| data analysis web app: Web App Development and Real-Time Web Analytics with Python Tshepo Chris Nokeri, 2021-11-20 Learn to develop and deploy dashboards as web apps using the Python programming language, and how to integrate algorithms into web apps. Author Tshepo Chris Nokeri begins by introducing you to the basics of constructing and styling static and interactive charts and tables before exploring the basics of HTML, CSS, and Bootstrap, including an approach to building web pages with HTML. From there, he’ll show you the key Python web frameworks and techniques for building web apps with them. You’ll then see how to style web apps and incorporate themes, including interactive charts and tables to build dashboards, followed by a walkthrough of creating URL routes and securing web apps. You’ll then progress to more advanced topics, like building machine learning algorithms and integrating them into a web app. The book concludes with a demonstration of how to deploy web apps in prevalent cloud platforms. Web App Development and Real-Time Web Analytics with Python is ideal for intermediate data scientists, machine learning engineers, and web developers, who have little or no knowledge about building web apps that implement bootstrap technologies. After completing this book, you will have the knowledge necessary to create added value for your organization, as you will understand how to link front-end and back-end development, including machine learning. What You Will Learn Create interactive graphs and render static graphs into interactive ones Understand the essentials of HTML, CSS, and Bootstrap Gain insight into the key Python web frameworks, and how to develop web applications using them Develop machine learning algorithms and integrate them into web apps Secure web apps and deploy them to cloud platforms Who This Book Is For Intermediate data scientists, machine learning engineers, and web developers. |
| data analysis web app: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data analysis web app: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2023-08-01 An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. |
| data analysis web app: Mastering Shiny Hadley Wickham, 2021-04-29 Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production |
| data analysis web app: Smart Applications and Data Analysis Mohamed Hamlich, Ladjel Bellatreche, Anirban Mondal, Carlos Ordonez, 2020-06-04 This volume constitutes refereed proceedings of the Third International Conference on Smart Applications and Data Analysis, SADASC 2020, held in Marrakesh, Morocco. Due to the COVID-19 pandemic the conference has been postponed to June 2020. The 24 full papers and 3 short papers presented were thoroughly reviewed and selected from 44 submissions. The papers are organized according to the following topics: ontologies and meta modeling; cyber physical systems and block-chains; recommender systems; machine learning based applications; combinatorial optimization; simulations and deep learning. |
| data analysis web app: Data Analysis and Computer Application Satyaprakash Sethy, 2022-01-03 Information Technology that refers to the convergence of computer, communication and content technologies plays catalytic role in emergence of new socio-economic applications. Need of introducing Data Analysis and Computer Application as SEC Paper to all the undergraduate students. It was needed to build problem-solving skills among the students just before they enter the professional courses. And this book has been published as per the CBCS Syllabus. The purpose of this course is to introduce basic computer skills to students at UG level in non technical subjects. After completion of this course, the students are expected to acquire some basic knowledge about computers and to develop some basic skills in using computers for data storage, compilation, analysis and presentations. |
| data analysis web app: A General Introduction to Data Analytics João Moreira, Andre Carvalho, Tomás Horvath, 2018-07-18 A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms. |
| data analysis web app: Web Data Mining Bing Liu, 2011-06-25 Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online. |
| data analysis web app: Getting Started with Streamlit for Data Science Tyler Richards, 2021-08-20 Create, deploy, and test your Python applications, analyses, and models with ease using Streamlit Key Features Learn how to showcase machine learning models in a Streamlit application effectively and efficiently Become an expert Streamlit creator by getting hands-on with complex application creation Discover how Streamlit enables you to create and deploy apps effortlessly Book DescriptionStreamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.What you will learn Set up your first development environment and create a basic Streamlit app from scratch Explore methods for uploading, downloading, and manipulating data in Streamlit apps Create dynamic visualizations in Streamlit using built-in and imported Python libraries Discover strategies for creating and deploying machine learning models in Streamlit Use Streamlit sharing for one-click deployment Beautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebar Implement best practices for prototyping your data science work with Streamlit Who this book is for This book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you’re a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered. |
