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| automated data analysis tools: Automated Data Analysis Using Excel Brian D. Bissett, 2020 This new edition includes some key topics relating to the latest version of MS Office, including use of the ribbon, current Excel file types, Dashboard, and basic Sharepoint integration. It shows how to automate operations, such as curve fitting, sorting, filtering, and analyzing data from a variety of sources. The book allows users to analyze data and automate the preparation of custom reports and demonstrates how to assign Excel VBA code to the new Ribbon user interface. |
| automated data analysis tools: Automated Data Analysis Using Excel Brian D. Bissett, 2007-06-15 Because the analysis of copious amounts of data and the preparation of custom reports often take away time from true research, the automation of these processes is paramount to ensure productivity. Exploring the core areas of automation, report generation, data acquisition, and data analysis, Automated Data Analysis Using Excel illustrates how to m |
| automated data analysis tools: Automated Data Analytics Soraya Sedkaoui, 2024-10-11 The human mind is endowed with a remarkable capacity for creative synthesis between intuition and reason; this mental alchemy is the source of genius. A new synergy is emerging between human ingenuity and the computational capacity of generative AI models. Automated Data Analytics focuses on this fruitful collaboration between the two to unlock the full potential of data analysis. Together, human ethics and algorithmic productivity have created an alloy stronger than the sum of its parts. The future belongs to this symbiosis between heart and mind, human and machine. If we succeed in harmoniously combining our strengths, it will only be a matter of time before we discover new analytical horizons. This book sets out the foundations of this promising partnership, in which everyone makes their contribution to a common work of considerable scope. History is being forged before our very eyes. It is our responsibility to write it wisely, and to collectively pursue the ideal of augmented intelligence progress. |
| automated data analysis tools: Automated Data Analysis Using Excel Brian D. Bissett, 2007-06-15 Because the analysis of copious amounts of data and the preparation of custom reports often take away time from true research, the automation of these processes is paramount to ensure productivity. Exploring the core areas of automation, report generation, data acquisition, and data analysis, Automated Data Analysis Using Excel illustrates how to minimize user intervention, automate parameter setup, obtain consistency in both analysis and reporting, and save time through automation. Focusing on the built-in Visual Basic® for Applications (VBA) scripting language of Excel®, the book shows step-by-step how to construct useful automated data analysis applications for both industrial and academic settings. It begins by discussing fundamental elements, the methods for importing and accessing data, and the creation of reports. The author then describes how to use Excel to obtain data from non-native sources, such as databases and third-party calculation tools. After providing the means to access any required information, the book explains how to automate manipulations and calculations on the acquired data sources. Collecting all of the concepts previously discussed in the book, the final chapter demonstrates from beginning to end how to create a cohesive, robust application. With an understanding of this book, readers should be able to construct applications that can import data from a variety of sources, apply algorithms to data that has been imported, and create meaningful reports based on the results. |
| automated data analysis tools: Practical Data Analysis Dhiraj Bhuyan, 2019-11-30 “Practical Data Analysis – Using Python & Open Source Technology” uses a case-study based approach to explore some of the real-world applications of open source data analysis tools and techniques. Specifically, the following topics are covered in this book: 1. Open Source Data Analysis Tools and Techniques. 2. A Beginner’s Guide to “Python” for Data Analysis. 3. Implementing Custom Search Engines On The Fly. 4. Visualising Missing Data. 5. Sentiment Analysis and Named Entity Recognition. 6. Automatic Document Classification, Clustering and Summarisation. 7. Fraud Detection Using Machine Learning Techniques. 8. Forecasting - Using Data to Map the Future. 9. Continuous Monitoring and Real-Time Analytics. 10. Creating a Robot for Interacting with Web Applications. Free samples of the book is available at - http://timesofdatascience.com |
| automated data analysis tools: Automated Data Analysis Using Excel Brian D. Bissett, 2020-08-18 This new edition covers some of the key topics relating to the latest version of MS Office through Excel 2019, including the creation of custom ribbons by injecting XML code into Excel Workbooks and how to link Excel VBA macros to customize ribbon objects. It now also provides examples in using ADO, DAO, and SQL queries to retrieve data from databases for analysis. Operations such as fully automated linear and non-linear curve fitting, linear and non-linear mapping, charting, plotting, sorting, and filtering of data have been updated to leverage the newest Excel VBA object models. The text provides examples on automated data analysis and the preparation of custom reports suitable for legal archiving and dissemination. Functionality Demonstrated in This Edition Includes: Find and extract information raw data files Format data in color (conditional formatting) Perform non-linear and linear regressions on data Create custom functions for specific applications Generate datasets for regressions and functions Create custom reports for regulatory agencies Leverage email to send generated reports Return data to Excel using ADO, DAO, and SQL queries Create database files for processed data Create tables, records, and fields in databases Add data to databases in fields or records Leverage external computational engines Call functions in MATLAB® and Origin® from Excel |
