Category Archives: Data Science
Getting Nurses Comfortable With Big Data – HealthLeaders Media
How comfortable are nurses with mining Big Data?
"Zero, zero, zero, zero,"responds Roy Simpson, DNP, RN, DPNAP, FAAN, FACMI, assistant dean of technology management and clinical professor at the Emory University Nell Hodgson Woodruff School of Nursing.
Thats why Simpson and Vicki Stover Hertzberg, PhD, FASA, a professor and director of Emorys Center for Data Science, helped create an online, self-paced data science certificate programto help nurses use Big Data to solve problems in healthcare settings.
Big Data is a relatively new concept for nursingits been around two, perhaps three years, Simpson saysbut its capabilities are unlimited in developing patterns of patient care.
"To compare six patients and 10 patients and 30 patients and 400 patients is not a good indicator of evidence. You need large trillion data sets,"Simpson says.
"Large data gives you patterns; you cannot get patterns out of small data sets,"Simpson says. "So, if you're looking for whatever you're doing in nursing, whether it's getting a med, turning a patient, or deciding if it's the right room for them, you cannot gather evidence and research on small data sets today. You have to have large data sets to develop patterns of care."
For example, from Big Data, nurses know that new patients to a hospital who are over 65 and dehydrated will develop pressure ulcers, which can result in longer lengths of stay. Knowing that helps to develop a care plan.
"We're the only profession in the organization that is there 24 by 7every other healthcare provider is an episodic engager with the patientso we have to develop and understand care needs for our patients,"he says. "We have to know what interventions we need to do for patients to decrease length of stay for the patient because our goal is to get a patient out of a hospital."
Thats not only for the patients sake but for the organization, as well.
If a patient is admitted with a pressure ulcer or develops one while hospitalized, it becomes the responsibility of the healthcare organization to discharge that patient with no pressure ulcer; otherwise, the hospital will not be reimbursed, Simpson notes.
Despite the benefits of Big Data, nurses tend to be uncomfortable with it for a couple of reasons.
"Evidence is hard to accept for change,"he says.
Simpson referred to a recent announcement by a World Health Organization agency that artificial sweetener aspartame, used in low-calorie products such as Diet Coke, sugar-free gum, and tabletop sweeteners is "possibly carcinogenic to humans."
"I've had more people call me, asking, Should I drink Diet Cokes or not?"he says. "I say, If you drink 20 a day you probably shouldn't drink it, but if you're drinking three or four, you're probably ok."
"How do you translate the evidence?"he says. "That's not a human behavior to follow the true evidence; people's inquisitions are not that strong."
The newness of Big Data is also a factor. "You have early adopters,"he says, "and you have laggards and Big Data is a huge component."
The new certificate program provides students with access to Emory's own vast stores of dataProject NeLL,the School of Nursings "pioneering"suite of apps that provides access to 2.7 million de-identified patient records and more than 37 trillion data points, providing information on diverse populations, countless conditions, and a wide spectrum of care.
Project NeLL, which stands for Nurses Electronic Learning Library, is singular in its presentation of data, Simpson says.
"There are other large data sets, but they don't have the clinical text data transcribed into natural languages that can be retrieved,"Simpson says.
"For instance, MIMIC-III is a Massachusetts General data set which a lot of people use in research, but it is only data that is put in as data,"he says. "NeLL looks at other types of data sets, so it has a lot of uniqueness to the marketplace."
Emory nursing students who used NeLL to complete capstones and dissertations discovered racial disparities in opioid administration for breast cancer patients, a cost value associated with nurse anesthetists compared to other provider types, and predictors of death among patients with pressure ulcers, according to Emory University.
The new data science certificate program was conceived by Simpson and Hertzberg to move nurses forward in understanding Big Data and evidence and to advance Emorys Doctorate in Nursing program to include a focus on evidence and systems work, he says.
"What we learned was not all nurses are interested in getting doctoral degrees,"Simpson says. "They're looking at more scalable certificates as a way to advance their knowledge base and their criteria for work or being hired. We felt that more people wanted to understand informatics and Big Data before they decided whether they should go for degree granting in informatics."
Nurses completing the program will earn an Emory Nursing digital certificate and badge and receive continuing professional development contact hours.
Getting comfortable with Big Data can only help nurses in their clinical practice.
