Category Archives: Data Science
What is a Machine Learning Engineer? Salary & Responsibilities – Unite.AI
The world of artificial intelligence (AI) is growing exponentially, with machine learning playing an instrumental role in bringing intelligent systems to life. As a result, machine learning engineers are in high demand in the tech industry. If youre contemplating a career in this captivating domain, this article will give you a comprehensive understanding of a machine learning engineers role, their primary responsibilities, average salary, and the steps to becoming one.
A machine learning engineer is a specialized type of software engineer who focuses on the design, implementation, and optimization of machine learning models and algorithms. They serve as a link between data science and software engineering, working in close collaboration with data scientists to transform prototypes and ideas into scalable, production-ready systems. Machine learning engineers play a vital role in converting raw data into actionable insights and ensuring that AI systems are efficient, accurate, and dependable.
Machine learning engineers have a wide range of responsibilities, including:
The average salary of a machine learning engineer can vary based on factors such as location, experience, and company size. According to Glassdoor, as of 2023, the average base salary for a machine learning engineer in the United States is approximately $118,000 per year. However, experienced professionals and those working in high-demand areas can earn significantly higher salaries.
To become a machine learning engineer, follow these steps:
the key traits that contribute to the success of a machine learning engineer.
Machine learning engineers often face complex challenges that require innovative solutions. A successful engineer must possess excellent analytical and problem-solving skills to identify patterns in data, understand the underlying structure of problems, and develop effective strategies to address them. This involves breaking down complex problems into smaller, more manageable components, and using a logical and methodical approach to solve them.
A solid foundation in mathematics and statistics is crucial for machine learning engineers, as these disciplines underpin many machine learning algorithms and techniques. Engineers should have a strong grasp of linear algebra, calculus, probability, and optimization methods to understand and apply various machine learning models effectively.
Machine learning engineers must be proficient in programming languages such as Python, R, or Java, as these are often used to develop machine learning models. Additionally, they should be well-versed in software engineering principles, including version control, testing, and code optimization. This knowledge enables them to create efficient, scalable, and maintainable code that can be seamlessly integrated into production environments.
Successful machine learning engineers must be adept at using popular machine learning frameworks and libraries such as TensorFlow, PyTorch, and Scikit-learn. These tools streamline the development and implementation of machine learning models, allowing engineers to focus on refining their algorithms and optimizing their models for better performance.
The field of machine learning is constantly evolving, with new techniques, tools, and best practices emerging regularly. A successful machine learning engineer must possess an innate curiosity and a strong desire for continuous learning. This includes staying up-to-date with the latest research, attending conferences and workshops, and engaging in online communities where they can learn from and collaborate with other professionals.
Machine learning projects often require engineers to adapt to new technologies, tools, and methodologies. A successful engineer must be adaptable and flexible, willing to learn new skills and pivot their approach when necessary. This agility enables them to stay ahead of the curve and remain relevant in the fast-paced world of AI.
Machine learning engineers frequently work in multidisciplinary teams, collaborating with data scientists, software engineers, and business stakeholders. Strong communication and collaboration skills are essential for effectively conveying complex ideas and concepts to team members with varying levels of technical expertise. This ensures that the entire team works cohesively towards a common goal, maximizing the success of machine learning projects.
Developing effective machine learning models requires a high degree of precision and attention to detail. A successful engineer must be thorough in their work, ensuring that their models are accurate, efficient, and reliable. This meticulous approach helps to minimize errors and ensures that the final product meets or exceeds expectations.
Becoming a machine learning engineer requires a strong foundation in mathematics, computer science, and programming, as well as a deep understanding of various machine learning algorithms and techniques. By following the roadmap outlined in this article and staying current with industry trends, you can embark on a rewarding and exciting career as a machine learning engineer. Develop an understanding of data preprocessing, feature engineering, and data visualization techniques.
Learn about different machine learning algorithms, including supervised, unsupervised, and reinforcement learning approaches. Gain practical experience through internships, personal projects, or freelance work. Build a portfolio of machine learning projects to showcase your skills and knowledge to potential employers.
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What is a Machine Learning Engineer? Salary & Responsibilities - Unite.AI
This bill would pay colleges and universities to offer more data science – Federal News Network
Rep. Don Beyer (D-Va.) recently introduced a bill to boost data science education. It would offer up to $10 million in grants to schools from nursery school to four-year colleges. Is it a good idea? For one answer, theFederal Drive with Tom Temin with Laura Albert, a University of Wisconsin Professor of Industrial and Systems Engineering.
