Embark on a transformative journey with APTRON Solutions, the leading Data Science Institute in Noida. Whether you're a beginner or an industry professional seeking to upskill, our tailored programs cater to all. Choose APTRON for a learning experience that transcends traditional boundaries, propelling you towards success in the dynamic field of data science. Discover the power of data with us, and unlock a world of opportunities. Enroll today to shape a future driven by knowledge and innovation.
https://aptronsolutions.com/best-data-science-training-in-noida.html
Unveiling Tomorrow_ The Future of Data Science.pdfCIOWomenMagazine
In this exploration, we delve into the burgeoning realm of data science, examining the current state, anticipating future trends, and understanding the transformative potential that lies ahead.
This document provides a review of data science initiatives for social good. It discusses how data-driven approaches have been used to address challenges in healthcare accessibility, poverty alleviation, and environmental sustainability through initiatives leveraging machine learning, predictive modeling, and other techniques. The review highlights both the positive impacts and potential challenges of applying data science to effect social change.
Social Media Datasets for Analysis and Modeling Drug Usageijtsrd
This paper based on the research carried out in the area of data mining depends for managing bulk amount of data with mining in social media on using composite applications for performing more sophisticated analysis. Enhancement of social media may address this need. The objective of this paper is to introduce such type of tool which used in social network to characterised Medicine Usage. This paper outlined a structured approach to analyse social media in order to capture emerging trends in medicine abuse by applying powerful methods like Machine Learning. This paper describes how to fetch important data for analysis from social network. Then big data techniques to extract useful content for analysis are discussed. Sindhu S. B | Dr. B. N Veerappa "Social Media Datasets for Analysis and Modeling Drug Usage" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25246.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/25246/social-media-datasets-for-analysis-and-modeling-drug-usage/sindhu-s-b
University of Virginia School of Data SciencePhilip Bourne
March 6, 2020 presentation to the University of Virginia Board of Visitors on the prior work and development of the School of Data Science over the next several years.
The future interface of mental health with information technology: high touch...HealthXn
The document discusses the future of mental health and technology, including:
- Technology may help address challenges in healthcare systems but also presents pitfalls if not implemented carefully.
- The roles of health professionals and patients may change as technology becomes more integrated in care, requiring new skills.
- Data and information from various sources can provide insights if analyzed properly, but also raise privacy and security concerns.
- Future health systems will rely more on knowledge management and using data/analytics to provide personalized, predictive care while maintaining the human touch.
The document discusses the rise of data science and its disruptive impact on higher education. It analyzes precedents like bioinformatics that were enabled by new digital data sources and technologies. The author advocates that universities should embrace data science by establishing interdisciplinary collaborations, investing in data infrastructure, and ensuring research has societal value and responsibility.
Big Data Analytics using in the Field of Education Systemijtsrd
This paper is a study on the use of big data in education analyzed how the big data and open data technology can actually involve in educational system. Present days we analyze how big mounts of unused data can benefit and improve to education sector. Big data has dramatically changed the ways in which leaders make decisions in natural science, Agriculture science, banking and retail business, healthcare and in education. In educations sector wide verity of digital data produced in every institution. For example the forms of data like videos, texts, voices etc. the digital educations improves both teachers and students understandings and improve teaching effectiveness. In education big data we use econometrics, causal inference models, social network analysis, text analysis, and linguistic analysis methods. Using different types of technologies adopting in education are mobile devices, teleconferences and remote access systems, educational platforms and services. This method is effectively used by students, teachers, academic faculty, specialists, and researchers in education. Gagana H. S | Sandhya B N | Gouthami H. S "Big Data Analytics using in the Field of Education System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31196.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31196/big-data-analytics-using-in-the-field-of-education-system/gagana-h-s
Embark on a transformative journey with APTRON Solutions, the leading Data Science Institute in Noida. Whether you're a beginner or an industry professional seeking to upskill, our tailored programs cater to all. Choose APTRON for a learning experience that transcends traditional boundaries, propelling you towards success in the dynamic field of data science. Discover the power of data with us, and unlock a world of opportunities. Enroll today to shape a future driven by knowledge and innovation.
https://aptronsolutions.com/best-data-science-training-in-noida.html
Unveiling Tomorrow_ The Future of Data Science.pdfCIOWomenMagazine
In this exploration, we delve into the burgeoning realm of data science, examining the current state, anticipating future trends, and understanding the transformative potential that lies ahead.
This document provides a review of data science initiatives for social good. It discusses how data-driven approaches have been used to address challenges in healthcare accessibility, poverty alleviation, and environmental sustainability through initiatives leveraging machine learning, predictive modeling, and other techniques. The review highlights both the positive impacts and potential challenges of applying data science to effect social change.
