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Big data is the new technology for healthcare management system. Present day's big data analytics are using in everywhere because of its good data management and its large storage capacity. In hospital managements the patients and doctors record keeping safe is the important role in healthcare system. In worldwide the big data method is extended use in the area of medicine and healthcare system. In this sector so many problems are there in implementing big data in healthcare system especially in relation to securities, privacy matters, standard records, good governance, managing of data, data storing and maintenance, etc. It is critical that these challenges to overcome before big data can be implemented successfully in healthcare. The amount of data being digitally collected and stored safely in big data Hadoop clusters. This paper introduces healthcare data, big data in healthcare systems, applications, advantages, issues of Big Data analytics in healthcare sector. Gagana H. S | Bhavani B. T | Gouthami H. S "Big Data Analytics using in Healthcare Management 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/ijtsrd31014.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31014/big-data-analytics-using-in-healthcare-management-system/gagana-h-s

Big data is the new technology for healthcare management system. Present day's big data analytics are using in everywhere because of its good data management and its large storage capacity. In hospital managements the patients and doctors record keeping safe is the important role in healthcare system. In worldwide the big data method is extended use in the area of medicine and healthcare system. In this sector so many problems are there in implementing big data in healthcare system especially in relation to securities, privacy matters, standard records, good governance, managing of data, data storing and maintenance, etc. It is critical that these challenges to overcome before big data can be implemented successfully in healthcare. The amount of data being digitally collected and stored safely in big data Hadoop clusters. This paper introduces healthcare data, big data in healthcare systems, applications, advantages, issues of Big Data analytics in healthcare sector. Gagana H. S | Bhavani B. T | Gouthami H. S "Big Data Analytics using in Healthcare Management 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/ijtsrd31014.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31014/big-data-analytics-using-in-healthcare-management-system/gagana-h-s

NationBuilder, a Los Angeles based nonpartisan political software provider, set out to make voter data easily accessible to the many campaigns that have neither access to major party databases nor funds available to work with costly consultants and data vendors.

Data analytics has revolutionized Product Lifecycle Management PLM in the medical technology industry by enabling data driven decision making, enhancing regulatory compliance, and optimizing manufacturing processes. This paper explores the transformative impact of analytics across PLM stages, including ideation, design, manufacturing, and post market surveillance. Key challenges, such as fragmented data, cybersecurity concerns, and integration complexities, are analyzed alongside innovative solutions like AI driven threat detection, blockchain for secure data management, and predictive maintenance. Emerging trends such as adaptive manufacturing and digital twins highlight the evolving role of advanced technologies in improving product efficiency and patient safety. Additionally, the integration of regulatory compliance frameworks like HIPAA and GDPR ensures ethical data handling while maintaining operational excellence. This study underscores the critical importance of analytics in fostering innovation, reducing costs, and enhancing trust in medical technologies, offering a roadmap for future advancements in this rapidly evolving domain. Sagar Sabbani "Data-Driven Excellence: Integrating Analytics into Medical Technology Product Lifecycle Management" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-1 , February 2025, URL: https://www.ijtsrd.com/papers/ijtsrd73817.pdf Paper Url: https://www.ijtsrd.com/medicine/other/73817/datadriven-excellence-integrating-analytics-into-medical-technology-product-lifecycle-management/sagar-sabbani

