer power is available.
Developing a data workbench
Software tools are also critical, says Mather. We expect single cases of upcoming (LASSO) simulations of deep convection to be on the order of 100 terabytes each. Mining those data will require sophisticated tools to visualize, filter and manipulate data.
Imagine, for instance, he said, LASSO trying to visualize convective cloud fields in three dimensions. Its a daunting software challenge.
Challenges like that require more engagement than ever with the atmospheric research community to identify the right software tools.
More engagement helped shape the Decadal Vision document. To gather information for it, Mather drew from workshops and direct contact with users and staff to cull ideas on increasing ARMs science impact.
Given the growth in data volume, there was a clear need to give a broader audience of data users even more seamless access to the ARM Data Centers resources. They already have access to ARM data, analytics, computing resources and databases. ARM data users can also select data by date range or conditional statements.
For deeper access, ARM is developing an ARM Data Workbench.
Prakash envisions the workbench as an extension of the current Data Discovery interfaceone that will provide transformative knowledge discovery by offering an integrated data-computing ecosystem. It would allow users to discover data of interest using advanced data queries. Users could perform advanced data analytics by using ARMs vast trove of data as well as software tools and computing resources.
The workbench will allow users to tap into open-source visualization and analytic tools. Open-source code, free to anyone, can also be redistributed or modified. They could also use technologies such as Apache Cassandra or Apache Spark for large-scale data analytics.
By early 2023, said Prakash, a preliminary version of the workbench will be online. Getting there will require more hours of consultations with ARM data users to nail down their workbench needs.
From that point on, he adds, the workbench will be continuously developed until the end of fiscal year 2023.
Prakash calls the workbench, with its enhanced access and open-source tools, a revolutionary way to interact with ARM data.
ARM recently restructured its open-source code capabilities and has added data service organizations on the software-sharing site GitHub.
Within ARM, we have a limited capacity to develop the processing and analysis codes that are needed, said Mather. But these open-source software practices offer a way for us to pool our development resources to implement the best ideas and minimize any duplication of effort.
In the end, he added, this is all about enhancing the impact of ARM data.
UT-Battelle manages ORNL for the Department of Energys Office of Science, the single largest supporter of basic research in the physical sciences in the United States. The Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Editors note: Adapted from an article by Corydon Ireland of Pacific Northwest National Laboratory, where ARM is headquartered.
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ARM plans upgrades as it marks 30 years of collecting atmospheric data - EurekAlert
Aunalytics to Showcase its Daybreak for Financial Services Solution at Illinois, Southeast, Ohio, and Michigan Banking Events in June – GlobeNewswire
SOUTH BEND, Ind., June 08, 2022 (GLOBE NEWSWIRE) -- Aunalytics, a leading data platform company delivering Insights-as-a-Service for mid-market businesses, today announced its participation at the Illinois Bankers Association Annual Conference, June 8-9, Southeast Credit Union Conference & Expo, June 15-17, Ohio Bankers League 2022 Convention, June 16-19, and the Michigan Bankers Association Annual Convention, June 22-24. The company will showcase its DaybreakTM for Financial Services solution, designed to help mid-market banks leverage artificial intelligence (AI)-powered data analytics to compete more effectively against their large, national counterparts. Aunalytics is also sponsoring the New York Credit Union Association's EXCEL22 Annual Meeting & Convention, June 16-19.
Personalized marketing in a digital world matters more than ever before, especially for mid-market banks that have traditionally relied on hometown, white glove service to win customers, said Katie Horvath, Chief Marketing Officer of Aunalytics. With Aunalytics Daybreak for Financial Services, midsize financial institutions can target-market more efficiently, reach high-value customers with the right product offering, and win business away from competitors to expand value. We look forward to meeting with bankers and credit unions from Ohio, Michigan, and the southeast, and demonstrating how Daybreak for Financial Services can help them strengthen their position in regional markets and compete more effectively.
Daybreak for Financial Services enables midsize financial institutions to gain customer intelligence and grow their lifetime value, predict churn, determine which products to introduce to customers and when, based upon deep learning models that are informed by data. Built from the ground up, Daybreak for Financial Services is a cloud-native data platform that enables users to focus on critical business outcomes. The solution seamlessly integrates and cleanses data for accuracy, ensures data governance, and employs artificial intelligence (AI) and machine learning (ML) driven analytics to glean customer intelligence and timely actionable insights that drive strategic value.
