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| collaboration in data science: Development of Linguistic Linked Open Data Resources for Collaborative Data-Intensive Research in the Language Sciences Antonio Pareja-Lora, Maria Blume, Barbara C. Lust, Christian Chiarcos, 2020-01-07 Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zinn |
| collaboration in data science: Human-Centered Data Science Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, Gina Neff, 2022-03-01 Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns. |
| collaboration in data science: Collaborative Technologies and Data Science in Smart City Applications Aram Hajian, Wolfram Luther, A. J. Han Vinck, 2018-08-30 In September 2018, researchers from Armenia, Chile, Germany and Japan met in Yerevan to discuss technologies with applications in Smart Cities, Data Science and Information-Theoretic Approaches for Smart Systems, Technical Challenges for Smart Environments, and Smart Human Centered Computing. This book presents their contributions to the CODASSCA 2018 workshop on Collaborative Technologies and Data Science in Smart City Applications, a cutting-edge topic in Computer Science today. |
| collaboration in data science: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| collaboration in data science: Mastering Data Science Cybellium Ltd, Unleash the Power of Insights from Data Are you ready to embark on a transformative journey into the world of data science? Mastering Data Science is your comprehensive guide to unlocking the full potential of data for extracting valuable insights and driving informed decisions. Whether you're an aspiring data scientist looking to enhance your skills or a business leader seeking to leverage data-driven strategies, this book equips you with the knowledge and tools to master the art of data science. Key Features: 1. Dive into Data Science: Immerse yourself in the realm of data science, understanding its core principles, methodologies, and applications. Build a solid foundation that empowers you to extract meaningful insights from complex datasets. 2. Data Exploration and Visualization: Master the art of data exploration and visualization. Learn how to analyze datasets, uncover patterns, and create compelling visualizations that reveal hidden trends. 3. Statistical Analysis and Hypothesis Testing: Uncover the power of statistical analysis and hypothesis testing. Explore techniques for making data-driven inferences, validating assumptions, and drawing meaningful conclusions. 4. Machine Learning Fundamentals: Delve into machine learning concepts and techniques. Learn about supervised and unsupervised learning, feature engineering, model selection, and evaluation. 5. Predictive Analytics: Discover the realm of predictive analytics. Learn how to build predictive models that forecast future outcomes, enabling proactive decision-making. 6. Natural Language Processing (NLP) and Text Mining: Explore NLP and text mining techniques. Learn how to process and analyze textual data, extract sentiments, and uncover insights from unstructured content. 7. Time Series Analysis: Master time series analysis for modeling sequential data. Learn how to forecast trends, identify seasonality, and make predictions based on temporal patterns. 8. Big Data and Data Wrangling: Dive into big data analytics and data wrangling. Learn how to handle and preprocess large datasets, ensuring data quality and usability. 9. Deep Learning and Neural Networks: Uncover the world of deep learning and neural networks. Learn how to build and train deep learning models for tasks like image recognition and natural language understanding. 10. Real-World Applications: Gain insights into real-world applications of data science across industries. From healthcare to finance, explore how organizations harness data science for strategic decision-making. Who This Book Is For: Mastering Data Science is an indispensable resource for aspiring data scientists, analysts, and business professionals who want to excel in extracting insights from data. Whether you're new to data science or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of data for innovation. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| collaboration in data science: 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. |
| collaboration in data science: Doing Business in Asia Gabriele Suder, Terence Tsai, Sumati Varma, 2020-10-12 A focused look into the business and management practices across Asia, from an author team located across three Asian-Pacific countries and experience of leading organisations spanning over more than two decades. |
| collaboration in data science: Veridical Data Science Bin Yu, Rebecca L. Barter, 2024-10-15 Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven results Teaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making process Cultivates critical thinking throughout the entire data science life cycle Provides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutions Suitable for advanced undergraduate and graduate students, domain scientists, and practitioners |
| collaboration in data science: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| collaboration in data science: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| collaboration in data science: 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. |
| collaboration in data science: 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. |
| collaboration in data science: Practical DataOps Harvinder Atwal, 2019-12-09 Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production. |
