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Unlocking the Power of Data Science at Data Summit 2023 – Database Trends and Applications

Financial institutions have rich, customer-centric data and are in a strong position when building AI solutions. Since the functions differ, AI use cases also differ.

The recent intense interest in generative AI has given rise to a new aspect of data science: Prompt Engineering, which is basically how humans train models like GPT by creating appropriate prompts.

At Data Summit 2023, Supreet Kaur,AVP,Morgan Stanley, discussed Leveraging Data Science and Generative AI during her session.

The annual Data Summit conference returned to Boston, May 10-11, 2023, with pre-conference workshops on May 9.

AI is transforming the quality of products, and such enhancements are making it possible to solve breakthrough problems and transform the day-to-day operations of companies in this area.

Drivers behind AI adaptation include big data, infrastructure, competition and automation, she explained.

Generative AI is an unsupervised and semi-supervised algorithm that enables computers to create new and original data that looks like humans generated it.

Youve all heard about GPT but, I feel that GANs arent getting enough attention, Kaur said.

Generative Adversarial Networks (GAN) helps identify misinformation, she noted.

Examples of generative AI use cases include financial and healthcare industries. In healthcare, generative AI can generate medical reports and clinical data based on conversations with doctors and provide support. Generative AI can provide personalized experiences to patients using their vitals, and generic conditions for preventative care.

Generative AI works by using prompt engineering, she said. Prompts are a set of instructions given to the model to generate the desired output. Prompt engineering is a natural language processing technique to create and fine tune prompts to get accurate responses from the model.

The future looks bright as we are going toward a low-code/no-code approach, Kaur said.

However, there are a few risks that generative AI brings. These risks include information leaks, bias, model hallucinations, ethical implications, and harm to society.

Data is the soul of any AI and ML algorithm, and hence spending massive amounts of resources and time can reap benefits in the future.

Choosing the suitable model is an inevitable step in your ML lifecycle as that will ultimately dictate the long-term success of your model, she said.

The industry is heading toward creating a more job roles in this area. This includes:

AI wont replace us, but a person using AI will, she said.

Many Data Summit 2023 presentations are available for review athttps://www.dbta.com/DataSummit/2023/Presentations.aspx.

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Analytics and Data Science News for the Week of May 12; Updates … – Solutions Review

Solutions Review editors curated this list of the most noteworthy analytics and data science news items for the week of May 12, 2023.

Keeping tabs on all the most relevant analytics and data science news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week, in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy analytics and data science news items.

The strategic acquisition of Merilytics will be the foundation of Accordions Data & Analytics Practice to strengthen long-term support for its CFO clients. Financial terms of the private transaction were not disclosed. Founded in 2011 and headquartered in Hyderabad, India, Merilytics uses decision sciences and an analytics-based approach to generate superior data-driven returns for its PE-focused clients.

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More than 10,000 data and analytics professionals participated in the virtual conference, held on April 26, 2023, to explore the development of the semantic layer technology category. The conference featured more than 50 industry leaders, including professionals from AWS, Databricks, Dremio, Google Cloud, InterSystems, Monte Carlo, Snowflake, Snowplow, Stardog, Toric, and BigEye.

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The ability to view Power BI files directly in OneDrive and SharePoint preview requires Power BI Admins toopt-in to enable the preview.The ability to share links to Power BI reports saved in OneDrive and SharePoint from Power BI Desktop preview ison by defaultand requires Power BI Admins toopt-outto disable the preview.

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Petal uses this innovative approach to serve consumers directly, while Prism Data provides cash flow underwriting technology to financial institutions, fintechs, and other businesses, helping them leverage open banking to build better products and make better credit decisions.

Read on for more.

The use of ChatGPT within Qrveys platform allows end users to quickly identify outliers, patterns, and forecasts, and even to suggest other questions or ways of visually presenting the findings. Together, these functions make it even easier for business users to quickly derive actionable information from even the most complex data.

Read on for more.

The billion-dollar investment includes direct research and development, industry-focused line-of-business teams, and industry marketing efforts. It will fund the innovative work of SAS data scientists, statisticians and software developers working with consultants, systems engineers and marketers with specific industry experience.

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At Tableau Conference 2023, the company announced new product innovations that help it continue its mission by using generative AI to simplify analytics while reimagining data experiences for everyone. In addition, Tableau shared how new developer capabilities bring analytics everywhere and how the Salesforce Data Cloud helps harmonize data with seamless analysis in Tableau.

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New capabilities enable users to experience data through generative AI, collaborate in context, connect and leverage new data sources and platforms, all delivered through an intuitive user experience in ThoughtSpot or within other third party applications. ThoughtSpot also announcedMonitor for Mobile, delivering users proactive notifications pushed to their mobile device as KPIs change.

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With the next Solutions Spotlight event, the team at Solutions Review has partnered with leading reliability vendor Monte Carlo to provide viewers with a unique webinar calledFrom Symptom to Source: A Guide to Root Cause Analysis for Data Engineers.During this workshop, well share a five-step process analytics engineering teams can use to conduct root cause analysis in a collaborative, quick, and effective manner.

