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| data science platform market: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science platform market: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science platform market: Data Science on the Google Cloud Platform Valliappa Lakshmanan, 2017-12-12 Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you’ll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You’ll learn how to: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines |
| data science platform market: Concise Survey of Computer Methods Peter Naur, 1974 |
| data science platform market: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data science platform market: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| data science platform market: 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 platform market: Data Science and Digital Business Fausto Pedro García Márquez, Benjamin Lev, 2019-01-04 This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business. |
| data science platform market: Disruptive Platforms Tymoteusz Doligalski, Michał Goliński, Krzysztof Kozłowski, 2021-12-20 It has taken platforms only twenty years to become digital economy hubs. They have changed markets, enterprises, and society. They have expedited communication, collaboration, and trade for consumers, winning their attention and collecting their data. In doing so, they have made processes, products, and industries obsolete, and disrupted the expectations and behaviours of market players. This raises the question, are digital platforms global innovators or disruptive monopolists? Are they a solution to problems of the past or emissaries of a problematic future? This book provides a multi-faceted approach to platforms and their profound impact on markets and ecosystems. Economic, managerial, social, and political aspects are analysed, and the differentiation of platforms and their disruptive potential is reviewed. The book also examines the mechanism of achieving a monopolistic position, including in the international supply chain, and the greater influence of platforms on political activity and contemporary democracy. With examples from Poland, USA, and China, the contributions offer an international evaluation of disruptive platforms across a multitude of industries. The edited collection, prepared by scholars from the SGH Warsaw School of Economics, will be valuable to researchers and academics across the fields of strategic management, marketing, innovations, international business, and the digital economy. |
| data science platform market: Data Science Xiaohui Cheng, Weipeng Jing, Xianhua Song, Zeguang Lu, 2019-09-13 This two volume set (CCIS 1058 and 1059) constitutes the refereed proceedings of the 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019 held in Guilin, China, in September 2019. The 104 revised full papers presented in these two volumes were carefully reviewed and selected from 395 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including data mining; data base; net work; security; machine learning; bioinformatics; natural language processing; software engineering; graphic images; system; education; application. |
| data science platform market: Data Science Handbook Kolla Bhanu Prakash, 2022-10-07 DATA SCIENCE HANDBOOK This desk reference handbook gives a hands-on experience on various algorithms and popular techniques used in real-time in data science to all researchers working in various domains. Data Science is one of the leading research-driven areas in the modern era. It is having a critical role in healthcare, engineering, education, mechatronics, and medical robotics. Building models and working with data is not value-neutral. We choose the problems with which we work, make assumptions in these models, and decide on metrics and algorithms for the problems. The data scientist identifies the problem which can be solved with data and expert tools of modeling and coding. The book starts with introductory concepts in data science like data munging, data preparation, and transforming data. Chapter 2 discusses data visualization, drawing various plots and histograms. Chapter 3 covers mathematics and statistics for data science. Chapter 4 mainly focuses on machine learning algorithms in data science. Chapter 5 comprises of outlier analysis and DBSCAN algorithm. Chapter 6 focuses on clustering. Chapter 7 discusses network analysis. Chapter 8 mainly focuses on regression and naive-bayes classifier. Chapter 9 covers web-based data visualizations with Plotly. Chapter 10 discusses web scraping. The book concludes with a section discussing 19 projects on various subjects in data science. Audience The handbook will be used by graduate students up to research scholars in computer science and electrical engineering as well as industry professionals in a range of industries such as healthcare. |
| data science platform market: 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 platform market: Big Data, IoT, and Machine Learning Rashmi Agrawal, Marcin Paprzycki, Neha Gupta, 2020-07-29 The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features Addresses the complete data science technologies workflow Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning Covers data processing and security solutions in IoT and Big Data applications Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems Presents security issues and data migration techniques of NoSQL databases |
