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| data science in fashion: Artificial Intelligence for Fashion Industry in the Big Data Era Sébastien Thomassey, Xianyi Zeng, 2018-05-16 This book provides an overview of current issues and challenges in the fashion industry and an update on data-driven artificial intelligence (AI) techniques and their potential implementation in response to those challenges. Each chapter starts off with an example of a data-driven AI technique on a particular sector of the fashion industry (design, manufacturing, supply or retailing), before moving on to illustrate its implementation in a real-world application |
| data science in fashion: Artificial Intelligence for Fashion Leanne Luce, 2018-12-08 Learn how Artificial Intelligence (AI) is being applied in the fashion industry. With an application focused approach, this book provides real-world examples, breaks down technical jargon for non-technical readers, and provides an educational resource for fashion professionals. The book investigates the ways in which AI is impacting every part of the fashion value chain starting with product discovery and working backwards to manufacturing. Artificial Intelligence for Fashion walks you through concepts, such as connected retail, data mining, and artificially intelligent robotics. Each chapter contains an example of how AI is being applied in the fashion industry illustrated by one major technological theme. There are no equations, algorithms, or code. The technological explanations are cumulative so you'll discover more information about the inner workings of artificial intelligence in practical stages as the book progresses. What You’ll Learn Gain a basic understanding of AI and how it is used in fashion Understand key terminology and concepts in AI Review the new competitive landscape of the fashion industry Conceptualize and develop new ways to apply AI within the workplaceWho This Book Is For Fashion industry professionals from designers, managers, department heads, and executives can use this book to learn about how AI is impacting roles in every department and profession. |
| data science in fashion: Big Data in Practice Bernard Marr, 2016-03-22 The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter |
| data science in fashion: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
| data science in fashion: Information Systems for the Fashion and Apparel Industry Tsan-Ming Jason Choi, 2016-04-13 Information Systems for the Fashion and Apparel Industry brings together trends and developments in fashion information systems, industrial case-studies, and insights from an international team of authors. The fashion and apparel industry is fast-growing and highly influential. Computerized information systems are essential to support fashion business operations and recent developments in social media, mobile commerce models, radio frequency identification (RFID) technologies, and ERP systems are all driving innovative business measures in the industry. After an introductory chapter outlining key decision points and information requirements in fast fashion supply chains, Part One focuses on the principles of fashion information systems, with chapters covering how decision making in the apparel supply chains can be improved through the use of fuzzy logic, RFID technologies, evolutionary optimization techniques, and artificial neural networks. Part Two then reviews the range of applications for information systems in the fashion and apparel industry to improve customer choice, aid design, implement intelligent forecasting and procurement systems, and manage inventory and returns. - Provides systematic and comprehensive coverage of information systems for the fashion and apparel industry - Combines recent developments and industrial best-practices in apparel supply chain management in order to meet the needs of the fashion and apparel industry professionals and academics - Features input from a team of highly knowledgeable authors with a range of professional and academic experience, overseen by an editor who is a leading expert in the field - Reviews the range of applications for information systems in the fashion and apparel industry to improve customer choice, aid design, implement intelligent forecasting and procurement systems, and manage inventory and returns |
| data science in fashion: Data Science and Intelligent Systems Radek Silhavy, Petr Silhavy, Zdenka Prokopova, 2021-11-16 This book constitutes the second part of refereed proceedings of the 5th Computational Methods in Systems and Software 2021 (CoMeSySo 2021) proceedings. The real-world problems related to data science and algorithm design related to systems and software engineering are presented in this papers. Furthermore, the basic research’ papers that describe novel approaches in the data science, algorithm design and in systems and software engineering are included. The CoMeSySo 2021 conference is breaking the barriers, being held online. CoMeSySo 2021 intends to provide an international forum for the discussion of the latest high-quality research results |
| data science in fashion: Data Science and Its Applications Aakanksha Sharaff, G R Sinha, 2021-08-17 The term data being mostly used, experimented, analyzed, and researched, Data Science and its Applications finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications. |
