I never had a professor who was better at explaining difficult topics than Dr. Behseta. He makes learning math enjoyable and I would recommend him to any of my peers looking to get the most out of their education.

Dr. Behseta demonstrated deep knowledge, contagious enthusiasm and encouragement. The course subject is difficult, and his great attitude and confidence helped us learn.

He is always willing to answer questions and make students feel comfortable asking them.

These are just a handful of student comments in support of Sam Behseta, professor of mathematics, this years recipient of Cal State Fullertons 2022 Outstanding Professor Award. Behseta was selected based on his exemplary contributions as an educator and scholar. CSUF President Fram Virjee announced Behsetas selection at todays (April 14) Academic Senate meeting.

In presenting the award, Virjee noted, If the goal of a professor is to ignite students passion for learning and hope they fall in love with the subject matter for life, Sam Behseta has achieved this. He has transformed classroom opportunities, changed his office hours and developed programs where students learn through participation. He offers real-world research experience to match what students will find in the field and to which they are entitled.

During my tenure at Cal State Fullerton, I have been fortunate to teach a large number of graduate and undergraduate classes in statistics and applied mathematics, Behseta said. Some of my personal favorites are introductory classes. In those classrooms, I have found some of the most outstanding research students, who in their own right, are now pathbreaking educators or visionary data scientists.

In data science, it is critical for students to develop a solid understanding of the basic concepts in theoretical and practical aspects of probability, statistics and computing, early on. Many students mistakenly believe this will be too hard for them. I tell them, If you fail, its because I failed as a teacher. Its my job to help you understand.

Behseta has supervised the work of more than 50 undergraduate researchers, many of whom are first-generation students.

Beyond being an approachable and beloved teacher, Behseta is also an accomplished researcher who has published extensively, presented at prestigious conferences and encouraged students to achieve far more than they thought possible. In fact, one of his former students is now an associate professor in the mathematics department.

When I look at my students and their success, it reinforces in me the idea of what we can accomplish at Cal State Fullerton, he said. It is quite important and makes a lifelong difference one classroom and one student at a time.

Among Behsetas accomplishments are:

Fellow of the American Statistical AssociationThe American Statistical Association recognizes fellows as those who have an established reputation and have made outstanding contributions to statistical science. The number of new fellows per year is limited to one third of 1% of the membership of ASA the second oldest organization in the U.S., formed in 1839. Behseta was selected to become a fellow in 2017.

Director of CSUFs Center for Computational and Applied MathematicsIn 2015, Behseta with a group of colleagues, initiated an effort to form an interdisciplinary scholarship with a computational component. In 2016, Behseta was appointed director of the Center for Computational and Applied Mathematics. Since its inception, CCAM has become a viable and highly active organization, supporting a significant number of data science research activities on campus.

High-Performance Computing ClusterIn 2021, CCAM secured two large grants: $600,000 from the U.S. Army for a new high-performance computing (HPC) clusterthat enabled science and mathematics faculty and students to engage in leading-edge research activities.The supercomputer, which is part of the Center for Computational and Applied Mathematics in the College of Natural Sciences and Mathematics, gives researchers the ability to process a large amount of data in a fraction of the time.

In 2022, CCAM secured a second supercomputer, ushering in an exciting era of advanced research for students, faculty and staff. The new high-performance computing clusters efficiency coupled with CCAM facultys diverse research topics, will not only pave the way for advancement in a multitude of fields: biology, biochemistry, statistics and applied mathematics, but it will also meet the demands of modern scientific research.

California Data Science Experience Transformation ProgramMore recently, in collaboration with researchers at UC Irvine and Cypress College, Behseta and colleague Jessica Jaynes, obtained a $1.5 million grant to provide opportunities for underrepresented and historically underserved students to receive training on the foundations and modern applications of data science, and to get involved in high-level research.

Statistical Modeling of COVID-19 DataSoon after the outbreak of the pandemic, Behseta and colleague Derdei Bichara, began building machine learning and mathematical models for predicting the spread of COVID-19. This model accounted for mobility among communities and human behavior and was showcased extensively in the media.

Serving Minorities in STEMBehseta was involved in securing National Science Foundation funding of $1.46 million to examine the effects of dual-language programs on increasing mathematics and science achievement among junior high students in the Anaheim School District.