Tweet this: .@Aunalytics to Showcase its Daybreak for Financial Services Solution at Illinois, Southeast, Ohio, and Michigan Banking Events in June #FinancialServices #Banks #CreditUnions #Dataplatform #DataAnalytics #Dataintegration #Dataaccuracy #AdvancedAnalytics #ArtificialIntelligence #AI #Masterdatamanagement #MDM #DataScientist #MachineLearning #ML #DigitalTransformation #FinancialServices
About AunalyticsAunalytics is a data platform company delivering answers for your business. Named a Digital Innovator by analyst firm Intellyx, and selected for the prestigious Inc. 5000 list, Aunalytics provides Insights-as-a-Service to answer enterprise and mid-sized companies most important IT and business questions. The Aunalytics cloud-native data platform is built for universal data access, advanced analytics and AI while unifying disparate data silos into a single golden record of accurate, actionable business information. Its DaybreakTM industry intelligent data mart combined with the power of the Aunalytics data platform provides industry-specific data models with built-in queries and AI to ensure access to timely, accurate data and answers to critical business and IT questions. Through its side-by-side digital transformation model, Aunalytics provides on-demand scalable access to technology, data science, and AI experts to seamlessly transform customers businesses. To learn more contact us at +1 855-799-DATA or visit Aunalytics at http://www.aunalytics.com or on Twitter and LinkedIn.
PR Contact: Denise NelsonThe Ventana Group for Aunalytics (925) 858-5198dnelson@theventanagroup.com
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NetWitness Selected by Ubiquo as Exclusive XDR Partner to Provide Integrated and Rapid Threat Detection and Response Against Advanced Attacks – Yahoo…
NetWitness XDR Technology Uses a Combination of Network and Endpoint Detection, Behavioral Analysis, Data Science, and Threat Intelligence to Detect and Resolve Known and Unknown Attacks
BEDFORD, Mass., June 08, 2022--(BUSINESS WIRE)--NetWitness, an RSA business, and globally trusted provider of cybersecurity technologies and incident response services, today announced a new partnership for Extended Detection and Response (XDR) with Ubiquo, a newly launched company in Chile owned by Telecom Argentina and specialized cybersecurity provider for Latin America. Ubiquo provides enterprises with a full suite of cybersecurity services and works with businesses to help protect and monitor their critical systems and respond to emerging cybersecurity threats.
"The threat of cyber criminals is ever present, and businesses are constantly struggling against a never-ending wave of attacks that can severely disrupt key business operations. Organizations throughout the region require a combination of best-of-breed technologies and industry expertise to keep these threats at bay," said Mauricio Chiabrando, Cybersecurity Solutions Director at Ubiquo. "Were confident that the strength of a global cybersecurity leader like NetWitness and our expert team will give our customers an advantage in the battle against cyberattacks, ensuring unsurpassed visibility, smarter threat detection, and faster analytics."
In addition to XDR solutions, NetWitness is the foundation of Ubiquo's new state-of-the-art Managed Detection and Response (MDR) center, which will provide outsourced threat detection services designed to deliver visibility into critical systems, advanced insights into attacks, and the ability to take action to mitigate the impact and disruption of threat actors. The MDR center is staffed by analysts fully trained on NetWitness technologies.
"Keeping enterprises safe from cyberattacks requires innovative and forward-thinking approaches that enable those businesses to stay on the cutting-edge of technology, as well as ahead of a rapidly transforming threat landscape, where new methods of attack are emerging daily," said Marcos Nehme, Vice President of Latin America and the Caribbean at NetWitness. "Ubiquo in Chile is taking just that approach, and were proud to work with their team on XDR and MDR offerings that will significantly strengthen the security capabilities of their customers; this includes Incident Response services powered by NetWitness experienced threat hunters for rapid discovery and response. We look forward to continuing our work with Ubiquo to help keep Chile and all Latin America-based organizations protected from cyber threats."
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The NetWitness Platform is an evolved SIEM and open XDR platform that enables security teams to detect threats, understand the full scope of a compromise, and automatically respond to security incidents across modern IT infrastructures. The NetWitness Platform collects and analyzes data across all capture points, including logs, network packets, NetFlow, endpoint, and IoT, on physical, virtual, and cloud computing platforms. It applies threat intelligence and user behavior analytics to detect, prioritize, investigate threats, and automate response, improving the effectiveness and efficiency of security operations.
Using a centralized combination of network and endpoint analysis, behavioral analysis, data science techniques, and threat intelligence, NetWitness Platform for XDR helps analysts detect and resolve known and unknown attacks while automating and orchestrating the incident response lifecycle. With these capabilities on one platform, security teams can integrate disparate tools and data into a powerful and intuitive user interface for rapid and effective response.
To learn more, visit http://www.netwitness.com.
ABOUT NetWitness
NetWitness, an RSA Business, provides comprehensive and highly scalable threat detection and response capabilities for organizations around the world. The NetWitness Platform delivers complete visibility combined with applied threat intelligence and user behavior analytics to detect, prioritize, investigate threats, and automate response. This empowers security analysts to be more efficient and stay ahead of business-impacting threats. For more information, visit netwitness.com.
2022 RSA Security LLC or its affiliates. All rights reserved. RSA and the RSA logo are trademarks of RSA Security LLC or its affiliates. For a list of RSA trademarks visit https://www.rsa.com/en-us/company/rsa-trademarks. Other trademarks are trademarks of their respective owners. RSA believes the information in this document is accurate. The information is subject to change without notice.
