| dataiku data science studio: Data for Learning Husein Abdul-Hamid, 2017-09-21 Data are a crucial ingredient in any successful education system, but building and sustaining a data system are challenging tasks. Many countries around the world have spent significant resources but still struggle to accomplish a functioning Education Management Information System (EMIS). On the other hand, countries that have created successful systems are harnessing the power of data to improve education outcomes. Increasingly, EMISs are moving away from using data narrowly for counting students and schools. Instead, they use data to drive system-wide innovations, accountability, professionalization, and, most important, quality and learning. This broader use of data also benefits classroom instruction and support at schools. An effective data system ensures that education cycles, from preschool to tertiary, are aligned and that the education system is monitored so it can achieve its ultimate goal—producing graduates able to successfully transition into the labor market and contribute to the overall national economy. Data for Learning: Building a Smart Education Data System and its forthcoming companion volume shed light on challenges in building a data system and provide actionable direction on how to navigate the complex issues associated with education data for better learning outcomes and beyond. Data for Learning details the key ingredients of successful data systems, including tangible examples, common pitfalls, and good practices. It is a resource for policy makers working to craft the vision and strategic road map of an EMIS, as well as a handbook to assist teams and decision makers in avoiding common mistakes. It is designed to provide the “how-to†? and to guide countries at various stages of EMIS deployment. A forthcoming companion volume will focus on digging deeper into the practical applications of education data systems by various user groups in different settings. |
| dataiku data science studio: Building Cloud Data Platforms Solutions Anouar BEN ZAHRA, Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects. |
| dataiku data science studio: Data Science Michael Oettinger, 2020-05-18 Das Thema Data-Science wird häufig diskutiert. Seit der ersten Auflage dieses Buches im Jahr 2017 hat sich an diesem Trend wenig verändert. Data-Scientisten (m/w/d) erfahren eine steigende Nachfrage auf dem Job-Markt, da immer mehr Unternehmen ihre Analytics-Abteilungen auf- bzw. ausbauen und hierfür entsprechende Mitarbeiter suchen. Hier stellt sich die Frage, worin eigentlich der Tätigkeitsbereich eines Data-Scientisten besteht. Das Aufgabenfeld ist nicht eindeutig definiert und reicht über künstliche Intelligenz, Machine-Learning, Data-Mining, Python-Programmierung bis zu Big Data. Im vorliegenden Buch soll eine praxisorientierte Einführung und ein aktueller Überblick darüber gegeben werden, was Data-Science und der Beruf Data-Scientist umfassen. |
| dataiku data science studio: Data Science und AI Michael Oettinger, 2024-03-17 Im vorliegenden Buch soll eine praxisorientierte Einführung und ein aktueller Überblick darüber gegeben werden, was Data-Science und der Beruf Data-Scientist umfassen. Nach 4 Jahren seit Erscheinen der zweiten Auflage wurde die dritte Auflage notwendig, da sich Data-Science als Thema und vor allem die dazugehörende Softwaretechnologie weiterentwickelt. Spätestens mit der Veröffentlichung von ChatGPT ist das Thema künstliche Intelligenz in aller Munde und eine Einordnung von Data- Science, Machine Learning und Artificial Intelligence scheint dringend notwendig. Das Buch enthält neben einer Übersicht über Theorie und Praxis der Daten-Analyse nun auch Code-Beispiele in Python bzw. SQL und Cheat-Sheets zu ChatGPT und GenAI Tools. |
| dataiku data science studio: Operating AI Ulrika Jagare, 2022-04-19 A holistic and real-world approach to operationalizing artificial intelligence in your company In Operating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jägare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including key areas such as; data mesh, data fabric, aspects of security, data privacy, data rights and IPR related to data and AI models. In the book, you’ll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI) The importance of efficient and reproduceable data pipelines, including how to manage your company's data An operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI/CD/CT techniques, that generates value in the real world Key competences and toolsets in AI development, deployment and operations What to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real world—and not just the lab—Operating AI is a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment. |
| dataiku data science studio: Deep Learning Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy, 2020-06-22 This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. |