Tom Temin This bill then seeks to improve data literacy. I guess, my question is, if data literacy is such an important field, wouldnt schools and colleges offer it anyway?
Laura Albert They do, but we need more of it. Data is here to stay. Our economy is driven by data, and todays students need to be prepared to work with data, drive decisions, drive innovation in all sectors, not just the data sector. Whats unique about this bill, is that it expands that data education beyond just math to help make those connections across different fields, for example, across into social studies. And this will really help teach students the skills they need to be data literate, but also maybe spark an interest in pursuing a data driven career.
Tom Temin And Id like to define that term data literate, because you could be data literate in understanding the difference between a cell in an Oracle database and a cell in some other database, and what the compatibilities are and how to make them work in a same application together. Thats one form of data literacy. The other form is, what is the problem Im trying to solve and what information do I need to get the answer Im looking for? Thats a different level of data literacy. What should this best aim at? Do you think?
Laura Albert The second one that you mentioned, is thinking really broad about data. The ways that were coming into contact with data in our everyday lives, our data visualizations, we see a lot of charts on the news even and in the newspaper, and understanding how to use numbers to help us understand the bigger picture and to drive decisions. So its not necessarily programing and getting into spreadsheets, but we want to see some of that later on. In K-12, sometimes it just starts with understanding and telling stories and seeing visualizations.
Tom Temin Because people that have a point of view about this or that topic often, the old story statistics and lies and so forth. Would data literacy help people understand when theyre reading propaganda, and have the ability to say, well, they only took a sample from point A to point B, and really the phenomenon went to point D. And we need a bigger base of data to really understand in contrary to what Im reading here, that kind of literacy. How do you get that to people?
Laura Albert Absolutely. That is something that will be a nice side effect of data literacy. A lot of working with numbers, especially statistics, really focuses on trying to figure out what happened. And we have these clues that are in data. And the data are often numbers, but not entirely numbers. And we use those clues to sort through what happened. And that can help us tackle topics such as misinformation or understanding, assumptions that may or may not make sense. That led to an analysis that drew certain outcomes. And the skills can be quite broad.
Tom Temin All right. And then theres the issue of math education, itself. If you look at a city like Philadelphia, Baltimore, Washington, D.C., Chicago, these great big educational machinery, lets say. And yet, 50, 80%, depending on the city of the graduates of high school, are barely, charitably, only 20%. In some cases, you could say charitably, math literate. And so do you think that the bill should, perhaps or the effort should be expanded to teaching math better? Because data doesnt do anything unless theres math to tie it together.
Laura Albert These are really synergistic efforts. And I will say that math education at the K-12 level has gotten a lot of attention in the past few decades, and we obviously have a lot more work to do, in particular in regards to COVID. One of the things were seeing in higher ed is that math education was interrupted during COVID, and were seeing that students arent quite prepared for taking the next level of math. And this is ripple effects that were seeing at the universities, but at all levels. But thats really a sidetrack. And back to your question, is that we need great math education and we need good data education, and students need to see some of these topics outside of math, and thats going to help strengthen some of their education. And thats really going to help our economy and put us in the best position to have a strong economy in the future.
Tom Temin Were speaking with Dr. Laura Albert. Shes a professor of industrial and systems engineering at the University of Wisconsin. And by the way, you are part of a consortium of people like you that think about these things at a policy level, correct?
Laura Albert Absolutely. Im the president of informs. It stands for the Institute for Operations Research and Management Sciences. And its mostly professors, but also professionals that have a background in data analytics and using that to drive decisions. And my background is industrial engineering, but we have folks from a variety of different academic backgrounds, including management, science, applied math, various engineering disciplines and computer science. And were really interested in solving problems in a data driven manner. And the need for this and the opportunities for us has grown and we want to help the next generation come up behind us.
Tom Temin And in the release and in some of the text of that bill, it does mention, as I said at the top. From preschool to four year colleges and universities eligible for these grants, not a ton of money. $10 million nationwide its a nice touch. But, I guess the question is, you cant get a grant to do the same thing in university, that you can get a grant to do the same thing in nursery school. And is there something that can be taught at this level to the very young, do you think?
