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| data science en français: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data science en français: Statistical Learning and Data Science Mireille Gettler Summa, Leon Bottou, Bernard Goldfarb, Fionn Murtagh, Catherine Pardoux, Myriam Touati, 2011-12-19 Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor |
| data science en français: Data Science : fondamentaux et études de cas Michel Lutz, Eric Biernat, 2015-10-15 Nous vivons une époque très excitante, qui ramène l'analyse de données et les méthodes quantitatives au coeur de la société. L'aboutissement de nombreux projets de recherche, la puissance de calcul informatique disponible et des données à profusion permettent aujourd'hui d'incroyables réalisations, grâce au travail des data scientists. Un livre de référence pour les data scientists La data science est l'art de traduire des problèmes industriels, sociaux, scientifiques, ou de toute autre nature, en problèmes de modélisation quantitative, pouvant être résolus par des algorithmes de traitement de données. Cela passe par une réflexion structurée, devant faire en sorte que se rencontrent problèmes humains, outils techniques/informatiques et méthodes statistiques/algorithmiques. Chaque projet de data science est une petite aventure, qui nécessite de partir d'un problème opérationnel souvent flou, à une réponse formelle et précise, qui aura des conséquences réelles sur le quotidien d'un nombre plus ou moins important de personnes. Éric Biernat et Michel Lutz proposent de vous guider dans cette aventure. Ils vous feront visiter les vastes espaces de la data science moderne, de plus en plus présente dans notre société et qui fait tant parler d'elle, parfois par l'intermédiaire d'un sujet qui lui est corollaire, les big data. Des études de cas pour devenir kaggle master Loin des grands discours abstraits, les auteurs vous feront découvrir, claviers à la main, les pratiques de leur métier de data scientist chez OCTO Technology, l'un des leaders français du domaine. Et vous mettrez également la main à la pâte : avec juste ce qu'il faut de théorie pour comprendre ce qu'impliquent les méthodes mathématiques utilisées, mais surtout avec votre ordinateur personnel, quelques logiciels gratuits et puissants, ainsi qu'un peu de réflexion, vous allez participer activement à cette passionnante exploration ! À qui s'adresse cet ouvrage ? Aux développeurs, statisticiens, étudiants et chefs de projets ayant à résoudre des problèmes de data science. Aux data scientists, mais aussi à toute personne curieuse d'avoir une vue d'ensemble de l'état de l'art du machine learning. |
| data science en français: Data Science with Python and Dask Jesse Daniel, 2019-07-08 Summary Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you're already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You'll find registration instructions inside the print book. About the Technology An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease. About the Book Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you'll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you'll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker. What's inside Working with large, structured and unstructured datasets Visualization with Seaborn and Datashader Implementing your own algorithms Building distributed apps with Dask Distributed Packaging and deploying Dask apps About the Reader For data scientists and developers with experience using Python and the PyData stack. About the Author Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company. Table of Contents PART 1 - The Building Blocks of scalable computing Why scalable computing matters Introducing Dask PART 2 - Working with Structured Data using Dask DataFrames Introducing Dask DataFrames Loading data into DataFrames Cleaning and transforming DataFrames Summarizing and analyzing DataFrames Visualizing DataFrames with Seaborn Visualizing location data with Datashader PART 3 - Extending and deploying Dask Working with Bags and Arrays Machine learning with Dask-ML Scaling and deploying Dask |
| data science en français: Les data sciences en 100 questions/réponses Younes Benzaki, 2020-10-01 Un livre à la fois théorique et pratique Cet ouvrage a pour ambition de couvrir un large spectre du domaine des data sciences. Il va plus loin qu'un simple tour sur les algorithmes d'apprentissage automatique et s'attaque aux autres aspects, malheureusement négligés mais fondamentaux pour tout data scientist : concepts généraux mais poussés, dont la maîtrise est indispensable ; algorithmes d'apprentissage automatique les plus connus ; aspects liés à l'exploration des données ; mesures de performances et d'autres métriques utilisées par les algorithmes ; différents concepts fondamentaux en mathématiques à connaître pour mieux explorer et comprendre les données ; notions importantes des big data ; études de cas pratiques en langage Python. La première partie de ce livre est théorique et adopte un format questions/réponses qui présente plusieurs avantages, dont la possibilité pour le lecteur de lire distinctement chacune des questions pour parfaire son savoir. Un autre point fort de cette structure est qu'elle incite à entrer dans un dialogue. Ainsi, grâce aux questions posées, le lecteur est poussé vers une réflexion où il confronte ses réponses à celles données par le livre. La deuxième partie est pratique et propose deux exemples d'implémentation de modèles d'apprentissage automatique. Vous y trouverez des codes écrits en Python et un aperçu de différentes difficultés que peut rencontrer un spécialiste lors de l'exercice de son métier. À qui s'adresse cet ouvrage ? Le présent ouvrage est adapté à toute personne ayant une certaine maîtrise de la data science et du Machine Learning. Il aidera notamment à se rappeler des concepts importants, mais suppose que le lecteur soit initié sur le sujet. Il sera particulièrement utile à ceux qui veulent se préparer pour un concours, un examen ou un entretien. |