Read on for more.

A panel of experts from Snowflake, Denodo, and their mutual customer Syngenta explore key considerations for modernizing your companys data architecture and discuss critical aspects of scaling your cloud data infrastructure. The 60-minute virtual event is moderated by an independent industry analyst, with a topic introduction hosted by Solutions Review all broadcast live to an audience of registered attendees.

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Ataccama Innovate 2023 will feature the launch of the companys new data management capabilities with a resourceful discussion on self-service, ensuring data quality, monitoring, analysis, remediation, and more. Speakers include Ataccamas Group Product Manager Lenka Studnicna, VP of Data Governance David Kolinek, and Chief Product & Technology Officer Martin Zahumensky.

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For consideration in future data science news roundups, send your announcements to the editor: tking@solutionsreview.com.

Tim is Solutions Review's Executive Editor and leads coverage on data management and analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in Data Management, Tim is a recognized industry thought leader and changemaker. Story? Reach him via email at tking@solutionsreview dot com.

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ISU trustees approve tuition increase and engineering and data … – WGLT

The sticker price to be an Illinois State University student is now a little bit more expensive after a Board of Trustees vote that increased tuition, fees and housing costs for new students next year.

Trustees during a regularly scheduled meeting Friday morning approved a 1.9% increase in tuition and fees for incoming freshman and new graduate students for the 2023-24 academic year.

The 1.9% increase in tuition puts ISU's per-credit-hour cost for in-state students at just over $402 an hour; the 1.9% increase in mandatory fees raises that figure to just over $113 per hour to fund, among other things, increases in counseling staff salaries, new programming and various needs at Bone Student Center.

Interim president Andover Tarhule told trustees the need to increase fees stemmed from institutional costs increasing faster than revenue.

"Even with this tuition increase, we're proposing that the university is still being pinched by a reduced margin of operations," he said.

Tarhule added that student financial aid is one of ISU's fastest-growing expenses, increasing from $25 million a year to about $47 million, currently. That figure has risen, he said, "with no increase in enrollment or credit hour generation."

The trustees' vote also approved a 4% increase in the cost of university housing and dining services, a measure that will affect between 5-6,000 students, most of them freshmen and sophomores.

Some student fee funding will go to new program

ISU Vice President for Student Affairs Levester Johnson said that the increased funding the university receives from mandatory fees will, in part, be directed to developing a new intervention program aimed at retaining students.

Called a co-responder program, Johnson said the goal is to pair case managers or counselors with ISU housing staff and ISU police to address "late-night issues that we have within our residential environments in order to support our students before we lose them."

"We [currently] have a system by which if housing staff is having a challenge and they don't have the counseling background, what they do then is outreach maybe to counseling services, and maybe we have to call someone," Johnson explained to WGLT after the meeting. "Sometimes they have to call if it escalates to a point where ... they may have to call a police officer first. So in this case, they will tell one individual and then all three would then work together on the issue."

'Opportunities for cost savings' in ISU Athletics

Student trustee Aselimhe Ebikhumi noted Friday that the board was not approving any athletics fee increases despite additional costs in that department stemming from the addition of more teams to the Missouri Valley Conference.

Tarhule pledged during an Academic Senate meeting last month that the Athletics fee increase would be cancelled amid questions about department spending raised in WGLT reporting.

"There are still 450 students that have a greater need for travel, for study, so on and so forth: The athletes, "Ebikhumi said. "That's about $1 million that we're losing now, without that fee increase. What are we going to do to kind of address that deficit?"

Vice President of Finance Dan Stephens said officials would "work with our Athletics division and examine their budgets and continue to see whether there are opportunities for cost savings and to also look at some reserves that they may have."

"We are very confident that we'll be able to to absorb those costs as we work in kind of a partnership together," he added.

Differential tuition

Trustees also took a preliminary look at an amendment to their oversight guidelines that would allow them to set differing tuition rates for different programs.

Current language in the board's guidelines allows different tuition costs in only three circumstances, including undergraduate versus graduate rates and in-state versus out-of-state students. The amendment would allow trustees to set prices on top of base tuition depending on what program a student chooses.

Tarhule said this is a common university practice and named the University of Illinois system as a example. He said costs tend to be higher in departments where delivering the program itself requires more equipment or technology like nursing and engineering.

"The students that graduate from those programs also make more money when they graduate, so we want to make sure that the people who are getting the benefits for these hardware costs are also paying a little bit more to cover their cost," he said. "So it's an overall comprehensive look at pricing strategy, tuition strategy, and not directly related to engineering although engineering happens to be one of those programs that we would like to have a differential tuition for because it's more expensive."

The board may take a vote on the proposed amendment at its October meeting.

New degrees approved, some in engineering

ISU's College of Arts and Sciences will now offer a data science major after trustees' approval Friday. The program will be offered through the math department and is expected to draw 50-60 students each year, according to meeting documentation. Trustee approval followed the approval of the Academic Senate last month.

Trustees also voted to approve the creation of both an engineering and a mechanical engineering degree program. ISU has targeted a fall 2025 start date for those programs.