| dataiku data science studio: Data Science Quick Reference Manual - Advanced Machine Learning and Deployment Mario A. B. Capurso, 2023-09-08 This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Advanced aspects associated with modeling are described such as loss and optimization functions such as gradient descent, techniques to analyze model performance such as Bootstrapping and Cross Validation. Deployment scenarios and the most common platforms are analyzed, with application examples. Mechanisms are proposed to automate machine learning and to support the interpretability of models and results such as Partial Dependence Plot, Permuted Feature Importance and others. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data. |
| dataiku data science studio: Azure Modern Data Architecture Anouar BEN ZAHRA, Key Features Discover the key drivers of successful Azure architecture Practical guidance Focus on scalability and performance Expert authorship Book Description This book presents a guide to design and implement scalable, secure, and efficient data solutions in the Azure cloud environment. It provides Data Architects, developers, and IT professionals who are responsible for designing and implementing data solutions in the Azure cloud environment with the knowledge and tools needed to design and implement data solutions using the latest Azure data services. It covers a wide range of topics, including data storage, data processing, data analysis, and data integration. In this book, you will learn how to select the appropriate Azure data services, design a data processing pipeline, implement real-time data processing, and implement advanced analytics using Azure Databricks and Azure Synapse Analytics. You will also learn how to implement data security and compliance, including data encryption, access control, and auditing. Whether you are building a new data architecture from scratch or migrating an existing on premises solution to Azure, the Azure Data Architecture Guidelines are an essential resource for any organization looking to harness the power of data in the cloud. With these guidelines, you will gain a deep understanding of the principles and best practices of Azure data architecture and be equipped to build data solutions that are highly scalable, secure, and cost effective. What You Need to Use this Book? To use this book, it is recommended that readers have a basic understanding of data architecture concepts and data management principles. Some familiarity with cloud computing and Azure services is also helpful. The book is designed for data architects, data engineers, data analysts, and anyone involved in designing, implementing, and managing data solutions on the Azure cloud platform. It is also suitable for students and professionals who want to learn about Azure data architecture and its best practices. |
| dataiku data science studio: The Digital Journey of Banking and Insurance, Volume III Volker Liermann, Claus Stegmann, 2021-10-27 This book, the third one of three volumes, focuses on data and the actions around data, like storage and processing. The angle shifts over the volumes from a business-driven approach in “Disruption and DNA” to a strong technical focus in “Data Storage, Processing and Analysis”, leaving “Digitalization and Machine Learning Applications” with the business and technical aspects in-between. In the last volume of the series, “Data Storage, Processing and Analysis”, the shifts in the way we deal with data are addressed. |
| dataiku data science studio: Introduction To Data Science Course Brian Smith, 2024-03-13 Welcome to the Introduction to Data Science course! This comprehensive course will take you through the fundamental concepts and techniques of data science. You will learn about the history and applications of data science, as well as the key methods and tools used in the field. The course covers topics such as data analysis and visualization, statistical methods, machine learning fundamentals, big data and data mining, predictive analytics, natural language processing, deep learning, data ethics and privacy, data science tools and technologies, data engineering, data science in business, case studies in data science, data science career paths, and future trends in data science. With this course, you will gain a solid understanding of data science principles and be equipped with the skills and knowledge necessary to embark on a successful data science career. Whether you are a beginner or have some experience in the field, this course will provide you with the foundation to excel in the exciting field of data science. |