| data science en français: Advances in Geospatial Data Science Rodrigo Tapia-McClung, Oscar Sánchez-Siordia, Karime González-Zuccolotto, Hugo Carlos-Martínez, 2022-05-17 This book presents a selection of manuscripts submitted to the 2nd International Conference on Geospatial Information Sciences 2021, a virtual conference held on November 3-5, 2021. These papers were selected by the Scientific Program Committee of the Conference after a rigorous peer-review process. They represent the vast scope of the interdisciplinary research areas that characterize the Geospatial Information Sciences that is done in the discipline. It especially represents a fabulous opportunity to showcase research carried out by young Mexican researchers and showcase it to the rest of the world and enhance the growth of the sciences in the country while, at the same time, enforces them to level up with other research at the international level. |
| data science en français: Linguistic Linked Data Philipp Cimiano, Christian Chiarcos, John P. McCrae, Jorge Gracia, 2020-01-13 This is the first monograph on the emerging area of linguistic linked data. Presenting a combination of background information on linguistic linked data and concrete implementation advice, it introduces and discusses the main benefits of applying linked data (LD) principles to the representation and publication of linguistic resources, arguing that LD does not look at a single resource in isolation but seeks to create a large network of resources that can be used together and uniformly, and so making more of the single resource. The book describes how the LD principles can be applied to modelling language resources. The first part provides the foundation for understanding the remainder of the book, introducing the data models, ontology and query languages used as the basis of the Semantic Web and LD and offering a more detailed overview of the Linguistic Linked Data Cloud. The second part of the book focuses on modelling language resources using LD principles, describing how to model lexical resources using Ontolex-lemon, the lexicon model for ontologies, and how to annotate and address elements of text represented in RDF. It also demonstrates how to model annotations, and how to capture the metadata of language resources. Further, it includes a chapter on representing linguistic categories. In the third part of the book, the authors describe how language resources can be transformed into LD and how links can be inferred and added to the data to increase connectivity and linking between different datasets. They also discuss using LD resources for natural language processing. The last part describes concrete applications of the technologies: representing and linking multilingual wordnets, applications in digital humanities and the discovery of language resources. Given its scope, the book is relevant for researchers and graduate students interested in topics at the crossroads of natural language processing / computational linguistics and the Semantic Web / linked data. It appeals to Semantic Web experts who are not proficient in applying the Semantic Web and LD principles to linguistic data, as well as to computational linguists who are used to working with lexical and linguistic resources wanting to learn about a new paradigm for modelling, publishing and exploiting linguistic resources. |
| data science en français: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| data science en français: XML and Web Technologies for Data Sciences with R Deborah Nolan, Duncan Temple Lang, 2013-11-29 Web technologies are increasingly relevant to scientists working with data, for both accessing data and creating rich dynamic and interactive displays. The XML and JSON data formats are widely used in Web services, regular Web pages and JavaScript code, and visualization formats such as SVG and KML for Google Earth and Google Maps. In addition, scientists use HTTP and other network protocols to scrape data from Web pages, access REST and SOAP Web Services, and interact with NoSQL databases and text search applications. This book provides a practical hands-on introduction to these technologies, including high-level functions the authors have developed for data scientists. It describes strategies and approaches for extracting data from HTML, XML, and JSON formats and how to programmatically access data from the Web. Along with these general skills, the authors illustrate several applications that are relevant to data scientists, such as reading and writing spreadsheet documents both locally and via Google Docs, creating interactive and dynamic visualizations, displaying spatial-temporal displays with Google Earth, and generating code from descriptions of data structures to read and write data. These topics demonstrate the rich possibilities and opportunities to do new things with these modern technologies. The book contains many examples and case-studies that readers can use directly and adapt to their own work. The authors have focused on the integration of these technologies with the R statistical computing environment. However, the ideas and skills presented here are more general, and statisticians who use other computing environments will also find them relevant to their work. Deborah Nolan is Professor of Statistics at University of California, Berkeley. Duncan Temple Lang is Associate Professor of Statistics at University of California, Davis and has been a member of both the S and R development teams. |