New trustee takes oath of office

Lia Merminga took her oath of office Friday morning, filling an open seat left by Robert Dobski. Merminga is currently the Laboratory Director for Fermi National Accelerator Laboratory in Batavia, a city south of Chicago.

ISU isn't Merminga's first stop in higher education: According to a news release, Merminga has served as an Adjunct Professor at the College of William and Mary, University of British Columbia, University of Victoria, and Stanford University in their departments of Physics and Astronomy.

Merminga was appointed to ISU's trustee board by Gov. JB Pritzker in February. Her appointment had been pending approval by the state Senate.

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Data Science Students Win Poster Competition | University of … – University of Arkansas Newswire

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From left: Amy Hoang, Laasya Ravipati, Trace Freeman and Shantel Romer.

Researchers involved in the Arkansas NSF EPSCoR DART program met in April at the Data Analytics that are Robust and Trusted (DART) All Hands Meeting and Student Poster Competition in Springdale.

Amy Hoang and Laasya Ravipati won first place in the Undergraduate Poster Competition at the conference. Hoang and Ravipati's project, which included help from their faculty advisor Karl Schubert and their staff advisor Lee Shoultz, involved comparing courses of the two-year Arkansas colleges with University of Arkansas courses to develop pathways for two-year data science students to transfer to four-year colleges and complete a B.S. in data science for colleges and universities within the "Data Science for Arkansas (DS4A)" ecosystem.

This project is important and insightful as the Data Science Program at the university, which launched in fall 2020, is the first of its kind in Arkansas. Hoang and Ravipati's award-winning poster, titled "2-Year Course Equivalencies," earned them a prize of $1,500 each to be used toward attending a future conference of their choice.

Trace Freeman won third place in the Undergraduate Poster Competition at the conference. Freeman's work entails the establishment of a study abroad partnership with the University of Nicosia located in Cyprus and applicable course equivalencies. Freeman's award-winning poster, titled "University of Arkansas Semester Abroad in Nicosia, Cyprus" earned him a prize of $500 to be used toward attending a future conference of his choice.

Shantel Romer, a graduate assistant of the DART project, won second place in the Graduate Poster Competition during the conference. Shantel's poster titled "The Year of the 2-Years: Course Track Overview," entails her continuous work in establishing the 2-plus program with colleges and universities across Arkansas. Her work is important to the overall DART project as it helps give students of all backgrounds and financial status the opportunity to receive an education in data science. Romer's award-winning poster earned her a prize of $750 to be used toward attending a future conference of her choice.

Karl Schubert, associate director of the Data Science Program, said, "I am very proud of Amy, Laasya, Trace and Shantel for their contribution to our statewide Data Science ecosystem, 'Data Science for Arkansas,' as recognized by their winning posters. Their work is helping us, and our two-year and four-year Data Science Program partners fully establish their programs across the state of Arkansas."

About the NSF EPSCoR DART-Education Theme:The Arkansas NSF EPSCoR program is a multi-institutional, interdisciplinary, statewide grant program leveraging $24 million over 5-years to expand research, workforce development, and STEM (science, technology, engineering, and math) educational outreach in Arkansas. The DART project is additionally will establish a statewide data science educational ecosystem by defining a combination of model programs, degrees, pedagogy and curriculum, providing resources and training for K20 educators, providing educational opportunities inside and outside the classroom for K20 students, and ensuring broad participation to impact the state's pipeline of data science skilled workers.

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Shifting gears: How data science led Madeleine Bonsma-Fisher … – University of Toronto

When Madeleine Bonsma-Fisher bikes through Toronto, she sees where her research meets the road.

Each street she pedals down presents as a series of data points: Shell count 15 people weaving past one another on the sidewalk, while three cars cruise down a road that takes up 80 per cent of the space.

A cycling activist, Bonsma-Fisher is studying traffic patterns as part of her post-doctoral research at the University of Torontos Data Sciences Institute, an institutional strategic initiative that is a tri-campus hub for number crunchers across disciplines. Before that, she modelled evolutionary interactions between microbes.

The common thread? Data and data analysis.

I don't want to say that data science is the answer to everything, but I am finding that there is so much you can do, Bonsma-Fisher says. It gave me a lot of freedom to really just do whatever I wanted.

Her current research focuses on what might seem like a simple question: At any point in Toronto, can you cycle to essential destinations grocery stores, health care and schools within 30 minutes, using only bike lanes and traffic-calmed roads?

The answer, she says, is far from straightforward. It requires sophisticated data analysis to make a map of the entire city and rate each road according to traffic stress, which accounts for factors such as traffic volume, speed limits and physical separation.

The next step, Bonsma-Fisher says, is to pinpoint places where infrastructure could improve access to cycling as a comfortable and convenient mode of transportation, such as dedicated bike lanes and physical separation from car traffic.

As she searches for active transportation solutions, Bonsma-Fisher is working with two advisers at the Data Sciences Institute: Shoshanna Saxe, an associate professor in the department of civil and mineral engineering, and Timothy Chan, a professor of mechanical and industrial engineering both in the Faculty of Applied Science & Engineering.