| data analysis web app: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data analysis web app: Methods and Applications of Longitudinal Data Analysis Xian Liu, 2015-09-01 Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: - descriptive methods for delineating trends over time - linear mixed regression models with both fixed and random effects - covariance pattern models on correlated errors - generalized estimating equations - nonlinear regression models for categorical repeated measurements - techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. - From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis - Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection - Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples. |
| data analysis web app: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| data analysis web app: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| data analysis web app: Morgan Kaufmann series in data management systems Stefano Ceri, Piero Fraternali, Aldo Bongio, Marco Brambilla, Sara Comai, Maristella Matera, 2003 This text represents a breakthrough in the process underlying the design of the increasingly common and important data-driven Web applications. |
| data analysis web app: Clojure Data Analysis Cookbook - Second Edition Eric Rochester, 2015-01-27 This book is for those with a basic knowledge of Clojure, who are looking to push the language to excel with data analysis. |
| data analysis web app: Real Time Data Analytics and R Programming Dr.J.Chenni Kumaran, Dr.M.Sivaram, Dr.A.Manimaran, Dr.A.Selvakumar, Dr.S. Ramesh, 2024-02-05 Dr.J.Chenni Kumaran, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.M.Sivaram, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.A.Manimaran, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.A.Selvakumar, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.S. Ramesh, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. |
| data analysis web app: Seeing Cities Through Big Data Piyushimita (Vonu) Thakuriah, Nebiyou Tilahun, Moira Zellner, 2016-10-07 This book introduces the latest thinking on the use of Big Data in the context of urban systems, including research and insights on human behavior, urban dynamics, resource use, sustainability and spatial disparities, where it promises improved planning, management and governance in the urban sectors (e.g., transportation, energy, smart cities, crime, housing, urban and regional economies, public health, public engagement, urban governance and political systems), as well as Big Data’s utility in decision-making, and development of indicators to monitor economic and social activity, and for urban sustainability, transparency, livability, social inclusion, place-making, accessibility and resilience. |
| data analysis web app: Single Page Web Applications Michael Mikowski, Josh Powell, 2013-09-19 Summary In Single Page Web Applications you'll learn to build modern browser-based apps that take advantage of stronger client platforms and more predictable bandwidth. You'll learn the SPA design approach, and then start exploring new techniques like structured JavaScript and responsive design. And you'll learn how to capitalize on trends like server-side JavaScript and NoSQL data stores, as well as new frameworks that make JavaScript more manageable and testable as a first-class language. About this Book If your website is a jumpy collection of linked pages, you are behind. Single page web applications are your next step: pushing UI rendering and business logic to the browser and communicating with the server only to synchronize data, they provide a smooth user experience, much like a native application. But, SPAs can be hard to develop, manage, and test. Single Page Web Applications shows how your team can easily design, test, maintain, and extend sophisticated SPAs using JavaScript end-to-end, without getting locked into a framework. Along the way, you'll develop advanced HTML5, CSS3, and JavaScript skills, and use JavaScript as the language of the web server and the database. This book assumes basic knowledge of web development. No experience with SPAs is required. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. What's Inside Design, build, and test a full-stack SPA Best-in-class tools like jQuery, TaffyDB, Node.js, and MongoDB Real-time web with web sockets and Socket.IO Touch controls for tablets and smartphones Common SPA design mistakes About the Authors The authors are architects and engineering managers. Michael Mikowski has worked on many commercial SPAs and a platform that processes over 100 billion requests per year. Josh Powell has built some of the most heavily trafficked sites on the web. Table of Contents PART 1: INTRODUCING SPAS Our first single page application Reintroducing JavaScript PART 2: SPA CLIENT Develop the Shell Add feature modules Build the Model Finish the Model and Data modules PART 3: THE SPA SERVER The web server The server database Readying our SPA for production |