| data science in fashion: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| data science in fashion: Fast Fashion, Fashion Brands and Sustainable Consumption Subramanian Senthilkannan Muthu, 2018-06-21 This book discusses the connection between fast fashion brands and customer-centric sustainability. It highlights what consumers can do with fast fashion and the important aspects that need to be addressed to make fast fashion sustainable. Fast fashion is an inevitable element in today’s fashion business cycle and its adverse impacts on sustainable fashion are a major issue. |
| data science in fashion: 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 in fashion: Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications Abhishek Majumder, Joy Lal Sarkar, Arindam Majumder, 2023-08-16 Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications captures the state of the art in usage of artificial intelligence in different types of recommendation systems and predictive analysis. The book provides guidelines and case studies for application of artificial intelligence in recommendation from expert researchers and practitioners. A detailed analysis of the relevant theoretical and practical aspects, current trends and future directions is presented. The book highlights many use cases for recommendation systems: · Basic application of machine learning and deep learning in recommendation process and the evaluation metrics · Machine learning techniques for text mining and spam email filtering considering the perspective of Industry 4.0 · Tensor factorization in different types of recommendation system · Ranking framework and topic modeling to recommend author specialization based on content. · Movie recommendation systems · Point of interest recommendations · Mobile tourism recommendation systems for visually disabled persons · Automation of fashion retail outlets · Human resource management (employee assessment and interview screening) This reference is essential reading for students, faculty members, researchers and industry professionals seeking insight into the working and design of recommendation systems. |
| data science in fashion: Proceedings of International Conference on Data Science and Applications Mukesh Saraswat, Chandreyee Chowdhury, Chintan Kumar Mandal, Amir H. Gandomi, 2023-02-06 This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing. |
| data science in fashion: Advanced Fashion Technology and Operations Management Vecchi, Alessandra, 2017-03-01 Fashion has been steadily moving from the brick and mortar to the digital market. As such, it is increasingly vital to research new methods that will help businesses to grow and succeed in this new sphere. Advanced Fashion Technology and Operations Management is a pivotal reference source for the latest development management strategies, fashion marketing, international business, and fashion entrepreneurship. Featuring extensive coverage across a range of relevant perspectives and topics, such as online shopping behavior, digital fashion, and e-commerce, this book is ideally designed for professionals, entrepreneurs, students, and researchers. |
| data science in fashion: Fashion Trend Forecasting Gwyneth Holland, Rae Jones, 2017-08-08 An understanding of trends is a fundamental skill for anyone working in the fashion industry. In this book Gwyneth Holland and Rae Jones look at how to produce a well-researched trend, from initial inspiration to concrete idea and, eventually, real product. Illustrated throughout with insights from practicing trend forecasters and industry insiders, it is an invaluable guide for fashion students and practitioners alike. |
| data science in fashion: Fashion Supply Chain Management Using Radio Frequency Identification (RFID) Technologies Calvin Wong, Z. X. Guo, 2014-02-16 Fashion Supply Chain Management Using Radio Frequency Identification (RFID) Technologies looks at the application of RFID technologies in such areas as order allocation, garment manufacturing, product tracking, distribution and retail. As supply chains in the textiles and fashion industry become ever more complex and global, and as the shift to mass customization puts more pressure on a rapid and flexible response to customer needs, monitoring and improving supply chain efficiency in the industry becomes crucial. Radio frequency identification (RFID) technologies offer a unique opportunity to achieve these goals. This book reviews the role of RFID technologies in the textiles and fashion supply chain to improve distribution, process management and product tracking, garment manufacturing, and assembly line operations. It also explores how RFID technologies can improve order allocation in the supply chain, and how these technologies can also be used for intelligent apparel product cross-selling. Its chapters also discuss measuring the impact of RFID technologies in improving the efficiency of the textile supply chain, and modeling the effectiveness of RFID technologies in improving sales performance in fashion retail outlets. Fashion Supply Chain Management Using Radio Frequency Identification (RFID) Technologies is a comprehensive resource for academic researchers, industry managers, and professionals within the fashion industry. - Looks at the application of RFID technologies in order allocation, garment manufacturing, product tracking, distribution, and retail - Reviews RFID technologies in the textiles and fashion supply chain for improving distribution, process management and product tracking, garment manufacturing, and assembly line operations - Focuses on measuring the impact of RFID technologies on efficiency, and modeling the effectiveness of RFID technologies in improving retail outlet sales |