Laura Albert Yes. As a parent, Im always amazed. I always study the education that happens at that young age. And some of the work they do with data is polling students in the class and making visualizations, histograms, charts, the type of thing that we see on the TV news every night. And whats fascinating to me as a college professor is that its also kind of developmentally appropriate to start with some of those data topics and preschool. And they can see that chocolate ice cream is maybe preferred to vanilla. And its pretty exciting as a professor to see where it all begins, and starting early is advantageous.
Tom Temin And maybe one of the crucial questions for this bill is, who makes the decision? As to who gets grants? What do you think would constitute a good proposal to get a grant under this bill from Don Beyer (D-Va.), the Data Science and Literacy Act.
Laura Albert Thats probably above my pay grade. You pointed out that its only $10 million. And Im hoping that this is a bipartisan effort, and it should be, because this is really a bipartisan issue. And I hope that this is one of many efforts that will come out of the federal government to support data literacy. So Im hoping that theres a lot more opportunity in the entire education ecosystem for data.
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This bill would pay colleges and universities to offer more data science - Federal News Network
Top 10 Data Analytics Skills and Platforms for 2023 – Analytics Insight
Master the top 10 data analytics skills and platforms of 2023 for business successIntro
Data analytics has become an essential aspect of business decision-making in recent years. With the increasing availability of data, companies require professionals who can extract meaningful insights from this data to drive business success. However, with the field of data analytics evolving at a rapid pace, professionals must stay up-to-date with the latest skills and platforms. In this article, well cover the top 10 data analytics skills and platforms that professionals in the field should master in 2023. From data visualization to machine learning and cloud computing, well explore the essential skills and platforms that can help data analysts provide valuable insights to their organizations. So, lets dive in and explore the top data analytics skills and platforms for 2023.
One of the essential skills for data analytics is data visualization. Visualization allows data analysts to effectively communicate their findings to decision-makers. Tools like Tableau, Power BI, and QlikView are popular platforms for data visualization. These tools can help businesses create interactive dashboards, charts, and graphs to communicate insights and trends effectively.
Machine learning is another crucial skill for data analytics. It involves training algorithms to learn from data and make predictions or decisions based on that learning. Python, R, and TensorFlow are popular platforms for machine learning. TensorFlow is preferred for deep learning applications, while Scikit-learn is used for traditional machine learning. Understanding how to apply machine learning algorithms is an important skill for data analysts
Data cleaning involves the process of identifying and fixing inaccuracies, inconsistencies, and errors in datasets. This skill is important because dirty data can negatively impact the accuracy of insights derived from data analysis. Platforms like Trifacta, OpenRefine, and Talend are popular for data cleaning.
Data warehousing is the process of storing and managing data from various sources in a single location. Its important to have an efficient data warehousing system in place to ensure quick and easy access to data. Platforms like Snowflake, Amazon Redshift, and Google BigQuery are popular for data warehousing.
Data mining involves the process of extracting patterns and insights from large datasets. This skill is important because it can help businesses make informed decisions based on trends and patterns in their data. Platforms like RapidMiner, KNIME, and SAS are popular for data mining.
Data governance involves the process of managing the availability, usability, integrity, and security of data used in an organization. This skill is important because it helps ensure data is used ethically and effectively. Platforms like Collibra, Informatica, and Alation are popular for data governance.
Data science is a multidisciplinary field that involves using scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Platforms like Anaconda, Jupyter, and Databricks are popular for data science.
Cloud computing involves the delivery of computing services over the Internet. Its important for data analytics because it allows easy access to large datasets and powerful computing resources. Platforms like Amazon Web Services, Microsoft Azure, and Google Cloud Platform are popular for cloud computing. Amazon Web Services is known for its scalability and flexibility, while Microsoft Azure is preferred for its integration with other Microsoft tools.
Business intelligence involves the process of analyzing data to help businesses make informed decisions. This skill is important because it helps businesses understand their data and use it to drive success. Platforms like MicroStrategy, Oracle BI, and IBM Cognos are popular for business intelligence.
Finally, its important to have a solid data analytics strategy in place. This involves identifying the business goals and objectives, selecting appropriate data analytics tools and platforms, and creating a roadmap for implementation. The ability to develop and execute a data analytics strategy is crucial for success in data analytics.