Social Media Datasets for Analysis and Modeling Drug Usageijtsrd
This paper based on the research carried out in the area of data mining depends for managing bulk amount of data with mining in social media on using composite applications for performing more sophisticated analysis. Enhancement of social media may address this need. The objective of this paper is to introduce such type of tool which used in social network to characterised Medicine Usage. This paper outlined a structured approach to analyse social media in order to capture emerging trends in medicine abuse by applying powerful methods like Machine Learning. This paper describes how to fetch important data for analysis from social network. Then big data techniques to extract useful content for analysis are discussed. Sindhu S. B | Dr. B. N Veerappa "Social Media Datasets for Analysis and Modeling Drug Usage" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25246.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/25246/social-media-datasets-for-analysis-and-modeling-drug-usage/sindhu-s-b
University of Virginia School of Data SciencePhilip Bourne
March 6, 2020 presentation to the University of Virginia Board of Visitors on the prior work and development of the School of Data Science over the next several years.
The future interface of mental health with information technology: high touch...HealthXn
The document discusses the future of mental health and technology, including:
- Technology may help address challenges in healthcare systems but also presents pitfalls if not implemented carefully.
- The roles of health professionals and patients may change as technology becomes more integrated in care, requiring new skills.
- Data and information from various sources can provide insights if analyzed properly, but also raise privacy and security concerns.
- Future health systems will rely more on knowledge management and using data/analytics to provide personalized, predictive care while maintaining the human touch.
The document discusses the rise of data science and its disruptive impact on higher education. It analyzes precedents like bioinformatics that were enabled by new digital data sources and technologies. The author advocates that universities should embrace data science by establishing interdisciplinary collaborations, investing in data infrastructure, and ensuring research has societal value and responsibility.
Big Data Analytics using in the Field of Education Systemijtsrd
This paper is a study on the use of big data in education analyzed how the big data and open data technology can actually involve in educational system. Present days we analyze how big mounts of unused data can benefit and improve to education sector. Big data has dramatically changed the ways in which leaders make decisions in natural science, Agriculture science, banking and retail business, healthcare and in education. In educations sector wide verity of digital data produced in every institution. For example the forms of data like videos, texts, voices etc. the digital educations improves both teachers and students understandings and improve teaching effectiveness. In education big data we use econometrics, causal inference models, social network analysis, text analysis, and linguistic analysis methods. Using different types of technologies adopting in education are mobile devices, teleconferences and remote access systems, educational platforms and services. This method is effectively used by students, teachers, academic faculty, specialists, and researchers in education. Gagana H. S | Sandhya B N | Gouthami H. S "Big Data Analytics using in the Field of Education System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31196.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31196/big-data-analytics-using-in-the-field-of-education-system/gagana-h-s
The document provides an overview of big data analytics for healthcare. It begins with motivating examples that demonstrate how big data can help improve healthcare outcomes and lower costs. It then discusses the main sources of healthcare data, including structured EHR data like billing codes, labs, and medications, as well as unstructured clinical notes. The document outlines challenges in analyzing these different types of complex healthcare data. It also introduces a healthcare analytics platform that can extract and select features from various data sources to build predictive models. Finally, it discusses techniques for clinical text mining, including named entity recognition and negation analysis.
The future of data analytics education is marked by diverse trends and innovations. Online learning, micro-credentials, and interdisciplinary approaches are democratizing access and specialization. Technology integration, such as AI and cloud-based labs, enhances learning experiences, while project-based and personalized learning foster practical skills and adaptability. Ethical considerations and industry collaboration are integrated, and interactive tools, gamification, and VR/AR provide engaging education. Challenges include content updates, equitable access, data privacy, and quality assurance. Overall, data analytics education is evolving to meet the demands of a data-driven world, emphasizing adaptability, inclusivity, and ethical practices.
Abstract Data-drivenhealthcareistrulyvaluableandpromising.Aslongasrele- vant data are gathered, probed, used, and managed in a good fashion, significant improvements in the dependability of healthcare practices are achievable. Neverthe- less, unless privacy facets of relevant sensitive data are addressed, there are notable concerns regarding data-driven healthcare policies and applications. In general, tech- nical and engineering facets of such interventions are concentered on to a greater extent, but privacy facets are not adequately addressed. This chapter highlights and discusses privacy issues in data-driven health care. A comprehensive review and distillation of pertinent literature and works yielded relevant results and interpreta- tions. Purposefully, generic privacy issues are elaborated in the beginning. Addition- ally, areas for improvement regarding privacy issues in data-driven health care are underlined and discussed. People, policy, and technology aspects are also explained and deliberated. Moreover, how privacy is related to people and policy to ensure the success in data-driven healthcare practices is discussed in this chapter. Besides, people’s perceptions about privacy are distilled and reported. The focal impact of this chapter is to deliver a contemporary interpretation and discussion regarding privacy issues in data-driven health care. Product developers and managers, policy-makers, and pertinent researchers might benefit from this chapter in order to improve related knowledge and implementations.