| dataiku data science studio: Optical Spectroscopy And Imaging For Cancer Diagnostics: Fundamentals, Progress, And Challenges Noureddine Melikechi, 2023-01-06 This is an interdisciplinary book that presents the applications of novel laser spectroscopy and imaging techniques for the detection of cancers recently developed by some of the world's most renown researchers. The book consists of three parts and a total of 16 chapters. Each chapter is written by leading experts who are actively seeking to develop novel spectroscopic and analytical methods for cancer detection and diagnosis.In Part I, the authors present fundamentals on optics, atoms and molecules, biophysics, cancer and machine learning. These chapters are intended for those who are not experts in the field but wish to learn about fundamentals' aspects of some of the key topics that are addressed in this book. Particular attention has been given to providing key references for those who wish to go further into the fundamental aspects of atoms and molecules, light-matter interaction, optical instrumentation, machine learning and cancer.In Part II, the authors present key applications of various laser spectroscopic methods in cancer diagnosis. They have provided recent progress in cancer diagnostics obtained by combining laser spectroscopy and machine learning for the analysis of the spectra acquired from biomedical tissues and biofluids.In Part III, the authors present chapters that discuss key developments in the applications of various laser imaging techniques for cancer detection.This is one of the few books that addresses cancer detection and diagnosis using laser spectroscopic and imaging tools with an eye on providing the reader the scientific tools, including machine learning ones. |
| dataiku data science studio: Artificial Intelligence, Co-Creation and Creativity Francisco Tigre Moura, 2024-08-01 Artificial intelligence (AI) has deeply impacted our understanding of creativity and the human ability to generate creative outputs. New applications for creative tasks are rapidly evolving, and new tools are constantly being developed with much greater optimal capabilities. Importantly, the success of implementing such tools for creative tasks is still heavily dependent on human supervision and input. Therefore, it is vital to understand and critically reflect on the nature of co-creative processes between humans and AI. This book addresses such issues and provides insights into how humans can augment their capabilities for generating creative and innovative outputs by successfully co-creating with AI. The book is intentionally divided into three main parts to allow for a comprehensive and holistic perspective on human and AI co-creation for creative tasks. The sections are divided as follows: Part 1: “Principles of AI and Creativity”, Part 2: “Critical Issues on Artificial Co-Creation”, and Part 3: “Industry-Specific Discussions”. Consequently, the book provides a holistic insight on the topic, covering various issues and perspectives and enabling an accessible read to a broad audience. For example, chapters cover examples across different industry sectors, including music, arts, science, and management. Furthermore, the book covers critical questions involving copyrights, ethical concerns, relationship with algorithms, and context-based issues. Only by critically reflecting on the intrinsic issues of AI and learning how to work with it effectively for creative purposes will we be able to benefit from its full potential to augment human creative abilities in an appropriate manner. This novel, edited collection is an essential read for scholars working on the intersection of AI, creativity, arts, and management. |
| dataiku data science studio: Big Data Analysis: New Algorithms for a New Society Nathalie Japkowicz, Jerzy Stefanowski, 2015-12-16 This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges. |
| dataiku data science studio: Data Science Manuale Italiano – Advanced Machine Learning e Deployment Mario A. B. Capurso, 2023-09-08 Questa opera segue il curriculum 2021 della Association for Computing Machinery per specialisti in Scienze dei Dati, con l’obiettivo di costituire un “Bignami” della Scienza ed Ingegneria dei Dati e facilitare il percorso di formazione personale a partire da competenze specialistiche in Informatica o Matematica o Statistica per un lettore di lingua madre italiana. Parte di una serie di testi, riepiloga prima di tutto la metodologia di lavoro standard CRISP DM utilizzata in questa opera e in progetti di Scienza dei Dati. Poichè questo testo utilizza Orange per gli aspetti applicativi, ne descrive l’installazione ed i widget. La fase di modellizzazione dei dati viene considerata nell’ottica dell’apprendimento automatico riepilogando i tipi di apprendimento automatico, i tipi di modelli, i tipi di problemi e i tipi di algoritmi. Sono descritti gli aspetti avanzati associati alla modellizzazione quali le funzioni di perdita e di ottimizzazione come la gradient descent, le tecniche per analizzare le prestazioni dei modelli come il Bootstrapping e la Cross Validation. Vengono analizzati gli scenari di deployment e le più comuni piattaforme, con esempi applicativi. Vengono proposti i meccanismi per automatizzare l’apprendimento automatico e per supportare l’interpretabilità dei modelli e dei risultati come Partial Dependence Plot, Permuted Feature Importance e altre. Gli esercizi sono descritti con Orange e Python con l’uso della libreria Keras/Tensorflow. Il testo è corredato di materiale di supporto ed è possibile scaricare gli esempi in Orange e i dati di prova. |