View source version on businesswire.com: https://www.businesswire.com/news/home/20220608005327/en/
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SHIFT Communicationsnetwitness@shiftcomm.com
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Going Out on Top | The UCSB Current – The UCSB Current
With Commencement Week officially underway at UC Santa Barbara, the university has announced recipients of its most prestigious student honors, awarded for scholastic achievement, extraordinary service and personal courage and persistence.
Emily Elizabeth Lopez has won the Thomas More Storke Award for Excellence, the campuss highest honor, for her outstanding scholarship and extraordinary service to the university, its students and the community.
Michael Zargari has won the Jeremy D. Friedman Memorial Award, which recognizes outstanding leadership, superior scholarship and contributions to undergraduate life on campus.
Hugh Darius David Cook has won the Alyce Marita Whitted Memorial Award, which recognizes a nontraditional students endurance, persistence and courage in the face of extraordinary challenges while pursuing an academic degree.
An award ceremony for winners of these and other student awards, as well as for their families, faculty and staff, will be held at 3:30 p.m. Friday, June 10 in Corwin Pavilion.
The Yonie Harris Award for Civility in Public Discourse will be presented to Vonnie Feng Wei. The honor is bestowed upon graduates who best exemplify the principles of free speech and respectful dialogue and who foster a campus climate of civility and open-mindedness. Timnit Kefela and Ryan Flaco Rising will receive the Michael D. Young Engaged Scholar Award for students who have successfully integrated their scholarly knowledge and/or values into action.
Prizes for the University Service Award, the University Award of Distinction, and the Vice Chancellors Award for Scholarship, Leadership and Citizenship will be presented to multiple graduating seniors and graduate students. The winner of the 2022 Mortar Board Award, which recognizes the student who earned the highest cumulative GPA of the graduating class, will be announced at the ceremony.
Lopez, the Storke Award winner, is cited for her scholarly excellence and positive contributions to the campus community at UC Santa Barbara, where she promoted better access for underrepresented groups in math and science. Her pursuit of challenging academic goals, her persistence through adversity and her dedication to fostering opportunities for others are what led to her selection for the universitys highest honor.
A transfer student from the College of the Canyons, Lopez arrived at UC Santa Barbara in 2018 to study mathematics in the College of Creative Studies. She is a first-generation college student and the only Latina in her cohort of 22 students a fact in which she took pride and used as motivation to persist as a mathematician and a role model.
In addition to completing her coursework, Lopez was involved in multiple research and mentorship initiatives, including service as an undergraduate research assistant, a research intern at Williams College and a UCSB McNair Scholar. Her research led to a peer-reviewed publication, an award-winning poster presentation and talks at the SACNAS National Diversity Conference and the Northeastern Mathematics Research Experience for Undergraduates Conference, among other events.
Lopez is a member of multiple academic societies, including the Society for Advancement of Chicanos/Hispanics and Native Americans in Science, the American Mathematical Society and the Association for Women in Mathematics. The winner of multiple scholarships and awards, she is moving on to a graduate program at Cornell University, where she will be a Deans Scholar, a Deans Excellence Fellow and an NSF Graduate Research Fellow.
Friedman Award winner Zargari is a UCSB Promise Scholar who in his freshman year founded the Promise Scholars Advisory Board to create networking opportunities and space for education for low-income, first-generation and underrepresented students. He also served on the Student Health Advisory Committee, as well as the UCSB Student Health Insurance Plan Advisory Committee and the systemwide UCSHIP Committee.
Working as a budget analyst for the Associated Students Office of the Controller, Zargari sought to restructure budget allocations and spending procedures for AS organizations to boost student engagement without increasing fees. He also served on the Student Fee Advisory Committee and volunteered with the Community Financial Aid Fund.
Zargari is a double major in economics and in statisics and data science, and a double-minor in Iranian studies and translation studies. His research contributions include the creation and administration of a survey to assess student mental health before and after completion of economics courses and work with endangered languages, filming and translating videos of older generation Persian Jews from Farsi and ancient Judeo-Persian in to English.
One of two inaugural Promise Fellows, a new graduate initiative at UCSB, Zargari will next pursue a masters degree in environmental data science at the Bren School.
Cook, the Whitted Award winner, dedicated his time at UCSB to helping students in recovery. As a Peer Recovery Intern for Gauchos in Recovery, he focused on overdose prevention education, mentorship, and the production of articles and blog posts for the Alcohol and Drug Program. In this capacity he also testified before the California State Assembly as an advocate for college students in recovery.
Characterized as a talented and inspirational writer who used words to foster connections among students, Cook wrote for the Daily Nexus and The Bottom Line and participated in the campus Poets Club, writing and performing many poems. He served as president of the Postal Art Club.
After his graduation from UC Santa Barbara, where he majored in writing and literature, Cook will pursue a masters degree in counseling psychology from Pacifica Graduate School.
