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| data science for restaurants: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data science for restaurants: Data Science & Business Analytics Sneha Kumari, K. K. Tripathy, Vidya Kumbhar, 2020-12-04 Data Science & Business Analytics explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications. |
| data science for restaurants: Data Science Uncovering the Reality Pulkit Bansal, Kunal Kishore, Pankaj Gupta, Srijan Saket, Neeraj Kumar, 2020-04-15 Data Science has become a popular field of work today. However a good resource to understand applied Data Science is still missing. In Data Science Uncovering the Reality, a group of IITians unravel how Data Science is done in the industry. They have interviewed Data Science and technology leaders at top companies in India and presented their learnings here. This book will give you honest answers to questions such as: How to build a career in Data Science? How A.I. is used in the world’s most successful companies. How Data Science leaders actually work and the challenges they face. |
| data science for restaurants: Fundamentals of Data Science Sanjeev J. Wagh, Manisha S. Bhende, Anuradha D. Thakare, 2021-09-26 Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals. |
| data science for restaurants: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics Patil, Bhushan, Vohra, Manisha, 2020-10-23 Analyzing data sets has continued to be an invaluable application for numerous industries. By combining different algorithms, technologies, and systems used to extract information from data and solve complex problems, various sectors have reached new heights and have changed our world for the better. The Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics is a collection of innovative research on the methods and applications of data analytics. While highlighting topics including artificial intelligence, data security, and information systems, this book is ideally designed for researchers, data analysts, data scientists, healthcare administrators, executives, managers, engineers, IT consultants, academicians, and students interested in the potential of data application technologies. |
| data science for restaurants: Marketing Data Science Thomas W. Miller, 2015-05-02 Now, a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance. |
| data science for restaurants: Tourism and ICTs: Advances in Data Science, Artificial Intelligence and Sustainability Antonio J. Guevara Plaza, |
| data science for restaurants: 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS) IEEE Staff, 2019-09-19 Artificial Intelligence, Data Sciences |
| data science for restaurants: Data Science And Knowledge Engineering For Sensing Decision Support - Proceedings Of The 13th International Flins Conference Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre, 2018-07-30 FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to include Computational Intelligence for applied research. The contributions of the FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, with special focuses on data science and knowledge engineering for sensing decision support, both from the foundations and the applications points-of-view. |
| data science for restaurants: The Underground Culinary Tour Damian Mogavero, Joseph D'Agnese, 2017-01-24 The Underground Culinary Tour is a high-octane, behind-the-scenes narrative about how the restaurant industry, historically run by gut and intuition, is being transformed by the use of data. Sixteen years ago, entrepreneur Damian Mogavero brought together an unlikely mix of experts—chefs and code writers—to create a pioneering software company whose goal was to empower restaurateurs, through the use of data, to elevate and enhance the guest experience. Today, his data gathering programs are used by such renown chefs as Danny Meyer, Tom Colicchio, Daniel Boulud, Guy Fieri, Giada De Laurentiis, Gordon Ramsay, and countless others. Mogavero describes such restaurateurs as the New Guard, and their approach to their art and craft is radically different from that of their predecessors. By embracing data and adapting to the new trends of today’s demanding consumers, these innovative chefs and owners do everything more nimbly and efficiently—from the recipes they create to the wines and craft beers they stock, from the presentations they choreograph to the customized training they give their servers, making restaurants more popular and profitable than ever before. Finally, Damian takes readers behind the scenes of his annual, invitation-only culinary tour for top chefs and industry CEOs, showing us how today’s elite restaurants embrace new trends to create unforgettable meals and transform how we eat. From the glittering nightclubs of Las Vegas to a packed seasonal restaurant on the Long Island Sound, from Brennan’s storied, family-run New Orleans dynasty to today’s high-stakes celebrity chef palaces, The Underground Culinary Tour takes readers on an epicurean adventure they won’t soon forget. |