| data analysis web app: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| data analysis web app: Python for Everyone: Learn to Code Like a Pro M.B. Chatfield, Take your Python skills to the next level! Python for Everyone is a comprehensive guide for anyone who wants to learn Python programming. This book is perfect for beginners who want to learn the basics of Python, as well as experienced programmers who want to take their skills to the next level. In this book, you will learn: Advanced Python syntax Object-oriented programming Data structures and algorithms Functional programming Python for data analysis and machine learning And much more! With Python for Everyone, you will be able to: Write complex Python programs Use Python to solve real-world problems Build powerful and efficient applications Become a professional Python programmer So what are you waiting for? Start learning Python today! |
| data analysis web app: IoT Fundamentals with a Practical Approach Neera Batra, Sonali Goyal, 2024-08-19 IoT Fundamentals with a Practical Approach is an insightful book that serves as a comprehensive guide to understanding the foundations and key concepts of Internet of Things (IoT) technologies. The book begins by introducing readers to the concept of IoT, explaining the significance and potential impact on various industries and domains. It covers the underlying principles of IoT, including its architecture, connectivity, and communication protocols, providing readers with a solid understanding of how IoT systems are structured and how devices interact within an IoT ecosystem. This book dives into the crucial components that form the backbone of IoT systems. It explores sensors and actuators, explaining their roles in collecting and transmitting data from the physical environment. The book also covers electronic components used in IoT devices, such as microcontrollers, communication modules, and power management circuits. This comprehensive understanding of the building blocks of IoT allows readers to grasp the technical aspects involved in developing IoT solutions. Security is a vital aspect of IoT, and the book dedicates a significant portion to exploring security challenges and best practices in IoT deployments. It delves into topics such as authentication, encryption, access control, and secure firmware updates, providing readers with essential insights into safeguarding IoT systems against potential threats and vulnerabilities. This book also addresses the scalability and interoperability challenges of IoT. It discusses IoT platforms and frameworks that facilitate the development and management of IoT applications, highlighting their role in enabling seamless integration and communication between devices and systems. The book is written in a clear and accessible manner and includes real-world examples, making it suitable for both beginners and professionals looking to enhance their understanding of IoT. It serves as a valuable resource for engineers, developers, researchers, and decision-makers involved in IoT projects and provides them with the knowledge and tools necessary to design, implement, and secure IoT solutions. |
| data analysis web app: Python for Data Analysis Brady Ellison, Ready to learn Data Science through Python language? Python for Data Analysis is a step-by-step guide for beginners and dabblers-alike. This book is designed to offer working knowledge of Python and data science and some of the tools required to apply that knowledge. It’s possible that you have little experience with or knowledge of data analysis and are interested in it. You might have some experience in coding. You may have worked with data before and want to use Python. We have made this book in a way that will be helpful to all these groups and more besides in varying ways. This can serve as an introduction to the most current tools and functions of those tools used by data scientists. In this book You will learn: Data Science/Analysis and its applications IPython and Jupyter - an introduction to the basic tools and how to navigate and use them. You will also learn about its importance in a data scientist’s ecosystem. Pandas - a powerful data management Python library that lets you do interesting things with data. You will learn all the basics you need to get started. NumPy - a powerful numerical library for Python. You will learn more about its advantages. Get your copy now |