| data science in fashion: Clothing Appearance and Fit J Fan, W Yu, L Hunter, 2004-09-20 Fashion and beauty have helped shape history and today more than ever, we find ourselves under increasing pressure to think about what we wear, what we look good in and how best to enhance our body shape and size. Behind this seemingly superficial industry, however, lies a technical thinking firmly grounded in science and technology. In one fully comprehensive book, Clothing appearance and fit: Science and technology provides a critical appreciation of the technological developments and scientific understanding of the appearance and fit of clothing. The authors bridge the science of beauty and fashion design with garment evaluation technology, garment drape and human anthropometrics and sizing.The ten chapters of the book provide a detailed coverage of clothing appearance and fit. Chapter 1 considers body attractiveness and how it relates to clothing material and design parameters and discusses classical and contemporary theories of beauty. Chapters 2 and 3 present the industry's techniques, methods and standards for assessing clothing appearance and fit and Chapters 4 and 5 review the research and development of objective measurement technologies for evaluating clothing appearance and fit. Fabric objective measurement, fabric properties and garment drape are covered in Chapters 6 and 7 and the R & D of body measurement, anthropometrics and sizing systems are detailed in Chapters 8 and 9. The final chapter reviews published work on garment design and pattern alteration for achieving good clothing appearance and fit.This book is an essential reference for researchers, academics, professionals and students in clothing and textile academia and industry. It includes many industrial standards, techniques and practices. - Offers a critical appreciation of technological developments - Incorporates user-friendly illustrations and photographs - Valuable reference for students, researchers and professionals in the clothing and textile industries |
| data science in fashion: Data Science and Applications Satyasai Jagannath Nanda, |
| data science in fashion: HBR's 10 Must Reads 2020 Harvard Business Review, Michael E. Porter, Nitin Nohria, Katrina Lake, Paul R. Daugherty, 2019-10-01 A year's worth of management wisdom, all in one place. We've reviewed the ideas, insights, and best practices from the past year of Harvard Business Review to keep you up-to-date on the most cutting-edge, influential thinking driving business today. With authors from Michael E. Porter to Katrina Lake and company examples from Alibaba to 3M, this volume brings the most current and important management conversations right to your fingertips. This book will inspire you to: Ask better questions to boost your learning, persuade others, and negotiate more effectively Create workplace conditions where gender equity can thrive Boost results by allowing humans and AI to enhance one another's strengths Make better connections with your customers by giving them a glimpse inside your company Scale your agile processes from a few teams to hundreds Build a commitment to both economic and social values in your organization Prepare your company for a rapidly aging workforce and society This collection of articles includes The Surprising Power of Questions, by Alison Wood Brooks and Leslie K. John; Strategy Needs Creativity, by Adam Brandenburger; What Most People Get Wrong about Men and Women, by Catherine H. Tinsley and Robin J. Ely; Collaborative Intelligence: Humans and AI Are Joining Forces, by H. James Wilson and Paul R. Daugherty; Stitch Fix's CEO on Selling Personal Style to the Mass Market, by Katrina Lake; Strategy for Start-Ups, by Joshua Gans, Erin L. Scott, and Scott Stern; Agile at Scale, by Darrell K. Rigby, Jeff Sutherland, and Andy Noble; Operational Transparency, by Ryan W. Buell; The Dual-Purpose Playbook, by Julie Battilana, Anne-Claire Pache, Metin Sengul, and Marissa Kimsey; How CEOs Manage Time, by Michael E. Porter and Nitin Nohria; and When No One Retires, by Paul Irving. |
| data science in fashion: Fashionable Technology Sabine Seymour, 2008-05-19 The interplay of electronic textiles and wearable technology, wearables for short, and fashion, design and science is a highly promising and topical subject. Offered here is a compact survey of the theory involved and an explanation of the role technology plays in a fabric or article of clothing. The practical application is explained in detail and numerous illustrations serve as clarification. Over 50 well-known designers, research institutes, companies and artists, among them Philips, Burton, MIT Media Lab, XS Labs, New York University, Hussein Chalayan, Cute Circuit or International Fashion Machines are introduced by means of their latest, often still unpublished, project, and a survey of their work to date. Given for the first time is a list of all the relevant information on research institutes, materials, publications etc. A must for all those wishing to know everything about fashionable technology. |
| data science in fashion: Social Media for Fashion Marketing Wendy K. Bendoni, 2020-08-06 Social Media for Fashion Marketing uses cutting edge case studies and detailed interviews to show how the business of fashion is changing in the digital landscape. Bendoni (@BendoniStyle) also considers the psychological impact of being a hyper-connected consumer and the generational gaps in social media communication. Using academic research, alongside her 25 years of fashion marketing experience, Bendoni offers a clear picture of the changing narrative of storytelling, social confirmation, digital nesting and how to use data to shape a brand's online presence. With practical and critical thinking activities to hone your skills into professional practice, this is the ultimate guide to social marketing, promotion, SEO, branding and communication. Featured topics - Rules of Digital Storytelling - Rethinking Gamification - Strategic Digital Marketing - The Role of Citizen Journalists - The Social Media Looking Glass - World of Influencer Marketing - Visual Consumption Economy - Global Perspective of Social Media |