| data science for restaurants: Data Science Foundations Tools and Techniques Michael Freeman, Joel Ross, 2018-11-23 The Foundational Hands-On Skills You Need to Dive into Data Science “Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills.” –From the foreword by Jared Lander, series editor Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills you need to get started, even if you have no programming or data science experience. Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you’ve uncovered. Step by step, you’ll master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything’s focused on real-world application, so you can quickly start analyzing your own data and getting answers you can act upon. Learn to Install your complete data science environment, including R and RStudio Manage projects efficiently, from version tracking to documentation Host, manage, and collaborate on data science projects with GitHub Master R language fundamentals: syntax, programming concepts, and data structures Load, format, explore, and restructure data for successful analysis Interact with databases and web APIs Master key principles for visualizing data accurately and intuitively Produce engaging, interactive visualizations with ggplot and other R packages Transform analyses into sharable documents and sites with R Markdown Create interactive web data science applications with Shiny Collaborate smoothly as part of a data science team Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data science for restaurants: Ethical Data Science Anne L. Washington, 2023 Can data science truly serve the public interest? Data-driven analysis shapes many interpersonal, consumer, and cultural experiences yet scientific solutions to social problems routinely stumble. All too often, predictions remain solely a technocratic instrument that sets financial interests against service to humanity. Amidst a growing movement to use science for positive change, Anne L. Washington offers a solution-oriented approach to the ethical challenges of data science. Ethical Data Science empowers those striving to create predictive data technologies that benefit more people. As one of the first books on public interest technology, it provides a starting point for anyone who wants human values to counterbalance the institutional incentives that drive computational prediction. It argues that data science prediction embeds administrative preferences that often ignore the disenfranchised. The book introduces the prediction supply chain to highlight moral questions alongside the interlocking legal and commercial interests influencing data science. Structured around a typical data science workflow, the book systematically outlines the potential for more nuanced approaches to transforming data into meaningful patterns. Drawing on arts and humanities methods, it encourages readers to think critically about the full human potential of data science step-by-step. Situating data science within multiple layers of effort exposes dependencies while also pinpointing opportunities for research ethics and policy interventions. This approachable process lays the foundation for broader conversations with a wide range of audiences. Practitioners, academics, students, policy makers, and legislators can all learn how to identify social dynamics in data trends, reflect on ethical questions, and deliberate over solutions. The book proves the limits of predictive technology controlled by the few and calls for more inclusive data science. |
| data science for restaurants: Developing Analytic Talent Vincent Granville, 2014-03-24 Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. |
| data science for restaurants: Data Science and Digital Business Fausto Pedro García Márquez, Benjamin Lev, 2019-01-04 This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business. |
| data science for restaurants: Becoming a Data Head Alex J. Gutman, Jordan Goldmeier, 2021-04-13 Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful. Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You’ve heard the hype around data—now get the facts. In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You’ll learn how to: Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what’s really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you’re a business professional, engineer, executive, or aspiring data scientist, this book is for you. |
| data science for restaurants: Data Science Doug Rose, 2016-11-17 Learn how to build a data science team within your organization rather than hiring from the outside. Teach your team to ask the right questions to gain actionable insights into your business. Most organizations still focus on objectives and deliverables. Instead, a data science team is exploratory. They use the scientific method to ask interesting questions and run small experiments. Your team needs to see if the data illuminate their questions. Then, they have to use critical thinking techniques to justify their insights and reasoning. They should pivot their efforts to keep their insights aligned with business value. Finally, your team needs to deliver these insights as a compelling story. Insight!: How to Build Data Science Teams that Deliver Real Business Value shows that the most important thing you can do now is help your team think about data. Management coach Doug Rose walks you through the process of creating and managing effective data science teams. You will learn how to find the right people inside your organization and equip them with the right mindset. The book has three overarching concepts: You should mine your own company for talent. You can’t change your organization by hiring a few data science superheroes. You should form small, agile-like data teams that focus on delivering valuable insights early and often. You can make real changes to your organization by telling compelling data stories. These stories