We are on the other side of \"big data\" hype, but what is the future of analytics and how does AI fit in? Till and Adithya from MotherDuck join us to discuss why DuckDB is taking the analytics and AI world by storm. We dive into what makes DuckDB, a free, in-process SQL OLAP database management system, unique including its ability to execute lighting fast analytics queries against a variety of data sources, even on your laptop! Along the way we dig into the intersections with AI, such as text-to-sql, vector search, and AI-driven SQL query correction.Join the discussionChangelog++ members save 9 minutes on this episode because they made the ads disappear. Join today!Sponsors:Fly.io - The home of Changelog.com - Deploy your apps close to your users - global Anycast load-balancing, zero-configuration private networking, hardware isolation, and instant WireGuard VPN connections. Push-button deployments that scale to thousands of instances. Check out the speedrun to get started in minutes.Timescale - Real-time analytics on Postgres, seriously fast. Over 3 million Timescale databases power loT, sensors, Al, dev tools, crypto, and finance apps - all on Postgres. Postgres, for everything.Notion - Notion is a place where any team can write, plan, organize, and rediscover the joy of play. It's a workspace designed not just for making progress, but getting inspired. Notion is for everyone - whether you're a Fortune 500 company or freelance designer, starting a new startup or a student juggling classes and clubs.Featuring:Till Dohmen – Website, GitHub, LinkedIn, XAdithya Krishnan – Website, GitHub, LinkedIn, XChris Benson – Website, GitHub, LinkedIn, XDaniel Whitenack – Website, GitHub, XShow Notes:DuckDBMotherDuckIntroducing FixIt: an unreasonably effective AI error fixer for SQLSomething missing or broken? PRs welcome!

The " Data Science Course " by Imarticus Learning is a comprehensive program designed to equip learners with essential skills in data science and analytics. The course covers key areas such as data manipulation, statistical analysis, machine learning, and data visualization using industry-standard tools like Python, R, and Tableau. With a blend of theoretical knowledge and practical experience, including real-world projects and case studies, the program aims to prepare students for various roles in the data science field. Additionally, participants benefit from personalized mentorship, career support, and access to a network of industry professionals, ensuring a well-rounded educational experience.

The proliferation of mobile devices, such as smartphones and Internet of Things (IoT) gadgets, results in the recent mobile big data (MBD) era. Collecting MBD is unprofitable unless suitable analytics and learning methods are utilized for extracting meaningful information and hidden patterns from data. This article presents an overview and brief tutorial of deep learning in MBD analytics and discusses a scalable learning framework over Apache Spark. Specifically, a distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall MBD, and a master deep model is then built by averaging the parameters of all partial models. This Spark-based framework speeds up the learning of deep models consisting of many hidden layers and millions of parameters. We use a context-aware activity recognition application with a real-world dataset containing millions of samples to validate our framework and assess its speedup effectiveness.

Sheer increase in volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark is gaining popularity for exhibiting superior scale-out performance on the commodity machines, the impact of data volume on the performance of Spark based data analytics in scale-up configuration is not well understood. We present a deep-dive analysis of Spark based applications on a large scale-up server machine. Our analysis reveals that Spark based data analytics are DRAM bound and do not benefit by using more than 12 cores for an executor. By enlarging input data size, application performance degrades significantly due to substantial increase in wait time during I/O operations and garbage collection, despite 10\% better instruction retirement rate (due to lower L1 cache misses and higher core utilization). We match memory behaviour with the garbage collector to improve performance of applications between 1.6x to 3x.

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Big data is the new technology for healthcare management system. Present day's big data analytics are using in everywhere because of its good data management and its large storage capacity. In hospital managements the patients and doctors record keeping safe is the important role in healthcare system. In worldwide the big data method is extended use in the area of medicine and healthcare system. In this sector so many problems are there in implementing big data in healthcare system especially in relation to securities, privacy matters, standard records, good governance, managing of data, data storing and maintenance, etc. It is critical that these challenges to overcome before big data can be implemented successfully in healthcare. The amount of data being digitally collected and stored safely in big data Hadoop clusters. This paper introduces healthcare data, big data in healthcare systems, applications, advantages, issues of Big Data analytics in healthcare sector. Gagana H. S | Bhavani B. T | Gouthami H. S "Big Data Analytics using in Healthcare Management 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/ijtsrd31014.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31014/big-data-analytics-using-in-healthcare-management-system/gagana-h-s

Data analytics,power bi

Tri 1 2022/2023 (Term 2215)

Tri 2 2022/2023

Tri 2 2022/2023

This project curates maternal, neonatal and child health (MNCH) data, to support robust spatiotemporal data analytics.