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Clinch Partners with DeepIntent to Bring New Campaign Automation and Omnichannel Personalization Capabilities to Healthcare Marketers – PR Newswire
Partnership brings Clinch's industry-leading platform, Flight Control, with advanced ad serving, DCO and consumer intelligence capabilities to healthcare brands looking to optimize outcomes and Rx conversions through DeepIntent's DSP
NEW YORK, June 8, 2022 /PRNewswire/ -- Clinch, the leader in dynamic ad serving and personalization and creator of Flight Control, the Omnichannel Campaign Management Platform, today announced a partnership with DeepIntent, the leading independent healthcare advertising technology company, to bring new workflow automation and omnichannel DCO capabilities to healthcare marketers, to optimize business outcomes and Rx conversions.
Healthcare marketers leveraging Clinch's Flight Control platform now can streamline their entire digital campaign lifecycle, from strategy through activation and measurement; and through Clinch's partnership with DeepIntent, scale personalized campaigns across DeepIntent's marketplace of curated, premium, and brand-safe endemic and non-endemic inventory across all channels and devices, including Connected TV (CTV), desktop, and mobile.
"DCO is an incredibly powerful tool for personalizing brand messages to the most receptive audiences but Pharmaceutical and Healthcare brands are often creatively limited due to strict regulations," said Oz Etzioni, CEO of Clinch. "By partnering with DeepIntent, Clinch is unlocking new personalization and targeting capabilities, and a greater level of consumer intelligence previously unavailable to healthcare marketers."
Flight Control includes several native features that are especially beneficial to advertisers in the healthcare vertical, including built-in integrations with leading fraud monitoring/blocking services (e.g. DoubleVerify, and Integral Ad Science), enhanced QA capabilities that enable real-time feedback sharing within the platform, and specialization in CTV, a top channel for advertisers in the healthcare category.
Aaron Letscher, VP CTV Development at DeepIntent said, "Given CTV's ability to more precisely target consumers and provide actionable campaign data than linear TV, DeepIntent has grown 25-fold in CTV spend on our DSP in the last year. We partnered with Clinch not only because of their advanced DCO and Flight Control offerings, but also because they are leaders in the CTV space, bringing proprietary partnerships with some of the top CTV OEMs."
Healthcare marketers can also tap into Clinch's creative engagement insights to enrich DeepIntent's healthcare datasets of anonymized clinical, behavioral, CRM, and first-party publisher data in real-time, and without the reliance on cookies.
The joint offering is currently available for Display, Online Video and CTV with a plan to soon support Audio and Video-based Digital-out-of-home (DOOH).
To learn more about this partnership, contact [emailprotected].
About ClinchClinch is the recognized leader in omnichannel personalization, campaign management and ad serving. The company's AI-driven dynamic personalization technology delivers custom-tailored ad experiences at scale across all channels, driving best-in-class performance and consumer intelligence. Flight Control, Clinch's Omnichannel Campaign Management Platform, enables agencies and brands to manage the entire campaign lifecycle, from strategy through activation and measurement, on a single data-driven, automated platform that makes them massively more efficient, and profitable. Clinch campaigns run across all digital channels including programmatic, Connected TV (CTV), social media, in-app, native and Digital Out of Home (DOOH).Learn more at https://clinch.co/.
About DeepIntentDeepIntent is leading the healthcare advertising industry with data-driven solutions built for the future. Built purposefully for healthcare marketers, DeepIntent's platform is proven to drive higher audience quality and script performance. It enables marketers to plan, activate, measure, and optimize their campaigns all within a single platform. Conceived by former Memorial Sloan Kettering data scientists, DeepIntent empowers nine of the top ten pharmaceutical companies and the leading healthcare advertising agencies to improve patient outcomes through the artful use of advertising, data science, and real-world health data. For more information, visit DeepIntent.com or find us on Twitter, Facebook, or LinkedIn.
Clinch Media Contacts:Kate Tumino[emailprotected]212-896-1252
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Holberton Partners With Jigsaw Academy to Offer a Certificate in Full Stack Development in India – GlobeNewswire
SAN FRANCISCO, June 08, 2022 (GLOBE NEWSWIRE) -- Holberton, which is making software engineering education affordable and accessible globally, announced today its partnership with Indias leading data science and emerging technologies institute, Jigsaw Academy. Under this partnership, students in India will receive postgraduate (PG) certification in Full-Stack Development.
This partnership comes as part of Holbertons Operating System (OS) of Education initiative to support its primary objective of making world-class education accessible to the many. Holberton will provide the projects, platform, tools, and services required for the purposes of training, whereas Jigsaw Academy will be responsible for delivering the programs and providing mentoring support to enrolled students.
Highlighting this partnership, Sarita Digumarti, Co-founder of Jigsaw Academy, said, Jigsaw Academy is excited to be partnering with Holberton for our PG certificate program in Full Stack Development. Holbertons state of the art platform allows students to gain project experience that will make their full-stack skills more relevant and real-world focused. We are fully committed to offering the best experience to all our students and are looking forward to working closely with Holberton to create the next generation of skilled software engineers in India.