Whats cool about the Data Sciences Institute is that the vision is to bring people together with different experience and allow people to make that jump to a different field.

The winding road of Bonsma-Fishers research career and the data focus that underpins it began when she arrived at U of Ts School of Graduate Studies in 2014 with a physics degree and an interest in using the fields principles to solve biological problems.

Her supervisor, Sidhartha Goyal, an associate professor in the department of physics in the Faculty of Arts & Science, suggested she look into CRISPR a term she hadnt heard before, but one that would become the subject of both her masters and doctoral studies.

You may have heard of CRISPR in the context of genome editing, but the technology is derived from a bacterial defence mechanism that is analogous to adaptive immunity in humans. Many bacteria have an immune system called CRISPR that allows them to store memories of viruses in their own DNA like a genetic gallery of viral mug shots, Bonsma-Fisher explains.

As part of her PhD research, Bonsma-Fisher built a simple mathematical model to explore how computer-simulated interactions between populations of bacteria and viruses shape CRISPR immune memories.

The paper, published in the journal eLife earlier this year, provides fresh insight into the evolutionary arms race between viruses and bacteria with viruses mutating to evade immune recognition, while CRISPR builds bacterias DNA database of previous attackers. The simplicity of the model helped narrow down the most prominent processes in a complicated system, Bonsma-Fisher says.

Down the road, Bonsma-Fisher says the model could contribute to our understanding of immunity in more complex organisms, including humans.

Some of the conclusions we think are going to apply to any type of immune system-virus interaction.

While she was chipping away at her microbial models, Bonsma-Fisher made another discovery: data analysis skills were in short supply and high demand among her fellow graduate students. So, she co-founded the U of T Coders group to give researchers across all disciplines a chance to learn the basics of programming and teach each other new techniques through hands-on, member-led tutorials.

A lot of people would try to learn by themselves, she says, and there would be a lot of struggle and tears. U of T coders was a place for people to support each other through all of that.

Bonsma-Fisher is interviewed by CBC about cycling infrastructure in Ottawa.

Bonsma-Fishers turn toward sustainability-oriented research around cycling came naturally.

Like many university students, Bonsma-Fisher relied on her bike to commute to campus and was all too familiar with the challenges of being a cyclist in a car-focused Canadian city.

Upon moving to Ottawa, Bonsma-Fisher joined the board of advocacy group Bike Ottawa, where she contributed data analysis to report on how the COVID-19 crisis has influenced cycling trends and advocated for a bike-share program.

The more she learned about transportation infrastructure, the faster the wheels in her head began to turn. What if she could combine her passions cycling and data analysis to make the streets safer and cities more sustainable?

It felt like there were these two parts of me, she says. I [used data analysis] to bring together a lot of things I care about: environmental sustainability and having a more human-scale place to live.

Saxe, who is Canada Research Chair in Sustainable Infrastructure, says Bonsma-Fishers personal investment in the subject is foundational to her work. I find people do better research when they are intrinsically motivated by the topic, she says.

Bonsma-Fisher notes that quantitative data alone cant solve every problem, particularly when it comes to questions of equity and peoples lived experiences. Nevertheless, she says surveys suggest that most adults would be willing to bike if they were physically protected from cars and data can help point policymakers to the places where infrastructure is needed most.

I know from my experience what I want to bike on and what it feels to be on a road that feels unsafe, she says. If the city wants to get people biking and they do they need to make it safe.

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This Job Role will Still be Relevant When Data Scientists be Gone – Analytics India Magazine

In an era when being a Data Scientist was the epitome of cool, college graduates flocked to the field, drawn by the allure of its potential. The hype was real, and the demand for these professionals was skyrocketing. However, as artificial intelligence (AI) and machine learning (ML) continue to advance at an astonishing pace, doubts have arisen about the very existence of Data Scientists. The rapid adoption of AI and ML has ignited a passionate debate about the future of this once-revered profession.

On one side of the argument, there are those who assert that the recent announcement of OpenAI that the company will be introducing plugins to ChatGPt while teasing the launch of a code interpreter and web browser plugin will render traditional data science roles obsolete. They believe that the plugins may replace many of the common workflows of a data scientist, including visualization, trend analysis, and even data transformation. When looking at the code interpreter in tandem with the other advancements in the data science field, there is a notion that the algorithms and automation offered by AI will replace the need for human intervention in data analysis. Conversely, there are those who staunchly maintain that AI and ML will open up new and exciting opportunities in the field of data science.

One such role is of ML Engineers, where experts believe that the role of Data Scientists will gradually transform into. According to a report by Indeed, the job title machine learning engineer is growing at a rate of 344%, while the job title data scientist is growing at a rate of 25%. While another report by OReilly Media found that 80% of data scientists are planning to learn machine learning in the next year.

Since the age of generative AI is catching up, and the models often involve large-scale data processing and sophisticated algorithmic architectures, ML Engineers will be in more demand than ever. The engineers possess the technical expertise to handle the computational challenges associated with training and deploying these models effectively. They have a deep understanding of distributed computing, parallel processing, and GPU acceleration, allowing them to optimize the performance of generative AI models and scale them to handle vast amounts of data.