The Future of Data Analytics Education_ Trends and Innovations (2).pdfUncodemy
The future of data analytics education, particularly the Data Analytics Course in Dehradun with Uncodemy, embodies dynamic innovation, adaptability, and an unwavering commitment to preparing individuals for the data-driven world. In an evolving industry, it's imperative to keep education aligned with shifting demands. This entails staying updated with swiftly evolving technologies, addressing concerns about equitable access, navigating the intricacies of data privacy and ethics, and ensuring high quality and consistency in online and micro-credential courses. To fully unlock the potential of data analytics education, it is of utmost importance to invest dedicated efforts, champion inclusivity, and uphold ethical standards. By doing so, we can empower individuals to embark on a journey of learning and professional growth in the field of data analytics, thereby fostering innovation and progress in our data-centric society. Explore the Data Analytics Course in Dehradun with Uncodemy and seize valuable opportunities in this dynamic field.
Data science uses statistical and computational methods to extract insights from data. In public health, data science is being used to improve disease surveillance, predict outbreaks, and develop targeted interventions. It enables identification of health disparities and data-driven decision making. Machine learning algorithms analyze datasets to identify patterns and develop predictive models for outbreaks and at-risk populations. The future of data science in public health is promising but challenges around privacy, security, and access need to be addressed.
Data Management and Broader Impacts: a holistic approachMegan O'Donnell
This document summarizes a presentation on taking a holistic approach to data management and broader impacts. It discusses the National Science Foundation's broader impacts criterion, which requires research to benefit society. It argues that examining data through a broader impacts lens highlights the benefits of good data management, data management plans, and the value of data information literacy skills. Taking this holistic approach can help researchers understand why data management plans are important, justify spending more time on data practices, and encourage embracing data sharing.
Big data has the potential to transform nursing education and healthcare. It allows analysis of large, diverse datasets to reveal patterns and trends. Nursing has a long history of using data to improve patient care. Now, with big data and analytics, insights can be gained from vast amounts of structured and unstructured data from various sources. This can help personalize learning and predict outcomes. However, challenges include technical issues, privacy concerns, and developing a data-driven culture. With collaboration across sectors and letting the data speak, big data can advance nursing knowledge and the learning healthcare system.
Deep-learning-or-health-informatics-recent-trends-and-future-directions By Ra...raihansikdar
Deep learning techniques show promise in developing intelligent applications for healthcare and health informatics due to the large amounts of data available. Deep learning can be used for disease prediction by learning patterns in data and images to replicate medical practitioners' decision making. It also aids in data visualization by enabling analysis and visualization of medical images. Deep learning assists technology development by processing biomedical signals for applications like brain-computer interfaces and prosthetics. However, challenges remain around data preprocessing, feature engineering, reliability of results, and handling high-dimensional data.
This document discusses the promise and challenges of data analytics in healthcare and biomedical research. It notes that we are at a point of deception, where digitization is disrupting traditional models through increased data volume, velocity and variety. The document outlines NIH's Big Data to Knowledge initiative to accelerate biomedical discovery through open data sharing and improved analytics. Precision medicine is highlighted as one area that could see major breakthroughs through these approaches. Challenges around data standards, privacy, workforce needs and demonstrating value are also discussed.
Data Science for Social Good Transforming Communities through KnowledgeAttitude Tally Academy
This course is explained to the introductory generalities and morals bolstering Computer Science Training System. ATTITUDE Academy has been approved as Web Development training institute for conducting C, C++, Data Structure, SQL, Java Programming in Yamuna Vihar, Uttam Nagar Delhi along with instrument. After this course, you will brief yourselves with ideas to perform the Real Time Industries.
Usefull Link: https://www.attitudetallyacademy.com/functionalarea/computer-science
Running head Impact of technology on healthcare1Impact of t.docxjeanettehully
Running head: Impact of technology on healthcare
1
Impact of technology on healthcare
7
Impact of technology on healthcare
Woodrow Rowell
11/24/19
The healthcare industry and technology are interrelated. Behavioural health is mainly attributed to the type of information a patient has access to. Technology thus plays a big role in giving the correct amount of information (Cypriano, 2011). Through it, the society is able to access information influencing their thought on public health care. In turn this adds on to the way the public and health professionals search for information related to health matters. This shapes the way decisions related to health are made and how they respond to this. Thus the main goal of technology in health care is to help improve the quality of health care and outcomes in health related matters.