| automated data analysis tools: Automated Data Analysis Using Excel Brian D Bissett, 2018-03-31 Because the analysis of copious amounts of data and the preparation of custom reports often take away time from true research, the automation of these processes is paramount to ensure productivity. Exploring the core areas of automation, report generation, data acquisition, and data analysis, Automated Data Analysis Using Excel illustrates how to minimize user intervention, automate parameter setup, obtain consistency in both analysis and reporting, and save time through automation. Focusing on the built-in Visual Basic for Applications (VBA) scripting language of Excel, the book shows step-by-step how to construct useful automated data analysis applications for both industrial and academic settings. It begins by discussing fundamental elements, the methods for importing and accessing data, and the creation of reports. The author then describes how to use Excel to obtain data from non-native sources, such as databases and third-party calculation tools. After providing the means to access any required information, the book explains how to automate manipulations and calculations on the acquired data sources. Collecting all of the concepts previously discussed in the book, the final chapter demonstrates from beginning to end how to create a cohesive, robust application. With an understanding of this book, readers should be able to construct applications that can import data from a variety of sources, apply algorithms to data that has been imported, and create meaningful reports based on the results. |
| automated data analysis tools: Deep Learning Techniques and Optimization Strategies in Big Data Analytics Thomas, J. Joshua, Karagoz, Pinar, Ahamed, B. Bazeer, Vasant, Pandian, 2019-11-29 Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry. |
| automated data analysis tools: Practical Machine Learning for Data Analysis Using Python Abdulhamit Subasi, 2020-06-05 Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. - Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas - Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data - Explores important classification and regression algorithms as well as other machine learning techniques - Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features |
| automated data analysis tools: Driving Midsized Growth: People Robert Sher, 2021-08 |
| automated data analysis tools: Automated Database Applications Testing Rana Farid Mikhail, Donald J. Berndt, Abraham Kandel, 2010 This book introduces SpecDB, an intelligent database created to represent and host software specifications in a machine-readable format, based on the principles of artificial intelligence and unit testing database operations. SpecDB is demonstrated via two automated intelligent tools. The first automatically generates database constraints from a rule-base in SpecDB. The second is a reverse engineering tool that logs the actual execution of the program from the code. |
| automated data analysis tools: Intelligent Data Analysis Deepak Gupta, Siddhartha Bhattacharyya, Ashish Khanna, Kalpna Sagar, 2020-07-13 This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis tools. |
| automated data analysis tools: Intelligent Data Analysis for Biomedical Applications D. Jude Hemanth, Deepak Gupta, Valentina Emilia Balas, 2019-03-15 Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers, scientists, engineers and practitioners interested in intelligent data analysis, knowledge discovery, and decision support in databases. - Provides the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collection - Contains an analysis of medical databases to provide diagnostic expert systems - Addresses the integration of intelligent data analysis techniques within biomedical information systems |
| automated data analysis tools: From Manual to Magical: Unleashing the Power of Business Automation Shu Chen Hou, Are you ready to unlock the secrets of business automation and transform your organization into a powerhouse of efficiency and success? Look no further than our groundbreaking eBook, From Manual to Magical: Unleashing the Power of Business Automation.” In today's competitive landscape, manual processes simply won't cut it. It's time to embrace the future and harness the transformative power of automation. This comprehensive guide will take you on a journey, equipping you with the knowledge and tools to revolutionize your business operations. Discover the Definition of Business Automation and its significance in today's fast-paced world. Gain insights into the Benefits of Automation, including increased efficiency, reduced costs, improved accuracy, and scalable growth. We debunk Common Myths and Misconceptions surrounding automation, setting the record straight and paving the way for success. But don't just take our word for it! Dive into our Real-Life Examples and explore case studies of businesses that have implemented automation to achieve extraordinary results. Witness firsthand how automation can propel your organization