are the best way to communicate your insights about your customers, challenges, and industry. What Your Will Learn: Create data science teams from existing talent in your organization to cost-efficiently extract maximum business value from your organization’s data Understand key data science terms and concepts Follow practical guidance to create and integrate an effective data science team with key roles and the responsibilities for each team member Utilize the data science life cycle (DSLC) to model essential processes and practices for delivering value Use sprints and storytelling to help your team stay on track and adapt to new knowledge Who This Book Is For Data science project managers and team leaders. The secondary readership is data scientists, DBAs, analysts, senior management, HR managers, and performance specialists. |
| data science for restaurants: Data Analytics in Marketing, Entrepreneurship, and Innovation Mounir Kehal, Shahira El Alfy, 2021-01-12 Innovation based in data analytics is a contemporary approach to developing empirically supported advances that encourage entrepreneurial activity inspired by novel marketing inferences. Data Analytics in Marketing, Entrepreneurship, and Innovation covers techniques, processes, models, tools, and practices for creating business opportunities through data analytics. It features case studies that provide realistic examples of applications. This multifaceted examination of data analytics looks at: Business analytics Applying predictive analytics Using discrete choice analysis for decision-making Marketing and customer analytics Developing new products Technopreneurship Disruptive versus incremental innovation The book gives researchers and practitioners insight into how data analytics is used in the areas of innovation, entrepreneurship, and marketing. Innovation analytics helps identify opportunities to develop new products and services, and improve existing methods of product manufacturing and service delivery. Entrepreneurial analytics facilitates the transformation of innovative ideas into strategy and helps entrepreneurs make critical decisions based on data-driven techniques. Marketing analytics is used in collecting, managing, assessing, and analyzing marketing data to predict trends, investigate customer preferences, and launch campaigns. |
| data science for restaurants: Data Analytics Subhashish Samaddar, Satish Nargundkar, 2019-02-18 If you are a manager who receives the results of any data analyst’s work to help with your decision-making, this book is for you. Anyone playing a role in the field of analytics can benefit from this book as well. In the two decades the editors of this book spent teaching and consulting in the field of analytics, they noticed a critical shortcoming in the communication abilities of many analytics professionals. Specifically, analysts have difficulty in articulating in business terms what their analyses showed and what actionable recommendations were made. When analysts made presentations, they tended to lapse into the technicalities of mathematical procedures, rather than focusing on the strategic and tactical impact and meaning of their work. As analytics has become more mainstream and widespread in organizations, this problem has grown more acute. Data Analytics: Effective Methods for Presenting Results tackles this issue. The editors have used their experience as presenters and audience members who have become lost during presentation. Over the years, they experimented with different ways of presenting analytics work to make a more compelling case to top managers. They have discovered tried and true methods for improving presentations, which they share. The book also presents insights from other analysts and managers who share their own experiences. It is truly a collection of experiences and insight from academics and professionals involved with analytics. The book is not a primer on how to draw the most beautiful charts and graphs or about how to perform any specific kind of analysis. Rather, it shares the experiences of professionals in various industries about how they present their analytics results effectively. They tell their stories on how to win over audiences. The book spans multiple functional areas within a business, and in some cases, it discusses how to adapt presentations to the needs of audiences at different levels of management. |
| data science for restaurants: Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications; Volume 1 Amit Kumar, |
| data science for restaurants: BIG DATA Prabhu TL, Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves. The use of Big Data is becoming common these days by the companies to outperform their peers. In most industries, existing competitors and new entrants alike will use the strategies resulting from the analyzed data to compete, innovate and capture value. Big Data helps the organizations to create new growth opportunities and entirely new categories of companies that can combine and analyze industry data. These companies have ample information about the products and services, buyers and suppliers, consumer preferences that can be captured and analyzed. While the term “big data” is relatively new, the act of gathering and storing large amounts of information for eventual analysis is ages old. The concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three Vs: Volume. Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden. The name 'Big Data' itself is related to a size which is enormous. Size of data plays very crucial role in determining value out of data. Also, whether a particular data can actually be considered as a Big Data or not, is dependent upon volume of data. Hence, 'Volume' is one characteristic which needs to be considered while dealing with 'Big Data'. Velocity. Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time. The term 'velocity' refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data. Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks and social media sites, sensors, Mobile devices, etc. The flow of data is massive and continuous. Variety. Data comes in all types of formats – from structured datasets numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions. Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. During earlier days, spreadsheets and databases were the only sources of data considered by most of the applications. Now days, data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. is also being considered in the analysis applications. This variety of unstructured data poses certain issues for storage, mining and analysing data. |
| data science for restaurants: EurSafe2024 Proceedings Mona Giersberg, Franck Meijboom, Bernice Bovenkerk, 2024-09-10 EurSafe2024 Back to the future: Sustainable innovations for ethical food production and consumption |
| data science for restaurants: International Conference on Innovative Computing and Communications Deepak Gupta, Ashish Khanna, Siddhartha Bhattacharyya, Aboul Ella Hassanien, Sameer Anand, Ajay Jaiswal, 2020-07-30 This book includes high-quality research papers presented at the Third International Conference on Innovative Computing and Communication (ICICC 2020), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on 21–23 February, 2020. Introducing the innovative works of scientists, professors, research scholars, students and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications. |
| data science for restaurants: Digital Economy, Business Analytics, and Big Data Analytics Applications Saad G. Yaseen, 2022-09-26 This book is about turning data into smart decisions, knowledge into wisdom and business into business intelligence and insight. It explores diverse paradigms, methodologies, models, tools and techniques of the emerging knowledge domain of digitalized business analytics applications. The book covers almost every crucial aspect of applied artificial intelligence in business, smart mobile and digital services in business administration, marketing, accounting, logistics, finance and IT management. This book aids researchers, practitioners and decisions makers to gain enough knowledge and insight on how to effectively leverage data into competitive intelligence. |
| data science for restaurants: Machine Learning Algorithms for Industrial Applications Santosh Kumar Das, Shom Prasad Das, Nilanjan Dey, Aboul-Ella Hassanien, 2020-07-18 This book explores several problems and their solutions regarding data analysis and prediction for industrial applications. Machine learning is a prominent topic in modern industries: its influence can be felt in many aspects of everyday life, as the world rapidly embraces big data and data analytics. Accordingly, there is a pressing need for novel and innovative algorithms to help us find effective solutions in industrial application areas such as media, healthcare, travel, finance, and retail. In all of these areas, data is the crucial parameter, and the main key to unlocking the value of industry. The book presents a range of intelligent algorithms that can be used to filter useful information in the above-mentioned application areas and efficiently solve particular problems. Its main objective is to raise awareness for this important field among students, researchers, and industrial practitioners. |
| data science for restaurants: Food Safety Management Systems Hal King, 2020-07-01 This foodborne disease outbreak prevention manual is the first of its kind for the retail food service industry. Respected public health professional Hal King helps the reader understand, design, and implement a food safety management system that will achieve Active Managerial Control in all retail food service establishments, whether as part of a multi-restaurant chain or for multi-restaurant franchisees. According to the most recently published data by the Centers for Disease Control and Prevention (CDC), retail food service establishments are the most commonly reported locations (60%) leading to foodborne disease outbreaks in the United States every year. The Food and Drug Administration (FDA) has reported that in order to effectively reduce the major foodborne illness risk factors in retail food service, a food service business should use Food Safety Management Systems (FSMS); however less than 11% of audited food service businesses in a 2018 report were found using a well-documented FSMS. Clearly, there needs to be more focus on the prevention of foodborne disease illnesses and outbreaks in retail food service establishments. The purpose of this book is to help retail food service businesses implement FSMS to achieve Active Managerial Control (AMC) of foodborne illness risk factors. It is a key resource for retail professionals at all levels of the retail food service industry, and those leaders tasked to build and manage food safety departments within these organizations. |