In order to improve the quality of life have been major technological advancements. The advancements have had a lot of impact in nearly all fields related to health care. This are mainly by updating the way health records are handled by digitizing them and use of big storage data and cloud computing. Digital health records have replaced the previously used paper records that had their own bit of challenges. In the world of medicine this is a bold move and a major boost. Through the help of coding professionals health professionals have been able to manage tasks and roles more effectively. Implementation of the roles are tasked to nurses and at times technicians.
Nurses are mandated with the task of collecting data and manually uploading it on a digitized central system void of any errors. As soon as the data is uploaded patient records are updated by the medical billers with a unique code of diagnostics. In turn the billings are submitted to insurance companies for medical claims. Patients are therefore able to access their health records easily and are able to spot an error in their records early enough in their treatment. There are benefits attributed to the digital health records just to mention a few.
Data collected is easy to feed into a computer as compared to the paper based methods, thus saves on time. Both nurses and patients are able to spot an error early enough regarding data on the patients diagnostics, financial records or treatment. Productivity is increased the nurses and medical coders can work from anywhere as there is digital access to the medical records. While billing and coding there is ease in the amount to be handled as they are can handle many records at a go. From the collected data it is easier to conduct research and come up with solutions for the various health related problems. Medical researchers can therefore improve on the available knowledge in medicine.
Digital platforms go along way into reducing the cost of health care. For instance using less paper means less space is used for collecting data in files, arranging in an orderly manner and using up space to secure the reco ...
Data mining technique has a key role in knowledge
extraction from databases to promote efficient decision making.
This paper presents an approach for knowledge extraction from
a sample database of some school dropped students using
association rule generation and classification algorithms to
demonstrate how knowledge-based development policy making
decisions can be processed from the extracted knowledge. A
system architecture is proposed considering mobile computing
devices as user interface to the system connecting mass people
database with cloud computing environment resources. The
causes of education termination are investigated by analyzing the
sample database in terms of attribute value relationship in the
form of association rules to reason about the causes based on the
computed support and confidence. It is observed that if the
affected family had no service holders, the dropped student had
to stop his education because of financial problem. Classification
is applied to classify the dropped students in different groups
based on their level of education.
Future And Scope of Data Science Online Program.pptxlearn bay
Welcome to the presentation on the future and scope of data science online programs.
In this session, we will explore the growing significance of online data science programs and the opportunities they offer.
Unveiling the Power of Data Science.pdfKajal Digital
Data science is an interdisciplinary field that combines techniques from statistics, computer science, and domain expertise to extract insights and knowledge from data. It involves the collection, cleaning, analysis, and interpretation of data to make informed decisions and predictions. The goal is to uncover hidden patterns, trends, and correlations that might otherwise remain obscured.
HSC Event, https://www.youtube.com/watch?v=g0FakQaUvPM
,digital health ,orcha ,digital phenotype ,dynamic consent ,real world data ,data in the wild ,ecological momentary assessment
Framework for understanding data science.pdfMichael Brodie
The objective of my research is to provide a framework with which the data science community can understand, define, and develop data science as a field of inquiry. The framework is based on the classical reference framework (axiology, ontology, epistemology, methodology) used for 200 years to define knowledge discovery paradigms and disciplines in the humanities, sciences, algorithms, and now data science. I augmented it for automated problem-solving with (methods, technology, community) [1][2]. The resulting data science reference framework is used to define the data science knowledge discovery paradigm in terms of the philosophy of data science addressed in [1] and the data science problem-solving paradigm, i.e., the data science method, and the data science problem-solving workflow, addressed in [2][3]. The framework is a much called for unifying framework for data science as it contains the components required to define data science. For insights to better understand data science, this paper uses the framework to define the emerging, often enigmatic, data science problem-solving paradigm and workflow, and to compare them with their well-understood scientific counterparts – scientific problem-solving paradigm and workflow.
The objective of my current research [4] is to develop a 21st C re-conception of data. Unlike 20th C data that are assets, 21st C data science data is phenomenological – a resource in which to discover phenomena and their properties, previously and otherwise impossible.
[1] Brodie, M.L., Defining data science: a new field of inquiry, arXiv preprint https://doi.org/10.48550/arXiv.2306.16177 Harvard University, July 2023.
[2] Brodie, M.L., A data science axiology: the nature, value, and risks of data science, arXiv preprint http://arxiv.org/abs/2307.10460 Harvard University, July 2023.
[3] Brodie, M.L., A framework for understanding data science, arXiv preprint https://arxiv.org/abs/2403.00776 Harvard University, March 2024.
[4] Brodie, M.L., Re-conceiving data in the 21st Century. Work in progress, Harvard University.