| dataiku data science studio: OECD Skills Studies OECD Skills Strategy Implementation Guidance for Latvia Developing Latvia’s Education Development Guidelines 2021-2027 OECD, 2020-10-07 In order to pave the path to future success, Latvia has developed its Education Development Guidelines 2021-2027, which identifies key policy initiatives that are critical for skills development. The Guidelines outline how Latvia will equip its citizens with skills to flourish in work and in society. |
| dataiku data science studio: プログラミングレスデータ分析のすすめ 株式会社インテック テクノロジー＆マーケティング本部 先端技術研究所, 2020-12-23 本書は、Dataiku 社（https://www.dataiku.com/）が提供する分析ツールDataiku Data Science Studio（Dataiku DSS）を使って、実際の現場でデータ分析を進めるための第⼀歩を踏み 出すための⼊⾨書です。 本書では、T シャツショップの売上向上というユースケースを通じて、実際のデータ分析の現場 で必要となる分析プロセスを体験することができます。具体的には、データ加⼯やデータの傾向を 把握するためのデータの可視化、たくさん商品を購⼊してくれる顧客を判断するための機械学習モ デルの作成、およびその評価⽅法までの⼀連の分析作業を体験することができます。初⼼者の⽅で もデータ分析のことを知っていただけるように、Python やR などの分析⾔語の知識がなくても、 Dataiku のGUI を使ってデータ分析を体験できるようになっています。 そのため、現場でのデータ分析をどのように進めていけばよいか知りたい⽅には、きっと役⽴て ていただける内容となっていると思っております。本書を通じて、実際のビジネス現場でのデータ 分析で活⽤いただれば幸いです。 |
| dataiku data science studio: Operations Management for Social Good Adriana Leiras, Carlos Alberto González-Calderón, Irineu de Brito Junior, Sebastián Villa, Hugo Tsugunobu Yoshida Yoshizaki, 2019-10-14 This volume showcases the presentations and discussions delivered at the 2018 POMS International Conference in Rio. Through a collection of selected papers, it is possible to review the impact and application of operations management for social good, with contributions across a wide range of topics, including: humanitarian operations and crisis management, healthcare operations management, sustainable operations, artificial intelligence and data analytics in operations, product innovation and technology in operations management, marketing and operations management, service operations and servitization, logistics and supply chain management, resilience and risk in operations, defense, and tourism among other emerging Operations Management issues. The Production and Operations Management Society (POMS) is one of the most important and influential societies in the subject of Production Engineering and, as an international professional and academic organization, represents the interests of professionals and academics in production management and operations around the world. |
| dataiku data science studio: Cloud Data Science: Harnessing Azure Machine Learning with Python Peter Jones, 2024-10-15 Unlock the full potential of your data with Cloud Data Science: Harnessing Azure Machine Learning with Python. This comprehensive guide equips you with the knowledge and skills to leverage the power of Azure Machine Learning and the versatility of Python to innovate and streamline your machine learning workflows. From setting up your Azure Machine Learning workspace to deploying sophisticated models, this book covers essential techniques and advanced methodologies in a clear, practical format. Dive into core topics such as data management, automated machine learning workflows, model optimization, and real-time monitoring to ensure your projects are scalable, efficient, and effective. Whether you're a data scientist, machine learning engineer, or a professional seeking to enhance your understanding of cloud-based machine learning, this book offers invaluable insights and hands-on examples to help you transform vast amounts of data into actionable insights. Explore real-world case studies across various industries, learn to overcome common challenges, and discover best practices for implementing machine learning projects successfully. Cloud Data Science: Harnessing Azure Machine Learning with Python is your gateway to mastering data science in the cloud and advancing your professional capabilities in the future of technology. |
| dataiku data science studio: Analytics and Big Data for Accountants Jim Lindell, 2020-10-29 Why is big data analytics one of the hottest business topics today? This book will help accountants and financial managers better understand big data and analytics, including its history and current trends. It dives into the platforms and operating tools that will help you measure program impacts and ROI, visualize data and business processes, and uncover the relationship between key performance indicators. Key topics covered include: Evidence-based techniques for finding or generating data, selecting key performance indicators, isolating program effects Relating data to return on investment, financial values, and executive decision making Data sources including surveys, interviews, customer satisfaction, engagement, and operational data Visualizing and presenting complex results |