| data analysis web app: AI Strategies For Web Development Anderson Soares Furtado Oliveira, 2024-09-30 From fundamental to advanced strategies, unlock useful insights for creating innovative, user-centric websites while navigating the evolving landscape of AI ethics and security Key Features Explore AI's role in web development, from shaping projects to architecting solutions Master advanced AI strategies to build cutting-edge applications Anticipate future trends by exploring next-gen development environments, emerging interfaces, and security considerations in AI web development Purchase of the print or Kindle book includes a free PDF eBook Book Description If you're a web developer looking to leverage the power of AI in your projects, then this book is for you. Written by an AI and ML expert with more than 15 years of experience, AI Strategies for Web Development takes you on a transformative journey through the dynamic intersection of AI and web development, offering a hands-on learning experience.The first part of the book focuses on uncovering the profound impact of AI on web projects, exploring fundamental concepts, and navigating popular frameworks and tools. As you progress, you'll learn how to build smart AI applications with design intelligence, personalized user journeys, and coding assistants. Later, you'll explore how to future-proof your web development projects using advanced AI strategies and understand AI's impact on jobs. Toward the end, you'll immerse yourself in AI-augmented development, crafting intelligent web applications and navigating the ethical landscape.Packed with insights into next-gen development environments, AI-augmented practices, emerging realities, interfaces, and security governance, this web development book acts as your roadmap to staying ahead in the AI and web development domain. What you will learn Build AI-powered web projects with optimized models Personalize UX dynamically with AI, NLP, chatbots, and recommendations Explore AI coding assistants and other tools for advanced web development Craft data-driven, personalized experiences using pattern recognition Architect effective AI solutions while exploring the future of web development Build secure and ethical AI applications following TRiSM best practices Explore cutting-edge AI and web development trends Who this book is for This book is for web developers with experience in programming languages and an interest in keeping up with the latest trends in AI-powered web development. Full-stack, front-end, and back-end developers, UI/UX designers, software engineers, and web development enthusiasts will also find valuable information and practical guidelines for developing smarter websites with AI. To get the most out of this book, it is recommended that you have basic knowledge of programming languages such as HTML, CSS, and JavaScript, as well as a familiarity with machine learning concepts. |
| data analysis web app: Applied Machine Learning and Data Analytics M. A. Jabbar, Fernando Ortiz-Rodríguez, Sanju Tiwari, Patrick Siarry, 2023-05-26 This book constitutes the refereed proceedings of the 5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022, held in Reynosa, Tamaulipas, Mexico, during December 22–23, 2022. The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 89 submissions. They were organized in topical sections as follows: Machine learning, Healthcare and medical imaging informatics; biometrics; forensics; precision agriculture; risk management; robotics and satellite imaging. |
| data analysis web app: Advancement in Gene Set Analysis: Gaining Insight From High-throughput Data Farhad Maleki, Renee Menezes, Sorin Draghici, Anthony Kusalik, 2022-08-01 |
| data analysis web app: Internet of Things (IoT) Shrikant Patel, S. R. Jena, 2022-12-14 This book aims to provide a broad introduction to Internet of Things (IOT) - Theory and Applications. It also explains the fundamentals of this subject. It provides a logical method of explaining various complicated concepts and step wise methods to explain important topics. Each chapter is well supported with necessary illustrations. All the chapters in the book are arranged in a proper sequence that permits each topic to build upon earlier studies.Nowadays, IoT is in the limelight of research and innovation. The techniques developed in this area so far require to be summarized appropriately. In this book, the fundamental theories of these techniques are introduced. |
| data analysis web app: Big Data Processing Using Spark in Cloud Mamta Mittal, Valentina E. Balas, Lalit Mohan Goyal, Raghvendra Kumar, 2018-06-16 The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data’s immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data. The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments. |
| data analysis web app: Manufacturing Driving Circular Economy Holger Kohl, Günther Seliger, Franz Dietrich, 2023-05-27 This is an open access book. It gathers the proceedings of the 18th Global Conference on Sustainable Manufacturing, held on October 5-7, 2022, as a hybrid event, in/from Berlin, Germany. With a focus on manufacturing advances and practices driving the circular economy, the chapters selected for this book report on sustainable manufacturing technologies for the mobility, energy and construction sector, and for machines and equipments, covering applications of artificial intelligence and industry 4.0. Moreover, they discuss energy-efficient process, waste reuse, and CO2 neutral production, giving a special emphasis to developing sustainable manufacturing in emerging countries. This book offers extensive and timely information for both researchers and professionals in the field of manufacturing and business development. |