Companies desires for making productive discoveries from big data have motivated academic institutions offering variety of different data science (DS) programs, in order to increases their graduates' ability to be data scientists who are capable to face the challenges of the new age. These data science programs represent a combination of subject areas from several disciplines. There are few studies have examined data science programs within a particular discipline, such as Business (e.g. Chen et al.). However, there are very few empirical studies that investigate DS programs and explore its curriculum structure across disciplines. Therefore, this study examines data science programs offered by American universities. The study aims to depict the current state of data science education in the U.S. to explore what discipline DS programs covers at the graduate level. The current study conducted an exploratory content analysis of 30 DS programs in the United States from a variety of disciplines. The analysis was conducted on course titles and course descriptions level. The study results indicate that DS programs required varying numbers of credit hours, including practicum and capstone. Management schools seem to take the lead and the initiative in lunching and hosting DS programs. In addition, all DS programs requires the basic knowledge of database design, representation, extraction and management. Furthermore, DS programs delivered information skills through their core courses. Moreover, the study results show that almost 40 percent of required courses in DS programs is involved information representations, retrieval and programming. Additionally, DS programs required courses also addressed communication visualization and mathematics skills.
For More Information Visit Here: https://medium.com/@niet.greaternoidancr/niets-approach-to-project-based-learning-fostering-practical-skills-and-innovation-709b4eb2d9a1
The document provides an overview of big data analytics for healthcare. It begins with motivating examples that demonstrate how big data can help improve healthcare outcomes and lower costs. It then discusses the main sources of healthcare data, including structured EHR data like billing codes, labs, and medications, as well as unstructured clinical notes. The document outlines challenges in analyzing these different types of complex healthcare data. It also introduces a healthcare analytics platform that can extract and select features from various data sources to build predictive models. Finally, it discusses techniques for clinical text mining, including named entity recognition and negation analysis.
The future of data analytics education is marked by diverse trends and innovations. Online learning, micro-credentials, and interdisciplinary approaches are democratizing access and specialization. Technology integration, such as AI and cloud-based labs, enhances learning experiences, while project-based and personalized learning foster practical skills and adaptability. Ethical considerations and industry collaboration are integrated, and interactive tools, gamification, and VR/AR provide engaging education. Challenges include content updates, equitable access, data privacy, and quality assurance. Overall, data analytics education is evolving to meet the demands of a data-driven world, emphasizing adaptability, inclusivity, and ethical practices.
Abstract Data-drivenhealthcareistrulyvaluableandpromising.Aslongasrele- vant data are gathered, probed, used, and managed in a good fashion, significant improvements in the dependability of healthcare practices are achievable. Neverthe- less, unless privacy facets of relevant sensitive data are addressed, there are notable concerns regarding data-driven healthcare policies and applications. In general, tech- nical and engineering facets of such interventions are concentered on to a greater extent, but privacy facets are not adequately addressed. This chapter highlights and discusses privacy issues in data-driven health care. A comprehensive review and distillation of pertinent literature and works yielded relevant results and interpreta- tions. Purposefully, generic privacy issues are elaborated in the beginning. Addition- ally, areas for improvement regarding privacy issues in data-driven health care are underlined and discussed. People, policy, and technology aspects are also explained and deliberated. Moreover, how privacy is related to people and policy to ensure the success in data-driven healthcare practices is discussed in this chapter. Besides, people’s perceptions about privacy are distilled and reported. The focal impact of this chapter is to deliver a contemporary interpretation and discussion regarding privacy issues in data-driven health care. Product developers and managers, policy-makers, and pertinent researchers might benefit from this chapter in order to improve related knowledge and implementations.
The Future of Data Analytics Education_ Trends and Innovations (2).pdfUncodemy
The future of data analytics education, particularly the Data Analytics Course in Dehradun with Uncodemy, embodies dynamic innovation, adaptability, and an unwavering commitment to preparing individuals for the data-driven world. In an evolving industry, it's imperative to keep education aligned with shifting demands. This entails staying updated with swiftly evolving technologies, addressing concerns about equitable access, navigating the intricacies of data privacy and ethics, and ensuring high quality and consistency in online and micro-credential courses. To fully unlock the potential of data analytics education, it is of utmost importance to invest dedicated efforts, champion inclusivity, and uphold ethical standards. By doing so, we can empower individuals to embark on a journey of learning and professional growth in the field of data analytics, thereby fostering innovation and progress in our data-centric society. Explore the Data Analytics Course in Dehradun with Uncodemy and seize valuable opportunities in this dynamic field.
Data science uses statistical and computational methods to extract insights from data. In public health, data science is being used to improve disease surveillance, predict outbreaks, and develop targeted interventions. It enables identification of health disparities and data-driven decision making. Machine learning algorithms analyze datasets to identify patterns and develop predictive models for outbreaks and at-risk populations. The future of data science in public health is promising but challenges around privacy, security, and access need to be addressed.