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Top 10 Data Analytics Skills and Platforms for 2023 - Analytics Insight
New research by Bryant professor explores AI’s role in finding novel … – Bryant University
Tingting Zhao, Ph.D., has dedicated her research to developing new tools to process the world around us. By applying that skillset to the health sciences, shes finding ways to use cutting edge data science techniques to aid others. This is a topic that I am really passionate about, the idea that something I was able to work on might make life easier for patients someday, says the assistant professor of Information Systems and Analytics and a faculty fellow with the School of Health and Behavioral Sciences at Bryant University.
Her recent publication, Identification of significant gene expression changes in multiple perturbation experiments using knockoffs, published in the scientific journal Briefings in Bioinformatics, offers insight into how studies in biology and data science can augment one another to great effect. Using machine learning a branch of artificial intelligence and computer science that focuses on the use of data and algorithms to imitate the way that humans learn Zhao and her collaborators developed and honed algorithms that can help identify how genes respond to specific stressor stimuli. They then compared those reactions to the effects of various small molecule drugs.
Zhao was the lead author for the paper. Her co-authors include Harsh Vardhan Dubey, a Ph.D. student in Statistics at the University of Massachusetts Amherst; Guangyu Zhu, an assistant professor in the Department of Computer Science and Statistics at the University of Rhode Island; and Patrick Flaherty, associate professor of Mathematics and Statistics at the University of Massachusetts Amherst.
This is a very fast evolving field, and there is a lot of competition because people all over the world want to make the next breakthrough.
The information they discovered through their work can help us develop a more detailed understanding of the molecular pathways that respond to genetic and environmental changes as well as the underlying mechanisms of disease. Zhao is particularly excited for the potential the algorithms have regarding drug repurposing. Creating and approving a new drug takes a great deal of work and costs a great deal of money, Zhao explains. We want to explore if we can use cheap, previously existing, drugs for new purposes, including being used in place of more expensive drugs.
The study is part of the growing field of bioinformatics, an interdisciplinary space that involves the development of methods and tools for understanding biological data, with applications to the understanding of health, disease, and medical care. That health-based focus, she says, adds a greater purpose to her data science work. We are not just developing algorithms in a vacuum, says Zhao. We develop these techniques to solve a real problem and that problem, which should always motivate people doing this work, is How can we help people?
The bioinformatic fields rise, she says, speaks to a larger embrace of computational analysis within the health sciences. Peoples opinions have changed over time. At the start, there was some skepticism of this sort of research because it was not just biology, but very computational as well, says Zhao. But over time people became more comfortable with it because they came to realize how helpful these tools could be.
Today, bioinformatics is a hotbed for research. This is a very fast evolving field, and there is a lot of competition because people all over the world want to make the next breakthrough, says Zhao, who notes that this competition is part of what draws her to the field. There is a thrill in knowing that I could create an algorithm that could beat all of the others.
But while healthy competition can be a spur for innovation, Zhao states, in the end, collaboration is the real goal. You always hope that others can use your research and make their own improvements and help more people, she reflects.
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New research by Bryant professor explores AI's role in finding novel ... - Bryant University
Significance and Purpose of Data Science in 2023 – Rebellion Research
Significance and Purpose of Data Science in 2023
Alt-Text: Significance and Purpose of Data Science in 2023
Data Science is an emerging subject that integrates statistics, computer science, and domain expertise to extract insights and information from data. Data Science is fundamentally concerned with gathering, cleaning, analyzing, and displaying data in order to identify patterns, correlations, and insights that may be used to inform decision-making.
Data Science uses a wide range of techniques and technologies, including statistical modeling, machine learning, data mining, and data visualization, to elicit useful insights from complicated and frequently large-scale information. Data Science has applications in many industries, including healthcare, finance, marketing, and government, and it is critical for companies that want to use data-driven insights to inform their decision-making processes.
Table of Contents:
Data Science involves a four-step process:
Data Sciences importance may be linked to its capacity to:
The purpose of Data Science can be summarized as follows:
Data Science has several applications in a variety of businesses and areas. Following are some of the most important Data Science applications:
Data Science has several applications in a variety of businesses and areas. Organizations may make better-informed decisions, streamline their processes, and enhance their goods and services by employing statistical and computational tools to extract insights from data. As the amount and complexity of data continue to expand, so will the importance of Data Science.