| data analysis web app: Mastering AI App Development with MERN Stack Anik Acharjee, 2024-11-05 TAGLINE Transform Your Web App Development Journey with MERN and AI KEY FEATURES ● Utilize AI for code generation, debugging, and optimizing performance in MERN applications. ● Build AI-powered web apps with real-time data processing and user behavior insights. ● Integrate AI capabilities seamlessly with MongoDB, Express.js, React, and Node.js for scalable web solutions. DESCRIPTION With AI applications driving a projected $15.7 trillion boost to the global economy by 2030, combining AI with the popular MERN stack has become a game-changer for developers and businesses alike. Mastering AI App Development with MERN Stack is a hands-on guide designed for developers ready to bring AI capabilities to their MERN applications, covering everything from foundational machine learning to advanced, real-world solutions. Starting with the essentials of setting up a MERN development environment, the book guides readers through machine learning basics in JavaScript, enabling AI integration with Node.js and TensorFlow.js. Each chapter provides practical insights into building intelligent interfaces with React, effective data handling with MongoDB, and AI middleware using Express.js. Readers will learn to create features like AI-powered chatbots, image and voice recognition, and personalized recommendation systems. Real-world scenarios and case studies demonstrate how AI can elevate MERN applications. With guidance on security practices, deployment, and scaling, this book is a complete toolkit for building secure, production-ready AI solutions with MERN. Mastering AI with the MERN Stack empowers developers to unlock the full potential of AI in the MERN ecosystem, creating innovative, impactful applications for an AI-driven world. WHAT WILL YOU LEARN ● Integrate AI into MERN applications for improved user experiences. ● Build AI-powered web apps using the MERN stack effectively. ● Implement real-time data processing and personalized content features. ● Leverage pre-trained AI models for language and analytics tasks. ● Design scalable AI architectures to enhance performance and capacity. WHO IS THIS BOOK FOR? This book is tailored for JavaScript developers, full-stack developers and frontend or backend developers interested in AI integration into their web applications. It’s also ideal for web developers aiming to create dynamic applications and MERN stack enthusiasts exploring AI's potential. With a basic understanding of the MERN stack, readers will find this guide a valuable resource for advancing their web development careers by incorporating AI capabilities. TABLE OF CONTENTS 1. Introduction to AI and the MERN Ecosystem 2. Setting Up the MERN Development Environment 3. Fundamentals of Machine Learning with JavaScript 4. Implementing AI with Node.js and TensorFlow.js 5. Creating Intelligent User Interfaces with React 6. Data Management for AI with MongoDB 7. Building AI Middleware with Express.js 8. Crafting AI-Powered Chatbots 9. Image and Voice Recognition Capabilities 10. Personalization with Recommendation Systems 11. Deploying MERN and AI Applications 12. Security Practices for AI-Enabled MERN Applications 13. Scaling AI Features in Production 14. Emerging Trends in AI and MERN Development 15. Case Studies and Real-World Success Stories Index |
| data analysis web app: IoT Communication Protocols Dr.Vijendra Pratap Singh, Mr.Neeraj Kumar, Mr.Ambuj Kumar Misra, Dr.Prathyusha.Kuncha, 2023-03-01 Dr.Vijendra Pratap Singh Assistant Professor Department of Computer Science and Applications, Mahatma Gandhi Kashi Vidyapith, Varanasi, Uttar Pradesh, India. Pin Code:221002 Mr.Neeraj Kumar Research Scholar School of Information Technology, University Teaching Department, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh, India. Pin Code:462033 Mr.Ambuj Kumar Misra Assistant Professor Department of Computer Science and Applications, Mahatma Gandhi Kashi Vidyapith, Varanasi, Uttar Pradesh, India. Pin Code:221002 Dr.Prathyusha.Kuncha Associate Professor Department of Electronics and Communication Engineering, NRI Institute of Technology, Pothavarappadu, Vijayawada, Andhra Pradesh, India. Pin Code:521212 |