Data Management and Broader Impacts: a holistic approachMegan O'Donnell
This document summarizes a presentation on taking a holistic approach to data management and broader impacts. It discusses the National Science Foundation's broader impacts criterion, which requires research to benefit society. It argues that examining data through a broader impacts lens highlights the benefits of good data management, data management plans, and the value of data information literacy skills. Taking this holistic approach can help researchers understand why data management plans are important, justify spending more time on data practices, and encourage embracing data sharing.
Big data has the potential to transform nursing education and healthcare. It allows analysis of large, diverse datasets to reveal patterns and trends. Nursing has a long history of using data to improve patient care. Now, with big data and analytics, insights can be gained from vast amounts of structured and unstructured data from various sources. This can help personalize learning and predict outcomes. However, challenges include technical issues, privacy concerns, and developing a data-driven culture. With collaboration across sectors and letting the data speak, big data can advance nursing knowledge and the learning healthcare system.
Deep-learning-or-health-informatics-recent-trends-and-future-directions By Ra...raihansikdar
Deep learning techniques show promise in developing intelligent applications for healthcare and health informatics due to the large amounts of data available. Deep learning can be used for disease prediction by learning patterns in data and images to replicate medical practitioners' decision making. It also aids in data visualization by enabling analysis and visualization of medical images. Deep learning assists technology development by processing biomedical signals for applications like brain-computer interfaces and prosthetics. However, challenges remain around data preprocessing, feature engineering, reliability of results, and handling high-dimensional data.
This document discusses the promise and challenges of data analytics in healthcare and biomedical research. It notes that we are at a point of deception, where digitization is disrupting traditional models through increased data volume, velocity and variety. The document outlines NIH's Big Data to Knowledge initiative to accelerate biomedical discovery through open data sharing and improved analytics. Precision medicine is highlighted as one area that could see major breakthroughs through these approaches. Challenges around data standards, privacy, workforce needs and demonstrating value are also discussed.
Data Science for Social Good Transforming Communities through KnowledgeAttitude Tally Academy
This course is explained to the introductory generalities and morals bolstering Computer Science Training System. ATTITUDE Academy has been approved as Web Development training institute for conducting C, C++, Data Structure, SQL, Java Programming in Yamuna Vihar, Uttam Nagar Delhi along with instrument. After this course, you will brief yourselves with ideas to perform the Real Time Industries.
Usefull Link: https://www.attitudetallyacademy.com/functionalarea/computer-science
Running head Impact of technology on healthcare1Impact of t.docxjeanettehully
Running head: Impact of technology on healthcare
1
Impact of technology on healthcare
7
Impact of technology on healthcare
Woodrow Rowell
11/24/19
The healthcare industry and technology are interrelated. Behavioural health is mainly attributed to the type of information a patient has access to. Technology thus plays a big role in giving the correct amount of information (Cypriano, 2011). Through it, the society is able to access information influencing their thought on public health care. In turn this adds on to the way the public and health professionals search for information related to health matters. This shapes the way decisions related to health are made and how they respond to this. Thus the main goal of technology in health care is to help improve the quality of health care and outcomes in health related matters.
In order to improve the quality of life have been major technological advancements. The advancements have had a lot of impact in nearly all fields related to health care. This are mainly by updating the way health records are handled by digitizing them and use of big storage data and cloud computing. Digital health records have replaced the previously used paper records that had their own bit of challenges. In the world of medicine this is a bold move and a major boost. Through the help of coding professionals health professionals have been able to manage tasks and roles more effectively. Implementation of the roles are tasked to nurses and at times technicians.
Nurses are mandated with the task of collecting data and manually uploading it on a digitized central system void of any errors. As soon as the data is uploaded patient records are updated by the medical billers with a unique code of diagnostics. In turn the billings are submitted to insurance companies for medical claims. Patients are therefore able to access their health records easily and are able to spot an error in their records early enough in their treatment. There are benefits attributed to the digital health records just to mention a few.
Data collected is easy to feed into a computer as compared to the paper based methods, thus saves on time. Both nurses and patients are able to spot an error early enough regarding data on the patients diagnostics, financial records or treatment. Productivity is increased the nurses and medical coders can work from anywhere as there is digital access to the medical records. While billing and coding there is ease in the amount to be handled as they are can handle many records at a go. From the collected data it is easier to conduct research and come up with solutions for the various health related problems. Medical researchers can therefore improve on the available knowledge in medicine.
Digital platforms go along way into reducing the cost of health care. For instance using less paper means less space is used for collecting data in files, arranging in an orderly manner and using up space to secure the reco ...