| dataiku data science studio: Guide pratique de l'intelligence artificielle dans l'entreprise 2e édition Stéphane Roder, 2024-01-11 Que répondrez-vous lorsque votre comité de direction vous demandera comment l’intelligence artificielle peut transformer votre business ? Et maintenant que l'IA a fait une entrée fracassante dans nos vies avec ChatGPT, que va-t-il se passer |
| dataiku data science studio: Big Data et Machine Learning - 2e éd. Pirmin Lemberger, Marc Batty, Médéric Morel, Jean-Luc Raffaëlli, 2016-10-05 Le Big Data s’est imposé comme une innovation majeure pour toutes les entreprises qui cherchent à construire un avantage concurrentiel grâce à l’exploitation de leurs données clients, fournisseurs, produits, processus, machines, etc. Ce livre est un guide pour comprendre les enjeux d’un projet Big Data, en appréhender les concepts sous-jacents (en particulier le machine learning) et acquérir les compétences nécessaires à la mise en place d’un data lab. Il combine la présentation de notions théoriques (traitement statistique des données, calcul distribué...), d’outils (écosystème Hadoop, Storm...) et d’exemples de machine learning ; Cette deuxième édition comporte des ajouts sur le deep learning et les réseaux de neurones, ainsi que des compléments et des mises à jour sur les moteurs de recommandations et Spark. Les compléments en ligne seront enrichis de nouveaux jeux de données pour un début de mise en pratique. |
| dataiku data science studio: Thriving in a Data World Sangeeta Krishnan, 2022-12-07 This book focuses on the foundations needed to be successful in managing and engaging with data analytics initiatives, bridging the gap between creators and users of data. Currently, every company, no matter its size, is data-driven in one way or another; using data to improve customer experience, as a new value stream, and to stay competitive. However, many business leaders, professionals, and students—such as executives, business analysts, UI/UX designers, project managers and marketing teams —are forced to interact with data and those who generate data, without being taught the general competencies needed to feel comfortable having these conversations. This book focuses on the foundations needed to be successful in managing and engaging with data analytics initiatives, bridging the gap between creators and users of data. As a management reference guide, it discusses the different types of data strategy needed for succeeding with data, covering topics such as data team composition, types of data analytics, the importance of data storytelling, and identifying data ROI. Framed by the author's personal story, the trove of information is made tangible through the compelling narrative with its unprecedented accessibility and readability for a non-technical audience. If you suffer from fear of data, anxiety around conversations with technical teams, this practical approach book can help with actions you can start implementing right away. |
| dataiku data science studio: Big Data et Machine Learning - 3e éd. Pirmin Lemberger, Marc Batty, Médéric Morel, Jean-Luc Raffaëlli, 2019-08-14 Cet ouvrage s’adresse à tous ceux qui cherchent à tirer parti de l’énorme potentiel des « technologies Big Data », qu’ils soient data scientists, DSI, chefs de projets ou spécialistes métier. Le Big Data s’est imposé comme une innovation majeure pour toutes les entreprises qui cherchent à construire un avantage concurrentiel grâce à l’exploitation de leurs données clients, fournisseurs, produits, processus, machines, etc. Mais quelle solution technique choisir ? Quelles compétences métier développer au sein de la DSI ? Ce livre est un guide pour comprendre les enjeux d’un projet Big Data, en appréhender les concepts sous-jacents (en particulier le Machine Learning) et acquérir les compétences nécessaires à la mise en place d’un data lab. Il combine la présentation : • de notions théoriques (traitement statistique des données, calcul distribué...) ; • des outils les plus répandus (écosystème Hadoop, Storm...) ; • d’exemples d’applications ; • d’une organisation typique d’un projet de data science. Les ajouts de cette troisième édition concernent principalement la vision d’architecture d’entreprise, nécessaire pour intégrer les innovations du Big Data au sein des organisations, et le Deep Learning pour le NLP (Natural Language Processing, qui est l’un des domaines de l’intelligence artificielle qui a le plus progressé récemment). |