"Every specialty in nursing has a component of informatics, and the weakness of those disciplines is the lack of informatics in their discipline,"Simpson says.
Nurses need Big Data, Simpson says.
"Big Data is a new opportunity for the world at large, not just nursing,"he says. "But for nursing to be successful in the future, we have to embrace it. We have to understand it and know how to use it."
Carol Davisis the Nursing Editor at HealthLeaders, an HCPro brand.
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Getting Nurses Comfortable With Big Data - HealthLeaders Media
Emory University Goizueta Business School: Today’s business … – Study International News
Angie Chen knew exactly what she was seeking to achieve her career goals a big city and a program that would advance her interest in data science.
In my previous role as an internet product manager, I sought to increase collaboration with the technology and data departments of the company, says the MS in Business Analytics (MSBA) graduate from Emory University Goizueta Business School. I believed an MSBA would bring me more hands-on exploration of the data and prepare me well for my next aimed role as a data scientist or analyst.
As an undergraduate student who majored in biological engineering, Chen is grateful for the chance to work with data from big companies, specifically Truists Wealth Department. Such is the potential of Goizuetas MSBA analytics practicum, which places students at the heart of a real-life business scenario. They act as consultants to deliver results with the MSBA curriculum advisory board and faculty supporting them every step of the way.
During the program, teams of students work with a real client ranging from Fortune 500 institutions to scrappy startups to deliver a data-driven business solution for the clients. The teams deliver the three core deliverables of a data science project: technical (code, cleansed data), dashboard visualization (Tableau, ggplot), and executive deck (business result).
The analytics practicum project serves as the pinnacle of a students academic journey. They leverage their expertise in data science, and manage big data, machine learning, and data visualization to develop innovative solutions tailored to the specific needs of our sponsor firms, says Scott Radcliffe, Managing Director of MSBA at Goizueta.
The MSBA is tailored to recent graduates with little or no professional experience who are seeking to advance their knowledge in data science. Source: Emory University Goizueta Business School
Goizuetas 10-month STEM-designated MSBA combines the knowledge of three fields management, information systems, and applied statistics to train students in solving real business problems. Here, theyll cover topics such as data visualization, machine learning, artificial intelligence, managing big data, network analytics, cloud analytics, and more.
Students can choose from two academic tracks: Business Analytics or AI in Business. These tracks will influence the projects they complete during the analytics practicum. Those on the Business Analytics track complete a general data science project, while students on the AI in Business track work on a project with an AI-focused problem or solution.
At the start, the MSBA program incorporates a series of boot camps that help refine ones knowledge in math, technology, business, and business problem-solving. The technology boot camp, for example, sees students conduct exercises in R, Python, SQL and other languages. They will also use various unix tools, file-transfer methods, and cloud-based services something graduates like Faarid Sanaan appreciate.
I could have gone anywhere, but the US is at the forefront of the data analytics and data science revolution, says the Fulbright scholar. I needed graduate school to build upon my basic knowledge. I felt like there was a missing piece that I needed to feel more confident and call myself an expert.
Romin Williams, an MSBA graduate and a student-athlete, started working as a business insight fellow at McKinsey & Company two months after graduation. Source: Emory University Goizueta Business School
Goizuetas MSBA program has a stellar track record of turning students into highly employable graduates. With some of the highest employment rates among MSBA programs, students have gone on to work at companies such as Amazon, EY, Morgan Stanley, Slalom Consulting, Tik Tok, and many more.
Romin Williams, an MSBA graduate and a student-athlete, started working as a business insight fellow at McKinsey & Company two months after graduation. One of the most rewarding moments in his professional career, he shared, was putting the skillsets he learned at Goizueta into practice at McKinsey.
Each graduating class continues to see strong demand for their skillsets, with the class of 2022 receiving a median base salary of US$100,00 and a median signing bonus of US$10,000.
Whats more, thanks to their programs STEM designation, students on an F-1 visa who graduate from the MS in Business Analytics program are eligible for a 24-month extension on their Optional Practical Training period (OPT) beyond the standard 12 months of OPT. The OPT lets international students continue to live and work in the US after graduation.