Data mining technique has a key role in knowledge
extraction from databases to promote efficient decision making.
This paper presents an approach for knowledge extraction from
a sample database of some school dropped students using
association rule generation and classification algorithms to
demonstrate how knowledge-based development policy making
decisions can be processed from the extracted knowledge. A
system architecture is proposed considering mobile computing
devices as user interface to the system connecting mass people
database with cloud computing environment resources. The
causes of education termination are investigated by analyzing the
sample database in terms of attribute value relationship in the
form of association rules to reason about the causes based on the
computed support and confidence. It is observed that if the
affected family had no service holders, the dropped student had
to stop his education because of financial problem. Classification
is applied to classify the dropped students in different groups
based on their level of education.
Future And Scope of Data Science Online Program.pptxlearn bay
Welcome to the presentation on the future and scope of data science online programs.
In this session, we will explore the growing significance of online data science programs and the opportunities they offer.
Unveiling the Power of Data Science.pdfKajal Digital
Data science is an interdisciplinary field that combines techniques from statistics, computer science, and domain expertise to extract insights and knowledge from data. It involves the collection, cleaning, analysis, and interpretation of data to make informed decisions and predictions. The goal is to uncover hidden patterns, trends, and correlations that might otherwise remain obscured.
HSC Event, https://www.youtube.com/watch?v=g0FakQaUvPM
,digital health ,orcha ,digital phenotype ,dynamic consent ,real world data ,data in the wild ,ecological momentary assessment
Framework for understanding data science.pdfMichael Brodie
The objective of my research is to provide a framework with which the data science community can understand, define, and develop data science as a field of inquiry. The framework is based on the classical reference framework (axiology, ontology, epistemology, methodology) used for 200 years to define knowledge discovery paradigms and disciplines in the humanities, sciences, algorithms, and now data science. I augmented it for automated problem-solving with (methods, technology, community) [1][2]. The resulting data science reference framework is used to define the data science knowledge discovery paradigm in terms of the philosophy of data science addressed in [1] and the data science problem-solving paradigm, i.e., the data science method, and the data science problem-solving workflow, addressed in [2][3]. The framework is a much called for unifying framework for data science as it contains the components required to define data science. For insights to better understand data science, this paper uses the framework to define the emerging, often enigmatic, data science problem-solving paradigm and workflow, and to compare them with their well-understood scientific counterparts – scientific problem-solving paradigm and workflow.
The objective of my current research [4] is to develop a 21st C re-conception of data. Unlike 20th C data that are assets, 21st C data science data is phenomenological – a resource in which to discover phenomena and their properties, previously and otherwise impossible.
[1] Brodie, M.L., Defining data science: a new field of inquiry, arXiv preprint https://doi.org/10.48550/arXiv.2306.16177 Harvard University, July 2023.
[2] Brodie, M.L., A data science axiology: the nature, value, and risks of data science, arXiv preprint http://arxiv.org/abs/2307.10460 Harvard University, July 2023.
[3] Brodie, M.L., A framework for understanding data science, arXiv preprint https://arxiv.org/abs/2403.00776 Harvard University, March 2024.
[4] Brodie, M.L., Re-conceiving data in the 21st Century. Work in progress, Harvard University.
Companies desires for making productive discoveries from big data have motivated academic institutions offering variety of different data science (DS) programs, in order to increases their graduates' ability to be data scientists who are capable to face the challenges of the new age. These data science programs represent a combination of subject areas from several disciplines. There are few studies have examined data science programs within a particular discipline, such as Business (e.g. Chen et al.). However, there are very few empirical studies that investigate DS programs and explore its curriculum structure across disciplines. Therefore, this study examines data science programs offered by American universities. The study aims to depict the current state of data science education in the U.S. to explore what discipline DS programs covers at the graduate level. The current study conducted an exploratory content analysis of 30 DS programs in the United States from a variety of disciplines. The analysis was conducted on course titles and course descriptions level. The study results indicate that DS programs required varying numbers of credit hours, including practicum and capstone. Management schools seem to take the lead and the initiative in lunching and hosting DS programs. In addition, all DS programs requires the basic knowledge of database design, representation, extraction and management. Furthermore, DS programs delivered information skills through their core courses. Moreover, the study results show that almost 40 percent of required courses in DS programs is involved information representations, retrieval and programming. Additionally, DS programs required courses also addressed communication visualization and mathematics skills.
For More Information Visit Here: https://medium.com/@niet.greaternoidancr/niets-approach-to-project-based-learning-fostering-practical-skills-and-innovation-709b4eb2d9a1
Top 5 Factors while choosing the Best Engineering Institute in Greater NoidaNIET Greater Noida ..