| data science platform market: The Elgar Companion to Information Economics Daphne R. Raban, Julia W_odarczyk, 2024-03-14 The Elgar Companion to Information Economics dexterously navigates this interdisciplinary field of research which celebrates the crucial contribution of information to decision making, market dynamics, and economic well-being. Offering a wealth of conceptual analysis, this erudite Companion embarks on an intellectual journey exploring the fundamentals of information economics. This title contains one or more Open Access chapters. |
| data science platform market: 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. |
| data science platform market: Think Bigger Mark Van Rijmenam, 2014-04-03 Offering real-world insight and explanations, this book provides a roadmap for organizations looking to develop a profitable big data strategy and reveals why it's not something they can leave to the I.T. department. Big data--the enormous amount of data that is created as virtually every movement, transaction, and choice we make becomes digitized--is revolutionizing business. Sharing best practices from companies that have implemented a big data strategy including Walmart, InterContinental Hotel Group, Walt Disney, and Shell, this helpful resource covers the most important big data trends affecting organizations, as well as key technologies like Hadoop and MapReduce, and several crucial types of analyses. In Think Bigger, you will find guidance on topics such as: how to ensure security, respecting the privacy rights of consumers, and how big data is impacting specific industries--and where opportunities can be found. Big data is changing the way businesses--and even governments--are operated and managed. Think Bigger is an essential resource for anyone who wants to ensure that their company isn't left in the dust. |
| data science platform market: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data science platform market: R for SAS and SPSS Users Robert A. Muenchen, 2011-08-27 R is a powerful and free software system for data analysis and graphics, with over 5,000 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R's built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches. The programs and practice datasets are available for download. The glossary defines over 50 R terms using SAS/SPSS jargon and again using R jargon. The table of contents and the index allow you to find equivalent R functions by looking up both SAS statements and SPSS commands. When finished, you will be able to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. This new edition has updated programming, an expanded index, and even more statistical methods covered in over 25 new sections. |
| data science platform market: Research Handbook in Data Science and Law Vanessa Mak, Eric Tjong Tjin Tai, Anna Berlee, 2024-08-06 This thoroughly updated Research Handbook examines the recent exponential growth of data use in society and its implications for legal research and practice. It explores contemporary research in the field of data science, as well as the operationalization of data for use in healthcare, urban governance and smart household devices, among others. |
| data science platform market: R for Stata Users Robert A. Muenchen, Joseph M. Hilbe, 2010-04-26 Stata is the most flexible and extensible data analysis package available from a commercial vendor. R is a similarly flexible free and open source package for data analysis, with over 3,000 add-on packages available. This book shows you how to extend the power of Stata through the use of R. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches. When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. A glossary defines over 50 R terms using Stata jargon and again using more formal R terminology. The table of contents and index allow you to find equivalent R functions by looking up Stata commands and vice versa. The example programs and practice datasets for both R and Stata are available for download. |
| data science platform market: राष्ट्रीय शिक्षा नीति 2020 और कौशल का महत्व डाॅ. कमलेश नारायण मिश्र, 2022-06-28 किसी भी देश की पहचान उसके नागरिकों से होती है। शिक्षित, समर्थ, कुशल सक्षम नागरिक तो समर्थ और सक्षम देश। भारत अरसे से अपनी क्षमताओं का अहसास कराने वाला देश बना हुआ है। विश्व गुरु की राह उसके लिये मुश्किल तो है लेकिन नामुमकिन नहीं। भारतीय मेधा ने नये जमाने के आई0टी0 क्षेत्र में अपनी प्रतिभा का लोहा मनवाया है। इसलिये आश्वस्ति होती है कि शिक्षा के क्षेत्र में जोरदार प्रयास करें तो भारत उन्नति के रास्ते पर सरपट आगे निकल सकता है। शिक्षा क्षेत्र में अभी तक देखा जाता रहा है कि हमारे बच्चे नंबरों के गेम में उलझकर रह गये हैं। मौजूदा शिक्षा के तौर तरीकों ने उन्हें रट्टू तोते बना रख छोड़ा है। लेकिन इस हकीकत से मुँह नहीं चुराया जा सकता कि परीक्षा में हासिल अंक ही किसी की क्षमता को परिलक्षित नहीं करते। व्यक्तित्व के सर्वांगीण विकास में अन्य तमाम कारक हैं, जिनमें निपुणता जरुरी है। व्यवसाय हो या कोई अन्य क्षेत्र श्रम की महत्ता का भान भी होना चाहिये। शिक्षा के उद्देश्य तभी मुकम्मल हो सकते हैं जब हर बच्चा आत्म विश्वास से लवरेज हो। मौजूदा शिक्षा ने कागजी किस्म के नागरिक ज्यादा ढ़ाले जबकि जरुरत है हर फन में माहिर नागरिकों की। नयी शिक्षा नीति में कोशिश है कि अच्छी शिक्षा सार्थक शिक्षा के रास्ते में आ रही अड़चनों को हटाया जाये। |
| data science platform market: Data-Driven Digital Marketing The Art and Science of Intelligent Decision-Making Milad Shemshaki, The book combines theoretical foundations with practical insights, case studies, and actionable strategies, making it an invaluable resource for marketing professionals, business leaders, students, and entrepreneurs alike. By exploring cutting-edge technologies and emphasizing the alignment of data-driven tactics with broader business objectives, Shemshaki equips readers with the knowledge and tools necessary to thrive in the digital age. Whether you're looking to enhance your data analysis skills, drive business growth through intelligent marketing decisions, or gain a competitive edge in the marketplace, this book provides a roadmap for leveraging data to create more meaningful connections with your audience and achieve measurable business results. |