| collaboration in data science: Data Matters National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Government-University-Industry Research Roundtable, Planning Committee for the Workshop on Ethics, Data, and International Research Collaboration in a Changing World, 2019-01-28 In an increasingly interconnected world, perhaps it should come as no surprise that international collaboration in science and technology research is growing at a remarkable rate. As science and technology capabilities grow around the world, U.S.-based organizations are finding that international collaborations and partnerships provide unique opportunities to enhance research and training. International research agreements can serve many purposes, but data are always involved in these collaborations. The kinds of data in play within international research agreements varies widely and may range from financial and consumer data, to Earth and space data, to population behavior and health data, to specific project-generated dataâ€this is just a narrow set of examples of research data but illustrates the breadth of possibilities. The uses of these data are various and require accounting for the effects of data access, use, and sharing on many different parties. Cultural, legal, policy, and technical concerns are also important determinants of what can be done in the realms of maintaining privacy, confidentiality, and security, and ethics is a lens through which the issues of data, data sharing, and research agreements can be viewed as well. A workshop held on March 14-16, 2018, in Washington, DC explored the changing opportunities and risks of data management and use across disciplinary domains. The third workshop in a series, participants gathered to examine advisory principles for consideration when developing international research agreements, in the pursuit of highlighting promising practices for sustaining and enabling international research collaborations at the highest ethical level possible. The intent of the workshop was to explore, through an ethical lens, the changing opportunities and risks associated with data management and use across disciplinary domainsâ€all within the context of international research agreements. This publication summarizes the presentations and discussions from the workshop. |
| collaboration in data science: Agile Processes in Software Engineering and Extreme Programming – Workshops Rashina Hoda, 2019-08-30 This open access book constitutes the research workshops, doctoral symposium and panel summaries presented at the 20th International Conference on Agile Software Development, XP 2019, held in Montreal, QC, Canada, in May 2019. XP is the premier agile software development conference combining research and practice. It is a hybrid forum where agile researchers, academics, practitioners, thought leaders, coaches, and trainers get together to present and discuss their most recent innovations, research results, experiences, concerns, challenges, and trends. Following this history, for both researchers and seasoned practitioners XP 2019 provided an informal environment to network, share, and discover trends in Agile for the next 20 years. Research papers and talks submissions were invited for the three XP 2019 research workshops, namely, agile transformation, autonomous teams, and large scale agile. This book includes 15 related papers. In addition, a summary for each of the four panels at XP 2019 is included. The panels were on security and privacy; the impact of the agile manifesto on culture, education, and software practices; business agility – agile’s next frontier; and Agile – the next 20 years. |
| collaboration in data science: Agile Data Science Russell Jurney, 2013-10-15 Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track |
| collaboration in data science: Data Science Tiffany Timbers, Trevor Campbell, Melissa Lee, 2022-07-15 Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course. |
| collaboration in data science: Collaboration in a Data-Rich World Luis M. Camarinha-Matos, Hamideh Afsarmanesh, Rosanna Fornasiero, 2017-09-06 This book constitutes the refereed proceedings of the 18th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2017, held in Vicenza, Italy, in September 2017. The 68 revised full papers were carefully reviewed and selected from 159 submissions. They provide a comprehensive overview of identified challenges and recent advances in various collaborative network (CN) domains and their applications, with a strong focus on the following areas: collaborative models, platforms and systems for data-rich worlds; manufacturing ecosystem and collaboration in Industry 4.0; big data analytics and intelligence; risk, performance, and uncertainty in collaborative data-rich systems; semantic data/service discovery, retrieval, and composition in a collaborative data-rich world; trust and sustainability analysis in collaborative networks; value creation and social impact of collaboration in data-rich worlds; technology development platforms supporting collaborative systems; collective intelligence and collaboration in advanced/emerging applications: collaborative manufacturing and factories of the future, e-health and care, food and agribusiness, and crisis/disaster management. |
| collaboration in data science: Research Collaboration and Team Science Barry Bozeman, Craig Boardman, 2014-05-16 Today in most scientific and technical fields more than 90% of research studies and publications are collaborative, often resulting in high-impact research and development of commercial applications, as reflected in patents. Nowadays in many areas of science, collaboration is not a preference but, literally, a work prerequisite. The purpose of this book is to review and critique the burgeoning scholarship on research collaboration. The authors seek to identify gaps in theory and research and identify the ways in which existing research can be used to improve public policy for collaboration and to improve project-level management of collaborations using Scientific and Technical Human Capital (STHC) theory as a framework. Broadly speaking, STHC is the sum of scientific and technical and social knowledge, skills and resources embodied in a particular individual. It is both human capital endowments, such as formal education and training and social relations and network ties that bind scientists and the users of science together. STHC includes the human capital which is the unique set of resources the individual brings to his or her own work and to collaborative efforts. Generally, human capital models have developed separately from social capital models, but in the practice of science and the career growth of scientists, the two are not easily disentangled. Using a multi-factor model, the book explores various factors affecting collaboration outcomes, with particular attention on institutional factors such as industry-university relations and the rise of large-scale university research centers. |