Additionally, ML Engineers are skilled in the deployment and productionisation of ML models. Generative AI models are not just research prototypes; they are increasingly being integrated into real-world applications. ML Engineers have the know-how to deploy these models into production environments, ensuring their stability, scalability, and robustness. They are proficient in building end-to-end ML pipelines, handling data preprocessing, model deployment, and monitoring, which are crucial steps in incorporating generative AI into practical use cases.

Furthermore, generative AI models often require fine-tuning and customization to align with specific business objectives and user requirements. ML Engineers possess the expertise to fine-tune and adapt these models, leveraging techniques such as transfer learning and hyperparameter tuning. They can tailor generative AI models to address specific challenges and optimize their performance for the intended application domain. Moreover, ML Engineers have a comprehensive understanding of the ethical implications and considerations associated with generative AI. They are aware of the potential biases, fairness issues, and privacy concerns that can arise when deploying AI models that generate content. ML Engineers are equipped to address these challenges and implement safeguards to ensure the responsible and ethical use of generative AI.

The role of a Data Designer is also becoming increasingly crucial in todays data-driven organizations, particularly in the era of Generative AI. These professionals hold the responsibility of defining the organizations unique norm of data, encompassing aspects such as data literacy, models, topics, and ontology. Moreover, they play a pivotal role in establishing a unified and coherent data vision across the entire organization, ensuring that everyone adopts a common language when dealing with data.

The primary focus of a data designer is to establish a structured framework for data management, ensuring that data is organized, accessible, and usable across the organization. They design and implement data models, which serve as blueprints for how data is structured, stored, and interconnected. These models help in capturing and representing the relationships between different data elements, enabling efficient data analysis and interpretation.

In addition to data modelling, data designers also define data standards and guidelines for data governance. They establish data quality criteria and ensure that data is accurate, consistent, and reliable. Data designers collaborate with various stakeholders, including data engineers, data scientists, and business analysts, to understand their data requirements and translate them into practical data design solutions.

Another important aspect of a data designers role is to create a common language or ontology for data within the organization. They develop a standardized vocabulary and terminology that allows different teams and departments to communicate effectively when working with data. This helps in avoiding confusion, improving collaboration, and promoting data literacy across the organization.

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Boise State University wins three NSF Regional Innovation Engines … – Boise State University

Boise State University is included in three $1 million awards from the U.S. National Science Foundations Regional Innovation Engines, or NSF Engines, program. Boise State researchers will be represented among 40 unique teams to receive the first-ever NSF Engines Development Awards, which aim to help partners collaborate to create economic, societal and technological opportunities for their regions.

The three Engines and their points of contact at Boise State include:

Nancy Glenn, Advancing Autonomous Systems Technologies in the Northern Front (North Dakota, South Dakota, Montana, Idaho)

This project will expand Boise States expertise in autonomous vehicles, including unoccupied aerial systems (UAS) or drones, in both training and applications, in partnership with industry and network members. UAS applications range from infrastructure monitoring to natural resource management, geosciences, and beyond, along with expanding data science.

Lan Li, Advancing Quantum and Supporting Technologies in the Northern Intermountain States (Montana, Wyoming, Idaho)

This project is to establish a network of quantum computing and information systems, called the Quantum Capacity, Operational Resilience and Equity (QCORE) in a three-state region (Montana, Wyoming, and Idaho). Boise State will collaborate with Montana State University, University of Wyoming, and local industry partners to enhance economic development, research innovation, and workforce development in the field of quantum computing and information.

David Estrada, Advancing Semiconductor Technologies in the Northwest (Oregon, Idaho, Washington)

Aligned to the White House Office of Science and Technologys National Strategy for Advanced Manufacturing, this project includes federal, regional and state government bodies, private industry and public learning institutions to develop a regional innovation ecosystem that expands discovery and entrepreneurship for the semiconductor industry. Partnerships with academic institutions and nonprofit organizations will also advance pathways for careers in semiconductor manufacturing.

All three NSF Engines Development projects represent Boise States expertise in critical and emerging technologies, and will build upon existing workforce training programs and use-inspired research, said Vice President for Research and Economic Development Nancy Glenn. Furthermore, the projects will expand our industry and agency partnerships, ultimately providing new opportunities for students to gain workforce skills and attracting and retaining talent.

One of the greatest challenges facing the information and communications technology ecosystem is the amount of energy required to process and store the tremendous amounts of data we produce, said David Estrada, associate professor of materials science and engineering and site director of Boise States NSF Center for Atomically Thin and Multifunctional Coatings. The emerging Pacific Northwest Semiconductor Ecosystem is very well positioned to solve such semiconductor related challenges, and help reap the economic rewards for Idaho, Oregon, and Washington.