| dataiku data science studio: Implementing Analytics Nauman Sheikh, 2013-05-06 Implementing Analytics demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption across all functions of an organization. Implementing Analytics simplifies and helps democratize a very specialized discipline to foster business efficiency and innovation without investing in multi-million dollar technology and manpower. A technology agnostic methodology that breaks down complex tasks like model design and tuning and emphasizes business decisions rather than the technology behind analytics. - Simplifies the understanding of analytics from a technical and functional perspective and shows a wide array of problems that can be tackled using existing technology - Provides a detailed step by step approach to identify opportunities, extract requirements, design variables and build and test models. It further explains the business decision strategies to use analytics models and provides an overview for governance and tuning - Helps formalize analytics projects from staffing, technology and implementation perspectives - Emphasizes machine learning and data mining over statistics and shows how the role of a Data Scientist can be broken down and still deliver the value by building a robust development process |
| dataiku data science studio: Python for R Users Ajay Ohri, 2017-11-13 The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing. |
| dataiku data science studio: 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 |
| dataiku data science studio: R for Business Analytics A Ohri, 2012-09-14 This book examines common tasks performed by business analysts and helps the reader navigate the wealth of information in R and its 4000 packages to create useful analytics applications. Includes interviews with corporate users of R, and easy-to-use examples. |
| dataiku data science studio: Sport Data Revolution Andy Hyeans, 2016-04-13 À l’image des secteurs économiques traditionnels comme la banque ou l’assurance, le sport est un domaine soumis à l’incertitude, dont les besoins en termes de performance sont à la fois importants et très concrets. Les professionnels du sport ne peuvent plus se contenter de comprendre ce qui vient de se passer, ils doivent aussi anticiper ce qui va se produire. La Sport Data Revolution, ou révolution des données sportives, leur donne les moyens d’atteindre leurs objectifs : victoires, performance des activités et des structures, réduction des coûts, amélioration des résultats, développement des potentiels, rationalisation de la prise de décision, réduction des risques, etc. Que vous soyez sportif, entraîneur, manager, dirigeant ou juste fan de sport, ce livre vous propose un éclairage sur les enjeux, les opportunités et les méthodes de cette nouvelle science en s’aidant d’exemples concrets. De la performance humaine des athlètes à la performance économique des organisations, tous les secrets de la révolution des données sportives vous sont dévoilés. |
| dataiku data science studio: Building the Data Lakehouse Bill Inmon, Ranjeet Srivastava, Mary Levins, 2021-10 The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements. Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after. |
| dataiku data science studio: Database Management using AI: A Comprehensive Guide A Purushotham Reddy, 2024-10-20 Database Management Using AI: A Comprehensive Guide is a professional yet accessible exploration of how artificial intelligence (AI) is reshaping the world of database management. Designed for database administrators, data scientists, and tech enthusiasts, this book walks readers through the transformative impact of AI on modern data systems. The guide begins with the fundamentals of database management, covering key concepts such as data models, SQL, and the principles of database design. From there, it delves into the powerful role AI plays in optimizing database performance, enhancing security, and automating complex tasks like data retrieval, query optimization, and schema design. The book doesn't stop at theory. It brings AI to life with practical case studies showing how AI-driven database systems are being used in industries such as e-commerce, healthcare, finance, and logistics. These real-world examples demonstrate AI's role in improving efficiency, reducing errors, and driving intelligent decision-making. Key topics covered include: Introduction to Database Systems: Fundamentals of database management, from relational databases to modern NoSQL systems. AI Integration: How AI enhances database performance, automates routine tasks, and strengthens security. Real-World Applications: Case studies from diverse sectors like healthcare, finance, and retail, showcasing the practical impact of AI in database management. Predictive Analytics and Data Mining: How AI tools leverage data to make accurate predictions and uncover trends. Future Trends: Explore cutting-edge innovations like autonomous databases and cloud-based AI solutions that are shaping the future of data management. With its clear explanations and actionable insights, Database Management Using AI equips readers with the knowledge to navigate the fast-evolving landscape of AI-powered databases, making it a must-have resource for those looking to stay ahead in the digital age. |