| data science platform market: Data Science Yang Wang, Guobin Zhu, Qilong Han, Liehui Zhang, Xianhua Song, Zeguang Lu, 2022-08-10 This two volume set (CCIS 1628 and 1629) constitutes the refereed proceedings of the 8th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2022 held in Chengdu, China, in August, 2022. The 65 full papers and 26 short papers presented in these two volumes were carefully reviewed and selected from 261 submissions. The papers are organized in topical sections on: Big Data Management and Applications; Data Security and Privacy; Applications of Data Science; Infrastructure for Data Science; Education Track; Regulatory Technology in Finance. |
| data science platform market: Mastering Marketing Data Science Iain Brown, 2024-04-26 Unlock the Power of Data: Transform Your Marketing Strategies with Data Science In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively. Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science. Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science. Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape. Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative. Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable. |
| data science platform market: Big Data Infrastructure Technologies for Data Analytics Yuri Demchenko, |
| data science platform market: Data Science for Economics and Finance Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana, 2021 This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. |
| data science platform market: Flow Rob Handfield, Phd, Tom Linton, 2022-05-30 With supply chain disruptions increasingly discussed in the media and impacting our daily lives, Flow offers an important framework and solutions for remedying the rampant delays and bottlenecks that exist in global supply chains. This book describes the concept of flow, which evokes physical properties that exist in nature, such as the flow of electricity, the flow of materials, and the flow of time. In terms of process optimization, flow encompasses the integration of end-to-end supply chains and the movement toward relocation of global supply bases to nearshore/onshore geographies. Achieving flow is essential for organizations seeking to improve their supply chain performance in a time of increasing disruption. This book highlights the high-level effectiveness of business strategies that use predictions based on the sequence of world events, global supply chains, and data by exchanged smart technologies. By broadly applying physical laws to the global supply chain, Rob Handfield and Tom Linton explore the impact of supply chain physics on global market policies, such as tariffs, factory location, pandemic response, supply base geographies, and outsourcing. The authors provide specific recommendations on what to do to improve supply chain flows, and include important insights for managers with examples from companies such as Biogen, General Motors, Siemens, and Flex with regard to their response to COVID-19. Flow is an important resource not only for procurement and supply chain management professionals, but for any manager concerned with enterprise-level success. |
| data science platform market: Customer 360 Martin Kihn, Andrea Lin, 2024-11-13 Become more competitive by developing a superior customer experience through data, AI, and trust - and get your organization ready for AI agents like Agentforce Customer 360: How Data, AI, and Trust Changes Everything delivers key insight and vision on using emerging technologies to delight customers and become more competitive by providing a superior customer experience. Find out why AI agents like Agentforce need a strong foundation of customer data. This book helps readers attract and engage their customers across channels and throughout their journey, from acquisition and onboarding, through service, upsell, retention, and win-back. To demonstrate the influence and importance of these ideas, this book contains a multitude of real-world case studies from companies in a range of industries, with business models, and at various stages of digital maturity. Readers will learn about: Using exciting technologies like AI and GPT while building a commitment to ethical use, safety, and privacy through secure guardrails Getting ready to use exciting emerging technologies like AI agents and autonomous AI Organizing data around customers, prospects, and accounts—even if that data comes from many different sources in different formats Making new technologies an extension of your existing data investments so that both work better Choosing a strategy and implementation plan to minimize time-to-value and ensure success weighing build, buy, or partner Handling internal stakeholders and dealing with change in a way that benefits the business For business leaders, executives, managers, and entrepreneurs, Customer 360: How Data, AI, and Trust Changes Everything is an essential read to understand and connect technology, people, processes, and strategy—truly the future of customer engagement—and leave competitors wondering what just happened. |
| data science platform market: Data Science Vijay Kotu, Bala Deshpande, 2018-11-27 Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You'll be able to: - Gain the necessary knowledge of different data science techniques to extract value from data. - Master the concepts and inner workings of 30 commonly used powerful data science algorithms. - Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... - Contains fully updated content on data science, including tactics on how to mine business data for information - Presents simple explanations for over twenty powerful data science techniques - Enables the practical use of data science algorithms without the need for programming - Demonstrates processes with practical use cases - Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language - Describes the commonly used setup options for the open source tool RapidMiner |