Significance and Purpose of Data Science in 2023
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Significance and Purpose of Data Science in 2023 - Rebellion Research
Rupert raises $8 million seed round to build the future of analytics … – PR Web
NEW YORK (PRWEB) April 12, 2023
Rupert, the leading analytics distribution platform, announced that it raised $8 million in seed funding. Cortical Ventures and IA Ventures led the round with participation by Citi Ventures and Joule Ventures, as well as data leaders, including CEO of AtScale, Christopher Lynch, founders of Stitch, Jake Stein and Bob Moore and other executives from leading data organizations such as Snowflake, Looker, Weights & Biases, Snowplow, Alation, and SAP.
Speculative analytics projects are being discarded by analytics leaders who need to quantify and maximize their return on analytics investments, and are accountable for executive-level awareness of business outcomes and use cases.
Leading data-centric organizations like AppsFlyer, Coalition, GoodRx, and Mux are turning to Rupert as the markets first solution that closes the loop between analytics and measurable business results. With Rupert, their customer success teams reduce customer churn, revenue operations teams decrease customer acquisition costs, and marketing teams maximize their ROAS.
Harnessing the power of natural language capabilities, Ruperts platform connects analytics sources (e.g. BI tools, semantic layers), operational applications (e.g. CRMs, ad campaign managers, Jira), and communications tools (e.g. Slack, Teams, email), enabling analytics and operations teams to deliver hyper-personalized insight alerts to business stakeholders, at scale. Alerts are prompted by intelligent data triggers and accompanied by Action Modules that allow users to immediately act on critical insights thus ensuring that every business improvement opportunity is capitalized upon.
Rupert was co-founded by Ziv Wangenheim and Yoni Steinmentz after leading analyst and data teams at organizations such as Google, Jefferies, Lazard, and Palantir. "The most effective business teams take data-driven business actions. says Rupert CEO, Ziv Wangenheim.
While organizations invest heavily in infrastructure for analytics, the insights their business teams need remain reactive, hidden, and idle in endless reporting dashboards. These dashboards are designed for exploration, rather than being proactive and driving meaningful business actions. Our customers strive to be truly data-driven and need the ability to activate their insights and measure the business impact of their analytics.
We built Rupert to help organizations close the loop from data pipelines to business impact. Our customers see up to 14x increase in business actions triggered by analytics. We help analytics teams claim their seat at the business table and move out of "support mode" and into proactive mode, by connecting their work directly to business KPIs and impact.
Jeremy Achin, founder of DataRobot and Cortical Ventures: After a decade of working with thousands of enterprise customers building analytics and AI projects, I saw first hand the challenge of surfacing actionable insights to business users at the right time and in the right place. Rupert solved this massive problem with a novel approach as it learns the business users behavior and needs and caters them with tailored and timely actionable insights while empowering data teams to deliver ROI on massive investments in data teams and tools.
According to Christopher Lynch, CEO at AtScale, Organizations are spending billions of dollars on modernizing their data stacks for analytics and data science initiatives. With todays macro-economic environment, CFOs and company boards are tightening their purse strings on initiatives that dont drive quantifiable, positive business outcomes. Ruperts analytics distribution platform is the first of its kind to measure the revenue and cost savings impacts tied to analytics initiatives.
The funding will enable the company to invest in their product, add features and integrations that their customers and market are waiting for, and scale their go-to-market efforts.