The global community faces a significant challenge in achieving the ambitious goals outlined in the United Nations' Sustainable Development Goals (SDGs) by 2030. These multifaceted goals, encompassing issues like poverty eradication and climate change mitigation, necessitate a comprehensive approach. The SDGs emphasise the importance of quality education (SDG 4) and responsible consumption and production patterns (SDG 12). Higher education institutions (HEIs) play a vital role in achieving these goals, increasingly utilising educational data analytics (EDA) to monitor progress and optimise resource allocation. EDA offers immense potential to enhance educational experiences and contribute to a more sustainable future. For instance, HEIs can leverage EDA to analyse student behaviour patterns related to energy consumption in campus buildings and identify areas for improvement. As such, EDA serves as a crucial tool for informing decision-making, measuring progress, and pinpointing areas needing improvement. However, harnessing student data for sustainability initiatives presents ethical concerns regarding data privacy, particularly within the context of postgraduate programmes. Studies highlight the potential for the collection and analysis of educational data to reveal sensitive details about students' learning styles, academic progress, and areas of weakness. Within the realm of sustainable education, where students often grapple with complex social and environmental issues, additional privacy considerations arise. For instance, student data could inadvertently capture personal opinions on controversial topics like environmental policies. This could potentially lead to a proliferation of self-censorship or a reluctance to fully engage in learning activities. The use of big data has propelled data privacy to the forefront of ethical concerns. The collection, storage, and analysis of student data for EDA raises concerns about potential misuse and unauthorised disclosure of personal information. Striking a balance between leveraging the power of EDA for sustainability initiatives and safeguarding student privacy is paramount to fostering trust within the educational ecosystem. This is particularly crucial in postgraduate programmes, where students often engage in independent research on sensitive topics. Such research may generate data that is considered more private than undergraduate studies due to the nature of the inquiry. Additionally, postgraduate students possess greater autonomy over their research direction, highlighting the critical importance of informed consent regarding data utilisation.

With the emergence of cloud computing and sensor technologies, Big Data analytics for the Internet of Things (IoT) has become the main force behind many innovative solutions for our society's problems. This paper provides practical explanations for the question "why is the number of Big Data applications that succeed and have an effect on our daily life so limited, compared with all of the solutions proposed and tested in the literature?", with examples taken from Smart Grids. We argue that "noninvariants" are the most challenging issues in IoT applications, which can be easily revealed if we use the term "invariant" to replace the more common terms such as "information", "knowledge", or "insight" in any Big Data for IoT research. From our experience with developing Smart Grid applications, we produced a list of "noninvariants", which we believe to be the main causes of the gaps between Big Data in a laboratory and in practice in IoT applications. This paper also proposes Graph of Virtual Actors (GOVA) as a Big Data analytics architecture for IoT applications, which not only can solve the noninvariants issues, but can also quickly scale horizontally in terms of computation, data storage, caching requirements, and programmability of the system.

Assessment analytics research aims to better understand and improve online assessments. To achieve this, researchers are dependent on students’ willingness to share (“donate”) their online exam data. In a previous, cross-sectional investigation of students’ willingness to donate their data (see https://osf.io/2tzcw/), we found that data donation willingness for university students can be predicted using the Technology Acceptance Model and its extensions (TAM; Abdullah & Ward, 2016; Davis, 1989; Venkatesh et al., 2003). Students’ expectations of their perceived ease of use, the expected behavior of significant others, as well as the expected individual performance benefit predicted willingness to donate data. However, we found that organizational performance expectancy (rather than individual) was a better predictor of data donation willingness than individual performance expectancy. Students who thought donating their data would benefit the university as a whole, as opposed to themselves, were more likely to indicate to being willing to donate their data. A possible explanation is the perceived gap between data donation in e-exams and the implementation of measures to improve students’ experience based on these data. If the organizational, rather than the individual, perspective plays a bigger role in data donation willingness, then interventions aiming to increase organizational commitment/identification should increase data donation willingness in e-exams. The present research employs an experimental manipulation of organizational identity and investigates its impact on students’ data donation willingness.