| data science platform market: Comet for Data Science Angelica Lo Duca, Gideon Mendels, 2022-08-26 Gain the key knowledge and skills required to manage data science projects using Comet Key Features • Discover techniques to build, monitor, and optimize your data science projects • Move from prototyping to production using Comet and DevOps tools • Get to grips with the Comet experimentation platform Book Description This book provides concepts and practical use cases which can be used to quickly build, monitor, and optimize data science projects. Using Comet, you will learn how to manage almost every step of the data science process from data collection through to creating, deploying, and monitoring a machine learning model. The book starts by explaining the features of Comet, along with exploratory data analysis and model evaluation in Comet. You'll see how Comet gives you the freedom to choose from a selection of programming languages, depending on which is best suited to your needs. Next, you will focus on workspaces, projects, experiments, and models. You will also learn how to build a narrative from your data, using the features provided by Comet. Later, you will review the basic concepts behind DevOps and how to extend the GitLab DevOps platform with Comet, further enhancing your ability to deploy your data science projects. Finally, you will cover various use cases of Comet in machine learning, NLP, deep learning, and time series analysis, gaining hands-on experience with some of the most interesting and valuable data science techniques available. By the end of this book, you will be able to confidently build data science pipelines according to bespoke specifications and manage them through Comet. What you will learn • Prepare for your project with the right data • Understand the purposes of different machine learning algorithms • Get up and running with Comet to manage and monitor your pipelines • Understand how Comet works and how to get the most out of it • See how you can use Comet for machine learning • Discover how to integrate Comet with GitLab • Work with Comet for NLP, deep learning, and time series analysis Who this book is for This book is for anyone who has programming experience, and wants to learn how to manage and optimize a complete data science lifecycle using Comet and other DevOps platforms. Although an understanding of basic data science concepts and programming concepts is needed, no prior knowledge of Comet and DevOps is required. |
| data science platform market: Getting Started with Data Science Murtaza Haider, 2015-12-14 Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon. |
| data science platform market: Recent Developments in Data Science and Intelligent Analysis of Information Oleg Chertov, Tymofiy Mylovanov, Yuriy Kondratenko, Janusz Kacprzyk, Vladik Kreinovich, Vadim Stefanuk, 2018-08-04 This book constitutes the proceedings of the XVIII International Conference on Data Science and Intelligent Analysis of Information (ICDSIAI'2018), held in Kiev, Ukraine on June 4-7, 2018. The conference series, which dates back to 2001 when it was known as the Workshop on Intelligent Analysis of Information, was renamed in 2008 to reflect the broadening of its scope and the composition of its organizers and participants. ICDSIAI'2018 brought together a large number of participants from numerous countries in Europe, Asia and the USA. The papers presented addressed novel theoretical developments in methods, algorithms and implementations for the broadly perceived areas of big data mining and intelligent analysis of data and information, representation and processing of uncertainty and fuzziness, including contributions on a range of applications in the fields of decision-making and decision support, economics, education, ecology, law, and various areas of technology. The book is dedicated to the memory of the conference founder, the late Professor Tetiana Taran, an outstanding scientist in the field of artificial intelligence whose research record, vision and personality have greatly contributed to the development of Ukrainian artificial intelligence and computer science. |
| data science platform market: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data science platform market: Bioinformatics Tools and Big Data Analytics for Patient Care Rishabha Malviya, Pramod Kumar Sharma, Sonali Sundram, Rajesh Kumar Dhanaraj, Balamurugan Balusamy, 2022-08-31 Nowadays, raw biological data can be easily stored as databases in computers but extracting the required information is the real challenge for researchers. For this reason, bioinformatics tools perform a vital role in extracting and analyzing information from databases. Bioinformatics Tools and Big Data Analytics for Patient describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery for patient care. The book gives details about the recent developments in the fields of artificial intelligence, cloud computing, and data analytics. It highlights the advances in computational techniques used to perform intelligent medical tasks. Features: Presents recent developments in the fields of artificial intelligence, cloud computing, and data analytics for improved patient care. Describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery. Summarizes several strategies, analyses, and optimization methods for patient healthcare. Focuses on drug discovery and development by cloud computing and data-driven research The targeted audience comprises academics, research scholars, healthcare professionals, hospital managers, pharmaceutical chemists, the biomedical industry, software engineers, and IT professionals. |