About Rupert
Rupert is the leading analytics distribution platform that drives business outcomes from analytics. It helps analytics and operations teams to be proactive and deliver to business users personalized, actionable insights at scale, without the pain of servicing them. Rupert measures and boosts analytics assets ROI by maximizing actionability and engagement with them and by minimizing the analytics infrastructure and operations costs. For more information, visit https://www.hirupert.com/
About Cortical Ventures
Cortical Ventures is a venture capital firm focused on helping entrepreneurs building the next generation AI Companies. The firm was started by DataRobot founder Jeremy Achin and Igor Taber, who previously led Corporate Development at DataRobot and was an early investor in the company while he was at Intel Capital. Cortical Ventures was started to invent, incubate and invest in the companies leading the AI revolution. The firm is backed by leading VC firms and partners, AI luminaries and top founders and operators in the industry. https://cortical.vc/
About IA Ventures
IA Ventures is a seed-stage venture capital firm investing in software businesses ahead of product-market fit. We are a tight-knit, all-partner team that works directly with founders every step of the way. Since its founding in 2010, IA has backed more than 100 companies including The Trade Desk, Datadog, DigitalOcean, Wise, Flatiron Health, Recorded Future, Komodo Health, DataRobot, and Ironclad. https://www.iaventures.com/
About Citi Ventures:
Citi Ventures harnesses the power of Citi to help people, businesses, and communities thrive in a world of technological change. Headquartered in San Francisco with offices in New York, London, Palo Alto, Tel Aviv, and Singapore, Citi Ventures accelerates discovering new sources of value by exploring, incubating, and investing in new ideas, in partnership with Citi colleagues, our clients, and the innovation ecosystem. The team focuses on six key areas: Financial Services & Technology, Commerce & Payments, Data Analytics & Machine Intelligence, Security & Enterprise IT, Marketing & Customer Experience, and Property Technology. Citi Ventures accelerates its portfolio companies' ability to scale through collaboration with Citi's global businesses and industry experts. http://www.citi.com/ventures
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Rupert raises $8 million seed round to build the future of analytics ... - PR Web
USM Professor Earns NSF EPSCoR Fellowship to Develop New … – The University of Southern Mississippi
Tue, 04/11/2023 - 09:56am | By: Van Arnold
Dr. Xiaodan Gu, professor in the School of Polymer Science and Engineering at The University of Southern Mississippi (USM), has been awarded a $250,886 National Science Foundation grant titled, RII Track-4: Obtaining Data Science Expertise to Enable Rapid Data-Driven Material Discovery.
The one-year grant is part of a new round of research fellowships announced by NSF under its Office of Integrative Activities (OIA) Track 4 program, with the aim of improving research competitiveness in EPSCoR (Established Program to Stimulate Competitive Research) states.
Gus project will utilize cutting-edge data science techniques to accelerate the discovery and design of new polymeric materials with improved properties for various applications. The research will focus on leveraging large datasets, computational modeling, and machine learning algorithms to predict and optimize material properties, leading to the development of novel materials with enhanced performance and functionality.
This NSF OIA Track 4 award represents a significant opportunity for me to acquire expertise in this rapidly developing field of material science research, said Gu. By harnessing the power of data-driven approaches, we can accelerate the process of material discovery, reduce experimental trial-and-error, and uncover new materials with properties that were previously unattainable. It's like Moore's Law for materials. The faster the pace of discovery, the better the material's properties can be.
To acquire this new skillset, Gu will collaborate with Dr. Alexander Hexemer, a renowned data scientist in the X-ray scattering community, at the Center for Advanced Mathematics for Energy Research Applications (CAMERA) at the Lawrence Berkeley National Laboratory. Together, they will develop innovative algorithms and models that can analyze vast amounts of data, including material properties, chemical compositions, and processing conditions, to identify patterns and correlations that can guide the design of new materials.
The potential impact of this research is far-reaching, said Dr. Derek Patton, Director of the School of Polymer Science and Engineering. Data-driven material design has the potential to revolutionize industries such as aerospace, electronics, energy, and healthcare by enabling the development of advanced materials with tailored properties that can drive technological innovations.
The EPSCoR RII Track-4: EPSCoR Research Fellows program aligns with NSF EPSCoR's strategic goal of establishing sustainable pathways for Science, Technology, Engineering, and Mathematics (STEM) professional development, advancing workforce development in STEM, and promoting engagement in STEM at national and global levels.
The fellowship provides awards to build research capacity in institutions and transform the career trajectories of investigators by fostering collaborations with investigators from premier private, governmental, or academic research centers. The fellowship also provides opportunities for extended or periodic collaborative visits to a selected host site, with the aim of creating lasting impacts that enhance the fellows' research trajectories beyond the award period.
Gu's research under the NSF OIA Track 4 award on data-driven material design has the potential to contribute significantly to the field of material science and advance the development of new materials with enhanced properties. The fellowship will also provide valuable opportunities for collaboration and skill acquisition, ultimately benefiting the broader scientific community and driving innovations in various industries.
For more information about this award. One can find it on the NSFs website.