A postgraduate certificate program in Full Stack Development will assist in training talented Indian students to become software engineers who are ready to contribute from their first day on the job. With this partnership, we aim to prepare Indian students to meet tomorrows worlds challenges by equipping todays pragmatic youth with the necessary skill sets to become self-reliant, said Julien Barbier, Founder, and CEO of Holberton.
About Jigsaw Academy
Jigsaw Academy is the best data science institute in India that offers various courses in analytics to change the course of its students careers. Jigsaw Academy offers the most industry-relevant holistic interactive learning programs across emerging technologies. These SME-designed programs are delivered by expert faculty along with facilitating live interactions with industry experts for relevant guidance. These programs are aimed at making learners competent professionals with the relevant skill set for a successful career.
About Holberton
Founded in Silicon Valley in 2015,Holbertons innovative and flexible delivery of the OS of Education provides a unique portfolio of tools, auto-graded tailored curricula, and teaching methods to help its customerseducation institutions, universities, corporations, governments, andHolberton School franchiseessuccessfully train the next generation of highly skilled digital talent at scale.
For press inquiries, contact Alexandra Jouis press@holberton.us
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Council Post: A deep dive into the world of AI-Analytics-powered-claims management – Analytics India Magazine
My last article was about how insurers can leverage AI & analytics in underwriting. This article focuses on the next step of the insurance value chain, which is claims. We all have experience in claims, but we hardly know how it is processed at the backend and how AI is bringing more efficiency and effectiveness.
Claims is a formal request made to your insurance provider for reimbursement against losses covered under your insurance policy.
Today more than half of claims activities have been replaced by automation, according to McKinsey. For example, insurance providers have been using advanced algorithms to handle initial claims routing, increasing efficiency and accuracy.
IoT sensors and an array of data-capturing technologies have helped insurers replace manual methods of first notice of loss (FNOL), where claims triage and repair services are often triggered automatically upon loss. For example, in the case of an auto accident, the policyholder takes a streaming video of the damage, which is later translated into loss descriptions, and estimated amounts. At home, IoT devices are used to proactively monitor water levels, temperature, and other risk factors and will proactively alert both insurers and tenants of issues before they arise.
Meanwhile, automated customer service apps handle most policyholder interactions through text and voice directly following self-learning scripts that interface with the fraud, claims, medical service, policy, and repair systems due to faster resolution times. According to Accenture, nearly 74 percent of customers said they would interact with modern technology and appreciate the computer-generated system of insurance advice.
Human claims management currently focuses on a few areas. This includes complex and unusual claims and contested claims where data-driven insights and analytics empower human interactions.
In this article, we will discuss how insurance claims processing is getting transformed by the adoption of AI, NLP and analytics. As AI and NLP enable digitisation, the power of analytics boosts the effectiveness of extracted content. This is done by leveraging predictive modeling.
In claims management, the first process is FNOL, where the insured informs their insurer about the loss and lodges a claim with the insurer for the damages incurred.
Lodging a claim can go through multiple channels. This includes voice/on-call, non-voice-through portal, mobile application or sending an email. In most non-voice cases, the lodgement process is keying in 60-80 fields (insured details, vehicle details, loss details, customer details, etc.) in the claims management system (Guidewire, LexisNexis, etc.).
The information is read from emails, standard and non-standard claim forms, and other supporting documents that are key to the claims management system. These forms can be digital, handwritten, scanned or unscanned, and can be of various formats, including .eml, .msg, pdf, Docx., .rtf, TIFF, JPEG, etc.
The key steps involve automating the 60-80 features with AI. The nature of the fields is not standard; there would be free-text driven fields (claims description, type, etc.), which require ML and NLP models to classify and summarise to decide the loss type. Or, there would be documents such as police reports and survey reports which vary based on city and state and are mostly handwritten.
To yield significant impact, one needs an advanced pre-built ensemble of computer vision and NLP models that can extract, classify, and summarise details.
Further, the extracted information enables the building of ML models to identify duplicate and fraud claims. For example, the claims can come from the same customer again and again, or the same agent is sending across similar claims. All such cases are identified and sorted through ML models.
Lastly, the AI analytics intervention would be to direct the claim to the correct channel for processing and prioritisation. Alignment can be either auto-processing or handler driven depending upon the claim complexity, value and ageing. The models support in directing the case to the right-skilled handler.
Once the automated data pipeline has been established, you can leverage the rich, newly aggregated data to get powerful insights, leading to better underwriting decisions, products and customer experience.
Benefits of digitising claims set-up processing:
Setting up claims and proceeding with the same can be a long, effort-intensive process which can lead to revenue leakage. Applying AI and analytics in claims management improves cost optimisation and customer experience and curbs revenue leakage. Companies should adopt AI solutions which are domain-rich, scalable and flexible in delivering multiple use cases.