| data science in fashion: 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 in fashion: Handbook of Research on Global Fashion Management and Merchandising Vecchi, Alessandra, 2016-05-03 Innovation and novel leadership strategies have aided the successful growth of the fashion industry around the globe. However, as the dynamics of the industry are constantly changing, a deficit can emerge in the overall comprehension of industry strategies and practices. The Handbook of Research on Global Fashion Management and Merchandising explores the various facets of effective management procedures within the fashion industry. Featuring research on entrepreneurship, operations management, marketing, business modeling, and fashion technology, this publication is an extensive reference source for practitioners, academics, researchers, and students interested in the dynamics of the fashion industry. |
| data science in fashion: Advanced Studies in Classification and Data Science Tadashi Imaizumi, Akinori Okada, Sadaaki Miyamoto, Fumitake Sakaori, Yoshiro Yamamoto, Maurizio Vichi, 2020-09-25 This edited volume focuses on the latest developments in classification and data science and covers a wide range of topics in the context of data analysis and related areas, e.g. the analysis of complex data, analysis of qualitative data, methods for high-dimensional data, dimensionality reduction, data visualization, multivariate statistical methods, and various applications to real data in the social sciences, medical sciences, and other disciplines. In addition to sharing theoretical and methodological findings, the book shows how to apply the proposed methods to a variety of problems — e.g. in consumer behavior, decision-making, marketing data and social network structures. Both methodological aspects and applications to a wide range of areas such as economics, behavioral science, marketing science, management science and the social sciences are covered. The book is chiefly intended for researchers and practitioners who are interested in the latest developments and practical applications in these fields, as well as applied statisticians and data analysts. Its combination of methodological advances with a wide range of real-world applications gathered from several fields makes it of unique value in helping readers solve their research problems. |
| data science in fashion: Mastering Marketing Data Science Iain Brown, 2024-04-26 Unlock the Power of Data: Transform Your Marketing Strategies with Data Science In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively. Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science. Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science. Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape. Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative. Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable. |
| data science in fashion: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science in fashion: Textiles and Fashion Rose Sinclair, 2014-11-08 This major textbook is designed for students studying textiles and fashion at higher and undergraduate level, as well as those needing a comprehensive and authoritative overview of textile materials and processes. The first part of the book reviews the main types of natural and synthetic fibres and their properties. Part two provides a systematic review of the key processes involved first in converting fibres into yarns and then transforming yarns into fabrics. Part three discusses the range of range of finishing techniques for fabrics. The final part of the book looks specifically at the transformation of fabric into apparel, from design and manufacture to marketing. With contributions from leading experts in their fields, this major book provides the definitive one-volume guide to textile manufacture. - Provides comprehensive coverage of the types and properties of textile fibres to yarn and fabric manufacture, fabric finishing, apparel production and fashion - Focused on the needs of college and undergraduate students studying textiles or fashion courses - Each chapter ends with a summary to emphasise key points, a comprehensive self-review section, and project ideas are also provided |
| data science in fashion: Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) Mostafa Ezziyyani, 2019-03-06 This book includes the outcomes of the International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD-2018), held in Tangier, Morocco on July 12–14, 2018. Presenting the latest research in the field of computing sciences and information technology, it discusses new challenges and provides valuable insights into the field, the goal being to stimulate debate, and to promote closer interaction and interdisciplinary collaboration between researchers and practitioners. Though chiefly intended for researchers and practitioners in advanced information technology management and networking, the book will also be of interest to those engaged in emerging fields such as data science and analytics, big data, internet of things, smart networked systems, artificial intelligence, expert systems and cloud computing. |