He also helped secure $1 million for a project, Big Data Discovery and Diversity Through Research Education Advancement and Partnership, to inspire and train undergraduate students with diverse backgrounds about the approaches in big data analytics.

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Mathematics Educator and Researcher Named CSUF's 2022 Outstanding Professor | CSUF News - CSUF News

Data analytics are crucial for companies leveraging Fintech in driving valuable insights.

This article will explore the role of data analytics in Fintech operations, as the disruptive innovation space continues to grow

Making use of data using a single view of all relevant assets, in real-time, has never been more vital, and for financial service institutions this is no different. Data-driven decisions, aided by analytics, are crucial in an ever-competitive landscape where consumers will look elsewhere if their needs arent met.

With this in mind, we explore the role that data analytics can play in Fintech operations.

Historically, financial service companies have dealt with data in their own respective departments, leading to disconnected pictures of business progress and customer behaviour. In todays business world though, organisations cant afford to continue with this approach, and need a unified view to gain true insights.

Current market research projects the global financial analytics market to grow to $25.38 billion by 2028. While the role of analytics in Fintech-powered operations is becoming more prominent, complexities remain when it comes to gaining insights from rising amounts of data, which can be attributed to skills gaps plaguing tech generally. This can be mitigated by establishing strong partnerships with vendors such as cloud service providers (CSPs) such as AWS and Azure, which are continuously adapting analytics capabilities.

To effectively process data in financial services, democratisation across the workforce is a must. No longer can assets afford to be kept solely with traditionally skilled IT personnel.

James Corcoran, senior vice-president of customer value at KX, explained: Whether its seeking Alpha, managing risk, ensuring compliance or identifying fraud, the financial services sector has always been at the forefront of data analytics.

The challenge facing financial services firms today is that there is simply too much data to process intuitively and without support. Insights are no longer visible; they must be mined and they must be mined quickly before either a problem occurs or an opportunity passes.

Throw in the disruption brought about by the pandemic, an ever more complex regulatory environment and the relentless impact of digital transformation on the sector and its clear that the focus on data, its management and its analysis, has never been greater. At KX, were hearing a lot from financial services customers on the need to democratise access to data across an organisation, and the need for data to be viewed as an enterprise asset rather than through the lens of individual teams or domain-specific requirements.

The consequences of not doing so are blatantly clear in too many organisations: the dreaded data silos that are costly to manage and hard to eliminate. The global datasphere is growing at an incredible rate, and much of that growth is coming from data created in real-time. Financial services firms must ensure their data analytics strategy can keep pace.

Analytics is proving particularly fruitful in the open banking segment of Fintech. With open banking being all about portability of personal financial data, to offer consumers more personalised services, data science and analytics have a fundamental role to play

Its difficult to overstate just how important data analytics is in Fintech. Not only can it be the basis for a huge range of different business offerings it also plays a critical role in optimising and informing how companies operate, said Alistair Dent, chief strategy officer at Profusion.

Startups can differentiate themselves and gain a competitive edge by offering more creative and useful services. The only way to do this is to have a strong and innovative data science capability that can support development. Put simply, you cannot have AI-driven financial advisors or financial aggregation platforms without data analytics.

Advances in data science and fintech are inextricably linked they are driving each other forward. More creative fintech solutions require more complex data science techniques.

Of course, no tech deployment project can drive that all important business value if board leadership isnt involved in the vision. Once this is achieved, financial service boardrooms can use analytics to make data-driven decisions that affect the company bottom line.

Once an IT issue, data should be at the heart of business models and strategies. Board level decisions need to be based on accurate insights rather than on approaches used in the past, which are not fit for purpose in the current environment, explained Anurag Bhatia, senior vice-president and head of Europe at Mphasis.

To harness innovation and monetise their data, the first step for leaders is to instil the right digital infrastructure to eliminate siloes and make quality data more accessible. Cloud is a strong enabler here, facilitating continuous innovation and opening the door for the use of advanced AI and machine learning capabilities for optimum data analytics.

Once leaders gain full visibility of their data, not only can they use it to arrive at meaningful insights, but they can also more easily comply with a changing regulatory landscape particularly when it comes to data protection, fraud prevention, and risk management.