This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the formhere.
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15 top data science certifications | CIO
Data scientist is one of the hottest jobs in IT. Companies are increasingly eager to hire data professionals who can make sense of the wide array of data the business collects. If you are looking to get into this lucrative field, or want to stand out against the competition, certification can be key.
Data science certifications give you an opportunity not only to develop skills that are hard to find in your desired industry but also to validate your data science know-how so that recruiters and hiring managers know what theyre getting if they hire you.
Whether youre looking to earn a certification from an accredited university, gain experience as a new grad, hone vendor-specific skills, or demonstrate your knowledge of data analytics, the following certifications (presented in alphabetical order) will work for you.
The Certified Analytics Professional (CAP) is a vendor-neutral certification that validates your ability to transform complex data into valuable insights and actions, which is exactly what businesses are looking for in a data scientist: someone who understands data, can draw logical conclusions and express to key stakeholders why those data points are significant. Youll need to apply and meet certain criteria before you can take the CAP or the associate level aCAP exams. To qualify for the CAP certification exam, youll need three years of related experience if you have a masters degree in a related field, five years of related experience if you hold a bachelors in a related field, or seven years of experience if you have any degree unrelated to analytics. To qualify for the aCAP exam, you will need a masters degree and less than three years of related experience in data or analytics.
Cost: CAP exam: $495 for INFORMS members, $695 for non-members; aCAP exam: $200 for INFORMS members, $300 for non-members
Location: In person at designated test centers
Duration: Self-paced
Expiration: Valid for three years
Cloudera has discontinued its Cloudera Certified Professional (CCP) and Cloudera Certified Associate (CCA) certifications in favor of the new Cloudera Data Platform (CDP) Generalist certification, which verifies proficiency with the platform. The new exam tests general knowledge of the platform and applies to multiple roles, including administrator, developer, data analyst, data engineer, data scientist, and system architect. The exam consists of 60 questions and the candidate has 90 minutes to complete it.
Cost: $300
Location: Online
Duration: 90 minutes
Expiration: Valid for two years
The Data Science Council of America (DASCA) Senior Data Scientist (SDS) certification program is designed for professionals with five or more years of experience in research and analytics. Its recommended that students have knowledge of databases, spreadsheets, statistical analytics, SPSS/SAS, R, quantitative methods, and the fundamentals of object-oriented programming and RDBMS. The program includes five tracks that will appeal to a range of candidates each track has differing requirements in terms of degree-level, work experience, and prerequisites to apply. Youll need at least a bachelors degree and more than five years of experience in data science to be eligible for each track, while other tracks require a masters degree or past certifications.
Cost: $775
Location: Online
Duration: Self-paced
Expiration: 5 years
The Data Science Council of America (DASCA) offers the Principal Data Scientist (PDS) certification, which includes three tracks for data science professionals with 10 or more years of experience in big data. The exam covers everything from fundamental to advanced data science concepts such as big data best practices, business strategies for data, building cross-organizational support, machine learning, natural language processing, scholastic modeling, and more. The exam is designed for seasoned and high-achiever data science thought and practice leaders.
Cost: $850, track 1; $1,050, track 2; $750, Track 3; $1,250, track 4
Location: Online
Duration: Self-paced
Expiration: Credentials do not expire
The IBM Data Science Professional Certificate consists of nine courses on data science, open source tools, data science methodology, Python, Databases and SQL, data analysis, data visualization, machine learning, and a final applied data science capstone. The certification coursework takes place online through Coursera with a flexible schedule and takes an average of three months to complete, but you are free to take more or less time. The course includes hands-on projects to help you build a portfolio to showcase your data science talents to potential employers.
Cost: Free
Location: Online
Duration: Self-paced
Expiration: Credentials do not expire
Microsofts Azure AI Fundamentals certification validates your knowledge of machine learning and artificial intelligence concepts and how they relate to Microsoft Azure services. Its a fundamentals exam, so you dont need extensive experience to pass the exam. Its a good place to start if you are new to AI or AI on Azure and want to demonstrate your skills and knowledge to employers.
Cost: $99
Location: Online
Duration: Self-paced
Expiration: Credentials do not expire
The Azure Data Scientist Associate certification from Microsoft focuses your ability to utilize machine learning to implement and run machine learning workloads on Azure. Candidates for the exam are tested on ML, AI solutions, NLP, computer vision, and predictive analytics. You will need to be skilled in deploying and managing resources, managing identities and governance, implementing and managing storage, and configuring and managing virtual networks.
Cost: $165
Location: Online
Duration: Self-paced
Expiration: Credentials do not expire
The Open Group Professional Certification Program for the Data Scientist Professional (Open CDS) is an experience-based certification without any traditional training courses or exams. Youll start at level one as a Certified Data Scientist, then you can move to level two where youll become a Master Certified Data Scientist and finally, you can pass the third level to become a Distinguished Certified Data Scientist. Certification requires a three-step process that includes applying for the certification, completing the experience application form, and attending a board review.