Building a robust quantum innovation ecosystem is crucial for economics and national security, said Lan Li, associate professor of materials science and engineering. Quantum computing and information systems open new market opportunities in cybersecurity, artificial intelligence, financial services, and complex manufacturing. Boise State aims to explore a three-fold plan, including economic development, research innovation, and workforce development, which uniquely fits Boise State and its nationally recognized role in molecular and solid-state quantum materials development and characterization in support of quantum information applications as part of a regional quantum ecosystem.

The NSF Engines program is a transformational investment for the nation, ensuring the U.S. remains in the vanguard of competitiveness for decades to come.

These NSF Engines Development Awards lay the foundation for emerging hubs of innovation and potential future NSF Engines, said NSF Director Sethuraman Panchanathan. These awardees are part of the fabric of NSFs vision to create opportunities everywhere and enable innovation anywhere. They will build robust regional partnerships rooted in scientific and technological innovation in every part of our nation. Through these planning awards, NSF is seeding the future for in-place innovation in communities and to grow their regional economies through research and partnerships. This will unleash ideas, talent, pathways and resources to create vibrant innovation ecosystems all across our nation.

The awardees span a broad range of states and regions, reaching geographic areas that have not fully benefited from the technology boom of the past decades. These NSF Engines Development Awards will help organizations create connections and develop their local innovation ecosystems within two years to prepare strong proposals for becoming future NSF Engines, which will each have the opportunity to receive up to $160 million.

Launched by NSFs new Directorate for Technology, Innovation and Partnerships and authorized by the CHIPS and Science Act of 2022, the NSF Engines program uniquely harnesses the nations science and technology research and development enterprise and regional-level resources. NSF Engines aspire to catalyze robust partnerships to positively impact regional economies, accelerate technology development, address societal challenges, advance national competitiveness and create local, high-wage jobs.

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Cyber Security vs. Data Science Which Is the Right Career Path? – Analytics Insight

Here is the comparison between the most in-demand fields Cyber Security vs. Data Science

Todays IT-intensive environment has taught us two important lessons: we need solutions to transform tidal surges of data into something that organizations can utilize to make educated decisions. We must safeguard that data and the networks on which it is stored.

As a result, we have the fields of data science and cyber security. So, which is the better job path? You wont get far if you approach the debate between cyber security vs. data science in terms of which field is more in demand. Both fields are in desperate need of a workforce.

Cyber security is the discipline of securing data, devices, and networks against unauthorized use or access while assuring and maintaining information availability, confidentiality, and integrity. A career in cybersecurity entails entering a thriving industry with more available positions than qualified applicants.

Data science combines domain knowledge, programming abilities, and mathematical and statistical knowledge to generate usable, relevant insights from massive amounts of unstructured data, often known as Big Data.

A career in data science includes carrying out data processing responsibilities, data scientists often use algorithms, processes, tools, scientific methods, techniques, and systems, and then apply the derived insights across multiple domains.

Data science and cyber security are inextricably linked since the latter demands the defences and protection that the former supplies. To obtain their conclusions and assure the security of the resultant processed information, data scientists require clean, uncompromised data. As a result, the area of data science looks to cyber security to assist protect the information in any form.

For someone interested in a career in one of the more intriguing and busy IT disciplines, cyber security and data science present fantastic chances. The career trajectories in both fields are comparable.

Experts in cyber security often begin their careers with a bachelors degree in computer science, information technology, cyber security, or a related profession. Aspirants in the field of cyber security should also be proficient in fundamental subjects like programming, cloud computing, and network and system administration.

The prospective cyber security specialist joins a corporation as an entry-level employee after graduating. After a few years of work experience, its time to apply for a senior position, which normally calls for a masters degree and certification in a variety of cybersecurity-related fields.

Cyber security experts choose career paths like security analyst, ethical hacker, chief information security officer, penetration tester, security architect, and IT security consultant.

Data scientists demand more formal education than cyber security specialists. A masters or even a bachelors degree isnt required for cybersecurity professionals, though having those resources helps. A bachelors degree in data science, computer science, or a similar branch of study is required for most data science professions. After a few years in an entry-level role, the ambitious data scientist should seek a masters degree in Data Science, reinforced by a few relevant certifications, and apply for a position as a senior data analyst.

Data science experts choose career paths like data engineer, marketing manager, data leader, product manager, and machine learning leader.

According to Glassdoor, the average yearly salary for cyber security specialists in the United States is US$94,794, whereas this figure is 110,597 in India.

In the field of data science, Indeed reports that US-based data scientists make an average salary of US$124,074 annually, while their Indian counterparts earn an average salary of US$830,319 annually.

Depending on demand, the hiring of certain individuals, and the location, these numbers frequently change.

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You can get into these online master’s degree programs by … – Fortune

BY Sarah Thomas-OxtobyMay 12, 2023, 12:57 PM

General view of the Beneficence statue on the campus of Ball State University, as seen in November 2016 in Muncie, Indiana. (Photo by Michael Hickey/Getty Images)

Ball State University recently announced plans to re-envision the admissions process for two online masters degree programs in computer science and data science that will launch in fall 2023. The university is partnering with online learning platform Coursera to offer three introductory-level online courses in both fieldsand after students successfully complete the courses and earn an average grade of B or higher, they earn a spot in one of the masters degree programs.