| data analysis web app: Web Analytics Action Hero Brent Dykes, 2011-11-12 Companies need more than just web analysts and data-savvy marketers to be successful–they need action heroes! While most of us never battle evil scientists or defuse nuclear warheads, successful web analysts benefit from the same attributes that fictional action heroes embody. As a web analyst, your main goal is to improve your organization’s online performance. You can become an “action hero” by translating analysis insights into action that generates significant returns for your company. How you approach analysis is critical to your overall success. In this book, web analytics expert Brent Dykes addresses the unique challenges facing analysts and online marketers working within small and large companies, teaching you how to move beyond reporting and toward analysis to drive action and change. Taking a principle-based rather than a tool-specific approach, Brent introduces you to the Action Hero Framework that breaks down the analysis process into three key stages: Prioritize (what to analyze), Analyze (how to analyze), and Mobilize (how to drive action). And he reinforces these topics with real-world examples and practical tips from seasoned analysts at leading companies. Defines the type of environment in which action heroes thrive–not just survive–as well as how to defeat the villains of web analytics that stand in the way Arms web professionals with a strategic framework for executing online analysis, as well as an arsenal of analysis techniques Reveals how companies need to be both data-driven and action-agile to drive business value from web analytics For more action hero resources and information, check out the book’s companion site at www.Analyticshero.com. The ideas in this book will take you days (or even weeks) to work your way through, and they fly in the face of the emotional approach to marketing. The question is: would you rather have your competition lead the way with data and science when it comes to reaching your market, or are you going to go first? That's how it is with action heroes--no guts, no glory. - Seth Godin Author We Are All Weird Don't let the jaunty, breezy style of this book throw you off. Brent successfully - and entertainingly - packs years of experience into these pages along with case studies and insightful help on getting the most out of web analytics, adding value to your company and boosting your career trajectory. - Jim Sterne Founder of eMetrics Marketing Optimization Summit, author of Social Media Metrics and Chairman of the Digital Analytics Association |
| data analysis web app: Nonlinear Science And Complexity - Proceedings Of The Conference Albert C J Luo, Liming Dai, Hamid R Hamidzadeh, 2006-12-09 This volume provides useful tools in Lie group analysis to solve nonlinear partial differential equations. Many of important issues in nonlinear wave dynamics and nonlinear fluid mechanics are presented: Homotopy techniques are used to obtain analytical solutions; fundamental problems and theories in classic and quantum dynamical systems are discussed; and numerous interesting results about dynamics and vibration in sensor and smart systems are presented. Interval computation and nonlinear modeling in dynamics and control are also briefly included. |
| data analysis web app: Intelligent Data Engineering and Analytics Vikrant Bhateja, Fiona Carroll, João Manuel R. S. Tavares, Sandeep Singh Sengar, Peter Peer, 2023-11-25 The book presents the proceedings of the 11th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2023), held at Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Wales, UK, during April 11–12, 2023. Researchers, scientists, engineers, and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. This book is divided into two volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols, and architectures. This book is a valuable resource for postgraduate students in various engineering disciplines. |
| data analysis web app: Interactive Web-Based Data Visualization with R, plotly, and shiny Carson Sievert, 2020-01-30 The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics. |
| data analysis web app: A Digitalization Framework for Smart Maintenance of Historic Buildings Zhongjun Ni, 2023-08-31 Smart maintenance of historic buildings involves integration of digital technologies and data analysis methods to help maintain functionalities of these buildings and preserve their heritage values. However, the maintenance of historic buildings is a long-term process. During the process, the digital transformation requires overcoming various challenges, such as stable and scalable storage and computing resources, a consistent format for organizing and representing building data, and a flexible design to integrate data analytics to deliver applications. This licentiate thesis aims to address these challenges by proposing a digitalization framework that integrates Internet of Things (IoT), cloud computing, ontology, and machine learning. IoT devices enable data collection from historic buildings to reveal their latest status. Using a public cloud platform brings stable and scalable resources for storing data, performing analytics, and deploying applications. Ontologies provide a clear and concise way to organize and represent building data, which makes it easier to understand the relationships between different building components and systems. Combined with IoT devices and ontologies, parametric digital twins can be created to evolve with their physical counterparts. Furthermore, with machine learning, digital twins can identify patterns from data and provide decision-makers with insights to achieve smart maintenance. Papers I-III have shown that data can be reliably collected, transmitted, and stored in the cloud. Results of Paper IV indicate that a digital twin