NIET proudly ranks among the Top 10 Engineering institutes in Delhi NCR, delivering excellence in education. Our distinguished instructors and cutting-edge infrastructure foster a welcoming environment for aspiring engineers. We develop future IT leaders through industry-focused curriculum and hands-on training. Choose NIET Greater Noida for an enlightening engineering journey and a prosperous career in a competitive world.
NIET offers Best Computer Science and Engineering College in Delhi. It provides quality education, training and research in the latest industry trends and technologies. It is also an autonomous institute recognized by the UGC and awarded by several organizations for its academic and placement excellence.
Dive into the vast scope of artificial intelligence and uncover the myriad of career opportunities it presents, from machine learning to robotics, opening doors to a promising and impactful career path.
Choosing the best MBA institution might be difficult due to the multiple criteria involved. To prevent committing typical blunders, it is critical to properly investigate and analyse various universities.
Top Seven Advantages of Studying at Private Engineering College at UPNIET Greater Noida ..
NIET Greater Noida is the best placement college in Delhi NCR UP. It provides all types of technical courses like MBA, B.Tech, MTech, MCA, and so on.
https://nietgrnoida.wordpress.com/2023/05/10/top-seven-advantages-of-studying-at-private-engineering-college-at-up/
Why B.Tech in Computer Science is a Demanding Stream for Engineering AspirantsNIET Greater Noida ..
NIET is considered one of the best Computer Science and Engineering colleges in Delhi, and the college has had an excellent placement record over the years.
However, it is imperative to choose the right college to pursue your higher studies to ensure a higher quality of education and good placement opportunities. If you are looking for top computer engineering colleges in Greater Noida, NIET is the name to trust.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
Data Science for Social Good: Using Data to Solve Real-World Problems
1. Data Science for Social Good: Using Data to Solve Real-World
Problems
People are beginning to see the benefit of leveraging the abundance of data available today
to improve the world. Information is being used in a developing trend known as "Data
Science for Social Good," which aims to solve important issues in the real world and
advance society. When resolving humanity's most difficult problems, data science is a game-
changer. NIET, holding 42nd
all India rank by NIRF 2023, provides programs that help
students develop the knowledge and perspective needed to use data for good.
How programs offered by NIET are helping to solve real world problems?
Students can pursue academic opportunities while also working toward joining this
meaningful cause.
Some of the sectors where data science program is helpful are:
1. A data-driven healthcare revolution
The medical field is one where data science is making a noticeable impact. Medical records,
patient information, and clinical trial results may all be analyzed to reveal previously
unknown trends, patterns, and therapies. Healthcare providers may improve diagnosis,
foresee disease outbreaks, and tailor treatments to individual patients by employing ML
algorithms and predictive analytics.
These developments help improve healthcare systems and the health of people everywhere.
Some of the best computer engineering colleges in India can provide an excellent
education for aspiring data scientists enthusiastic about pushing such breakthroughs.
2. Accessibility and equity in education
Data science is becoming increasingly important in the fight to close the achievement gap
and improve all students' education quality. Researchers can better inform policymakers by
examining educational data to reveal patterns of success and failure.
2. As a result, when instructional content is developed utilizing a data-driven approach,
students are more likely to participate and produce higher results actively. Students who
graduate from the best engineering institute in Delhi NCR UP will be prepared to fulfill the
growing demand for data science specialists.
3. Sustainability via environmental protection
There is an immediate need for creative answers in the battle against global warming and
safeguarding Earth's resources. Tools from the field of data science may help examine
environmental data, forecast ecological changes, and maximize the use of available
resources.
Data-driven insights influence environmentally friendly habits and policy formation, from
monitoring deforestation patterns with satellite imagery to estimating the influence of policy
choices on greenhouse gas emissions. The best computer engineering colleges in India are
excellent places for aspiring environmental data scientists to perfect their talents.
4. Assistance in times of crisis
Data science may be a lifeline in a bind. Experts may better prepare for natural catastrophes
and track their progress in real time by evaluating historical data and real-time information.
This talent saves lives and lessens the severity of disasters immensely.
Data analysis also helps with humanitarian relief distribution by pinpointing the places most
urgently needed. The best computer engineering colleges in India, like NIET, provide
students with many resources as they begin their data science education.
Conclusion
Data science is a potent instrument for social good in today's digital age. We can overcome
obstacles that have looked intractable for a long time if we can access the insights buried in
massive databases. Data science is the engine powering revolutionary shifts in many
different arenas.
The best engineering institute in Delhi NCR UP, like NIET, is great places for aspiring data
scientists who want to make a difference and get the education and training they need to
succeed. Because of their hard work and knowledge, the world will be a better place where
data catalyzes change.