to new heights of productivity and profitability. Identifying Opportunities for Automation is crucial, and we provide you with the tools to analyze your existing processes, map workflows, and prioritize initiatives based on impact and feasibility. Conduct a Cost-Benefit Analysis and make informed decisions that maximize your return on investment. Choosing the Right Automation Tools is a breeze with our in-depth overview of the available technologies, such as robotic process automation (RPA), workflow management systems, and artificial intelligence (AI). Learn how to evaluate tool suitability and unleash their potential in your organization. Implementation is key, and our step-by-step guide ensures you plan and execute the implementation process flawlessly. Overcome challenges with Change Management and Employee Buy-In, and master Data Migration and Integration for seamless automation adoption. Leave no stone unturned with thorough Testing and Quality Assurance to ensure your success. But we don't stop there. Our eBook takes you into the future, exploring Emerging Technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning. Discover how these technologies will shape the future of automation and gain a competitive edge. Don't miss out on this opportunity to stay ahead of the curve and revolutionize your organization. From Manual to Magical: Unleashing the Power of Business Automation is your ticket to increased efficiency, reduced costs, and unprecedented growth. Embrace the power of automation and transform your business today! Take the first step towards a future of success and order your copy of From Manual to Magical: Unleashing the Power of Business Automation now! |
| automated data analysis tools: Examining Information Literacy in Academic Libraries Chizwina, Sabelo, Moyo, Mathew, 2024-04-09 In the 21st century, information literacy emerges as the cornerstone of educational development. Despite its paramount significance, a stark reality persists — students often traverse the corridors of academia without acquiring essential information literacy skills. This deficiency is exacerbated by a shortage of faculty training, leaving academic libraries to shoulder the responsibility of cultivating information-savvy individuals. Examining Information Literacy in Academic Libraries delves into the core challenges and solutions surrounding this critical educational imperative. This book illuminates the role of academic libraries as bastions of information literacy instruction. The capacity of students and information consumers to seek, evaluate, and utilize information is paramount for informed decision-making ethically and legally. Regardless of whether or not students were brought up in the digital age, many lack the fundamental information literacy skills required for higher education. This deficiency manifests in academic malpractices, such as plagiarism, which compromise the integrity of educational institutions. Moreover, this imperative work contends that the Fourth Industrial Revolution (4IR) is not a distant concept but a present reality. The recent global upheaval caused by the Coronavirus pandemic accelerated the adoption of new technologies, necessitating a swift reassessment of our collective ability to navigate this everchanging digital and information landscape. Focusing on media literacy, data literacy, and digital literacy, with information literacy as the overarching domain, this book serves as a beacon for educators, librarians, and policymakers. |
| automated data analysis tools: Microsoft Certified: Dynamics 365 Sales Functional Consultant Associate (MB-210) , Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| automated data analysis tools: Unleash Your Inner Authorpreneur Rae A. Stonehouse, 2024-01-03 In the dynamic digital-first world of publishing, succeeding as an author requires more than just writing talent. You need to embrace business skills, marketing savvy and an entrepreneurial drive to stand out. Author Rae A. Stonehouse offers a motivational yet practical guide that shows writers how to unleash their inner authorpreneur and launch a financially sustainable and creatively rewarding literary career. Within the pages of Unleash Your Inner Authorpreneur: DIY Marketing Strategies Writers Need Today, you'll discover the frameworks, strategies and actionable steps essential for establishing yourself as a thriving author brand. Learn how to conduct competitive analysis, identify your target audience, optimize online visibility, convert readers into buyers, collaborate with influencers and adapt to industry trends. Equip yourself with the well-rounded proficiencies needed to create engaging content, stimulate online discussions, maximize advertising returns, diversify income and ascend both the creative and business facets of your authorial pursuits. Guideposts include optimizing your online platform, leveraging email marketing, analyzing metrics, monetizing through multimedia and ultimately constructing the foundation for long term authorial success. This empowering and intensely practical playbook aims to help determined writers supersede barriers, fulfill their potential and boldly ascend to new heights of literary excellence. Follow these principles persistently, gather feedback, take calculated risks - before you know it, your authorpreneurial talents will unfold, connecting your stories with eager readers worldwide while you reap the financial rewards. The time has come to spread your wings and pursue your true calling. With this book as your guide, you now hold the map to realizing your ambitious author dreams. |