This new admissions approach to these programs is an effort to attract students who may not have been drawn to these fields previously. Both of these programs are built with the person in mind that doesnt necessarily have a background in computer science or any type of data analysis, or programming or math, Jill Coleman, associate dean of the College of Sciences and Humanities and program executive director at Ball State University, tells Fortune. Both masters degree programs are housed in the universitys College of Sciences and Humanities.

Whats more, the online introductory courses are offered at the same cost of $1,314 per three-credit course, regardless of a students state of residency. Once students enroll in either of the masters degree programs, they will continue paying the in-state tuition price of $438 per credit for the degree programs, as well.

By creating a new pathway to enrollment in a masters degree program, Ball State wants to offer a low-cost and inclusive approach to train graduates for jobs in these fast-growing fields. By 2031, the number of jobs is projected to grow 36% for data scientists and 21% for computer scientists, according to the U.S. Bureau of Labor Statistics. These jobs often pay in excess of six-figure salaries.

Heres what you need to know about the changes to Ball States admissions process for these masters degree programs.

Offering an equalizer for people new to these fields

Removing the traditional admittance requirements creates an equalizer for people who want to move into the fields of computer science or data science, Nancy Prater, executive director of market development for online and strategic learning at Ball State University, tells Fortune. Whereas applicants to other masters degree programs typically need to demonstrate their relevant experience in computer science or data science, that will no longer be the case at Ball State.

By first funneling prospective students through the introductory courses, the university can attract a broader cohort to these masters degree programsincluding students who may not have felt qualified to apply previously. Theres not a big involved process or wondering, Will I get in or not get into the program? Coleman says.

Students need only complete a form to register and pay tuition fees before starting the introductory courseswhich can be applied to either of the two degrees.

Streamlining the admissions process

Several elements typically go into an admissions process for an advanced degree program, including completing an application, securing recommendation letters, and preparing for and completing a standardized entrance exam. Theres also generally a minimum GPA requirement for acceptance into these programs.

For those people who are working full-time jobs with family commitments, finding a way to carve out time to complete the admissions process can feel dauntingand particularly for people looking to switch fields. Both the online computer science and online data science programs at Ball State University are designed with these types of student in mind.

The master of computer science has been redesigned and reimagined for this new audience, Coleman says. Both of these programs are looking for the working adult that is seeking a career change and new types of skill sets.

This effort to attract a new type of student is also why the university was receptive to a partnership with Coursera, Coleman notes. We saw this as an opportunity to have our Ball State faculty be spread out to the world, and to enable many more people to have the Ball State experience.

A humanities approach to computer science, data science

Both of the introductory courses, along with the program curriculum for the online masters in computer science and data science, were developed byand will be taught byfaculty from Ball States College of Sciences and Humanities. The colleges involvement is a natural framework that will benefit students, Coleman says.

Where humanities comes into play in both of these fieldsand especially in data scienceis that you have to communicate the information, be a decent writer, and a decent presenter. Because otherwise its just data, she adds.

The faculty intentionally designed the two online programs with a bit of overlap, says Prater. If youre not sure if you want to take computer science or data science, you can figure that out, she continues.

To start, students can take one to two courses which can be applied to either degree. That way students have a little time to test what path suits them best, says Prater.

How to apply to Ball States introductory courses

Beginning in August, students can complete a form to register and pay for the introductory courses. Students who successfully pass the three courses with a combined GPA of 3.0 or better will then be admitted into one of the two masters degree programs, according to Prater.

Each of the masters degree programs will take 24 months to complete. The computer science program requires 36 credits to graduate, with a total tuition price tag just short of $16,000. Meanwhile, the data science program requires 33 credits, and will cost just shy of $15,000.

For the three introductory courses, students can opt to take them at their own paceone at a time or all three at once. The courses each run for 16 weeks, meaning it will take students who are new to these fields anywhere from about four months to nearly a year to complete these prerequisites.

For students where this is a completely new field, I would recommend taking one to two courses to start, says Prater.

Check out all of Fortunes rankings of degree programs, and learn more about specific career paths.

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Building A Data-Driven Organization – Forbes

the Enterprise today.Getty Images

Building data-driven businesses is clearly the topic of most board agendas this year. Increasing technology maturity coupled with the macro-economic environment is driving a sharp pivot to accelerate data monetization and define analytics as a product for many organizations. The reality is that creating a data-driven organization is easier said than done. Research indicates that just 23 percent of executives reported that their companies had created a data-driven organization, down from 31 percent four years ago.

Here are three areas to consider on the journey to building a data-driven business:

Create an enterprise data platform

Building an enterprise data platform (EDP) with truth in data is key to unlocking the value of data across an enterprise. The before and after pictures of companies that started with fragmented data with ownership across distributed groups and moved to a single centralized governed EDP are strikingly clear. The clear and winning model that has evolved is data at the center, analytics at the edge. There is evidence that cloud is actually exploding the total cost of ownership when it comes to data and volume is the single largest problem many in the industry are facing. Breaking down the volume problem by focusing on quality over quantity is key. Leading corporations are setting up data intelligence factories a single place to manage, certify, and publish their data globally. Once in place it can be coupled with a front-end interface for business leaders where they access, and export trusted and reliable business data, directly from the source.