| data science platform market: Planning for Big Data Edd Wilder-James, 2012-03-12 In an age where everything is measurable, understanding big data is an essential. From creating new data-driven products through to increasing operational efficiency, big data has the potential to make your organization both more competitive and more innovative. As this emerging field transitions from the bleeding edge to enterprise infrastructure, it's vital to understand not only the technologies involved, but the organizational and cultural demands of being data-driven. Written by O'Reilly Radar's experts on big data, this anthology describes: The broad industry changes heralded by the big data era What big data is, what it means to your business, and how to start solving data problems The software that makes up the Hadoop big data stack, and the major enterprise vendors' Hadoop solutions The landscape of NoSQL databases and their relative merits How visualization plays an important part in data work |
| data science platform market: Building Data Science Teams DJ Patil, 2011-09-15 As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success. Topics include: What it means to be data driven. The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team. |
| data science platform market: Recent Developments in Data Science and Business Analytics Madjid Tavana, Srikanta Patnaik, 2018-03-27 This edited volume is brought out from the contributions of the research papers presented in the International Conference on Data Science and Business Analytics (ICDSBA- 2017), which was held during September 23-25 2017 in ChangSha, China. As we all know, the field of data science and business analytics is emerging at the intersection of the fields of mathematics, statistics, operations research, information systems, computer science and engineering. Data science and business analytics is an interdisciplinary field about processes and systems to extract knowledge or insights from data. Data science and business analytics employ techniques and theories drawn from many fields including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition, visualization, descriptive analytics, predictive analytics, prescriptive analytics, uncertainty modeling, big data, data warehousing, data compression, computer programming, business intelligence, computational intelligence, and high performance computing among others. The volume contains 55 contributions from diverse areas of Data Science and Business Analytics, which has been categorized into five sections, namely: i) Marketing and Supply Chain Analytics; ii) Logistics and Operations Analytics; iii) Financial Analytics. iv) Predictive Modeling and Data Analytics; v) Communications and Information Systems Analytics. The readers shall not only receive the theoretical knowledge about this upcoming area but also cutting edge applications of this domains. |
| data science platform market: Research Anthology on Big Data Analytics, Architectures, and Applications Management Association, Information Resources, 2021-09-24 Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians. |
| data science platform market: Enhancing Business Communications and Collaboration Through Data Science Applications Geada, Nuno, Leal Jamil, George, 2023-03-21 Digital evolution has become increasingly present in our lives, whether on cellphones, computers, watches, or other appliances. As a result of the wide access we have to the digital world, the amount of data generated daily is vast. This density of information generated at every moment can be the insight needed for the success of an organization. Much is said about data-based decision-making to generate the best results. The new capabilities of data intelligence unleashed by the emergence of cloud computing and artificial intelligence make it one of the most promising areas of digital transformation change management. Enhancing Business Communications and Collaboration Through Data Science Applications provides relevant theoretical frameworks and the latest empirical research findings in the area. It is written for professionals who wish to improve their understanding of the strategic role of trust at different levels of the information and knowledge society. Covering topics such as data science, online business communication, and user-centered design, this premier reference source is an ideal resource for business managers and leaders, entrepreneurs, data scientists, data analysts, sociologists, students and educators of higher education, librarians, researchers, and academicians. |