| data science in fashion: The Fashion Show Gill Stark, 2018-09-20 Take up your all-access pass to one of the most dynamic areas of the international fashion industry. Lavishly illustrated and packed with industry insights, The Fashion Show is the must-have guide to showing off a collection. You will learn about: The context of the fashion show and its significance for brands, designers, journalists and others working in the fashion industry; How a fashion show is produced, everything from agreeing a vision to casting the models to setting up backstage; What happens on show day, and how to use the impact of your show. Future fashion designers, fashion marketers, fashion managers, fashion PRs – and creative practitioners looking to learn more about this fascinating part of the industry, you are cordially invited to join Gill Stark in the front row of The Fashion Show. |
| data science in fashion: The Psychology of Fashion Carolyn Mair, 2018-04-09 The Psychology of Fashion offers an insightful introduction to the exciting and dynamic world of fashion in relation to human behaviour, from how clothing can affect our cognitive processes to the way retail environments manipulate consumer behaviour. The book explores how fashion design can impact healthy body image, how psychology can inform a more sustainable perspective on the production and disposal of clothing, and why we develop certain shopping behaviours. With fashion imagery ever present in the streets, press and media, The Psychology of Fashion shows how fashion and psychology can make a positive difference to our lives. |
| data science in fashion: The Science of Fashion Julie Danneberg, 2021-08-15 Find out the science behind your clothes in this fun, interactive book full of hands-on projects for middle schoolers! What kinds of sneakers are you wearing right now? What material is your shirt made out of? And how did all of these clothes and accessories get made and arrive at a store for you to try on and buy? In The Science and Engineering of Fashion, readers ages 12 to 15 learn how science and engineering makes the entire fashion industry possible. Fashion is one of the biggest, most influential industries in the world. But how much do you know about how your clothes are made? Most people tend to think of fashion designers and scientists as occupying two completely different worlds, but the truth is, the fashion business is completely dependent on and intertwined with science and engineering. The computer technology used in fashion design, the science behind dyes and special treatments such as waterproofing, the engineering innovations in manufacturing of fast fashion are just a few of the ways the fashion world and the science and engineering world meet. Throughout The Science and Engineering of Fashion, kids encounter essential topics and questions to encourage critical thinking skills, hands-on STEAM activities that encourage creative thinking, graphic novel style illustrations and more! Links to online resources provide a digital learning experience that integrates content with an interactive platform. This book breaks the stereotype that science and engineering are not part of creative endeavors by showing how fashion designers, scientists, and engineers work together to produce the clothing that shows up in your closet. The fashion industry is responding to the demands from the public to create more environmentally friendly processes and products. Science is helping them do this. Activities include creating natural dyes, making your own fashion design, weaving on a handmade loom, and exploring how the psychology of color plays out in your own life. Essential questions guide readers'' investigations while hands-on activities promote critical and creative problem solving, and text-to-world connections highlight the way the past provides context for the present-day world. Aligns with Next Generation Science Standards for middle school in Engineering Design. About the Inquire & Investigate series and Nomad Press Nomad Press books in the Inquire & Investigate series integrate content with participation, encouraging readers to engage in student-directed learning. Combining content with inquiry-based projects stimulates learning and makes it active and alive. Nomad''s unique approach simultaneously grounds kids in factual knowledge while allowing them the space to be curious, creative, and critical thinkers. All books are leveled for Guided Reading level and Lexile and align with Common Core State Standards and Next Generation Science Standards. All titles are available in paperback, hardcover, and ebook formats. |
| data science in fashion: Green Fashion Retail Jochen Strähle, 2016-10-18 This book focuses on sustainability in fashion retail, which is fast becoming the pivot point of future fashion retail strategies. Chapters in the book provide theoretical and practical insight on how going green may positively influence the strategy of fashion retailers and marketers, who have to react to the changing society and customer needs. Structured in four main parts, and based on distinct research questions, readers will be able to dig deep into the individual levers for possible adaptions. It thus provides a solid understanding on how to integrate green aspects into any fashion retailers business model. |