| dataiku data science studio: Открытые системы. СУБД No01/2014 Открытые системы, 2022-05-07 В номере:Все в одном: микропроцессор KaveriДля получения преимуществ на современном рынке микропроцессоров производители значительное внимание уделяют универсальности своих изделий, наделяя их возможностями самоопределения.Большие Данные для управления ИТАналитика Больших Данных становится необходима ИТ-менеджерам – существующие инструменты управления ИТ не позволяют оценить эффективность своего применения, предсказать динамику изменения производительности, а главное – современный уровень сложности ИТ-сред уже не оставляет места ручному управлению.Тестирование эластичных компьютерных системПоявившись вместе с облаками, эластичные компьютерные системы привлекают сегодня все больше внимания – они могут «сжиматься» и «растягиваться» в зависимости от рабочей нагрузки. Однако до сих пор неясно, как тестировать эластичные системы и каковы дальнейшие направления их развития.Защита критически важных систем управленияБесперебойная работа таких критически важных инфраструктур, как системы энергоснабжения, обеспечения водой или продуктами питания, – задача государственной важности. Какие сегодня имеются архитектуры автоматизированных систем управления, в чем состоят угрозы, где уязвимые места и как защищать такие инфраструктуры?Первоклассные объекты Всемирной паутиныДо недавнего времени в WWW не было единой модели аннотаций, независимой от контента, что затрудняло возможность их переноса между системами и предметными областями. Но сегодня спецификация Open Annotation Data Model консорциума W3C кардинально меняет принципы подготовки и распространения аннотаций.Закон Меткалфа сорок лет спустя после рождения EthernetСогласно закону Меткалфа, полезность сети пропорциональна квадрату числа ее пользователей. Правда, критики уверены, что это преувеличение, однако на реальных данных закон раньше никто не проверял. Изобретатель Ethernet и автор закона сам предпринял попытку сделать это.Системы автоматической обработки текстовМногообразие систем автоматической обработки неструктурированных текстов сегодня вызывает необходимость их систематизации и классификации с целью упрощения выбора решения, наиболее адекватного для конкретной задачи.и многое другое |
| dataiku data science studio: Automated Machine Learning Adnan Masood, 2021-02-18 Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key FeaturesGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choiceEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsFind out how you can make machine learning accessible for all users to promote decentralized processesBook Description Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What you will learnExplore AutoML fundamentals, underlying methods, and techniquesAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenarioFind out the difference between cloud and operations support systems (OSS)Implement AutoML in enterprise cloud to deploy ML models and pipelinesBuild explainable AutoML pipelines with transparencyUnderstand automated feature engineering and time series forecastingAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problemsWho this book is for Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial. |
| dataiku data science studio: Re-Entrepreneuring Charles-Edouard Bouée, Stefan Schaible, 2018-11-01 Re-entrepreneuring shows how organizations must re-invigorate entrepreneurial spirit at all levels to create new value and stay ahead in turbulent times. It has long been assumed that, in the development of any organization, the time for entrepreneurial activity is right at the beginning. Once an organization is established, qualities that were virtues in the organization's start-up and early stages can become vices, and the entrepreneurial founders must cede control to professional managers who can nurture the fruits of their original vision more efficiently. One unintended consequence of this assumption is that large, established organizations tend to be entrepreneur-free zones. Entrepreneurial thinking is tacitly discouraged because it can create novelty, and novelty is a threat to established organizations with large market shares. Re-entrepreneuring argues that organizations must revive the entrepreneurial out-look of their founders in order to survive in today's market. In an organization that encourages and nurtures an entrepreneurial outlook, everyone has the potential to unleash their inner entrepreneur and bring new and dynamic ways of thinking into their work environment. It has more to do with the ways of thinking encouraged by the organizational culture than by any inherent differences in talent or aptitude. The solution presented in this new book from ROLAND BERGER, edited by Charles-Edouard Bouée and Stefan Schaible, is piecemeal yet targeted 're-entrepreneuring'. With the help of international case studies and first-hand testimony from business leaders, the authors show how the entrepreneurial approach can be applied to any organization and at all levels, in order to spark innovation, remove operational obstacles and – ultimately – to create new value. |