| data science for restaurants: AI for Disease Surveillance and Pandemic Intelligence Arash Shaban-Nejad, Martin Michalowski, Simone Bianco, 2022-03-08 This book aims to highlight the latest achievements in the use of artificial intelligence for digital disease surveillance, pandemic intelligence, as well as public and clinical health surveillance. The edited book contains selected papers presented at the 2021 Health Intelligence workshop, co-located with the Association for the Advancement of Artificial Intelligence (AAAI) annual conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. While disease surveillance has always been a crucial process, the recent global health crisis caused by COVID-19 has once again highlighted our dependence on intelligent surveillance infrastructures that provide support for making sound and timely decisions. This book provides information for researchers, students, industry professionals, and public health agencies interested in the applications of AI in population health and personalized medicine. |
| data science for restaurants: Recent Trends in Computational Sciences Gururaj H L, Pooja M R, Francesco Flammini, 2023-10-12 This book is a compilation of research papers and presentations from the Fourth Annual International Conference on Data Science, Machine Learning and Blockchain Technology (AICDMB 2023, Mysuru, India, 16-17 March 2023). The book covers a wide range of topics, including data mining, natural language processing, deep learning, computer vision, big data analytics, cryptography, smart contracts, decentralized applications, and blockchain-based solutions for various industries such as healthcare, finance, and supply chain management. The research papers presented in this book highlight the latest advancements and practical applications in data science, machine learning, and blockchain technology, and provide insights into the future direction of these fields. The book serves as a valuable resource for researchers, students, and professionals in the areas of data science, machine learning, and blockchain technology. |
| data science for restaurants: Restaurant Technology CK Chong, 2023-11-16 This book sets the gold standard for Restaurant Technology (RestTech). In an era where technology drives Quick Service Restaurants (QSR), this book is a compass for food & beverage operators, innovators, digital transformation professionals, and enthusiasts alike. It defines RestTech standards, guiding QSR professionals to harness the power of digitalization, data, and innovation. The author’s wealth of experience, spanning global multi-national giants and iconic QSR brands, forms the cornerstone for a comprehensive guide that promises to reshape the way the industry leverages technology and data. This book isn't just a reference; it's a roadmap for QSR or any retail pioneers ready to embark on a digital revolution. |
| data science for restaurants: Advances on Intelligent Computing and Data Science Faisal Saeed, Fathey Mohammed, Errais Mohammed, Tawfik Al-Hadhrami, Mohammed Al-Sarem, 2023-08-16 This book presents the papers included in the proceedings of the 3rd International Conference of Advanced Computing and Informatics (ICACin’22) that was held in Casablanca, Morocco, on October 15–16, 2022. A total of 98 papers were submitted to the conference, but only 60 papers were accepted and published in this book with an acceptance rate of 61%. The book presents several hot research topics which include artificial intelligence and data science, big data analytics, Internet of Things (IoT) and smart cities, information security, cloud computing and networking, and computational informatics. |
| data science for restaurants: The A.I. Marketer Andrew W. Pearson, 2019-04-15 We seem to be living in the age of A.I. Everywhere you look, companies are touting their most recent A.I., machine learning, and deep learning breakthroughs, even when they are far short of anything that could be touted as a “breakthrough.” “A.I.” has eclipsed “Blockchain” and “Crypto” as the buzzword of today. Indeed, one of the best ways to raise VC funding is to stick ‘AI’ or ‘ML’ at the front of your prospectus and “.ai” at the end of your website. Separating fact from fiction is more important than it has ever been. The A.I. Marketer breaks down A.I., machine learning, and deep learning into five unique use cases—sound, time series, text, image, and video—and also reveals how marketing executives can utilize this powerful technology to help them more finely tune their marketing campaigns, better segment their customers, increase lead generation, and foster strong customer loyalty. Today, “Personalization”—the process of utilizing mobile, social, geo-location data, web morphing, context and even affective computing to tailor messages and experiences to an individual interacting with them—is becoming the optimum word in a radically new customer intelligence environment. The A.I. Marketer explains this complex technology in simple to understand terms and then shows how marketers can utilize the psychology of personalization with A.I. to both create more effective marketing campaigns as well as increase customer loyalty. Pearson shows companies how to avoid Adobe’s warning of not using industrial-age technology in the digital era. Pearson also reveals how to create a platform of technology that seamlessly integrates EDW and real-time streaming data with social media content. Analytical models and neural nets can then be built on both commerical and open source technology to better understand the customer, thereby strengthening the brand and, just as importantly, increasing ROI. |