| automated data analysis tools: Transforming US Intelligence for Irregular War Richard H. ShultzJr., 2020-04-01 When Joint Special Operations Command deployed Task Force 714 to Iraq in 2003, it faced an adversary unlike any it had previously encountered: al-Qaeda in Iraq (AQI). AQI’s organization into multiple, independent networks and its application of Information Age technologies allowed it to wage war across a vast landscape. To meet this unique threat, TF 714 developed the intelligence capacity to operate inside those networks, and in the words of commander Gen. Stanley McChrystal, USA (Ret.) “claw the guts out of AQI.” In Transforming US Intelligence for Irregular War, Richard H. Shultz Jr. provides a broad discussion of the role of intelligence in combatting nonstate militants and revisits this moment of innovation during the Iraq War, showing how the defense and intelligence communities can adapt to new and evolving foes. Shultz tells the story of how TF 714 partnered with US intelligence agencies to dismantle AQI’s secret networks by eliminating many of its key leaders. He also reveals how TF 714 altered its methods and practices of intelligence collection, intelligence analysis, and covert paramilitary operations to suppress AQI’s growing insurgency and, ultimately, destroy its networked infrastructure. TF 714 remains an exemplar of successful organizational learning and adaptation in the midst of modern warfare. By examining its innovations, Shultz makes a compelling case for intelligence leading the way in future campaigns against nonstate armed groups. |
| automated data analysis tools: Collaborative Filtering Using Data Mining and Analysis Bhatnagar, Vishal, 2016-07-13 Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material. Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering. |
| automated data analysis tools: Data Governance Handbook Wendy S. Batchelder, 2024-05-31 Build an actionable, business value driven case for data governance to obtain executive support and implement with excellence Key Features Develop a solid foundation in data governance and increase your confidence in data solutions Align data governance solutions with measurable business results and apply practical knowledge from real-world projects Learn from a three-time chief data officer who has worked in leading Fortune 500 companies Purchase of the print or Kindle book includes a free PDF eBook Book Description2.5 quintillion bytes! This is the amount of data being generated every single day across the globe. As this number continues to grow, understanding and managing data becomes more complex. Data professionals know that it’s their responsibility to navigate this complexity and ensure effective governance, empowering businesses with the right data, at the right time, and with the right controls. If you are a data professional, this book will equip you with valuable guidance to conquer data governance complexities with ease. Written by a three-time chief data officer in global Fortune 500 companies, the Data Governance Handbook is an exhaustive guide to understanding data governance, its key components, and how to successfully position solutions in a way that translates into tangible business outcomes. By the end, you’ll be able to successfully pitch and gain support for your data governance program, demonstrating tangible outcomes that resonate with key stakeholders. What you will learn Comprehend data governance from ideation to delivery and beyond Position data governance to obtain executive buy-in Launch a governance program at scale with a measurable impact Understand real-world use cases to drive swift and effective action Obtain support for data governance-led digital transformation Launch your data governance program with confidence Who this book is for Chief data officers, data governance leaders, data stewards, and engineers who want to understand the business value of their work, and IT professionals seeking further understanding of data management, will find this book useful. You need a basic understanding of working with data, business needs, and how to meet those needs with data solutions. Prior coding experience or skills in selling data solutions to executives are not required. |
| automated data analysis tools: Fostering Innovation in Community and Institutional Corrections Brian A. Jackson, 2015-01-06 The U.S. corrections enterprise is challenged by a variety of demographic, societal, and fiscal trends, and this report seeks to frame an innovation agenda by identifying and prioritizing potential improvements in technology, policy, and practice. |
| automated data analysis tools: Intraoperative Flow Cytometry Georgios Alexiou, Georgios Vartholomatos, 2023-07-26 This book describes intraoperative flow cytometry in solid tumours. Intraoperative flow cytometry is an innovative technique for assessing tumour margins and grade of malignancy intraoperatively. The authors have been working on this procedure for more than 14 years and have introduced it in the surgery of intracranial tumours, breast cancer, liver cancer and head and neck neoplasms, as described in the book. A detailed description of the main pathological findings will be followed by an explanation of the intraoperative flow cytometry’s role. In each chapter, information will be provided to stimulate further research on this topic. The book will also include new topics that have not been published till now. The book is aimed at neurosurgeons, general surgeons, ENT specialists, breast surgeons, radiologists, pathologists, oncologists, biologists, biochemists and scientists working with on flow cytometry. |