| data science platform market: Urban Platforms and the Future City Mike Hodson, Julia Kasmire, Andrew McMeekin, John G. Stehlin, Kevin Ward, 2020-11-30 This title takes the broadest possible scope to interrogate the emergence of “platform urbanism”, examining how it transforms urban infrastructure, governance, knowledge production, and everyday life, and brings together leading scholars and early-career researchers from across five continents and multiple disciplines. The volume advances theoretical debates at the leading edge of the intersection between urbanism, governance, and the digital economy, by drawing on a range of empirically detailed cases from which to theorize the multiplicity of forms that platform urbanism takes. It draws international comparisons between urban platforms across sites, with attention to the leading edges of theory and practice and explores the potential for a renewal of civic life, engagement, and participatory governance through “platform cooperativism” and related movements. A breadth of tangible and diverse examples of platform urbanism provides critical insights to scholars examining the interface of digital technologies and urban infrastructure, urban governance, urban knowledge production, and everyday urban life. The book will be invaluable on a range of undergraduate and postgraduate courses, as well as for academics and researchers in these fields, including anthropology, geography, innovation studies, politics, public policy, science and technology studies, sociology, sustainable development, urban planning, and urban studies. It will also appeal to an engaged, academia-adjacent readership, including city and regional planners, policymakers, and third-sector researchers in the realms of citizen engagement, industrial strategy, regeneration, sustainable development, and transport. |
Infographics Data Science Platform Market - Coherent Market …
through various recommendations about operational improvements, technologies, emerging market trends and new working methods. We o˛er both customized andsyndicated market …
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Feb 22, 2018 · Data science and machine-learning platforms enable organizations to take an end-to-end approach to building and deploying data science models. This Magic Quadrant …
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In a study conducted by marketing research firm Altimeter, 41 percent of the 500 strategists and executives surveyed reported increased market share thanks to digital transformation efforts, …
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Data Science Platform Market Deployment was valued at USD 100.9 billion in 2022. The Data Science Platform market industry is projected to grow from USD 120.27 Billion in 2023 to USD …
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Jun 3, 2022 · Global Data Science Platform Market (20222028) Research report - provides an in-depth market evaluation by highlighting information on various aspects covering global drivers, …
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The Global Data Science Platform Market size is expected to reach $165.5 billion by 2026, rising at a market growth of 27% CAGR during the forecast period. The data science platform can be …
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Jul 1, 2019 · leaders can understand the market for data science skills across the new economy: monitoring the demand for data science skills through job posting analysis from Burning Glass …
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Data Science and Analytics are no longer just buzzwords- they are essential business tools. Every day, 2.5 quintillion bytes of data are created,¹ and IDC estimates that by 2019 the Big Data …
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Generation: Move Data Around — data analytics started by moving data from one place to another, analyzing it locally and moving results back to the remote data source
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The “Data Science Platform Market – Forecasts from 2019 to 2024” is an exhaustive study which aims to present the key market trends through various chapters focusing on different aspects …
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augmented data pr eparation. Integration with adv anced data preparation tools (Monarch and Knowledge Hub) enables semistructur ed data to be easily extracted and included in ML …
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Data Science Platform Market Growth & Trends The global data science platform market size is expected to reach USD 25.94 billion by 2027, according to a new report by Grand View …
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Our survey revealed that Insights Leaders leverage a platform approach to their data science technology stack; in fact, 88% of Leaders reported using a fully functional platform, either with …
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2021 STATE OF DATA SCIENCE REPORT EXECUTIVE SUMMARY We conducted the 2021 State of Data Science survey focusing on how data science as a field is growing, the overall …
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needs to have a product that supports reproducibility and standardization. A data science platform can help a company meet the demands of its team. This white paper discusses the state of …
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data-driven decision-making Projected CAGR of the global machine learning market by 20293 38.8% Projected worldwide AI market in 2026 growing at 18.6% 2022-262 $900 Bn Projected …
FactSet Quantitative Research Environment
data science platform. FactSet Quantitative Research Environment provides you with all the tools you need to: • Examine investment ideas within one cohesive research environment, complete …
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1 Data science & AI Novo Nordisk ... unexpected contract breaches or terminations, government- mandated or market-driven price decreases for Novo Nordisk’s products, introduction of …
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ML and data science challenges By Pavel Klushin Data collection: Large amount of unorganized data (storage is cheap), a lot of data source (difficult to find right source) Data security and …
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Anaconda to power their data science and AI workflows, making Anaconda the world’s most popular data science platform and the foundation of modern machine learning. Anaconda has …