Related:

Blockchain technology in financial services This article will explore the most valuable business use cases for blockchain technology in the financial services sector.

Fintech disruption of the banking industry: innovation vs tradition? Hasan Nawaz, CEO at HUBUC, explores how fintech disruption continues to challenge the traditional banking industry.

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The role of data analytics in Fintech - Information Age

Theres no shortage of data in this world. Neither is there a shortage of data-driven business plans. In fact, we are sitting on gluts of both. So why are companies still struggling to get the right data in front of the right people at the right time? One of the big challenges, sources say, is melding established data access and data management patterns with the new decentralized data paradigm. Here are three ways to do it.

That familiar urge to centralize data is falling by the wayside as the volumes of data continue to pile up. That represents a massive reversal of trends, according to Sean Knapp, the CEO and founder of Ascend.io.

Five to 10 years ago, there was a very strong push to consolidate data, consolidate it into your late, consolidate it into your warehouse, Knapp said during yesterdays Data Automation Summit, which continues today. And were starting to see those trends change. Were starting to see that organizations are embracing silos.embracing the fact that they cannot consolidate all of their data and there is no one platform at the data insurer layer to suit them all.

While were moving away from data centralization, that doesnt mean we can say goodbye to ETL. Ascend.io sells tools to automate the creation and management of data pipelines, which are proliferating at a furious clip at the moment, as data engineers seek to connect the various silos to enable data analysts and data scientists to get their data work done.

Knapp wants to improve the state of that art, and help automate the low-level muck that many data engineers are living with on a daily basis.

Automation of ETL/ELT pipelines is one way to tackle the growth of big decentralized data (Agor2012/Shutterstock)

The world of data has just grown too fast. It is like swimming upstream as we watched companies compete over the years, to try and pull all of their data into one spot, Knapp said. There will always be multiple data technologies.

While many companies want to use data in profitable ways, theyre having a hard time turning that desire into reality. Gerrit Katzmaeir, the vice president and general manager for database, data analytics, and Looker at Google Cloud, cited a recent study that found 68% of companies say theyre not getting lasting value out of their data investments.

Thats profoundly interesting, Katzmaeir said during last weeks rollout of BigLake, the companys first formal data lakehouse offering, which is slated to go up against lakehouses from Databricks and others.

Everyone recognizes that theyre going to compete with data, Katzmaeir said. And on the other side, we recognize that only a few companies are actually successful with it. So the question is, what is getting in the way of these companies to transform?

The answer, Katzmaeir said, lies somewhere in the jurisdiction of three paradigm changes that are currently taking place. First, the data is growing. The generation and storage of data is continuing to explode, and companies are grappling with storing a variety of data types and formats in multiple locations.

Second, the applications are expanding. Companies want to process this data with all sorts of engines and frameworks, and deliver a variety of data products and rich data experiences from it. Lastly, the users are everywhere. Data touches many personas today, including employees, customers, and partners, and the number of use cases for a given piece of data is growing.

The lakehouse concept melds data warehouses and data lakes into a unified whole (ramcreations/Shutterstock)

Even a company as large and technologically advanced as Google seems to realize that it cannot be the unifying force to bring all of its customers data back together. With BigLake, its melding the previously separate universes of the tried-and-true data warehouse, where structured data reigns supreme, and the looser-but-more-scalable data lake, where semi-structured data is stored.

In a way, the lakehouse architecture seeks to split the difference between the older approach (DWs) and the newer approach (data lakes) and delivering a semblance of data unification that will deliver some salvation from all those pesky data pipelines that keep popping up.

While Google Cloud is arguably the most open of the big three cloud providersindeed, Google Cloud says it extend into the data lakes of Microsoft Azure and Amazon Web Services and enable it to be accessed with BigLakenot everybody is convinced that a cloud-centric approach ultimately will solve customers modern data problems.

Data automation and lakehouses undoubtedly will help some organizations solve their data problems. But there are other big data challenges that wont be adequately addressed with either of those technologies.

Molly Presley, the senior vice president of marketing for Hammerspace, says some customers with large numbers of unstructured datasuch as what is found in science, media, and advertisingmay be best suited by adopting what she terms a global data environment.