| data science en français: Trends and Applications in Knowledge Discovery and Data Mining Wei Lu, Kenny Q. Zhu, 2020-10-14 This book constitutes the thoroughly refereed post-workshop proceedings of the workshops that were held in conjunction with the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, in Singapore, Singapore, in May 2020. The 17 revised full papers presented were carefully reviewed and selected from a total of 50 submissions. The five workshops were as follows: · First International Workshop on Literature-Based Discovery (LBD 2020) · Workshop on Data Science for Fake News (DSFN 2020) · Learning Data Representation for Clustering (LDRC 2020) · Ninth Workshop on Biologically Inspired Techniques for Data Mining (BDM · 2020) · First Pacific Asia Workshop on Game Intelligence & Informatics (GII 2020) |
| data science en français: Data Science in Critical Care, An Issue of Critical Care Clinics, E-Book Rishikesan Kamaleswaran, Andre L. Holder, 2023-09-13 In this issue of Critical Care Clinics, guest editors Drs. Rishikesan Kamaleswaran and Andre L. Holder bring their considerable expertise to the topic of Data Science in Critical Care. Data science, the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. In this issue, top experts in the field cover key topics such as refining our understanding and classification of critical illness using biomarker-based phenotyping; predictive modeling using AI/ML on EHR data; classification and prediction using waveform-based data; creating trustworthy and fair AI systems; and more. - Contains 15 relevant, practice-oriented topics including AI and the imaging revolution; designing living, breathing clinical trials: lessons learned from the COVID-19 pandemic; the patient or the population: knowing the limitations of our data to make smart clinical decisions; weighing the cost vs. benefit of AI in healthcare; and more. - Provides in-depth clinical reviews on data science in critical care, offering actionable insights for clinical practice. - Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews. |
| data science en français: Big Data Eglantine Schmitt, 2020-11-17 Manipulating and processing masses of digital data is never a purely technical activity. It requires an interpretative and exploratory outlook - already well known in the social sciences and the humanities - to convey intelligible results from data analysis algorithms and create new knowledge. Big Data is based on an inquiry of several years within Proxem, a software publisher specializing in big data processing. The book examines how data scientists explore, interpret and visualize our digital traces to make sense of them, and to produce new knowledge. Grounded in epistemology and science and technology studies, Big Data offers a reflection on data in general, and on how they help us to better understand reality and decide on our daily actions. |
| data science en français: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data science en français: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| data science en français: Dialogues in Data Power Juliane Jarke, Jo Bates, 2024-09-03 Available open access digitally under CC-BY-NC-ND licence. This book presents emerging themes and future directions in the interdisciplinary field of critical data studies, loosely themed around the notion of shifting response-abilities in a datafied world. In each chapter an interdisciplinary group of scholars discuss a specific theme, ranging from questions around data power and the configuring of data subjects to the intersection of technology and the environment. The book is an invaluable dialogue between disciplines that introduces readers to cutting edge arguments within the field. It will be a key resource for scholars and students who require a guide to this rapidly evolving area of research. |
| data science en français: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data science en français: Science is Fiction Andy Masaki Bellows, Jean Painlevé, Marina McDougall, Brigitte Berg, 2001 Essays examining the work of maverick scientific documentary filmmaker Jean Painleve. |
| data science en français: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2023-08-01 An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. |
| data science en français: Grokking Deep Learning Andrew W. Trask, 2019-01-23 Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide |
| data science en français: Malware Data Science Joshua Saxe, Hillary Sanders, 2018-09-25 Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a big data problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You'll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve. |
| data science en français: The Dominance of English as a Language of Science Ulrich Ammon, 2001 CONTRIBUTIONS TO THE SOCIOLOGY OF LANGUAGE brings to students, researchers and practitioners in all of the social and language-related sciences carefully selected book-length publications dealing with sociolinguistic theory, methods, findings and applications. It approaches the study of language in society in its broadest sense, as a truly international and interdisciplinary field in which various approaches, theoretical and empirical, supplement and complement each other. The series invites the attention of linguists, language teachers of all interests, sociologists, political scientists, anthropologists, historians etc. to the development of the sociology of language. |