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Key Words: Data Science, Stock Market, Algorithms, Trade, Data Analysis 1.INTRODUCTION The stock market is quite complicated and prone to volatility, and it is influenced by a number …
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p. 3). The wealth of data collected and stored by various technologies can have transformative benefits for business organizations, but only if we can interpret it. That's where Data Science …
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• Big Data Market Growth: The Big Data market is projected to be worth $103 billion by 2027. • Job Opportunities: By 2026, there will be 11 million job openings in Data Science in India …
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2023 STATE OF DATA + AI 5 • The number of companies using SaaS LLM APIs (used to access services like ChatGPT) has grown 1310% between the end of November 2022 and the …
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digital platform market. To do so, I use data from Spotify, the world’s largest music streaming platform. Spotify provides an excellent context for this research as a) users typically consume …
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the production or improvement of a good or a service. Data exchanges thus involve at least two markets, an upstream data collection market and a downstream data use market. There can …
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Data on patent filings are sourced from Google Patents. — Research. Data on research publications are sourced from the Lens (www.lens.org). — News. Data on news publications …
NVIDIA Accelerated Data Science Solution with RAPIDS
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As traders buy more market data from a particular exchange, the overall volume of trading on that exchange can increase. This is because traders use market data to make order routing …
Kaggle AI Report 2023 - storage.googleapis.com
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: Markets and Markets / : 2022-03-01 / : 349 / Single User …
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data management implemented by a platform would impact online market competition and consumer welfare. In light of the importance of data in the Internet market, a thorough …
AWS Cloud Adoption Framework
As the proliferation of digital technologies continues to disrupt market segments and industries, adopting Amazon Web Services (AWS) can help you transform your ... Data Science Platform …
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embed processes in a data science platform; and use data science as a data governance catalyst. 2. Data Drift, Anomalies, and Errors Dirty, incomplete, inconsistent, non-valid, or changing …
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2017] PLATFORM MARKET POWER 1055 Finally, Questions Six, Seven, and Eight address issues arising out of the digital nature of platform markets, including when a platform’s …
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WORKING WITHOUT BORDERS
over time within Latin America as well as beyond; in 2018, the platform expanded its presence to Southeast Asia. 3 SoyFreelancer is an online freelancing platform based in San Salvador and …
ELEKS Data Science Platform
ELEKS Data Science Platform (eDSP) lets you manage all your data science models from one place, with significantly less effort. Using ... Market-leading Solutions For more information visit …
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Platform (GCP) stands out as a top-tier cloud computing platform, offering extensive services for data science and machine learning. This book is a comprehensive guide to learning GCP for …
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Revenue-Sharing, and Platform Design Hemant K. Bhargava,y May 12, 2021 Forthcoming, Management Science Abstract Many digital platforms give users a bundle of goods sourced …
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Market Definition/Description Analytics and business intelligence (ABI) platforms ar e characterized by easy-to-use functionality ... Another capability, currently on Alibaba Cloud’s …
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May 31, 2022 · data across operational databases, data warehouses, and data lakes. 11.Amazon EMR provides the cloud big data platform for processing vast amounts of data using open …
Fueling growth through data monetization - McKinsey
Data and analytics are also changing the nature of industry competition. Seventy percent of all executives report that data and analytics have caused at least moderate changes in their …
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and AWS is not as well-known in the ABI platform market. Nevertheless, Amazon QuickSight has huge potential to sell to the AWS installed base. Amazon QuickSight is a fully managed, cloud …
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largest online platform for data science with more than 1,000,000 members. While the platform is a large repository for public datasets and a place to exchange for data scientists through …
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tools to increase data science productivity, and empower end users to do basic analytics tasks, allowing data scientists to focus on core tasks. › Grow the data science talent pool by reskilling …
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pricing is an overdue pillar in data science research and practice. Data and information as goods discussed in this article are those that are ... tness in marketplace, market positioning, price …