TRI 2 2019/2020

Data Analytics Training in Chandigarh imparts practical skills through hands-on projects, providing an avenue to apply theoretical knowledge to real-world scenarios. Participants learn to interpret data and derive actionable recommendations, fostering critical thinking and problem-solving abilities.

The Data Analytics report provides a comprehensive analysis of the global market, taking into consideration its history, current scenario, and future development trends. It also examines market aspects such as trends, share, forecast, outlook, production, and future growth potential. The report also details various new trends and technologies that are being adopted by the various players in the Data Analytics to propel their business in a positive direction

Data Analytics is the process of collecting, analyzing, and interpreting data to uncover patterns, trends, and insights. It helps businesses make informed decisions, improve efficiency, and drive growth across various industries.

Mark is joined in this episode by Christian Steinert to talk about his solo journey building Steinert Analytics, a data analytics consultancy transforming data into actionable revenue and cost-saving insights for Central Ohio's SaaS startups.Steinert Analytics homepageChristian Steinert LinkedIn Profile\"Helping a Roofing Company Define Their Sales Process\"\"Transforming a Top 5 Fast Food Company's Operational Drive-Thru Reporting\"

Mark is joined in this episode by Christian Steinert to talk about his solo journey building Steinert Analytics, a data analytics consultancy transforming data into actionable revenue and cost-saving insights for Central Ohio's SaaS startups.Steinert Analytics homepageChristian Steinert LinkedIn Profile\"Helping a Roofing Company Define Their Sales Process\"\"Transforming a Top 5 Fast Food Company's Operational Drive-Thru Reporting\"

As of late there has been a growth in data. This paper presents a methodology to investigate the text classification of data gathered from twitter. In this study sentiment analysis has been done on online comment data giving us picture of how to discover the demands of a people. Ziya Fatima | Er. Vandana "Detailed Investigation of Text Classification and Clustering of Twitter Data for Business Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38527.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38527/detailed-investigation-of-text-classification-and-clustering-of-twitter-data-for-business-analytics/ziya-fatima

Full show notes and video at http://theAverageGuy.tv/cf061   This week on Cyber Frontiers Christian and Jim tackle the technology shaping society amidst the Coronavirus (COVID-19). We dive into the health (and growth) of the internet backbone, working from home, and distance learning to name a few. We then switch gears into discussing how data and analytic capabilities behind the virus tricked into the mainstream and is now widely available to society at large to make well informed decisions. We specifically highlight the importance of accurate data visualizations to not skew good data, and highlight Corona Data Scraper as a model of open-source, scalable, accurate data analytics and visualizations for COVID-19.   Cyber Frontiers is all about Exploring Cyber security, Big Data, and the Technologies Shaping the Future!   Christian Johnson will bring fresh and relevant topics to the show based on the current work he does.   Support the Average Guy: https://www.patreon.com/theaverageguy   WANT TO SUBSCRIBE? We now have Video Large / Small and Video iTunes options at http://theAverageGuy.tv/subscribe   You can contact us via email at [email protected]  

Welcome to our latest video, "The Future of Data Analytics in the Era of GenAI." In this video, we explore how artificial intelligence is changing the landscape of data analytics and transforming the way we act on data. We will discuss the latest trends, insights, and examples of how AI is being used in data analytics, and hear from experts in the field on how they are leveraging AI to build better software faster, improve code quality, and reduce development costs. In this episode, Firat Tekiner, from the Product Management team at Google, speaks with Bruno Aziza, Head of Data Analytics at Google Cloud. Whether you are a seasoned data analyst or just starting out, this video is a must-watch. Join us as we explore the exciting world of LLMs and generative AI, together with the future of data analytics, and discover how you can stay ahead of the curve in this rapidly evolving field. Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech

In this paper we review basic and emerging models and associated algorithms for large-scale tensor networks, especially Tensor Train (TT) decompositions using novel mathematical and graphical representations. We discus the concept of tensorization (i.e., creating very high-order tensors from lower-order original data) and super compression of data achieved via quantized tensor train (QTT) networks. The purpose of a tensorization and quantization is to achieve, via low-rank tensor approximations "super" compression, and meaningful, compact representation of structured data. The main objective of this paper is to show how tensor networks can be used to solve a wide class of big data optimization problems (that are far from tractable by classical numerical methods) by applying tensorization and performing all operations using relatively small size matrices and tensors and applying iteratively optimized and approximative tensor contractions. Keywords: Tensor networks, tensor train (TT) decompositions, matrix product states (MPS), matrix product operators (MPO), basic tensor operations, tensorization, distributed representation od data optimization problems for very large-scale problems: generalized eigenvalue decomposition (GEVD), PCA/SVD, canonical correlation analysis (CCA).

Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data.

Connecting different text attributes associated with the same entity (conflation) is important in business data analytics since it could help merge two different tables in a database to provide a more comprehensive profile of an entity. However, the conflation task is challenging because two text strings that describe the same entity could be quite different from each other for reasons such as misspelling. It is therefore critical to develop a conflation model that is able to truly understand the semantic meaning of the strings and match them at the semantic level. To this end, we develop a character-level deep conflation model that encodes the input text strings from character level into finite dimension feature vectors, which are then used to compute the cosine similarity between the text strings. The model is trained in an end-to-end manner using back propagation and stochastic gradient descent to maximize the likelihood of the correct association. Specifically, we propose two variants of the deep conflation model, based on long-short-term memory (LSTM) recurrent neural network (RNN) and convolutional neural network (CNN), respectively. Both models perform well on a real-world business analytics dataset and significantly outperform the baseline bag-of-character (BoC) model.

Engine icing researchers at the NASA Glenn Research Center use the Escort data acquisition system in the Propulsion Systems Laboratory (PSL) to generate and collect a tremendous amount of data every day. Currently these researchers spend countless hours processing and formatting their data, selecting important variables, and plotting relationships between variables, all by hand, generally analyzing data in a spreadsheet-style program (such as Microsoft Excel). Though spreadsheet-style analysis is familiar and intuitive to many, processing data in spreadsheets is often unreproducible and small mistakes are easily overlooked. Spreadsheet-style analysis is also time inefficient. The same formatting, processing, and plotting procedure has to be repeated for every dataset, which leads to researchers performing the same tedious data munging process over and over instead of making discoveries within their data. This paper documents a data analysis tool written in Python hosted in a Jupyter notebook that vastly simplifies the analysis process. From the file path of any folder containing time series datasets, this tool batch loads every dataset in the folder, processes the datasets in parallel, and ingests them into a widget where users can search for and interactively plot subsets of columns in a number of ways with a click of a button, easily and intuitively comparing their data and discovering interesting dynamics. Furthermore, comparing variables across data sets and integrating video data (while extremely difficult with spreadsheet-style programs) is quite simplified in this tool. This tool has also gathered interest outside the engine icing branch, and will be used by researchers across NASA Glenn Research Center. This project exemplifies the enormous benefit of automating data processing, analysis, and visualization, and will help researchers move from raw data to insight in a much smaller time frame.

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This online DATA ANALYST COURSE IN CHANDIGARH will transform you into a data analytics expert. In this certification course, you will learn the latest analytics tools and techniques, how to work with SQL, the languages of R and Python, the art of  creating data visualizations, and how to apply statistics and predictive analytics in a business environment.

In 2025, data analytics gets sharper—real-time dashboards, AI-powered insights, and ethical governance will dominate. Expect faster decisions, deeper personalization, and smarter automation across industries. Discover Interesting Data Analytics Trends Here https://bit.ly/3TrJie2

This article is from PLoS ONE , volume 9 . Abstract Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/.

This article is from PLoS ONE , volume 9 . Abstract Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/.