| dataiku data science studio: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| dataiku data science studio: Driving Digital Transformation through Data and AI Alexander Borek, Nadine Prill, 2020-11-03 Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI. |
| dataiku data science studio: Scheduling Problems Rodrigo Righi, 2020-07-08 Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems. |
| dataiku data science studio: Applied Data Science in Tourism Roman Egger, 2022-01-31 Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, and its advantages and disadvantages are weighed up and typical fields of application are presented. The correct methodical application is presented with a how-to approach, together with code examples, allowing a wider reader base including researchers, practitioners, and students entering the field. The book is a very well-structured introduction to data science – not only in tourism – and its methodological foundations, accompanied by well-chosen practical cases. It underlines an important insight: data are only representations of reality, you need methodological skills and domain background to derive knowledge from them - Hannes Werthner, Vienna University of Technology Roman Egger has accomplished a difficult but necessary task: make clear how data science can practically support and foster travel and tourism research and applications. The book offers a well-taught collection of chapters giving a comprehensive and deep account of AI and data science for tourism - Francesco Ricci, Free University of Bozen-Bolzano This well-structured and easy-to-read book provides a comprehensive overview of data science in tourism. It contributes largely to the methodological repository beyond traditional methods. - Rob Law, University of Macau |
| dataiku data science studio: Artificial Intelligence in HCI Helmut Degen, |
| dataiku data science studio: Open Source Intelligence Tools and Resources Handbook i-intelligence, 2019-08-17 2018 version of the OSINT Tools and Resources Handbook. This version is almost three times the size of the last public release in 2016. It reflects the changing intelligence needs of our clients in both the public and private sector, as well as the many areas we have been active in over the past two years. |
| dataiku data science studio: Data Management and Analysis Reda Alhajj, Mohammad Moshirpour, Behrouz Far, 2019-12-20 Data management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas. |
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RoyE Dataiker, Dataiku DSS Core Designer, Dataiku DSS ML Practitioner, Dataiku DSS Adv Designer, Registered Posts: 31 Dataiker July 2021 edited July 2024 Answer Hello
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Sep 7, 2022 · Hi @Usersyed,. There is a way to use a prepare recipe with a custom Python function if you enable the option Use a real Python process (instead of Jython) that will allow you to use …
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Jan 16, 2025 · Turribeach Dataiku DSS Core Designer, Neuron, Dataiku DSS Adv Designer, Registered, Neuron 2023 Posts: 2,489 Neuron January 16 Hi, yes, you can use local LLM models.
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May 10, 2017 · Welcome to the Dataiku Community. This confused me for a while with Dataiku. A Managed folder in Dataiku is not exactly like a folder on disk. It is sort of a handle designed to …
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Apr 1, 2021 · It works if I use : df = SQLExecutor2.query_to_df(sql) and then with dataiku.Dataset("my_dataset").get_writer() as writer: writer.write_dataframe(df) However the …
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Jun 8, 2025 · Controlling Access IP for Dataiku Design Node. Hello, How can I control the access IP addresses for the Dataiku design node? For example, I …
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How to set a project variables with Python — Dataiku Comm…
Nov 4, 2024 · Marlan Neuron 2020, Neuron, Registered, Dataiku Frontrunner Awards 2021 Finalist, Neuron 2021, Neuron 2022, Dataiku …
Writing to partitions - Dataiku Community
RoyE Dataiker, Dataiku DSS Core Designer, Dataiku DSS ML Practitioner, Dataiku DSS Adv Designer, Registered Posts: 31 Dataiker July 2021 edited …
Create if, then, else statements — Dataiku Community
Oct 18, 2022 · Sv3n-Sk4 Dataiku DSS Core Designer, Dataiku DSS ML Practitioner, Dataiku DSS Adv …