| data science en français: Teaching and Assessment in the Era of Education 5.0 Chemsi, Ghizlane, Elimadi, Imane, Sadiq, Mounir, Radid, Mohamed, 2024-07-17 In the rapidly evolving landscape of Education 5.0, educators and institutions grapple with unprecedented challenges in leveraging digital technologies to enhance teaching, learning, and assessment. The profound shift towards a more humanized educational experience, focusing on social and emotional growth alongside skill development, demands a paradigmatic transformation. However, a palpable gap exists in understanding and navigating the complexities of this digital transition. Educators, students, and administrators are left facing dilemmas related to pedagogical innovation, technology integration, and effective learning assessment in the digital age. Enter Teaching and Assessment in the Era of Education 5.0, a definitive guide poised to bridge the gap between the challenges posed by Education 5.0 and actionable solutions. The current educational milieu faces a conundrum as it attempts to adapt to the tenets of Education 5.0. The digital transition poses challenges, from incorporating immersive technologies to understanding the attitudes of educators and students towards digitization. Furthermore, the design and implementation of training and distance learning systems require a nuanced approach, calling for engineering expertise in training, pedagogy, and tutoring. The assessment landscape, crucial for gauging the effectiveness of learning in the digital era, grapples with contemporary trends, ethical considerations, and the ever-present specter of plagiarism. This multifaceted challenge necessitates a comprehensive resource that not only delineates the issues but offers actionable solutions to navigate this transformative journey. |
| data science en français: The Algorithmic Code of Ethics Jerome Beranger, 2018-10-08 The technical progress illustrated by the development of Artificial Intelligence (AI), Big Data technologies, the Internet of Things (IoT), online platforms, NBICs, autonomous expert systems, and the Blockchain let appear the possibility of a new world and the emergence of a fourth industrial revolution centered around digital data. Therefore, the advent of digital and its omnipresence in our modern society create a growing need to lay ethical benchmarks against this new religion of data, the dataisme. |
| data science en français: Language MOOCs Elena Martín-Monje, Elena Bárcena, 2015-08-17 Language MOOCs (or LMOOCs) are dedicated Web-based online courses for second languages with unrestricted access and potentially unlimited participation. They are generating interest and expectation in the contexts of university education, lifelong learning and online training in general. This pioneering book presents an initial analysis of the theoretical and methodological issues underlying LMOOCs and presents empirical evidence of their potential for the development of language communicative competences, based upon previously unpublished research. It provides a mosaic-like view of LMOOC research, not only with respect to the geographical and institutional origin of its authors, but also to the heterogeneous nature of their respective academic backgrounds, and suggests directions for future development. As in other types of online language courses, the integration of the results of multidisciplinary research projects and teaching experiences related to LMOOCs is fundamental to make the field advance steadily and respond to some of the real challenges and problems faced by individuals working and living in competitive plurilingual societies today. |
| data science en français: Healthcare and Artificial Intelligence Bernard Nordlinger, Cédric Villani, Daniela Rus, 2020-03-17 This book provides an overview of the role of AI in medicine and, more generally, of issues at the intersection of mathematics, informatics, and medicine. It is intended for AI experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious about this timely and important subject. Its goal is to provide clear, objective, and reasonable information on the issues covered, avoiding any fantasies that the topic “AI” might evoke. In addition, the book seeks to provide a broad kaleidoscopic perspective, rather than deep technical details. |
| data science en français: Introduction to Probability for Data Science Stanley H. Chan, 2021 Probability is one of the most interesting subjects in electrical engineering and computer science. It bridges our favorite engineering principles to the practical reality, a world that is full of uncertainty. However, because probability is such a mature subject, the undergraduate textbooks alone might fill several rows of shelves in a library. When the literature is so rich, the challenge becomes how one can pierce through to the insight while diving into the details. For example, many of you have used a normal random variable before, but have you ever wondered where the 'bell shape' comes from? Every probability class will teach you about flipping a coin, but how can 'flipping a coin' ever be useful in machine learning today? Data scientists use the Poisson random variables to model the internet traffic, but where does the gorgeous Poisson equation come from? This book is designed to fill these gaps with knowledge that is essential to all data science students. -- Preface. |