Governance is key in this matter being intentional about the way data is managed, governed, controlled, used, and owned. Were often so focused on the outcome that we forget that the input is really critical. Making data valuable requires the right governance and ownership data strategy, lineage, and clear lines of ownership - with accountability flowing back to the business. In addition, for data to deliver truly meaningful output, data needs to be contextualized at the industry or subindustry level, and building in a foundation for this context is crucial for todays data-driven enterprises.

But even for companies that are truly data-driven, the day-to-day management and governance over data quality and engineering is still an evolutionary discipline. In many ways, this is a journey, not a destination, and there needs to be a fundamental and continued willingness to learn, experiment and innovate, to get to a true data-driven business status.

Develop a clear strategy for data science teams

With the proliferation of data, there is a need to prioritize efforts of data science teams along a portfolio approach, keeping a strong focus on key stakeholders and learning when to follow, partner or lead, as discussed in a recent interview with Murli Buluswar, head of analytics for Citi.

The portfolio approach is tried and tested in the venture capital world, but it applies to data science as well. The data science organization needs to build relevance in the short, medium and long term. You have to be tackling a set of problems that satisfy the business in the near term while ensuring that you're driving step change intelligence in the medium term and then more fundamentally, transformation in the long term. Having that portfolio approach allows you to be strategic and relevant. That is true for capital allocation as well.

Putting yourself in the shoes of your key stakeholders is also a key success factor for a data-driven strategy. If you are thinking like a CEO, you're thinking of materiality. How is this bending the curve on the future of that business unit or the larger enterprise in a way that is meaningful and appreciable at scale? If you are thinking like a CFO, the measurement manifests in either the P&L or the balance sheet. And if you are thinking like a Head of Audit, you have the mindset of assessing whether the decision is having the impact that it intended to. Some examples of outcome metrics include 1. financials realized 2. financials identified, but not yet realized. 3. adoption and non-financial change, for instance the speed of decision-making in some particular area, or more clarity with which decisions are made. 4. new frontiers of innovation, new questions that we are asking that are more early stage and will hopefully manifest themselves in outcomes and financial metrics. 5. the beta of what we do, delivering on your operating commitments a critical part of ensuring that the entire ecosystem is operating effectively.

Looking at analytics and data science end to end is key. The risk for many data science functions is to measure success through a simple functional lens of delivering a set of insights. But those measures are an intermediate step, they are not necessarily the end metrics. A CEO might care about the fact that his or her decision sciences or analytics team is coming out with super useful insights, they are more focused on this is driving financial outcomes, the breadth or the speed or the depth of decision-making in some important area of their business. That requires the data science capability to not just think vertically as a function but think horizontally and understand the end-to-end process.

Establish a data-driven culture

The need to create the right culture within the business is key and the right culture is one that understands the value of data. Data is only valuable if you do something with it. And the best technology leaders play a large role in helping the business really understand how they can use data to achieve better outcomes, asking the questions the business does not know to ask. There is a need to move from data science to decision science within the enterprise. With this new focus, the organization is not just chartered with delivering analytics or insight, it is chartered with delivering clarity on the decisions being made. It therefore needs to have an acute sense of the outcome youre trying to drive.

One great example value of data-driven superior outcomes is digital twins instead of bringing a manufacturing line down and re-laying the parts and pieces, the ability to assemble, run and test a digital twin, before even touching the assembly line, presents significant cost savings for businesses and reimagines how enhancements are delivered.

The other critical success factor is culling ideas quickly and decisively and only developing and driving initiatives that will have an impact at scale and within a reasonable timeframe. Along with the data democratization we want to see, new issues emerge, especially around curiosity, accountability, ownership, and change management often clubbed together as data literacy. A framework can accelerate data literacy programs across the corporation, and the first piece is new tools that are designed for businesspeople instead of just data scientists. The second is driving agile programs across the company that demonstrate the journey from ideation to visualization to outcomes. The third is affecting the mindset of making data a first-class citizen. And the best practice here is driving mindsets top-down, not bottom-up, starting with the CEOs office.

I am Chief Digital Strategist at Genpact, Venture Partner at Masa Group, and Chair of the Executive Technology Board. I advise F500 technology CXOs on digital transformation at the intersection of people, process, data and technology, and I am board advisor to AI startups, limited partner in digital-focused venture funds, and mentor to startup CEOs.

Previously I served as CDO at Genpact, and earlier as CEO for a SaaS firm. Before that I was an entrepreneur and built four startups - in edge networking, data center automation, predictive applications, and enterprise SaaS (and sold to Akamai, BMC, FIS, and Genpact) and well before that I managed compute servers at HP.

I earned my graduate degree at the University of Minnesota, and my undergraduate degree at the Indian Institute of Technology, and studied in the executive education programs at Northwestern and Stanford Universities

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Building A Data-Driven Organization - Forbes

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