| data science for restaurants: Practicing Food Studies Amy Bentley, Fabio Parasecoli, Krishnendu Ray, 2024-03-26 An introduction to the burgeoning field of food studies Popular and intellectual interest in food is on the rise. The breadth of concerns surrounding food ranges from animal welfare and climate change’s impact on food production to debates on the healthfulness of carbohydrates and fats, and fair compensation for restaurant and farm workers. Not only is there an expanding conversation about the ways in which we produce and consume our food, but there is growing attention being placed on the myriad ways in which food expresses and shapes shifting identities. Practicing Food Studies details the turn of the twenty-first century development and flourishing of food studies as a multidisciplinary field, focusing on its establishment at New York University. Food studies scholars have come from various fields such as history, sociology, economics, political science, nutrition, or public policy, but often felt limited by the conventions of their traditional discipline. Many gravitated to food studies to be able to describe and critically examine their specific areas of interest beyond the borders of academic disciplines. This volume explores the history of knowledge in which NYU Food Studies emerged, providing the opportunity to reflect on how academic fields are created and evolve as a response to institutional constraints and opportunities, the landscape of ideas, social movements, and public conversations. Practicing Food Studies is a compelling collection of essays compiling the research, ideas, and experiences of faculty members and graduates of the NYU Food Studies program—mapping the paths for intellectual and social engagement with food systems and its most urgent issues. |
| data science for restaurants: Winning with Data Science Howard Steven Friedman, Akshay Swaminathan, 2024-01-30 Whether you are a newly minted MBA or a project manager at a Fortune 500 company, data science will play a major role in your career. Knowing how to communicate effectively with data scientists in order to obtain maximum value from their expertise is essential. This book is a compelling and comprehensive guide to data science, emphasizing its real-world business applications and focusing on how to collaborate productively with data science teams. Taking an engaging narrative approach, Winning with Data Science covers the fundamental concepts without getting bogged down in complex equations or programming languages. It provides clear explanations of key terms, tools, and techniques, illustrated through practical examples. The book follows the stories of Kamala and Steve, two professionals who need to collaborate with data science teams to achieve their business goals. Howard Steven Friedman and Akshay Swaminathan walk readers through each step of managing a data science project, from understanding the different roles on a data science team to identifying the right software. They equip readers with critical questions to ask data analysts, statisticians, data scientists, and other technical experts to avoid wasting time and money. Winning with Data Science is a must-read for anyone who works with data science teams or is interested in the practical side of the subject. |
| data science for restaurants: Artificial Intelligence for Marketing Management Park Thaichon, Sara Quach, 2022-11-10 Artificial intelligence (AI) has driven businesses to adopt new business practices rapidly, enhance product development and services, has helped to power AI-based market intelligence and customer insights, and improve customer relationship management. This timely book addresses the use of AI in marketing. This book also explores the dark side of AI in marketing management and discusses ethics and transparency of automated decision-making in AI applications, data privacy, cyber security issues, and biases in various facets of marketing. Emerging applications of AI such as DeepFakes which use deep learning technology could increase risks of manipulation and deception. Hence, apart from leveraging AI capabilities and advantages, the book cautions the need for prevention strategies to deal with potential issues that could arise from the adoption of AI in marketing management. This book will provide practical insights into the role of AI in marketing management. It will be a useful reference for those researching marketing and marketing professionals. |
| data science for restaurants: Big Data and Analytics for Infectious Disease Research, Operations, and Policy National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Global Health, Forum on Microbial Threats, 2016-11-30 With the amount of data in the world exploding, big data could generate significant value in the field of infectious disease. The increased use of social media provides an opportunity to improve public health surveillance systems and to develop predictive models. Advances in machine learning and crowdsourcing may also offer the possibility to gather information about disease dynamics, such as contact patterns and the impact of the social environment. New, rapid, point-of-care diagnostics may make it possible to capture not only diagnostic information but also other potentially epidemiologically relevant information in real time. With a wide