Excited about what youre reading? Kickstart your journey to becoming a business analytics expert with Emory University Goizueta Business School
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Emory University Goizueta Business School: Today's business ... - Study International News
Study Identifies Pitfalls, Solutions for Using AI to Predict Opioid Use … – Newswise
Newswise More than 10 million Americans misused prescription opioids in 2019, and nearly 75 percent of drug overdose deaths in 2020 involved an opioid. According to the United States Centers for Disease Control and Prevention, overdose deaths involving opioids, including prescription opioids, heroin and synthetic opioids such as fentanyl, have increased eightfold since 1999.
As scientists and the health care community search for effective ways to mitigate the opioid epidemic, rapid advances in machine learning are promising. Access to data and machine learning frameworks has led to the development of machine learning models that use health care data to deal with different facets of the opioid crisis. For example, health care databases can assist researchers and clinicians to identify patients at risk by leveraging various data and information.
But are these machine learning models built on health care data reliable at predicting opioid use disorder? Thats what researchers from Florida Atlantic Universitys College of Engineering and Computer Science wanted to explore. As such, they examined peer-reviewed journal papers and conducted the first systematic review analyzing not only the technical aspects of machine learning applied to predicting opioid use, but also the published results.
Their goal was to determine if these machine learning methods are useful and, more importantly, reproducible. For the study, they reviewed 16 peer-reviewed journal papers that used machine learning models to predict opioid use disorder and investigated how the papers trained and evaluated these models.
Findings, published in the journal Computer Methods and Programs in Biomedicine, reveal that while results from the reviewed papers show machine learning models applied to opioid use disorder prediction may be useful, there are important ways to improve transparency and reproducibility of these models, which will ultimately enhance their use for research.
For the systematic review, researchers searched Google Scholar, Semantic Scholar, PubMed, IEEE Xplore and Science.gov. They extracted data that included the study's goal, dataset used, cohort selected, types of machine learning models created, model evaluation metrics, and the details of the machine learning tools and techniques used to create the models.
Findings showed that of these 16 papers, three created their dataset, five used a publicly available dataset and the remaining eight used a private dataset. Cohort size ranged from the low hundreds to more than half a million. Six papers used one type of machine learning model, and the remaining 10 used up to five different machine learning models. Most papers did not sufficiently describe the machine learning techniques and tools used to produce their results. Only three papers published their source code.
The reproducibility of papers using machine learning for health care applications can be improved upon, said Oge Marques, Ph.D., co-author and a professor in FAUs Department of Electrical Engineering and Computer Science. For example, even though health care datasets can be hindered by privacy laws and ethical considerations, researchers should follow machine learning best practices. Ideally, the code should be publicly available.
The researchers recommendations are threefold: use the area under the precision/recall curve (AUPRC), a metric more useful in cases of imbalanced datasets when thenegative classismoreprevalent and there is low value in true-negativepredictions; and avoid non-interpretable models (also known as black-box models) in this critical health care area, and favor using interpretable models whenever possible. If that is not possible and a non-interpretable model must be deployed to predict opioid use disorder, they recommend defining the reasons that justify its use. Finally, to ensure transparency and reproducibility of results, the researchers recommend the adoption of checklists and other documentation practices before submitting machine-learning-based studies for review and publication. Better documented and publicly available studies will help the research community advance the field.
The researchers note that the lack of good machine learning reproducibility practices in the papers makes it impossible to verify their claims. For example, the evidence presented may fall short of the accepted standard, or the claim only holds in a narrower set of circumstances than asserted.
Journal papers would be more valuable to the research community and their suggested application if they follow good practices of machine learning reproducibility in order for their claims to be verified and used as a solid base for future work, said Marques. Our study recommends a minimum set of practices to be followed before accepting machine-learning-based studies for publication.
Study co-authors are Christian Garbin, first author and a Ph.D. candidate, and Nicholas Marques, an M.S. student in data science and analytics and a National Science Foundation Research Traineeship Program scholar, both within the College of Engineering and Computer Science.
Opioid use disorder is a public health concern of the first magnitude in the United States and elsewhere, said Stella Batalama, Ph.D., dean, FAU College of Engineering and Computer Science. Harnessing the power and potential of machine learning to predict and prevent ones risk of opioid use disorder holds great promise. However, to be effective, machine learning methods must be reliable and reproducible. This systematic review by our researchers provides important recommendations on how to accomplish that.