| data science en français: MICRO 2016: Fate and Impact of Microplastics in Marine Ecosystems Juan Baztan, Bethany Jorgensen, Sabine Pahl, Richard C. Thompson, Jean-Paul Vanderlinden, 2016-11-29 Fate and Impact of Microplastics in Marine Ecosystems: From the Coastline to the Open Sea brings together highlights from the conference proceedings for MICRO 2016: Fate and Impact of Microplastics in Marine Ecosystems: From the Coastline to the Open Sea. While the presence of microplastics in ecosystems has been reported in the scientific literature since the 1970's, many pressing questions regarding their impacts remain unresolved. This short format title draws from the shared scientific and technical material and summarizes the current research and future outlook. - Includes a range of topics, from macro- to microplastics - Presents data from source to sink, including occurrence and distribution of microplastics in freshwater bodies, coastal zones, and the open ocean - Presents the impacts of microplastics on marine life as well as microplastics as vectors of biological and chemical contaminants - Provides important analysis on solutions and next steps |
| data science en français: French Bibliographical Digest , 1960 |
| data science en français: Brentano's Book Chat , 1915 |
| data science en français: The French Imperial Nation-State Gary Wilder, 2005-12 France experienced a period of crisis following World War I when the relationship between the nation and its colonies became a subject of public debate. The French Imperial Nation-State focuses on two intersecting movements that redefined imperial politics—colonial humanism led by administrative reformers in West Africa and the Paris-based Negritude project, comprising African and Caribbean elites. Gary Wilder develops a sophisticated account of the contradictory character of colonial government and examines the cultural nationalism of Negritude as a multifaceted movement rooted in an alternative black public sphere. He argues that interwar France must be understood as an imperial nation-state—an integrated sociopolitical system that linked a parliamentary republic to an administrative empire. An interdisciplinary study of colonial modernity combining French history, colonial studies, and social theory, The French Imperial Nation-State will compel readers to revise conventional assumptions about the distinctions between republicanism and racism, metropolitan and colonial societies, and national and transnational processes. |
| data science en français: Resources in Education , 2001 |
| data science en français: After the Crash Sharyn O'Halloran, Thomas Groll, 2019-10-08 The 2008 crash was the worst financial crisis and the most severe economic downturn since the Great Depression. It triggered a complete overhaul of the global regulatory environment, ushering in a stream of new rules and laws to combat the perceived weakness of the financial system. While the global economy came back from the brink, the continuing effects of the crisis include increasing economic inequality and political polarization. After the Crash is an innovative analysis of the crisis and its ongoing influence on the global regulatory, financial, and political landscape, with timely discussions of the key issues for our economic future. It brings together a range of experts and practitioners, including Joseph Stiglitz, a Nobel Prize winner; former congressman Barney Frank; former treasury secretary Jacob Lew; Paul Tucker, a former deputy governor of the Bank of England; and Steve Cutler, general counsel of JP Morgan Chase during the financial crisis. Each poses crucial questions: What were the origins of the crisis? How effective were international and domestic regulatory responses? Have we addressed the roots of the crisis through reform and regulation? Are our financial systems and the global economy better able to withstand another crash? After the Crash is vital reading as both a retrospective on the last crisis and an analysis of possible sources of the next one. |
| data science en français: Inventaire Sélectif Des Services D'information Et de Documentation en Sciences Sociales Unesco. Social and Human Sciences Documentation Centre, 1998 Provides details of social science information services. |
| data science en français: Current Catalog National Library of Medicine (U.S.), 1993 First multi-year cumulation covers six years: 1965-70. |
| data science en français: Fundamentals of Data Science with MATLAB Arash Karimpour, 2020-07-31 |
| data science en français: Skepticism and Humanism Paul Kurtz, 2023-05-31 As we begin the third millennium there is cause for cautious optimism regarding the human prospect. Democratic revolutions and the doctrine of universal human rights have captured the imagination of large sectors of humanity, while major advances in science and technology continue to conquer disease and extend life, contributing to rising standards of living, affluence, and cultural freedom on a worldwide basis. Paradoxically, at the same time ancient authoritarian fundamentalist religions have grown in vitriolic intensity along with bizarre New Age, media-driven paranormal belief systems. Also surprising is the resurgence of primitive tribal and ethnic loyalties, unleashing wars of intolerance and bitterness. In Skepticism and Humanism, Paul Kurtz locates these threatening developments within a long-standing and largely unchallenged