The increase in the number and volume, and sources, of globally available Earth science data measurements and datasets have afforded Earth scientists and applications researchers unprecedented opportunities to study our Earth in ever more sophisticated ways. In fact, the NASA Earth Observing System Data Information System (EOSDIS) archives have doubled from 2007 to 2014, to 9.1 PB (Ramapriyan, 2009; and https:earthdata.nasa.govaboutsystem-- performance). In addition, other US agency, international programs, field experiments, ground stations, and citizen scientists provide a plethora of additional sources for studying Earth. Co--analyzing huge amounts of heterogeneous data to glean out unobvious information is a daunting task. Earth science data analytics (ESDA) is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. It can include Data Preparation, Data Reduction, and Data Analysis. Through work associated with the Earth Science Information Partners (ESIP) Federation, a collection of Earth science data analytics use cases have been collected and analyzed for the purpose of extracting the types of Earth science data analytics employed, and requirements for data analytics tools and techniques yet to be implemented, based on use case needs. ESIP generated use case template, ESDA use cases, use case types, and preliminary use case analysis (this is a work in progress) will be presented.

Norman Nie of Revolution Analytics goes deep on the concept of big data. Source: https://youtube.com/watch?v=3RmLjXmzcN0 Uploader: gigaom Upload date: 2013-05-28

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Market leaders are connecting everyone through data products augmented with context, insights, and actions. In this session, you will learn how to embed smart analytics in your applications and create new revenue streams, grounded in consistent live metrics. Speakers: Skander Larbi, , Sahir Azam Watch more: All sessions from Google Cloud Next → https://goo.gle/next23 #GoogleCloudNext ANA213

Data practitioners spend much of their time on complex, fragmented and sometimes, repetitive tasks. This limits their ability to focus on strategic insights and maximize the value of their data. Gemini in BigQuery shifts this paradigm by providing AI and data analytics capabilities that help streamline your workflows across the entire data lifecycle. In this video, we'll explore how Google Gemini in BigQuery transforms the experience through assistance and automation. It empowers you with features such as intent-driven development with SQL and Python code assistance, semantic search of data assets, workload optimization, AI-driven data pipelines through Google Cloud BigQuery data transformation, a natural-language-based data journey experience with BigQuery data canvas, and more. Ready to transform your data analysis workflows? Discover how Gemini in BigQuery can unlock new levels of efficiency and productivity. Resources: Learn more in the documentation → https://goo.gle/4aGJ5u9 Read the blog → https://goo.gle/gemini-in-bq-blog Gemini → https://goo.gle/4aKqqxj Try Gemini in Vertex AI → https://goo.gle/4bKd193 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech #gemini #googlecloud

Statistics for Data Science and Analytics Peter C. Bruce Peter Gedeck Janet Dobbins 2025

Recently, the Corona pandemic has swept the entire world and has led to many deaths, as monitoring the health of patients has become very difficult for the medical staff, with IoT technology has become the monitoring of patients' health remotely, and this facilitated a lot of work for physicians. In this research, connecting medical sensors, including blood oxygen sensors, temperature sensors, as well as pulse sensors) to patients, obtaining vital parameters and sending data to the IoT platform, used the technology of monitoring the health of corona patients remotely using the Internet of things. The ubidots platform was used where it is considered one of the easiest platforms to deal with sensors, as well as providing free and permanent data storage. High results were obtained in quickly transferring sensor data to the IoT platform, where the data transfer rate ranged between (10 - 15) seconds for each new data, and this is an excellent transfer rate.

Recently, the Corona pandemic has swept the entire world and has led to many deaths, as monitoring the health of patients has become very difficult for the medical staff, with IoT technology has become the monitoring of patients' health remotely, and this facilitated a lot of work for physicians. In this research, connecting medical sensors, including blood oxygen sensors, temperature sensors, as well as pulse sensors) to patients, obtaining vital parameters and sending data to the IoT platform, used the technology of monitoring the health of corona patients remotely using the Internet of things. The ubidots platform was used where it is considered one of the easiest platforms to deal with sensors, as well as providing free and permanent data storage. High results were obtained in quickly transferring sensor data to the IoT platform, where the data transfer rate ranged between (10 - 15) seconds for each new data, and this is an excellent transfer rate.