- FAU -
About FAUs College of Engineering and Computer Science:
The FAU College of Engineering and Computer Science is internationally recognized for cutting-edge research and education in the areas of computer science and artificial intelligence (AI), computer engineering, electrical engineering, biomedical engineering, civil, environmental and geomatics engineering, mechanical engineering, and ocean engineering. Research conducted by the faculty and their teams expose students to technology innovations that push the current state-of-the art of the disciplines. The College research efforts are supported by the National Science Foundation (NSF), the National Institutes of Health (NIH), the Department of Defense (DOD), the Department of Transportation (DOT), the Department of Education (DOEd), the State of Florida, and industry. The FAU College of Engineering and Computer Science offers degrees with a modern twist that bear specializations in areas of national priority such as AI, cybersecurity, internet-of-things, transportation and supply chain management, and data science. New degree programs include Master of Science in AI (first in Florida), Master of Science and Bachelor in Data Science and Analytics, and the new Professional Master of Science and Ph.D. in computer science for working professionals. For more information about the College, please visit eng.fau.edu.
About Florida Atlantic University: Florida Atlantic University, established in 1961, officially opened its doors in 1964 as the fifth public university in Florida. Today, the University serves more than 30,000 undergraduate and graduate students across six campuses located along the southeast Florida coast. In recent years, the University has doubled its research expenditures and outpaced its peers in student achievement rates. Through the coexistence of access and excellence, FAU embodies an innovative model where traditional achievement gaps vanish. FAU is designated a Hispanic-serving institution, ranked as a top public university by U.S. News & World Report and a High Research Activity institution by the Carnegie Foundation for the Advancement of Teaching. For more information, visitwww.fau.edu.
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Study Identifies Pitfalls, Solutions for Using AI to Predict Opioid Use ... - Newswise
Emerging Data Science Job Opportunities for 2023 – Analytics Insight
The demand for skilled data scientists continues to soar as the world becomes increasingly data-driven. Data science has emerged as one of the most sought-after fields, offering many exciting career opportunities. This article will explore six emerging data science job roles for 2023 to pursue in the coming year.
Machine learning engineers are crucial in designing and implementing machine learning models and algorithms. They work closely with data scientists and software engineers to develop and deploy intelligent systems that can make predictions, automate processes, and uncover insights from vast datasets. With their programming and statistical analysis expertise, machine learning engineers are in high demand across industries.
Data engineers specialize in building and maintaining the infrastructure required to store, process, and analyze large volumes of data. They design and optimize data pipelines, ensuring efficient data flow and storage. Data engineers also collaborate with data scientists to ensure the availability and accessibility of high-quality data for analysis and model development. Their data architecture and database management skills make them valuable assets to organizations.
Data analysts are crucial in interpreting and visualizing data to derive meaningful insights. They employ statistical techniques and data visualization tools to analyze trends, identify patterns, and communicate findings to stakeholders. Data analysts work closely with business teams to address specific data-related challenges and make data-driven decisions. Strong analytical skills, proficiency in data manipulation, and domain knowledge are key attributes of successful data analysts.
Data science consultants provide strategic guidance and solutions to organizations seeking to leverage data for business growth and innovation. They possess a deep understanding of data science techniques and industry trends, enabling them to advise clients on data-driven strategies, project planning, and implementation. Data science consultants often collaborate with cross-functional teams, bridging the gap between technical expertise and business objectives.
With increasing concerns about data privacy and security, the role of data privacy and security specialists has gained significant prominence. These professionals are responsible for ensuring compliance with data protection regulations, implementing robust security measures, and mitigating potential risks related to data breaches. Data privacy and security specialists work closely with legal teams, IT departments, and data science teams to safeguard sensitive information and maintain ethical data practices.
As artificial intelligence (AI) advances, ethical considerations become paramount. AI ethicists specialize in addressing the ethical implications of AI applications and algorithms. They work towards ensuring fairness, transparency, and accountability in AI systems. AI ethicists collaborate with data scientists, policymakers, and legal experts to establish ethical guidelines and frameworks that govern the responsible development and deployment of AI technologies.
Data science offers many career opportunities for aspiring professionals in 2023 and beyond. From machine learning engineers and data analysts to data science consultants and AI ethicists, these emerging job roles reflect the evolving needs of the data-driven world. Aspiring data scientists can carve a successful and fulfilling career path in this dynamic field by equipping themselves with the necessary skills and knowledge.