range of data available for analysis, decision-making and policy-making processes could be improved. While there are many opportunities for big data to be used for infectious disease research, operations, and policy, many challenges remain before it is possible to capture the full potential of big data. In order to explore some of the opportunities and issues associated with the scientific, policy, and operational aspects of big data in relation to microbial threats and public health, the National Academies of Sciences, Engineering, and Medicine convened a workshop in May 2016. Participants discussed a range of topics including preventing, detecting, and responding to infectious disease threats using big data and related analytics; varieties of data (including demographic, geospatial, behavioral, syndromic, and laboratory) and their broader applications; means to improve their collection, processing, utility, and validation; and approaches that can be learned from other sectors to inform big data strategies for infectious disease research, operations, and policy. This publication summarizes the presentations and discussions from the workshop. |
| data science for restaurants: Advances on Intelligent Informatics and Computing Faisal Saeed, Fathey Mohammed, Fuad Ghaleb, 2022-03-29 This book presents emerging trends in intelligent computing and informatics. This book presents the papers included in the proceedings of the 6th International Conference of Reliable Information and Communication Technology 2021 (IRICT 2021) that was held virtually, on Dec. 22-23, 2021. The main theme of the book is “Advances on Intelligent Informatics and Computing”. A total of 87 papers were submitted to the conference, but only 66 papers were accepted and published in this book. The book presents several hot research topics which include health informatics, artificial intelligence, soft computing, data science, big data analytics, Internet of Things (IoT), intelligent communication systems, cybersecurity, and information systems. |
| data science for restaurants: Artificial Intelligence: A Real Opportunity in the Food Industry Aboul Ella Hassanien, Mona Soliman, 2022-11-03 This book emphasizes the latest developments and achievements in AI and related technologies with a special focus on food quality. The book describes the applications, and conceptualization of ideas, and critical surveys covering most aspects of AI for food quality. |
| data science for restaurants: Expert Clouds and Applications I. Jeena Jacob, Selvanayaki Kolandapalayam Shanmugam, Ivan Izonin, 2023-07-01 The book features original papers from International Conference on Expert Clouds and Applications (ICOECA 2023), organized by RV Institute of Technology and Management, Bangalore, India, during February 9–10, 2023. It covers new research insights on artificial intelligence, big data, cloud computing, sustainability, and knowledge-based expert systems. The book discusses innovative research from all aspects including theoretical, practical, and experimental domains that pertain to the expert systems, sustainable clouds, and artificial intelligence technologies. The thrust of the book is to showcase different research chapters dealing with the design, development, implementation, testing and analysis of intelligent systems, and expert clouds, and also to provide empirical and practical guidelines for the development of such systems. |
| data science for restaurants: Food systems in Latin America and the Caribbean Graziano da Silva, J., Jales, M., Rapallo, R., Díaz-Bonilla, E., Girardi, G., del Grossi, M., Luiselli, C., Sotomayor, O., Rodríguez, A., Rodrigues, M., Wander, P., Rodríguez, M., Zuluaga, J., Pérez, D., 2021-09-24 The book has been prepared by authors from different international organizations – including FAO, IFPRI, UNCTAD and ECLAC, as well as legislators and academics from prestigious Latin American universities – seeking to foster reflections for the Global Food Systems Summit, to be held in September 2021. It contextualizes the region’s food systems within a post COVID-19 pandemic scenario and raises new challenges (and opportunities) for policy makers, decision makers, the private sector, and the general public. Likewise, it offers important reflections on sustainability, from production to consumption, with the call to promote better governance of the global and regional food system. In order to face what some authors have deemed “the Syndemic of the century”, the participation of companies, research centres, academia, NGOs, government agencies and international organizations will be necessary. |
| data science for restaurants: Intelligent Sustainable Systems Atulya K. Nagar, Dharm Singh Jat, Durgesh Kumar Mishra, Amit Joshi, 2023-01-24 This book provides insights of World Conference on Smart Trends in Systems, Security and Sustainability (WS4 2022) which is divided into different sections such as Smart IT Infrastructure for Sustainable Society; Smart Management Prospective for Sustainable Society; Smart Secure Systems for Next Generation Technologies; Smart Trends for Computational Graphics and Image Modeling; and Smart Trends for Biomedical and Health Informatics. The proceedings is presented in two volumes. The book is helpful for active researchers and practitioners in the field. |
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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 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 …