| collaboration in data science: Agile Data Science 2.0 Russell Jurney, 2017-06-07 Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track |
| collaboration in data science: Human-Centered Data Science Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, Gina Neff, 2022-03-01 Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns. |
| collaboration in data science: Data Science For Dummies Lillian Pierson, 2015-02-20 Discover how data science can help you gain in-depth insight into your business – the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer covering all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad aspects of the topic, including the sometimes intimidating field of big data and data science, it is not an instructional manual for hands-on implementation. Here’s what to expect in Data Science for Dummies: Provides a background in big data and data engineering before moving on to data science and how it’s applied to generate value. Includes coverage of big data frameworks and applications like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL. Explains machine learning and many of its algorithms, as well as artificial intelligence and the evolution of the Internet of Things. Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate. It’s a big, big data world out there – let Data Science For Dummies help you get started harnessing its power so you can gain a competitive edge for your organization. |
| collaboration in data science: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
| collaboration in data science: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| collaboration in data science: Opening Science Sönke Bartling, Sascha Friesike, 2013-12-16 Modern information and communication technologies, together with a cultural upheaval within the research community, have profoundly changed research in nearly every aspect. Ranging from sharing and discussing ideas in social networks for scientists to new collaborative environments and novel publication formats, knowledge creation and dissemination as we know it is experiencing a vigorous shift towards increased transparency, collaboration and accessibility. Many assume that research workflows will change more in the next 20 years than they have in the last 200. This book provides researchers, decision makers, and other scientific stakeholders with a snapshot of the basics, the tools, and the underlying visions that drive the current scientific (r)evolution, often called ‘Open Science.’ |
| collaboration in data science: Data Science for Beginners: A Hands-On Guide to Big Data Michael Roberts, Unlock the power of data with Data Science for Beginners: A Hands-On Guide to Big Data. This comprehensive guide introduces you to the world of data science, covering everything from the basics of data collection and preparation to advanced machine learning techniques and practical data science projects. Whether you're new to the field or looking to enhance your skills, this book provides step-by-step instructions, real-world examples, and best practices to help you succeed. Discover the tools and technologies used by data scientists, learn how to analyze and visualize data, and explore the vast opportunities that data science offers in various industries. Start your data science journey today and transform data into actionable insights. |
| collaboration in data science: Remote Work and Sustainable Changes for the Future of Global Business Ali, Mohammed, 2021-06-25 There is a void of research and other academic materials to support stakeholders operating within industry and the service sector with respect to their perceptions and experiences of remote work, particularly in the context of global business, sustainability, and change management. As more businesses consider remaining and maintaining a remote workforce, it is of paramount importance that new research be conducted regarding the multifaceted area of remote work and sustainable change for global business. Remote Work and Sustainable Changes for the Future of Global Business raises awareness of the multifaceted area of remote work in the context of sustainable change. In particular, it explores remote technology in an attempt to cope with the changing landscape of work environments amidst global change from a sociotechnical perspective. This book provides insight into the challenges both national and international businesses face during a world crisis. Covering topics such as crisis management, the human cloud, and virtual collaboration, this book is essential to business managers, project managers, business clusters, entrepreneurs, higher education practitioners, faculty and PhD researchers, educational boards, technology vendors and firms, and academic researchers. |
| collaboration in data science: Integrating Data Science and Earth Science Laurens M. Bouwer, Doris Dransch, Roland Ruhnke, Diana Rechid, Stephan Frickenhaus, Jens Greinert, 2022-07-14 This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows. |
| collaboration in data science: Data Mining and Decision Support Dunja Mladenic, Nada Lavrač, Marko Bohanec, Steve Moyle, 2012-12-06 Data mining deals with finding patterns in data that are by user-definition, interesting and valid. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, visualization and others. Decision support focuses on developing systems to help decision-makers solve problems. Decision support provides a selection of data analysis, simulation, visualization and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases and data warehouses. Independently, data mining and decision support are well-developed research areas, but until now there has been no systematic attempt to integrate them. Data Mining and Decision Support: Integration and Collaboration, written by leading researchers in the field, presents a conceptual framework, plus the methods and tools for integrating the two disciplines and for applying this technology to business problems in a collaborative setting. |