| automated data analysis tools: Intelligent Systems and Applications Kohei Arai, 2021-08-02 This book presents Proceedings of the 2021 Intelligent Systems Conference which is a remarkable collection of chapters covering a wider range of topics in areas of intelligent systems and artificial intelligence and their applications to the real world. The conference attracted a total of 496 submissions from many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-review process. Of the total submissions, 180 submissions have been selected to be included in these proceedings. As we witness exponential growth of computational intelligence in several directions and use of intelligent systems in everyday applications, this book is an ideal resource for reporting latest innovations and future of AI. The chapters include theory and application on all aspects of artificial intelligence, from classical to intelligent scope. We hope that readers find the book interesting and valuable; it provides the state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of the future research. |
| automated data analysis tools: Mastering Cloud Native Aditya Pratap Bhuyan, 2024-07-26 Mastering Cloud Native: A Comprehensive Guide to Containers, DevOps, CI/CD, and Microservices is your essential companion for navigating the transformative world of Cloud Native computing. Designed for both beginners and experienced professionals, this comprehensive guide provides a deep dive into the core principles and practices that define modern software development and deployment. In an era where agility, scalability, and resilience are paramount, Cloud Native computing stands at the forefront of technological innovation. This book explores the revolutionary concepts that drive Cloud Native, offering practical insights and detailed explanations to help you master this dynamic field. The journey begins with an Introduction to Cloud Native, where you'll trace the evolution of cloud computing and understand the myriad benefits of adopting a Cloud Native architecture. This foundational knowledge sets the stage for deeper explorations into the key components of Cloud Native environments. Containers, the building blocks of Cloud Native applications, are covered extensively in Understanding Containers. You'll learn about Docker and Kubernetes, the leading technologies in containerization, and discover best practices for managing and securing your containerized applications. The DevOps in the Cloud Native World chapter delves into the cultural and technical aspects of DevOps, emphasizing collaboration, automation, and continuous improvement. You'll gain insights into essential DevOps practices and tools, illustrated through real-world case studies of successful implementations. Continuous Integration and Continuous Deployment (CI/CD) are crucial for rapid and reliable software delivery. In the CI/CD chapter, you'll explore the principles and setup of CI/CD pipelines, popular tools, and solutions to common challenges. This knowledge will empower you to streamline your development processes and enhance your deployment efficiency. Microservices architecture, a key aspect of Cloud Native, is thoroughly examined in Microservices Architecture. This chapter highlights the design principles and advantages of microservices over traditional monolithic systems, providing best practices for implementing and managing microservices in your projects. The book also introduces you to the diverse Cloud Native Tools and Platforms, including insights into the Cloud Native Computing Foundation (CNCF) and guidance on selecting the right tools for your needs. This chapter ensures you have the necessary resources to build and manage robust Cloud Native applications. Security is paramount in any technology stack, and Security in Cloud Native Environments addresses the critical aspects of securing your Cloud Native infrastructure. From securing containers and microservices to ensuring compliance with industry standards, this chapter equips you with the knowledge to protect your applications and data. Monitoring and Observability explores the importance of maintaining the health and performance of your Cloud Native applications. You'll learn about essential tools and techniques for effective monitoring and observability, enabling proactive identification and resolution of issues. The book concludes with Case Studies and Real-World Applications, presenting insights and lessons learned from industry implementations of Cloud Native technologies. These real-world examples provide valuable perspectives on the challenges and successes of adopting Cloud Native practices. Mastering Cloud Native is more than a technical guide; it's a comprehensive resource designed to inspire and educate. Whether you're a developer, operations professional, or technology leader, this book will equip you with the tools and knowledge to succeed in the Cloud Native era. Embrace the future of software development and unlock the full potential of Cloud Native computing with this indispensable guide. |