| data science in fashion: Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing Lopa Mandal, Joao Manuel R. S. Tavares, Valentina E. Balas, 2022-08-17 This book includes selected papers presented at International Conference on Computational Intelligence, Data Science,, and Cloud Computing (IEM-ICDC 2021), organized by the Department of Information Technology Institute of Engineering and Management, Kolkata, India, during December 22 – 24, 2021. It covers substantial new findings about AI and robotics, image processing and NLP, cloud computing and big data analytics as well as in cyber-security, blockchain and IoT, and various allied fields. The book serves as a reference resource for researchers and practitioners in academia and industry. |
| data science in fashion: Data Science with Julia Paul D. McNicholas, Peter Tait, 2019-01-02 This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist.- Professor Charles Bouveyron, INRIA Chair in Data Science, Université Côte d’Azur, Nice, France Julia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science. This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist. Professor Charles Bouveyron INRIA Chair in Data Science Université Côte d’Azur, Nice, France |
| data science in fashion: Fashion Branding and Consumer Behaviors Tsan-Ming Choi, 2014-01-30 Fashion Branding and Consumer Behaviors presents eye-opening theory, literature review and original research on the mutual influence of branding strategies and consumer response. Contributors use multiple methods to analyze consumers' psychosocial needs and the extent that their fulfillment goes beyond the usefulness or value of the items they purchase as well as the fashion industry's means of communicating brand identity and enhancing brand loyalty. Along the way, these studies raise important questions about consumer behaviors, consumer welfare, environmental ethics and the future of consumer research. Included in the coverage: A symbolic interactionist perspective on fashion brand personality and advertisement response. Optimizing fashion branding strategies in a fluctuating market. An analysis of fashion brand extensions by artificial neural networks. Domestic or foreign luxury brands? A comparison of status- and non-status- seeking teenagers. The impact of consumers’ need for uniqueness on purchase perception. How brand awareness relates to market outcome, brand equity and the marketing mix. A breakthrough volume on the complexities of how and why we buy, Fashion Branding and Consumer Behaviors will captivate researchers and practitioners in the fields of consumer psychology, marketing and economics. |
| data science in fashion: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
| data science in fashion: Fashion Trends Eundeok Kim, Ann Marie Fiore, Alice Payne, Hyejeong Kim, 2021-01-14 In a fast-moving global industry, how can anyone know what the next trend will be? Fashion Trends: Analysis and Forecasting offers a clear pathway into the theory and practice of forecasting fashion, using professional case studies to demonstrate each technique and concept. This revised edition includes an updated model of the fashion trend analysis and forecasting process and expanded coverage of social media, digital influencers, sustainability and social responsibility. There are also first-hand visual materials relating to forecasts from leading firms. With the rise of individualism and concern for the sustainable world, the authors also walk you through the 'end of fashion' and what comes next, including: recycled and upcycled fashion, garment rental, subscription services, the circular economy, transparency and traceability, and the role of forecasting in encouraging sustainable lifestyles. Key topics – The characteristics of an innovation – The influence of consumer groups – Long- and short-term fashion forecasting – Sociocultural factors and their influence on trends – Fashion professionals' roles in creating and supporting trends – Consumer and industry trends accelerating product innovation and diffusion – Changing trend forecasting formats – The influence of trend forecasting on business decisions |
| data science in fashion: Data Science and Big Data Analytics Durgesh Mishra, |
| data science in fashion: Data Science at the Command Line Jeroen Janssens, 2014-09-25 This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools. Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on plain text, CSV, HTML/XML, and JSON Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow using Drake Create reusable tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines using GNU Parallel Model data with dimensionality reduction, clustering, regression, and classification algorithms |
| data science in fashion: High-Performance Tensor Computations in Scientific Computing and Data Science Edoardo Angelo Di Napoli, Paolo Bientinesi, Jiajia Li, André Uschmajew, 2022-11-08 |
| data science in fashion: Sustainability in Fashion and Textiles Miguel Angel Gardetti, Ana Laura Torres, 2017-09-08 There is no doubt that the textile industry – the production of clothing, fabrics, thread, fibre and related products – plays a significant part in the global economy. It also frequently operates with disregard to its environmental and social impacts. The textile industry uses large quantities of water and outputs large quantities of waste. As for social aspects, many unskilled jobs have disappeared in regions that rely heavily on these industries. Another serious and still unresolved problem is the flexibility textile industry companies claim to need. Faced with fierce international competition, they are increasingly unable to offer job security. This is without even considering the informal-sector work proliferating both in developing and developed countries. Child labour persists within this sector despite growing pressure