Cost: Contact for pricing
Location: On-site
Duration: Varies by level
Expiration: Credentials do not expire
The AI and Machine Learning Professional certification from SAS demonstrates your ability to use open source tools to gain insight from data using AI and analytics skills. The certification consists of several exams that cover topics such as machine learning, natural language processing, computer vision, and model forecasting and optimization. Youll need to pass the SAS Certified Specialist exams in Machine Learning, Forecasting and Optimization, and Natural Language Processing and Computer Vision to earn the AI and Machine Learning professional designation.
Cost: $180 per exam
Location: Online
Duration: Self-paced
Expiration: Credentials do not expire
The SAS Certified Advanced Analytics Professional Using SAS 9 credential validates your ability to analyze big data with a variety of statistical analysis and predictive modeling techniques. Youll need experience in machine learning and predictive modeling techniques, including their use with big, distributed, and in-memory data sets. You should also have experience with pattern detection, experimentation in business optimization techniques, and time-series forecasting. The certification requires passing three exams: Predictive Modeling Using SAS Enterprise Miner 7, 13, or 14; SAS Advanced Predictive Modeling; and SAS Text Analytics, Time Series, Experimentation, and Optimization.
Cost: $250 for the Predictive Modeling Using SAS Enterprise Miner exam; $180 each for the other two required exams
Location: Online
Duration: Self-paced
Expiration: Credentials do not expire
The SAS Certified Data Scientist certification is a combination of the other two data certifications offered through SAS. It covers programming skills; managing and improving data; transforming, accessing, and manipulating data; and how to work with popular data visualization tools. Once you earn both the Big Data Professional and Advance Analytics Professional certifications, you can qualify to earn your SAS Certified Data Scientist designation. Youll need to complete all 18 courses and pass the five exams between the two separate certifications.
Cost: $180 per exam
Location: Online
Duration: Self-paced
Expiration: Credentials do not expire
The TensorFlow Developer Certificate program is a foundational certificate for students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow. The exam tests your knowledge of and ability to integrate machine learning into various tools and applications. To pass the exam you will need to be experienced with the foundational principles of ML and deep learning, building ML models, image recognition algorithms, deep neural networks, and natural language processing.
Cost: $100 per exam
Location: Online
Duration: Self-paced
Expiration: Credentials do not expire
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Roadblocks to getting real-time AI right – VentureBeat
Analysts estimate that by 2025, 30% of generated data will be real-time data. That is 52 zettabytes (ZB) of real-time data per year roughly the amount of total data produced in 2020. Since data volumes have grown so rapidly, 52 ZB is three times the amount of total data produced in 2015. With this exponential growth, its clear that conquering real-time data is the future of data science.
Over the last decade, technologies have been developed by the likes of Materialize, Deephaven, Kafka and Redpanda to work with these streams of real-time data. They can transform, transmit and persist data streams on-the-fly and provide the basic building blocks needed to construct applications for the new real-time reality. But to really make such enormous volumes of data useful, artificial intelligence (AI) must be employed.
Enterprises need insightful technology that can create knowledge and understanding with minimal human intervention to keep up with the tidal wave of real-time data. Putting this idea of applying AI algorithms to real-time data into practice is still in its infancy, though. Specialized hedge funds and big-name AI players like Google and Facebook make use of real-time AI, but few others have waded into these waters.
To make real-time AI ubiquitous, supporting software must be developed. This software needs to provide:
Developers and data scientists want to spend their time thinking about important AI problems, not worrying about time-consuming data plumbing. A data scientist should not care if data is a static table from Pandas or a dynamic table from Kafka. Both are tables and should be treated the same way. Unfortunately, most current generation systems treat static and dynamic data differently. The data is obtained in different ways, queried in different ways, and used in different ways. This makes transitions from research to production expensive and labor-intensive.
To really get value out of real-time AI, developers and data scientists need to be able to seamlessly transition between using static data and dynamic data within the same software environment. This requires common APIs and a framework that can process both static and real-time data in a UX-consistent way.
The sexiest work for AI engineers and data scientists is creating new models. Unfortunately, the bulk of an AI engineers or data scientists time is devoted to being a data janitor. Datasets are inevitably dirty and must be cleaned and massaged into the right form. This is thankless and time-consuming work. With an exponentially growing flood of real-time data, this whole process must take less human labor and must work on both static and streaming data.
In practice, easy data cleaning is accomplished by having a concise, powerful, and expressive way to perform common data cleaning operations that works on both static and dynamic data. This includes removing bad data, filling missing values, joining multiple data sources, and transforming data formats.