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Altair to Showcase Pharmaceutical Process Optimization … – PR Newswire
Altair will feature pharmaceutical manufacturing process simulation and data analytics technologies
SEONGNAM, South Korea, April 13, 2023 /PRNewswire/ -- Altair(Nasdaq: ALTR), a global leader in computational science and artificial intelligence (AI), will be exhibiting at Korea Pharm&Bio, April 18-21 in Goyang City, Gyeonggi-do, South Korea. At the event, Altair will showcase technologies and approaches that optimize pharmaceutical test analytics and manufacturing processes.
"We look forward to sharing our solutions that converge AI technology and simulation, a new paradigm for manufacturing bio, and further expand our business scope as a company specializing in AI," said Eunha Yu, Altair Korea branch manager.
On April 19, on the third floor of KINTEX, Altair engineer Mikyung Yang will present a talk on "Digital Twin of Pharmaceutical Process," where he will explain how to optimize pharmaceutical process and accumulated resources and leverage a data analysis platform that can utilize accumulated data.
As more than 75% of pharmaceuticals are powder formulations, Altair plans to expand its business in the Korean pharmaceutical market by highlighting the need for fine powder process optimization technology. At the event, Altair will share insight into Altair EDEM, a particulate powder simulation widely used by global pharmaceutical companies.
In addition, data analytics solutions that utilize data and AI technology in the pharmaceutical bio industry will be introduced including Altair SLC which runs programs written in SAS language syntax without translation and without needing to license third-party products. Altair SLC's built-in SAS language compiler runs SAS language and SQL code and utilizes Python and R compilers to run Python and R code and exchange SAS language datasets, Pandas, and R data frames. Altair RapidMiner, Altair's data analytics and AI platform, will also be showcased, which includes massive data preparation, machine learning (ML), and visualization technologies.
AI technology is attracting attention as a technology that can reduce the cycle and cost of drug development, but it is difficult to secure a workforce with both an understanding of pharmaceutical manufacturing and AI technology capabilities. Altair's Center of Excellence (CoE) gives individuals, teams, and organizations the power to understand and implement data principles, tools, and approaches alongside Altair's experts. It is also a holistic, multi-faceted, continuous approach to learning and support that upskills anyone to the point where they can utilize basic data analysis and data science skills and techniques, even if they don't have a data background.
For more information, visit Altair booth #3G402 at Korea Pharm&Bio at KINTEX, April 18-21, or visit https://web.altair.com/pharmbio2023-0.
About AltairAltair is a global leader in computational science and artificial intelligence (AI) that provides software and cloud solutions in simulation, high-performance computing (HPC), data analytics, and AI. Altair enables organizations across all industries to compete more effectively and drive smarter decisions in an increasingly connected world all while creating a greener, more sustainable future. For more information, visit https://www.altair.com/.
Media contactsAltair South KoreaIseul Jeong+82.10.7339.0740[emailprotected]
Altair CorporateJennifer Ristic+1.216.849.3109[emailprotected]
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Altair to Showcase Pharmaceutical Process Optimization ... - PR Newswire
Melissa for Education Portal Empowers Students with Free Access to Data for Projects and Research – Yahoo Finance
Melissa
Melissa furthers commitment to the study and use of data in higher education by sponsoring Embark, UCIs inaugural collegiate datathon
RANCHO SANTA MARGARITA, Calif., April 12, 2023 (GLOBE NEWSWIRE) -- Melissa, a leading provider of global data quality, identity verification, and address management solutions, today debuted its Melissa for Education portal giving students and faculty access to a range of Melissa datasets and tools. This new resource, available to individuals with a valid .edu email address, is designed to introduce future data scientists to the inherent value of data and its global relevance in an ever-increasing range of industries and applications. The portal further enables a practical approach to active learning, empowering students to request specific datasets for use in research and capstone projects. Data and tools are free to students and offered at low cost to instructors in support of their own research projects outside the classroom.
To be data-driven in todays world is a business imperative. Without a solid grasp of data and what it means, youre limited in what you can achieve, said Daniel Kha Le, Vice President of Data & Analytics, Melissa. As the address experts, we here at Melissa are obsessed with data and its application across industries. Through our involvement in a range of student programs, including the Melissa for Education portal and the UCI Embark datathon, were dedicated to nurturing a passion for data in the next generation of data champions.
With the Melissa for Education portal, students have the opportunity to discover, leverage, and understand the types of tools that will allow them to better clean and manage data making it more useful and reliable for real-world application. By tapping into smarter and more relevant datasets supported by a comprehensive range of data tools, students can develop and refine their skills for use in an array of careers and industries.