Its the concept of I want to be able to make all my data globally available, no matter which storage silo or which storage system or which cloud region its sitting in, she says.

Being able to scale unstructured data storage broadly in a single name space with full high availability is important, Presley said. But distributed file systems and object systems can already do that. What is really moving the needle now is being able to simplify how users access and manage data, no matter where it sits, no matter what storage environment or protocol it uses, and meeting whatever performance requirements the customer needs.

Hammerspace offers what it calls a global data environment, but its mostly for unstructured data (Blue-Planet-Studio/Shutterstock)

Other environments are saying, Okay, I have NetApp, I have DDN, and I have some object store and I want to aggregate all of that data and make it available to my remote users who dont have connectivity to the data centers, dont have connectivity to the clusters, dont know how to interact with all those different technologies, Presley tells Datanami.

Hammerspace functions as that global data environment, which can function as a layer sitting atop other data stores, and smooth over the differences, while providing a common management and access layer to unstructured data. The key to Hammerspaces technology, Presley says, is the metadata.

So what well do is assimilate the metadataand now those remote users get local high-performance data access, she says. And they only have to interact with one thing, so IT doesnt have figure out how to make that user connected into all those different technologies.

While the cloud vendors are solving big data storage and processing challenges with infinitely scalable object storage systems that are completely separated from computenot to mention the data warehouses and lakehouses that offer a cornucopia of compute optionsthey still lack visibility into the legacy storage repositories that organization are still running on prem, Presley says. Thats the space that Hammerspace is attacking with its global data environment.

Its also why Microsoft is partnering with Hammerspace to help its Azure customers get access to large amounts of unstructured data that is still residing in on-prem data centers. Microsoft realizes that not all data and workloads are moving to the cloud, and it tapped Hammerspace to bring that into the cloud fold, Presley says.

What has changed is people are remote and data is distributed or decentralizedin a cloud data center, five data centers, whatever it isand the technologies that people are trying to use were designed for a single environment, she says. Theyre trying to say, Okay, I have all these technologies that were designed over the last 10 or 20 years for a single data center that were adapted a bit to use the cloud but werent adapted for multi-region simultaneously with remote users. And so theyre scratching their heads going Crud, what am I going to do? How do I put this together?

Weve mostly abandoned the idea that all data must live in a single place. The future of big data looks decidedly decentralized from this point forward. To keep data from becoming a distributed quagmire, there need to be some unifying themes. Theres a multitude of different methods to get there, including data automation, data lakehouses, and global data environment. Undoubtedly, there will be more.

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Hammerspace Hits the Market with Global Parallel File System

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Three Ways to Connect the Dots in a Decentralized Big Data World - Datanami

Organizations can harness great benefits from data, but understanding the importance of data quality, trust and avoiding bias allows them to make decisions and create profit.

At a fundamental level, data trust is when an enterprise has confidence that the data it is using is accurate, usable, comprehensive and relevant to its intended purposes. On a bigger-picture level, data trust has to do with context, ethics and biases.

"The narrow definition is looking at how data is used in organizations to drive their mission," said Danil Mikhailov, executive director at Data.org, a nonprofit backed by the Mastercard Center for Inclusive Growth and The Rockefeller Foundation.

Data.org promotes the use of data science to tackle society's greatest challenges. One of its projects is the data maturity assessment, a free tool that helps organizations determine where they stand on the data journey.

That narrow definition of data trust often gets support from tools that assess the quality of that data or automatically monitor the data across key metrics, Mikhailov said.

"Once you tick all the boxes, the organization can trust the data more," he said.

But that definition of data trust is limited because data is part of a broader context. Companies should consider other factors when evaluating data trust beyond the basic operational ones.

"Look not just at the specifics of data quality but who is the data for?" Mikhailov said. "Who is involved in the process of designing the systems, assessing the systems, using the data?"

The bigger picture is harder to quantify and operationalize but forgetting or ignoring it can lead to biases and failures, he added.

Organizations' bottom line reflects the importance of data quality. Poor data quality costs organizations, on average, $13 million a year, according to a Gartner report in July 2021. It's not just the immediate effect on revenue that's at stake. Poor data quality increases the complexity of data ecosystems and leads to poor decision-making.