Currently, there are a few technologies that allow users to implement data cleaning and manipulation logic just once and use it for both static and real-time data. Materialize and ksqlDb both allow SQL queries of Kafka streams. These options are good choices for use cases with relatively simple logic or for SQL developers. Deephaven has a table-oriented query language that supports Kafka, Parquet, CSV, and other common data formats. This kind of query language is suited for more complex and more mathematical logic, or for Python developers.
Many possibly even most new AI models never make it from research to production. This hold up is because research and production are typically implemented using very different software environments. Research environments are geared towards working with large static datasets, model calibration, and model validation. On the other hand, production environments make predictions on new events as they come in. To increase the fraction of AI models that impact the world, the steps for moving from research to production must be extremely easy.
Consider an ideal scenario: First, static and real-time data would be accessed and manipulated through the same API. This provides a consistent platform to build applications using static and/or real-time data. Second, data cleaning and manipulation logic would be implemented once for use in both static research and dynamic production cases. Duplicating this logic is expensive and increases the odds that research and production differ in unexpected and consequential ways. Third, AI models would be easy to serialize and deserialize. This allows production models to be switched out simply by changing a file path or URL. Finally, the system would make it easy to monitor in real time how well production AI models are performing in the wild.
Change is inevitable, especially when working with dynamic data. In data systems, these changes can be in input data sources, requirements, team members and more. No matter how carefully a project is planned, it will be forced to adapt over time. Often these adaptations never happen. Accumulated technical debt and knowledge lost through staffing changes kill these efforts.
To handle a changing world, real-time AI infrastructure must make all phases of a project (from training to validation to production) understandable and modifiable by a very small team. And not just the original team it was built for it should be understandable and modifiable by new individuals that inherit existing production applications.
As the tidal wave of real-time data strikes, we will see significant innovations in real-time AI. Real-time AI will move beyond the Googles and Facebooks of the world and into the toolkit of all AI engineers. We will get better answers, faster, and with less work. Engineers and data scientists will be able to spend more of their time focusing on interesting and important real-time solutions. Businesses will get higher-quality, timely answers from fewer employees, reducing the challenges of hiring AI talent.
When we have software tools that facilitate these four requirements, we will finally be able to get real-time AI right.
Chip Kent is the chief data scientist at Deephaven Data Labs.
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How to Become a Better Data Science Team – Built In
A lot of my articles, as well as much of the writing on data science in general, focus on the work of individual data scientists. In this article, though, I want to focus on something different: the data science team. But first, let's define what such a team usually consists of. Although this configuration isnt set in stone, here is an example of a data science team: a few data scientists, a data engineer, a business/data analyst and a data science manager.
The specific composition of the team is less important than how the team works together, however. With that being said, lets look at the tools and methods you can use to improve collaboration among your data science team, whether you are a data scientist, a manager or possibly a technical recruiter.
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This first tool is a combination of planning and grooming. These terms can be a little muddled, though, so lets define them first.
Grooming falls under the umbrella of organization, but what sets this process apart from planning (in certain companies) is that it serves as the first review of whatever is in your backlog. This queue may be composed of several Jira tickets or other general tasks that your team has come up with over time but has not yet prioritized into an active process.
You can think of planning as more specific on a sprint level. Even if you dont use Jira, you can still plan weekly, bi-weekly, or on whatever cadence you prefer, and log it with more check-ins. Typically, in these check-ins, youll discuss upcoming projects. More importantly, though, youll address the digestible tasks of a particular project for that given week or time period.
Here are a few takeaways and benefits that can come from collaborating on planning and grooming:
Once again, these terms might switch meanings or be interchangeable depending on your companys processes or if youre working in an agile environment. Whats important, however, is improving your teams overall organizational ability.
Stakeholder updates are not often discussed in the process of becoming a data scientist since the training is usually more focused on learning algorithms, coding, and the respective, underlying concepts of each of those.
Stakeholders are the people (or the single person) who assign your tasks or projects or who will digest your final project and its impact on the business. That being said, stakeholders do not all have the same role; they may be data science managers, product managers, sales engineers or in some other position, depending on the company.
You can always update stakeholders through Jira tickets, Slack messages, Google Slide decks and many other methods. The point is not the platform you use; its the way in which you share your information and updates.
Here are some ways that you and your team can effectively update stakeholders:
Also look at breakdowns of specific groups of data: You may organize it geographically, by type and so on.
There are many ways to explain and update your data science projects, but the most important thing is how you articulate them.
Finally, retrospectives are crucial to your data science team. This meeting is usually a thorough discussion of a few situations that your team has faced and can be held bi-weekly or monthly.
Planning and grooming take place before the project or task, and stakeholder updates occur during and often at the end of the task. The retrospective, however, encompasses everything that happened in the projects entire timeframe.
You will typically look at a few things in this retrospective discussion:
All these questions and areas will generally cover everything important that has happened over the given timeframe. You will gain a better sense of whats important to the team, the company and yourself.
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While improving your own work will improve the team, you can focus on other, more team-centric items to make your data science team even better overall.To summarize, here are three ways that your data science team as a whole can improve:
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