that depicts the latest status of a historic building can be created and fed with real-time sensor data. The insights discovered from the digital twin provide facts for improving the indoor climate to achieve both heritage conservation and human comfort. Papers V and VI have shown that deep learning methods exhibit strong capabilities in capturing tendency and uncertainty in building energy consumption. Incorporating future information that determines energy consumption is critical for making multi-horizon predictions. Moreover, changes in the operating mode of a building and activities held in a building bring more uncertainty in energy consumption and deteriorate the performance of point forecasts. Overall, this thesis contributes to the field of preservation of historic buildings by proposing a comprehensive digitalization framework that integrates various advanced digital technologies to provide a holistic approach to achieve smart maintenance of historic buildings. |
| data analysis web app: Translational Bioinformatics Applications in Healthcare Khalid Raza, Nilanjan Dey, 2021-04-20 Translational bioinformatics (TBI) involves development of storage, analytics, and advanced computational methods to harvest knowledge from voluminous biomedical and genomic data into 4P healthcare (proactive, predictive, preventive, and participatory). Translational Bioinformatics Applications in Healthcare offers a detailed overview on concepts of TBI, biological and clinical databases, clinical informatics, and pertinent real-case applications. It further illustrates recent advancements, tools, techniques, and applications of TBI in healthcare, including Internet of Things (IoT) potential, toxin databases, medical image analysis and telemedicine applications, analytics of COVID-19 CT images, viroinformatics and viral diseases, and COVID-19–related research. Covers recent technologies such as Blockchain, IoT, and Big data analytics in bioinformatics Presents the role of translational bioinformatic methods in the field of viroinformatics, as well as in drug development and repurposing Includes translational healthcare and NGS for clinical applications Illustrates translational medicine systems and their applications in better healthcare Explores medical image analysis with focus on CT images and novel coronavirus disease detection Aimed at researchers and graduate students in computational biology, data mining and knowledge discovery, algorithms and complexity, and interdisciplinary fields of studies, including bioinformatics, health-informatics, biostatistics, biomedical engineering, and viroinformatics. Khalid Raza is an Assistant Professor, the Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi. His research interests include translational bioinformatics, computational intelligence methods and its applications in bioinformatics, viroinformatics, and health informatics. Nilanjan Dey is an Associate Professor, the Department of Computer Science and Engineering, JIS University, Kolkata, India. His research interests include medical imaging, machine learning, computer-aided diagnosis, and data mining. |
| data analysis web app: Building Data Science Applications with FastAPI Francois Voron, 2021-10-08 Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications Key FeaturesCover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injectionDevelop efficient RESTful APIs for data science with modern PythonBuild, test, and deploy high performing data science and machine learning systems with FastAPIBook Description FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you'll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you'll cover best practices relating to testing and deployment to run a high-quality and robust application. You'll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you'll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you'll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you'll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI. What you will learnExplore the basics of modern Python and async I/O programmingGet to grips with basic and advanced concepts of the FastAPI frameworkImplement a FastAPI dependency to efficiently run a machine learning modelIntegrate a simple face detection algorithm in a FastAPI backendIntegrate common Python data science libraries in a web backendDeploy a performant and reliable web backend for a data science applicationWho this book is for This Python data science book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended. |