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As part of the companys ongoing commitment to data education, Melissa is also sponsoring the Embark datathon from April 15 to April 16 at the University of California, Irvine (UCI) Interdisciplinary Science and Engineering Building (ISEB). Open to college students in Southern California, this event will bring together data enthusiasts and business stakeholders to solve practical problems using data science tools and techniques. Admission is free and the event features a keynote, workshops, data challenges, prizes, networking opportunities, and refreshments. Register and learn more here.
Melissa has been an active partner of UCI for nearly a decade, providing insight and expertise in data science to faculty and students, said UCI Associate Dean, David Van Vranken. Their hands-on involvement in and sponsorship of our Embark datathon helps give participants an up-close look at the power of data and the endless possibilities it brings to myriad situations. Melissas new education portal is a welcome and much-needed resource that will enable students to learn and grow their data skills in preparation for their future endeavors.
Click here for access to the Melissa for Education portal and here to attend the UCI Embark datathon. To connect with members of Melissas global intelligence team, visit http://www.Melissa.com or call 1-800-MELISSA.
About MelissaSince 1985, Melissa has specialized in global intelligence solutions to help organizations unlock accurate data for a more compelling customer view. More than 10,000 clients worldwide in arenas such as retail, education, healthcare, insurance, finance, and government, rely on Melissa for full spectrum data quality and ID verification software, including data matching, validation, and enhancement services to gain critical insight and drive meaningful customer relationships. For more information or free product trials, visit http://www.Melissa.com or call 1-800-MELISSA (635-4772).
Media contactsGreg BrownVice President, Global Marketing, Melissagreg.brown@Melissa.com+1-800-635-4772 x1130
MPowered PR for Melissapr@mpoweredpr.com +1-877-794-6777
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Data-Enabled Agriculture Resilience | UArizona Research … – The University of Arizona Research
In the face of disruptive environmental and economic change, data and artificial intelligence may offer the best hope for resilience in agriculture, farming, ranching, and water use amid a drying climate.
The Institute for Computation and Data-Enabled Insight invites students, faculty and staff to join University of Arizona researchers, extension specialists and colleagues from across campus for a panel discussion and Q&A session on the benefits, challenges and ideas associated with the use of data and technology in agriculture. Topics and panelists include:
Arizona as Leaders in Open Agriculture
George Frisvold,Department of Agricultural and Resource Economics
Agriculture Technology and Cyberinfrastructure
Duke PauliandEric Lyons,School of Plant Sciences
The Social and Societal Impacts of Data and the People in Agriculture
Stephanie Carroll,Collaboratory for Indigenous Data Governance & Zuckerman College of Public Health
Cutting-edge Information Technologies, Addressing Challenges
Nirav MerchantandMaliaca Oxnam,Data Science Institute
Tyson Swetnam,Geoinformatics, BIO5 Institute
Increasing Investments in Cyberinfrastructure to Support Resilient Agriculture
Channah Rock, Cooperative Extension,Maricopa Agricultural Center
ICDI thanks theCollege of Agriculture and Life Sciences and Arizona Institute for Resilience for their collaboration in coordinating this event.
Strategic Plan In Action: Data-Enabled Agriculture Resilience
Friday, April 21, 2 - 4 p.m. |Main Library, Learning Studios (off the lobby)
Meet and mingle with attendees and panelists from 4 - 5 p.m. Light refreshments will be served.
TheInstitute for Computation and Data-Enabled Insightsupports the Grand Challenges pillar of the strategic plan, serving as a hub tounlock new research solutions and accelerate breakthrough discoveries using thepower of information technologies. By integrating data, networking and computing, researchers can target today's most complex and pressing challenges, including precision health care, cyberspace security, and climate resilience. ICDI prioritizes workforce development, faculty/student research, experiential learning opportunities, andhelping grow Arizonas industry and economy.
TheStrategic Plan In Actionevents provide an opportunity for all students, faculty, and staff toengage directly with an initiative team to explore their projects progress, collaborative partnerships, and impact on the campus and broader community. Join us to foster cross-campus connections with students and colleagues who are invested in supporting the Universitys strategic goals.
Register Here
2 p.m. to 4 p.m. April 21, 2023
Read more here:
Data-Enabled Agriculture Resilience | UArizona Research ... - The University of Arizona Research