| automated data analysis tools: Novel Measurement and Assessment Tools for Monitoring and Management of Land and Water Resources in Agricultural Landscapes of Central Asia Lothar Mueller, Abdulla Saparov, Gunnar Lischeid, 2013-10-04 The book aims to initiate a sustainable use of land and water resources in Central Asia by the transfer of scientific methods. It deals with the most advanced methods worldwide for better monitoring and management of water and land resources. We offer an array of methods of measuring, assessing, forecasting, utilizing and controling processes in agricultural landscapes. These are laboratory and field measurement methods, methods of resource evaluation, functional mapping and risk assessment, and remote sensing methods for monitoring and modeling large areas. The book contains methods and results of data analysis and ecosystem modeling, of bioremediation of soil and water, field monitoring of soils, and methods and technologies for optimizing land use systems as well. The chapter authors are inventors and advocators of novel transferrable methods. The book starts with an analysis of the current state of water and land resources. Finally concrete proposals for the applicability of novel methods are given. |
| automated data analysis tools: AI in Digital Marketing Maria Johnsen, 2024-07-09 This book presents a comprehensive and innovative exploration of the role of Artificial Intelligence (AI) in the digital marketing arena. It begins with historical context and fundamental AI principles, and subsequently, details AI's applications across a spectrum of areas, including data analytics, content creation, customer targeting, Search Engine Optimization (SEO), Pay Per Click (PPC) advertising, Social Media Marketing (SMM), and Email Marketing. A distinctive feature lies in the author's extensive real-world experience, providing many useful business applications of AI. The book is designed for marketing professionals, business executives, educators, and students, and offers numerous examples and case studies. FEATURES: Underscores the significance of content marketing in the AI era, covering AI-driven content creation, content curation, and search engine optimization Examines social media marketing in detail, with valuable insights into social media listening, chatbots, using Google AdsTM, and influencer marketing Provides a comprehensive section on paid advertising and paid social media, revealing programmatic advertising, ad creative, and machine learning-driven ad campaign optimization Includes ethical considerations and legal aspects about privacy and data protection |
| automated data analysis tools: Online Communication in Language Learning and Teaching M. Lamy, R. Hampel, 2007-11-12 This offers a framework for thinking about technologies that allow online communication, for example, forums, chats, real-time platforms as well as virtual worlds and mobile devices, and the practical issues of using them. The authors offer a thorough appraisal of the potential benefits and challenges of learning and teaching a language online. |
| automated data analysis tools: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| automated data analysis tools: Risk Thinking for Cloud-Based Application Services Eric Bauer, 2017-04-07 Many enterprises are moving their applications and IT services to the cloud. Better risk management results in fewer operational surprises and failures, greater stakeholder confidence and reduced regulatory concerns; proactive risk management maximizes the likelihood that an enterprise’s objectives will be achieved, thereby enabling organizational success. This work methodically considers the risks and opportunities that an enterprise taking their applications or services onto the cloud must consider to obtain the cost reductions and service velocity improvements they desire without suffering the consequences of unacceptable user service quality. |
| automated data analysis tools: Research and Technology Objectives and Plans Summary (RTOPS) , 1993 |
| automated data analysis tools: Handbook of Surface Plasmon Resonance Richard B. M. Schasfoort, 2017-05-30 Surface plasmon resonance (SPR) plays a dominant role in real-time interaction sensing of biomolecular binding events, this book provides a total system description including optics, fluidics and sensor surfaces for a wide researcher audience. |
| automated data analysis tools: Foundations of Intelligent Systems Aijun An, Stan Matwin, Zbigniew W Ras, Dominik Slezak, 2008-05-08 This volume contains the papers selected for presentation at the 17th Inter- tional Symposium on Methodologies for Intelligent Systems (ISMIS 2008), held in York University, Toronto, Canada, May 21–23, 2008. ISMIS is a conference series started in 1986. Held twice every three years, ISMIS provides an inter- tional forum for exchanging scienti?c research and technological achievements in building intelligent systems. Its goal is to achieve a vibrant interchange - tween researchers and practitioners on fundamental and advanced issues related to intelligent systems. ISMIS 2008featureda selectionof latestresearchworkandapplicationsfrom the following areas related to intelligent systems: active media human–computer interaction, autonomic and evolutionary computation, digital libraries, intel- gent agent technology, intelligent information retrieval, intelligent information systems, intelligent language processing, knowledge representation and integ- tion, knowledge discovery and data mining, knowledge visualization, logic for arti?cial intelligence, soft computing, Web intelligence, and Web services. - searchers and developers from 29 countries submitted more than 100 full - pers to the conference. Each paper was rigorously reviewed by three committee members and external reviewers. Out of these submissions, 40% were selected as regular papers and 22% as short papers. ISMIS 2008 also featured three plenary talks given by John Mylopoulos, Jiawei Han and Michael Lowry. They spoke on their recent research in age- oriented software engineering, information network mining, and intelligent so- ware engineering tools, respectively. |