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Emerging Data Science Job Opportunities for 2023 - Analytics Insight
Console Flare Empowers Non-IT Professionals with Cutting-edge Data Science Training in Hindi – mid-day.com
Moreover, the need to upskill is even direr today than ever before, as the world is gearing towards a massive change, which entails the implementation of the latest powerful tech developments such as Artificial Intelligence, Machine Learning, and Data Science.
CEO & Founder, Nihal Jaiswal and CTO and Co-Founder Mrinmai Sharma
Learners around the world have woken to the rewarding careers that come with the knowledge of emerging fields such as Data Science, which has become a mainstay in the operations of companies across industries. Answering this demand, numerous start-ups have mushroomed on the internet, sometimes leading the bright and the ambitious to their desired career goals.
Moreover, the need to upskill is even direr today than ever before, as the world is gearing towards a massive change, which entails the implementation of the latest powerful tech developments such as Artificial Intelligence, Machine Learning, and Data Science. In other words, jobs as we know them would change and become integrated with the cutting-edge technologies that have appeared on the block, leaving a huge void in the skill gap in domains such as Data Science. For example, let's look at the IT industry. A NASSCOM report mentions that by 2026, India will have a technology professionals shortage of a whopping 1.4-1.9 million.
At the same time, owing to its edge in population metrics, Young India is the world's best bet in solving the supply-demand issue around Data science jobs, as a massive tech-savvy Indian population can potentially power the major data-driven projects around the world. To bridge this massive skill gap, the Edtech sector will need to not just train the young, college-going populous but also those who have long-forayed into the market. However, these platforms often fail to cater to the key demographics in India. So, above all, what needs to be addressed is the language gap that the English-centric learning model of the EdTech industry poses.
In the Indian start-up ecosystem, teeming with Edtech start-ups, Console Flare, has, since its inception in 2020, successfully carved its own niche as a 360-degree learning platform for aspiring IT professionals. What sets the Console Flare apart from its peers is its focus on delivering industry-relevant data science & data analytics training in Hindi as opposed to the host of English-centric data science training institutes in the sector. By breaking the language barrier, Console Flare has become accessible to a wide pool of learners around India, especially given that a big chunk of the population, though familiar with English, finds Hindi to be second nature and thus prefers it as a mode of learning.
As the Indian market is more eager than ever to tap into the full potential of Data Science, the sector is growing exponentially in the country, creating a massive demand compared to the supply of eligible candidates. In this talent crunch scenario, concerns such as age bar and relevant job experience no longer hold water if one has the necessary technical expertise and domain knowledge. Therefore, riding the giant Data Science wave, Console Flare, in its last 3 years, has launched over 5000 IT & Non-IT students in the field of data science.
Another salient feature of Console Flare is that it caters to the need of both advanced learners as well as beginners in the field of Information Technology. The company believes in a for-all pedagogy around technical skills so that those without a computer science and engineering or any STEM background could upskill seamlessly and gun for rewarding positions such as Machine Learning Engineer, Business Analyst, Data Engineer, Data Analyst, Data Scientist, Business Intelligence Developer, and Big Data Analyst.
Additionally, to cater to IT professionals and non-technical learners comprehensively, Console Flare endeavours to impart relevant skills from scratch and build up to the current best practices in advanced modules such as Python, Data Analysis, Big Data Analytics, Machine Learning, AI, and Business Intelligence. Delivering this holistic grooming into the IT sector, the company has painstakingly curated a team of industry experts around the country who bring to the table their long-standing domain experience. As a result, the cohort also gets the unique opportunity to pick up on the current market trends and be ready to take up challenging industry positions.
One of the highlights of the learning experience with Console Flare that is worth mentioning is the company's policy- "Once a student, always a student" - which denotes the flexible and inclusive environment that the EdTech platform provides.
Further exemplifying the free-flowing learning structure that Console Flare is proud to offer, the instructors widely encourage questions and interactions in live classes, which not only lend a personal touch to the upskilling but also help to consolidate the material in real-time. Moreover, the regular live classes boost the learning curve by saving time from prolonged doubt-clearing sessions and also help learners build a rapport with the instructor, leading to seeking one-on-one mentorship.