| data analysis web app: Web App Development Made Simple with Streamlit Rosario Moscato, 2024-02-09 Unlock the full potential of Streamlit, mastering web app development from setup to deployment with practical guidance, advanced techniques, and real-world examples Key Features Identify and overcome web development challenges, crafting dedicated application skeletons using Streamlit Understand how Streamlit's widgets and components work to implement any kind of web app Manage web application development and deployment with ease using the Streamlit Cloud service Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis book is a comprehensive guide to the Streamlit open-source Python library and simplifying the process of creating web applications. Through hands-on guidance and realistic examples, you’ll progress from crafting simple to sophisticated web applications from scratch. This book covers everything from understanding Streamlit's central principles, modules, basic features, and widgets to advanced skills such as dealing with databases, hashes, sessions, and multipages. Starting with fundamental concepts like operation systems virtualization, IDEs, development environments, widgets, scripting, and the anatomy of web apps, the initial chapters set the groundwork. You’ll then apply this knowledge to develop some real web apps, gradually advancing to more complex apps, incorporating features like natural language processing (NLP), computer vision, dashboards with interactive charts, file uploading, and much more. The book concludes by delving into the implementation of advanced skills and deployment techniques. By the end of this book, you’ll have transformed into a proficient developer, equipped with advanced skills for handling databases, implementing secure login processes, managing session states, creating multipage applications, and seamlessly deploying them on the cloud.What you will learn Develop interactive web apps with Streamlit and deploy them seamlessly on the cloud Acquire in-depth theoretical and practical expertise in using Streamlit for app development Use themes and customization for visually appealing web apps tailored to specific needs Implement advanced features including secure login, signup processes, file uploaders, and database connections Build a catalog of scripts and routines to efficiently implement new web apps Attain autonomy in adopting new Streamlit features rapidly and effectively Who this book is for This book is for Python programmers, web developers, computer science students, and IT enthusiasts with a foundation in Python (or any programming language) who have a passion for creating visually appealing applications. If you already know how to write programs, this book will help you evolve into an adept web application developer skilled at converting command-line tools into impressive, cloud-hosted applications. |
| data analysis web app: Full Stack FastAPI, React, and MongoDB Marko Aleksendric, 2022-09-23 Wield the power of Python, React, and MongoDB to build web and data applications quickly and broaden your web development horizons Key FeaturesLearn how to build web applications without having to know the intricacies of the componentsBuild full stack projects without compromising on development speed and app performancePrepare yourself for the diverse world of web development, analytics, and data visualizationBook Description If you need to develop web applications quickly, where do you turn? Enter the FARM stack. The FARM stack combines the power of the Python ecosystem with REST and MongoDB and makes building web applications easy and fast. This book is a fast-paced, concise, and hands-on beginner's guide that will equip you with the skills you need to quickly build web applications by diving just deep enough into the intricacies of the stack's components. The book quickly introduces each element of the stack and then helps you merge them to build a medium-sized web application. You'll set up a document store with MongoDB, build a simple API with FastAPI, and create an application with React. Security is crucial on the web, so you'll learn about authentication and authorization with JSON Web Tokens. You'll also understand how to optimize images, cache responses with Redis, and add additional features to your application as well as explore tips, tricks, and best practices to make your development experience a breeze. Before you know it, you'll be deploying the application to different platforms. By the end of this book, you will have built a couple of functional applications efficiently and will have the springboard you need to delve into diverse and more specialized domains. What you will learnDiscover the flexibility of the FARM stackImplement complete JWT authentication with FastAPIExplore the various Python drivers for MongoDBDiscover the problems that React libraries solveBuild simple and medium web applications with the FARM stackDive into server-side rendering with Next.jsDeploy your app with Heroku, Vercel, Ubuntu Server and NetlifyUnderstand how to deploy and cache a FastAPI backendWho this book is for This book is for web developers and analysts who want to include the power of a modern asynchronous Python framework, a flexible data store and a powerful UI library with the combination of two of the most important programming languages today in their web development toolkit. Beginners in the field of information presentation will also find this book helpful. You must have a beginner-level understanding of Python, JavaScript, and HTML and CSS to get the most out of this book. |
| data analysis web app: Computerworld , 2004-12-20 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