| automated data analysis tools: Development Research in Practice Kristoffer Bjärkefur, Luíza Cardoso de Andrade, Benjamin Daniels, Maria Ruth Jones, 2021-07-16 Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University |
| automated data analysis tools: Database Management using AI: A Comprehensive Guide A Purushotham Reddy, 2024-10-20 Database Management Using AI: A Comprehensive Guide is a professional yet accessible exploration of how artificial intelligence (AI) is reshaping the world of database management. Designed for database administrators, data scientists, and tech enthusiasts, this book walks readers through the transformative impact of AI on modern data systems. The guide begins with the fundamentals of database management, covering key concepts such as data models, SQL, and the principles of database design. From there, it delves into the powerful role AI plays in optimizing database performance, enhancing security, and automating complex tasks like data retrieval, query optimization, and schema design. The book doesn't stop at theory. It brings AI to life with practical case studies showing how AI-driven database systems are being used in industries such as e-commerce, healthcare, finance, and logistics. These real-world examples demonstrate AI's role in improving efficiency, reducing errors, and driving intelligent decision-making. Key topics covered include: Introduction to Database Systems: Fundamentals of database management, from relational databases to modern NoSQL systems. AI Integration: How AI enhances database performance, automates routine tasks, and strengthens security. Real-World Applications: Case studies from diverse sectors like healthcare, finance, and retail, showcasing the practical impact of AI in database management. Predictive Analytics and Data Mining: How AI tools leverage data to make accurate predictions and uncover trends. Future Trends: Explore cutting-edge innovations like autonomous databases and cloud-based AI solutions that are shaping the future of data management. With its clear explanations and actionable insights, Database Management Using AI equips readers with the knowledge to navigate the fast-evolving landscape of AI-powered databases, making it a must-have resource for those looking to stay ahead in the digital age. |
| automated data analysis tools: Chemical Imaging Analysis Freddy Adams, Carlo Barbante, 2015-06-06 Chemical Imaging Analysis covers the advancements made over the last 50 years in chemical imaging analysis, including different analytical techniques and the ways they were developed and refined to link the composition and structure of manmade and natural materials at the nano/micro scale to the functional behavior at the macroscopic scale. In a development process that started in the early 1960s, a variety of specialized analytical techniques was developed – or adapted from existing techniques – and these techniques have matured into versatile and powerful tools for visualizing structural and compositional heterogeneity. This text explores that journey, providing a general overview of imaging techniques in diverse fields, including mass spectrometry, optical spectrometry including X-rays, electron microscopy, and beam techniques. - Provides comprehensive coverage of analytical techniques used in chemical imaging analysis - Explores a variety of specialized techniques - Provides a general overview of imaging techniques in diverse fields |
| automated data analysis tools: Software Quality Engineering Jeff Tian, 2005-02-18 The one resource needed to create reliable software This text offers a comprehensive and integrated approach to software quality engineering. By following the author's clear guidance, readers learn how to master the techniques to produce high-quality, reliable software, regardless of the software system's level of complexity. The first part of the publication introduces major topics in software quality engineering and presents quality planning as an integral part of the process. Providing readers with a solid foundation in key concepts and practices, the book moves on to offer in-depth coverage of software testing as a primary means to ensure software quality; alternatives for quality assurance, including defect prevention, process improvement, inspection, formal verification, fault tolerance, safety assurance, and damage control; and measurement and analysis to close the feedback loop for quality assessment and quantifiable improvement. The text's approach and style evolved from the author's hands-on experience in the classroom. All the pedagogical tools needed to facilitate quick learning are provided: * Figures and tables that clarify concepts and provide quick topic summaries * Examples that illustrate how theory is applied in real-world situations * Comprehensive bibliography that leads to in-depth discussion of specialized topics * Problem sets at the end of each chapter that test readers' knowledge This is a superior textbook for software engineering, computer science, information systems, and electrical engineering students, and a dependable reference for software and computer professionals and engineers. |
| automated data analysis tools: Informatics in Schools. New Ideas in School Informatics Sergei N. Pozdniakov, Valentina Dagienė, 2019-11-11 This book constitutes the proceedings of the 12th International Conference on Informatics in Schools: Situation, Evolution and Perspectives, ISSEP 2019, held in Larnaca, Cyprus, in November 2019. The 23 revised full papers presented were carefully reviewed and selected from 55 submissions. They are organized in topical sections named : teacher education in informatics, primary education in informatics, contemporary computer science ideas in school informatics, teaching informatics: from highschool to university levels, contests, competitions and games in informatics. |