to halt it.Fashion demands continuous consumption. In seeking to own the latest trends consumers quickly come to regard their existing garments as inferior, if not useless. Old items become unwanted as quickly as new ones come into demand. This tendency towards disposability results in the increased use of resources and thus the accelerated accumulation of waste. It is obvious to many that current fashion industry practices are in direct competition with sustainability objectives; yet this is frequently overlooked as a pressing concern.It is, however, becoming apparent that there are social and ecological consequences to the current operation of the fashion industry: sustainability in the sector has been gaining attention in recent years from those who believe that it should be held accountable for the pressure it places on the individual, as well as its contribution to increases in consumption and waste disposal.This book takes a wide-screen approach to the topic, covering, among other issues: sustainability and business management in textile and fashion companies; value chain management; use of materials; sustainable production processes; fashion, needs and consumption; disposal; and innovation and design.The book will be essential reading for researchers and practitioners in the global fashion business. |
TRADITIONAL VS. BIG-DATA FASHION TREND …
First, current literature on the use of big data tools in fashion companies focuses on merchandising strategies such as improving product assortments, markdown optimization, and …
The roles of data analytics in the fashion industry - MedCrave …
May 29, 2020 · In this research, it is intended to review the roles and importance of data analytics in the fashion merchandising process. AI-powered data analytics applications or services that …
Are fashion majors ready for the era of data science? A study …
As the fashion industry is becoming ever more data-driven, this study intends to understand whether the current fashion curriculums in U.S. education institutions have sufficiently …
FashionAI: A Hierarchical Dataset for Fashion Understanding
In light of this, we present FashionAI dataset with both attributes and key points for fashion understanding tasks. Specifically, we address above limitations by conducting the domain …
Big data and digital design models for fashion design - SAGE …
Oct 9, 2020 · Using big data and digital processing technologies, fashion designers identify the characteristics of popular fashions in the market, predict fashion trends, and create designs …
Data-driven AI techniques for fashion and apparel retailing
research on applications of data-driven AI in the fashion and apparel industry, demonstrate how various data sets and AI techniques can be utilised for improved decision support in different …
A Framework for Fashion Data Gathering, Hierarchical …
We present a design of a customized crawler that enables the user to crawl data based on category or attributes. Moreover, an efficient and collaborative web-solution is designed and …
Fashion and Data Mining - Iris Publishers
Aug 26, 2021 · have used the fashion data to discuss the fashion recommendation, branding prediction and even the next season trends. Given the importance of the influencers, Yusan L, …
Implementing The Use of AI for Analysis and Prediction in …
Deepfashion is a data set specifically made for fashion analytic needs, containing a collection of photos classified by type of garment or style, such as jeans, tie dye t-shirts, or baby doll dress …
The application of big data in fashion retailing: a narrative …
We uncover five main reasons that are driving the utilisation and application of Big Data analytics in the fashion industry. These are (1) trend prediction, (2) waste reduction, (3) consumer …
Using Data Mining to Prediction Fashion Sales - IJISRT
Affiliation mining is a strategy through which the model recommends that various information happen together habitually. In this paper, we endeavour to make a learned model utilizing …
The roles of data analytics in the fashion industry
In this research, it is intended to review the roles and importance of data analytics in the fashion merchandising process. AI-powered data analytics applications or services that are currently …
BIG DATA IN FASHION INDUSTRY - Marudhara College
data. Data science is having a huge impact on the fashion industry as it transitions from a "offer-based demand" to a "demand-based offer" paradigm. Predictive data analysis can help …
Data-Driven Dynamic Fashion Market Insights Using Data …
This paper delves into the pivotal role of web scraping, data analysis, and visualization techniques in empowering fashion e-commerce enterprises with the tools necessary for informed decision …
2023 Proceedings Baltimore, Maryland - iastatedigitalpress.com
Keywords: Fashion, Data Science, Artificial Intelligence, Deep Learning, Supply Chain Background: The fashion industry is undergoing an evolution where digital technologies, …
Data-Driven AI Techniques for Fashion and Apparel Retailing …
research on applications of data-driven AI in the fashion and apparel industry, demonstrate how various data sets and AI techniques can be utilised for improved decision support in different …
AFRIFASHION1600: A Contemporary African Fashion
This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes repre-senting some African …
Traditional vs. big-data fashion trend forecasting: an …
casting fashion trends along with the emergence of data science have inspired fashion companies to explore new ways of trend forecasting (Chaudhuri, 2018; Israeli & Avery, 2018).