Attesting to the superlative learning model of the platform, Console Flare has not only attracted a massive number of learners in the last three years but also has, as a bootstrap start-up hit a great growth trajectory. In fact, what remains remarkable about the company's journey is that it has, in the age of robust VC funding, made it big on a lean startup model.
All in all, Console Flare holistically solves the triple threat in the EdTech industry: the language barrier, age bar and quality of teaching, and thus is poised to continue on its path of excellence as among the leading data science training institutes in India.
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Want better job, pay hike? Boost your data science skills with these 5 best AI Chrome Extensions – HT Tech
In the fast-paced world of technology, numerous tools and extensions are continually emerging to simplify and enhance our digital experiences. For Data Scientists, these advancements are a boon, as they simplify and enrich their work processes. Here we have researched and curated a list of the top 5 AI-powered Chrome extensions that have become indispensable for Data Scientists in 2023. These extensions cater to a diverse range of needs, from research support to text processing, code optimisation, and smart note-taking.
SciSpace Copilot is a powerful tool that can answer scientific queries and explain complex data, including tables and charts, found in scientific papers. It serves as an invaluable guide for researchers, students, and curious readers, making it easier to understand intricate scientific content and streamlining the research process.
Data Scraper is an automatic parser that can analyze websites and extract valuable data efficiently. It allows users to save the extracted information in CSV or Excel formats, enabling easy integration with different data analysis tools and techniques.
Specifically designed for data science tasks, Code Squire.AI is an exceptional code assistant that works seamlessly with libraries like Pandas. It supports JupyterLab and Colab, streamlining the coding process and enhancing work efficiency.
For IT professionals and data scientists working with Generative Pretrained Transformers (GPT), the AIPRM extension offers a well-organized set of questions. It assists in fine-tuning GPT's responses for specific use cases and situations, such as training models or creating chatbots.
Codeium is a versatile tool that analyzes and optimizes code, supporting more than 20 programming languages. It provides valuable insights to improve program performance, making it useful for both experienced programmers and beginners seeking to learn best practices.
These AI Chrome extensions offer valuable assistance to data scientists in their day-to-day tasks, from simplifying research and data extraction to enhancing coding efficiency and program performance. Embracing these tools can undoubtedly elevate the productivity and effectiveness of data scientists in 2023 and beyond.
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QA Qa Xiong – UBNow: News and views for UB faculty and staff – University at Buffalo
Jinjun Xiongs years of experience with artificial intelligence (AI) are making a dramatic impact at UB.
SUNY Empire Innovation Professor of Computer Science and Engineering, Xiong is scientific director and co-director of the AI Institute for Exceptional Education, a national institute developing artificial intelligence systems that identify and assist young children with speech and/or language processing challenges. It was established earlier this year with a five-year, $20 million grant from the National Science Foundation.
Xiong also serves as co-director of UBsInstitute for Artificial Intelligence and Data Science (IAD),where he connects investigators including clinical and translational researchers with the power of AI.
These efforts include:
I am also always looking for new ideas for how we can make the IAD platform more useful and accessible for all UB investigators, Xiong says.
He believes it is important for researchers and the public to understand artificial intelligence, and the ways in which it is changing our world. In a Q&A with UBNow, Xiong discusses the impact of AI on research now and in the future, and analyzes how it will affect health care.
How is AI impacting clinical and translational research now, and how will it be in the future?
AI is already impacting clinical research in multiple ways, such as medical imagining analyses for skin cancer detection, MRI imaging segmentation, clinical trials data understanding, wearable sensors to improve patient monitoring the list just goes on and on. The future of clinical practices will incorporate more and more intelligent solutions enabled by more efficient and intelligent algorithms, all aiming to improve the patient quality of care. One such example is the growing capabilities of AI, especially the recent amazing results from generative AI like ChatGPT, where it is conceivable that AI-augmented agents such as chatbots can help with providing more accessible and higher-quality health literacy for patients.
To some degree, every future professional needs to understand a bit about AI and computing, by either talking to AI experts/researchers or learning online to gain a general understanding of how AI works, and what AI can do and cannot do right now and even in the near future. With that basic understanding, people working in a particular domain like medicine can revisit their daily practices and think out of the box about where AI can help in their current practice flows, and then engage with an AI expert to co-imagine and then co-design a possible AI-driven solution.