There's a rule of thumb called the "1-10-100" rule of data that dates back to 1992; it says a dollar spent on verifying data at the outset translates to a $10 cost for correcting bad data, and a $100 cost to the business if it is not fixed.

Eighty-two percent of senior data executives said data quality concerns represent a barrier to data integration projects, and 80% find it challenging to consistently enrich data with proper context at scale, according to a survey by Corinium Intelligence in June 2021.

Identifying data quality and trusting its accuracy, consistency and completeness is a challenge for any executive. This is true even in organizations where the importance of data quality is literally a matter of life and death.

Only 20% of healthcare executives said that they fully trust their data, according to an October 2021 survey by consulting firm Sage Growth Partners and data technology company InterSystems.

One mistake companies make is assuming data is good and safe just because it matches what the company wants to track or measure. Servaas VerbiestDirector of product field strategy, Sungard Availability Services

Trust starts with the collection process. One mistake companies make is assuming data is good and safe just because it matches what the company wants to track or measure, said Servaas Verbiest, director of product field strategy at Sungard Availability Services.

"It's all about understanding who provided the data, where it came from, why it was collected, how it was collected," he said.

Diversification also helps.

"A single source of truth is a single point of failure," Verbiest said. "That is a big task, but it is essential to prevent bias or adoption from being impacted by an individual's preference versus the data bias required by the organization."

It's also important to follow the chain of custody of the data after collecting it to ensure that it's not tampered with later. In addition, data may change over time, so quality control processes must be ongoing.

For example, Abe Gong, CEO of data collaboration company Superconductive, once built an algorithm to predict health outcomes. One critical variable was gender, coded as 1 for male and 2 for female. The data came from a healthcare company. Then a new batch of data arrived using 1, 2, 4 and 9.

The reason? People were now able to select "nonbinary" or "prefer not to say." The schema was coded for ones and twos, meaning the algorithm's predictions would have yielded erroneous results indicating that a person with code 9 was nine times more female -- with their associated health risks multiplied as well.

"The model would have made predictions about disease and hospitalization risk that made absolutely no sense," Gong said.

Fortunately, the company had tests in place to catch the problem and update the algorithms for the new data.

"In our open source library, they're called data contracts or checkpoints," he said. "As the new data comes in, it raises an alert that says the system was expecting only ones and twos, which gives us a heads up that something has fundamentally changed in the data."

Superconductive is one of several commercial vendors offering data scoring platforms. Other vendors in this market include Talend, Informatica, Anomalo, Datafold, Metaplane, Soda and Bigeye.

It's too simplistic to say that some data contains bias and some don't.

"There are no unbiased data stores," said Slater Victoroff, co-founder and CTO at Indico Data, an unstructured data management company. "In truth, it's a spectrum."

The best approach is to identify bias and then work to correct it.

"There's a large number of techniques that can be used to mitigate that bias," Victoroff said. "Many of these techniques are simple tweaks to sampling and representation, but in practice it's important to remember that data can't become unbiased in a vacuum."

Companies may need to look for new data sources outside the traditional ones or set up differential outcomes for protected classes.

"It's not enough to simply say: 'remove bias from the data,'" Victoroff said. "We have to explicitly look at differential outcomes for protected classes, and maybe even look for new sources of data outside of the ones that have traditionally been considered."

Other techniques companies can use to reduce bias include separating the people building the models from the fairness committee, said Sagar Shah, client partner at AI technology company Fractal Analytics. Companies can also make sure that developers can't see sensitive attributes so that they don't accidentally use that data in their models.

As with data quality checks, bias checks must also be continual, Shah said.

One of the biggest trends this year when it comes to data is the move to data fabrics. This approach helps break down data silos and uses advanced analytics to optimize the data integration process and create a single, compliant view of data.

Data fabrics can reduce data management efforts by up to 70%. Gartner recommends using technology such as artificial intelligence to reduce human errors and decrease costs.

Seventy-nine percent of organizations have more than 100 data sources -- and 30% have more than 1,000, according to a December 2021 IDC survey of global chief data officers. Meanwhile, most organizations haven't standardized their data quality function and nearly two-thirds haven't standardized data governance and privacy.