From Runway to Recommendation: Big Data’s Impact on …
This paper investigates the application of big data in Avenue Fashion's e-commerce platform, analyzing how data is used to personalize shopping experiences, predict style trends, optimize …
Can Small Business in Retailing Afford Digital Media Data …
Jul 10, 2020 · Digital media systems store useful data for businesses to accurately predict customers’ demand on products and services. The importance and the urgency of utilizing …
TRADITIONAL VS. BIG-DATA FASHION TREND FORECASTING:
First, current literature on the use of big data tools in fashion companies focuses on merchandising strategies such as improving product assortments, markdown optimization, and producing sales …
The roles of data analytics in the fashion industry
May 29, 2020 · In this research, it is intended to review the roles and importance of data analytics in the fashion merchandising process. AI-powered data analytics applications or services that are …
Are fashion majors ready for the era of data science? A study …
As the fashion industry is becoming ever more data-driven, this study intends to understand whether the current fashion curriculums in U.S. education institutions have sufficiently …
FashionAI: A Hierarchical Dataset for Fashion Understanding
In light of this, we present FashionAI dataset with both attributes and key points for fashion understanding tasks. Specifically, we address above limitations by conducting the domain …
Big data and digital design models for fashion design - SAGE …
Oct 9, 2020 · Using big data and digital processing technologies, fashion designers identify the characteristics of popular fashions in the market, predict fashion trends, and create designs …
Data-driven AI techniques for fashion and apparel retailing
research on applications of data-driven AI in the fashion and apparel industry, demonstrate how various data sets and AI techniques can be utilised for improved decision support in different …
A Framework for Fashion Data Gathering, Hierarchical …
We present a design of a customized crawler that enables the user to crawl data based on category or attributes. Moreover, an efficient and collaborative web-solution is designed and implemented …
Fashion and Data Mining - Iris Publishers
Aug 26, 2021 · have used the fashion data to discuss the fashion recommendation, branding prediction and even the next season trends. Given the importance of the influencers, Yusan L, et …
Implementing The Use of AI for Analysis and Prediction in the …
Deepfashion is a data set specifically made for fashion analytic needs, containing a collection of photos classified by type of garment or style, such as jeans, tie dye t-shirts, or baby doll dress …
The application of big data in fashion retailing: a narrative review
We uncover five main reasons that are driving the utilisation and application of Big Data analytics in the fashion industry. These are (1) trend prediction, (2) waste reduction, (3) consumer …
Using Data Mining to Prediction Fashion Sales - IJISRT
Affiliation mining is a strategy through which the model recommends that various information happen together habitually. In this paper, we endeavour to make a learned model utilizing WEKA. …
The roles of data analytics in the fashion industry
In this research, it is intended to review the roles and importance of data analytics in the fashion merchandising process. AI-powered data analytics applications or services that are currently …
BIG DATA IN FASHION INDUSTRY - Marudhara College
data. Data science is having a huge impact on the fashion industry as it transitions from a "offer-based demand" to a "demand-based offer" paradigm. Predictive data analysis can help fashion …
Data-Driven Dynamic Fashion Market Insights Using Data …
This paper delves into the pivotal role of web scraping, data analysis, and visualization techniques in empowering fashion e-commerce enterprises with the tools necessary for informed decision …
2023 Proceedings Baltimore, Maryland - iastatedigitalpress.com
Keywords: Fashion, Data Science, Artificial Intelligence, Deep Learning, Supply Chain Background: The fashion industry is undergoing an evolution where digital technologies, especially artificial …
Data-Driven AI Techniques for Fashion and Apparel Retailing
research on applications of data-driven AI in the fashion and apparel industry, demonstrate how various data sets and AI techniques can be utilised for improved decision support in different …
AFRIFASHION1600: A Contemporary African Fashion
This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes repre-senting some African fashion …
Traditional vs. big-data fashion trend forecasting: an …
casting fashion trends along with the emergence of data science have inspired fashion companies to explore new ways of trend forecasting (Chaudhuri, 2018; Israeli & Avery, 2018).
From Runway to Recommendation: Big Data’s Impact on …
This paper investigates the application of big data in Avenue Fashion's e-commerce platform, analyzing how data is used to personalize shopping experiences, predict style trends, optimize …
Can Small Business in Retailing Afford Digital Media Data …
Jul 10, 2020 · Digital media systems store useful data for businesses to accurately predict customers’ demand on products and services. The importance and the urgency of utilizing data …