The public should realize that the impact of AI to health care is real and inevitable. There is always an ethical and moral issue around AI in health care, as it may potentially remove autonomy from humans. But that is exactly why the public should be aware of the technology so they can be part of the conversation to find meaningful solutions. I believe the voices of the public should be heard in charting a new direction for humankind with AI.
The power of AI can only become real when it is applied to solve a particular domain problem.
For more information on IAD research initiatives, write to Xiong atjinjun@buffalo.edu.
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QA Qa Xiong - UBNow: News and views for UB faculty and staff - University at Buffalo
The Data Science Book: 10th Anniversary Edition – Fagen wasanni
The 10th anniversary edition of The Data Science Book by Brett Lantz is now available, featuring 50% new content for R 4.0.0 and beyond. This updated edition explores the essential aspects of data pre-processing, uncovering insights, making predictions, and visualizing findings.
The book includes several new chapters that highlight the advancements in machine learning over the past few years. These additions aim to help readers enhance their data science skills and tackle more complex problems. Topics covered in the book include building successful machine learning models, advanced data preparation techniques, and utilizing big data.
The end-to-end process of machine learning is thoroughly explained, starting from raw data and concluding with implementation. The book guides readers through classification using nearest neighbor and Bayesian methods, predicting future events using decision trees, rules, and support vector machines, forecasting numeric data, estimating financial values using regression methods, and modeling complex processes with artificial neural networks.
Data preparation is a crucial step in the data science process, and the book demonstrates how to prepare, transform, and clean data using the tidyverse. It also offers guidance on evaluating models and improving their performance.
Additionally, The Data Science Book provides insights into connecting R to SQL databases, as well as emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow.
This 10th anniversary edition is a valuable resource for intermediate R developers and anyone interested in artificial intelligence and machine learning. It offers practical knowledge and techniques that can be applied to real-world data science projects.
For more information on The Data Science Book and other featured books, visit the Book Watch section on I Programmers website or follow @bookwatchiprog on Twitter.
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The Data Science Book: 10th Anniversary Edition - Fagen wasanni
Jianwei Niu named interim dean of University College – UTSA
UTSAs Mission
The University of Texas at San Antonio is dedicated to the advancement of knowledge through research and discovery, teaching and learning, community engagement and public service. As an institution of access and excellence, UTSA embraces multicultural traditions and serves as a center for intellectual and creative resources as well as a catalyst for socioeconomic development and the commercialization of intellectual property - for Texas, the nation and the world.
To be a premier public research university, providing access to educational excellence and preparing citizen leaders for the global environment.
We encourage an environment of dialogue and discovery, where integrity, excellence, inclusiveness, respect, collaboration and innovation are fostered.
UTSA is a proud Hispanic Serving Institution (HSI) as designated by the U.S. Department of Education.
The University of Texas at San Antonio, a Hispanic Serving Institution situated in a global city that has been a crossroads of peoples and cultures for centuries, values diversity and inclusion in all aspects of university life. As an institution expressly founded to advance the education of Mexican Americans and other underserved communities, our university is committed to ending generations of discrimination and inequity. UTSA, a premier public research university, fosters academic excellence through a community of dialogue, discovery and innovation that embraces the uniqueness of each voice.
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Master of Science in Data Science – National University
National Universitys MS in Data Science program focuses on advanced topics like how to develop, implement, and maintain the hardware and software tools needed to make efficient and effective use of big data, including databases, data marts, data warehouses, machine learning, analytic programming, and artificial intelligence and optimization. With this knowledge, youll be equipped with the industry-current credentials needed to pursue in-demand positions* like:
Employer seeking data science professionals span a large range of service and manufacturing settings. For example, top employers of computer and IT professionals include IBM, Microsoft, and Facebook, while JP Morgan Chase, Wells Fargo, and Travelers Group regularly recruit finance and insurance professionals with an MS in Data Science.
In the consulting world, Deloitte, KPMG, and Accenture are top employers, and Humana and Anthem dominate the healthcare industry. If youre interested in biotech or pharmaceutical manufacturing, keep Johnson and Johnson and Bayer on your radar. Ryder and Uber are two top employers in the transportation sector.
With your MS in Data Science, you can not only expect to be in demand, youre also likely to be well compensated. The Bureau of Labor Statistics states that the median annual wage for management analysts $93,000 in May 2021, and the highest 10 percent earned more than $163,760.**
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