Organizations that optimize their data see numerous benefits. Operational efficiency was 117% higher, customer retention was 44% higher, profits were 36% higher and time to market was 33% faster, according to the IDC survey.

Read more:

Why businesses should know the importance of data quality - TechTarget

Building a Continuous Learning & Development Culture is Just One Way Pactera EDGE Is Staying Ahead of its Competition

REDMOND, Wash., April 14, 2022 /PRNewswire/ --Pactera EDGE, a world-class digital solution provider for the data-driven, intelligent enterprise, announced today a new partnership with Udemy Business, the corporate learning division of Udemy, a leading destination for learning and teaching online.

Through the partnership, full-time Pactera EDGE employees will have access to over 6,000 online training courses, labs, and certification programs taught by Udemy's faculty of real-world experts.

The partnership is the latest initiative in Pactera EDGE's ongoing commitment to employee well-being and professional development. The Udemy Business courses are well-suited to help the company's workforce members interpret and apply data, leverage emerging technologies, reimagine existing business models, and hone their business skills.

As Pactera EDGE's workforce continues to grow beyond 3,000 employees worldwide, creating scalable pathways for upskilling will ensure the company's learning and development culture continues to thrive.

"Ensuring our employees are continuously ahead of the technology curve is critical in driving the innovative business outcomes our customers rely on us for," said Pactera EDGE CEO, Venkat Rangapuram. "This new partnership with Udemy allows Pactera EDGE employees to update their skills and provides the opportunity to explore new fields of interest as well."

Udemy's network of real-world experts teaches topics ranging from programming and data science to leadership and team building. Udemy Business will provide Pactera EDGE employees a training and development platform with subscription access to thousands of courses, learning analytics, and in-depth certification preparation.

"Employees are our most valuable asset. The Udemy partnership will allow us to easily scale our investment in their personal and professional growth, enterprise-wide," said Pamela Pei, Chief Operating Officer at Pactera EDGE. "When employees take an active role in developing their skills and exploring new fields they're passionate about, they win, and our customers win."

Pactera EDGE provides top Fortune 500 clients with an array of IT services, delivering award-winning engineering and globalization services on an enterprise scale.

To learn more about Pactera EDGE and its services visit:https://www.pacteraedge.com

For media inquiries contact: [emailprotected]

About UdemyUdemy's (Nasdaq: UDMY) mission is to create new possibilities for people and organizations everywhere by connecting them to the knowledge and skills they need to succeed in a changing world. The Udemy marketplace platform, with thousands of up-to-date courses in dozens of languages, provides the tools learners, instructors, and enterprises need to achieve their goals and reach their full potential. Millions of people learn on Udemy from real-world experts in topics ranging fromprogramminganddata sciencetoleadershipandteam building. For companies, Udemy Business offers anemployee trainingand development platform with subscription access to thousands of courses, learning analytics, and the ability to host and distribute their own content. Udemy Business customers include Fender Instruments, Glassdoor, GoFundMe, On24, The World Bank, and Volkswagen. Udemy is headquartered in San Francisco with hubs in Ankara, Turkey; Austin, Texas; Boston, Massachusetts; Mountain View, California; Denver, Colorado; Dublin, Ireland; Melbourne, Australia; New Delhi, India; and Sao Paulo, Brazil.

About Pactera EDGEPactera EDGEis a global organization with offices in the US, Europe, India and Asia-Pacific. Clients include 100+ of the Global 500 companies, with industry concentration in Software and Technology, CPG, Retail, Logistics, Financial Services, Insurance, Healthcare, Food & Beverage, and Travel & Hospitality

With a core focus on Data, Intelligence and Experience, Pactera EDGE helps clients achieve new levels of performance, while adding brand new digital business capabilities to drive relevance, revenue, and growth. With clarity of vision, technological expertise, operational excellence, and a global footprint, Pactera EDGE is the partner of choice for enterprises that want to run smarter and for those that want to change the race.

PRESS CONTACT

NAMELynn MunroePHONE8455481211WEBSITEhttps://www.pacteraedge.com

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Pactera EDGE Announces New Partnership with Udemy Business To Provide Full-Time Employees Enhanced Skill-Building Opportunities - PR Newswire