theological worldview. He proposes, as an alternative to religion, a new cultural paradigm rooted in scientific naturalism, rationalism, and a humanistic outlook. An estimated 60 percent of scientists are atheists or agnostics. However, the skeptical world view has been given little currency even in advanced societies, because of a cultural prohibition against the criticism of religion. At the same time, science has become increasingly narrow and specialized so that few people can draw on its broader intellectual and cultural implications. Skepticism and Humanism attempts to meet this need. It defends skepticism as a method for developing reliable knowledge by using scientific inquiry and reason to test all claims to truth. It also defends scientific naturalism-an evolutionary view of nature, life, and the human species. Kurtz sees the dominant religious doctrines as drawn from an agricultural/nomadic past, and emphasizes the need for a new outlook applicable to the postindustrial information age. At the same time, he rejects postmodernism for abandoning science and embracing a form of nihilism. There can be no doubt that as a new global civilization emerges, scientific naturalism, rationalism, and secular humanism have something significant to say about the meaning of life. Skepticism and Humanism shows how they can to foster democratic values and social prosperity. The book will be important for philosophers, scientists, and all those concerned with contemporary issues. |
| data science en français: Digital Asset Valuation and Cyber Risk Measurement Keyun Ruan, 2019-05-29 Digital Asset Valuation and Cyber Risk Measurement: Principles of Cybernomics is a book about the future of risk and the future of value. It examines the indispensable role of economic modeling in the future of digitization, thus providing industry professionals with the tools they need to optimize the management of financial risks associated with this megatrend. The book addresses three problem areas: the valuation of digital assets, measurement of risk exposures of digital valuables, and economic modeling for the management of such risks. Employing a pair of novel cyber risk measurement units, bitmort and hekla, the book covers areas of value, risk, control, and return, each of which are viewed from the perspective of entity (e.g., individual, organization, business), portfolio (e.g., industry sector, nation-state), and global ramifications. Establishing adequate, holistic, and statistically robust data points on the entity, portfolio, and global levels for the development of a cybernomics databank is essential for the resilience of our shared digital future. This book also argues existing economic value theories no longer apply to the digital era due to the unique characteristics of digital assets. It introduces six laws of digital theory of value, with the aim to adapt economic value theories to the digital and machine era. - Comprehensive literature review on existing digital asset valuation models, cyber risk management methods, security control frameworks, and economics of information security - Discusses the implication of classical economic theories under the context of digitization, as well as the impact of rapid digitization on the future of value - Analyzes the fundamental attributes and measurable characteristics of digital assets as economic goods - Discusses the scope and measurement of digital economy - Highlights cutting-edge risk measurement practices regarding cybersecurity risk management - Introduces novel concepts, models, and theories, including opportunity value, Digital Valuation Model, six laws of digital theory of value, Cyber Risk Quadrant, and most importantly, cyber risk measures hekla and bitmort - Introduces cybernomics, that is, the integration of cyber risk management and economics to study the requirements of a databank in order to improve risk analytics solutions for (1) the valuation of digital assets, (2) the measurement of risk exposure of digital assets, and (3) the capital optimization for managing residual cyber risK - Provides a case study on cyber insurance |
| data science en français: Science, technology and art in the spoken expression of meaning Plinio Almeida Barbosa, Sandra Madureira, Åsa Abelin, 2023-08-31 |
| data science en français: Generative Deep Learning David Foster, 2019-06-28 Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN |
| data science en français: The Treaty Liam Weeks, Mícheál Ó Fathartaigh, 2018-09-17 What exactly did the split over the Anglo-Irish Treaty of 1921 actually mean? We know it both established the independent Irish state and that Ireland would not be a fully sovereign republic and provided for the partition of Northern Ireland. The Treaty was ratified 64 votes to 57 by the Sinn Fein members of the Revolutionary Dail Eireann, splitting Sinn Fein irrevocably and leading to the Irish Civil War, a rupture that still defines the Irish political landscape a century on. Drawing together the work of a diverse range of scholars, who each re-examine this critical period in Irish political history from a variety of perspectives, The Anglo-Irish Treaty Debates addresses this vexed historical and political question for a new generation of readers in the ongoing Decade of Commemorations, to determine what caused the split and its consequences that are still felt today. |
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 enable a …
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 to …
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 …