| automated data analysis tools: Python for Data Analysis Dr. Katta Padmaja, Imran Wadkar, Dr. Uma Patil, Dr. J. Vellingiri, 2024-07-29 Python for Data Analysis for data enthusiasts, scientists, and analysts looking to harness Python’s capabilities in data manipulation, processing, and visualization. Covering essential libraries like Pandas, NumPy, and Matplotlib, this data cleaning, aggregation, and exploratory data analysis techniques. It emphasizes hands-on examples and real-world datasets to build a strong foundation in Python-based data analysis, making it an ideal resource for both beginners and professionals aiming to deepen their data skills in Python's versatile ecosystem. |
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| automated data analysis tools: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action Duck Young Kim, Gregor von Cieminski, David Romero, 2022-09-16 This two-volume set, IFIP AICT 663 and 664, constitutes the thoroughly refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2022, held in Gyeongju, South Korea in September 2022. The 139 full papers presented in these volumes were carefully reviewed and selected from a total of 153 submissions. The papers of APMS 2022 are organized into two parts. The topics of special interest in the first part included: AI & Data-driven Production Management; Smart Manufacturing & Industry 4.0; Simulation & Model-driven Production Management; Service Systems Design, Engineering & Management; Industrial Digital Transformation; Sustainable Production Management; and Digital Supply Networks. The second part included the following subjects: Development of Circular Business Solutions and Product-Service Systems through Digital Twins; “Farm-to-Fork” Production Management in Food Supply Chains; Urban Mobility and City Logistics; Digital Transformation Approaches in Production Management; Smart Supply Chain and Production in Society 5.0 Era; Service and Operations Management in the Context of Digitally-enabled Product-Service Systems; Sustainable and Digital Servitization; Manufacturing Models and Practices for Eco-Efficient, Circular and Regenerative Industrial Systems; Cognitive and Autonomous AI in Manufacturing and Supply Chains; Operators 4.0 and Human-Technology Integration in Smart Manufacturing and Logistics Environments; Cyber-Physical Systems for Smart Assembly and Logistics in Automotive Industry; and Trends, Challenges and Applications of Digital Lean Paradigm. |
Automated Case Information
Apr 1, 2025 · Welcome to the Automated Case Information System. The following information relates to the primary case only. Please contact your local court if you need bond hearing …
AUTOMATED Definition & Meaning - Merriam-Webster
The meaning of AUTOMATED is operated automatically. How to use automated in a sentence.
AUTOMATED | English meaning - Cambridge Dictionary
AUTOMATED definition: 1. carried out by machines or computers without needing human control: 2. carried …
Automated - definition of automated by The Free Dicti…
Define automated. automated synonyms, automated pronunciation, automated translation, English dictionary definition of automated. v. au·to·mat·ed , au·to·mat·ing , …
AUTOMATED definition and meaning | Collins English Dict…
An automated factory, office, or process uses machines to do the work instead of people. The equipment …
Automated Case Information
Apr 1, 2025 · Welcome to the Automated Case Information System. The following information relates to the primary case only. Please contact your local court if you need bond hearing …
AUTOMATED Definition & Meaning - Merriam-Webster
The meaning of AUTOMATED is operated automatically. How to use automated in a sentence.
AUTOMATED | English meaning - Cambridge Dictionary
AUTOMATED definition: 1. carried out by machines or computers without needing human control: 2. carried out by machines…. Learn more.
Automated - definition of automated by The Free Dictionary
Define automated. automated synonyms, automated pronunciation, automated translation, English dictionary definition of automated. v. au·to·mat·ed , au·to·mat·ing , au·to·mates v. tr. 1. …
AUTOMATED definition and meaning | Collins English Dictionary
An automated factory, office, or process uses machines to do the work instead of people. The equipment was made on highly automated production lines.
AUTOMATE Definition & Meaning | Dictionary.com
Automate definition: to apply the principles of automation to (a mechanical process, industry, office, etc.).. See examples of AUTOMATE used in a sentence.
What does automated mean? - Definitions.net
Automated refers to a system, process, or piece of equipment that is operated with minimal or no human intervention, often using software or other technology to perform tasks or functions. …
Automatic vs. Automated — What’s the Difference?
Feb 22, 2024 · Automatic processes operate without human intervention, often based on pre-set mechanisms, while automated systems are designed to perform tasks autonomously using …
Automate - Definition, Meaning & Synonyms | Vocabulary.com
When you design a machine to complete a process once done by a human, you automate the process. Standardized tests once had to be graded by hand, now they're automated, i.e. done …
What Is Automation? | IBM
Jun 6, 2025 · Automation is the application of technology, programs, robotics or processes to achieve outcomes with minimal human input.