| collaboration in data science: National Collaboratories National Research Council, Computer Science and Telecommunications Board, Committee Toward a National Collaboratory: Establishing the User-Developer Partnership, 1993-02-01 Computing and communications are becoming essential tools of science. Together, they make possible new kinds and degrees of collaboration. This book addresses technical, scientific, and social aspects of fostering scientific collaboration using information technology. It explores issues in molecular biology, oceanography, and space physics, and derives recommendations for a partnership between scientists and technologists to develop better collaboration technology to support science. |
| collaboration in data science: Cracking the Data Science Interview Leondra R. Gonzalez, Aaren Stubberfield, 2024-02-29 Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much more Key Features Acquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learning Gain the confidence to explain complex statistical, machine learning, and deep learning theory Extend your expertise beyond model development with version control, shell scripting, and model deployment fundamentals Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.What you will learn Explore data science trends, job demands, and potential career paths Secure interviews with industry-standard resume and portfolio tips Practice data manipulation with Python and SQL Learn about supervised and unsupervised machine learning models Master deep learning components such as backpropagation and activation functions Enhance your productivity by implementing code versioning through Git Streamline workflows using shell scripting for increased efficiency Who this book is for Whether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews. |
| collaboration in data science: Introduction to Data Science and Machine Learning Keshav Sud, Pakize Erdogmus, Seifedine Kadry, 2020-03-25 Introduction to Data Science and Machine Learning has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four sections: the first section contains an introduction to the book, the second covers the field of data science, software development, and open-source based embedded hardware; the third section covers algorithms that are the decision engines for data science applications; and the final section brings together the concepts shared in the first three sections and provides several examples of data science applications. |
| collaboration in data science: The Strength in Numbers Barry Bozeman, Jan Youtie, 2020-07-14 Why collaborations in STEM fields succeed or fail and how to ensure success Once upon a time, it was the lone scientist who achieved brilliant breakthroughs. No longer. Today, science is done in teams of as many as hundreds of researchers who may be scattered across continents. These collaborations can be powerful, but they also demand new ways of thinking. The Strength in Numbers illuminates the nascent science of team science by synthesizing the results of the most far-reaching study to date on collaboration among university scientists. Drawing on a national survey with responses from researchers at more than one hundred universities, archival data, and extensive interviews with scientists and engineers in over a dozen STEM disciplines, Barry Bozeman and Jan Youtie establish a framework for characterizing different collaborations and their outcomes, and lay out what they have found to be the gold-standard approach: consultative collaboration management. The Strength in Numbers is an indispensable guide for scientists interested in maximizing collaborative success. |
| collaboration in data science: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| collaboration in data science: Microsoft Certified: Azure Data Scientist Associate (DP-100) Cybellium, Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| collaboration in data science: Data Science and Business Intelligence Heverton Anunciação, 2023-12-04 A professional, no matter what area he belongs to, I believe, should never think that his truth is definitive or that his way of doing or solving something is the best. And, logically, I had to get it right and wrong to reach this simple conclusion. Now, what does that have to do with the purpose of this book? This book that I have gathered important tips and advice from an elite of data science professionals from various sectors and reputable experience? After I've worked on hundreds of consulting projects and implementation of best practices in Relationship Marketing (CRM), Business Intelligence (BI) and Customer Experience (CX), as well as countless Information Technology projects, one truth is absolute: We need data! Most companies say they do everything perfect, but it is not shown in the media or the press the headache that the areas of Information Technology suffer to join the right data. And when they do manage to unite and make it available, the time to market has already been lost and possible opportunities. Therefore, if a company wants to be considered excellence in corporate governance and satisfy the legal, marketing, sales, customer service, technology, logistics, products, among other areas, this company must start as soon as possible to become a data driven and real-time company. For this, I recommend companies to look for their digital intuitions, and digital inspirations. So, with this book, I am proposing that all the employees and companies will arrive one day that they will know how to use, from their data, their sixth sense. The sixth sense is an extrasensory perception, which goes beyond our five basic senses, vision, hearing, taste, smell, touch. It is a sensation of intuition, which in a certain way allows us to have sensations of clairvoyance and even visions of future events. A company will only achieve this ability if it immediately begins to apply true data governance. And the illustrious data scientists who are part of this book will show you the way to take the first step: - Eric Siegel, Predictive Analytics World, USA - Bill Inmon, The Father of Datawarehouse, Forest Rim Technology, USA - Bram Nauts, ABN AMRO Bank, Netherlands - Jim Sterne, Digital Analytics Association, USA - Terry Miller, Siemens, USA - Shivanku Misra, Hilton Hotels, USA - Caner Canak, Turkcell, Turkey - Dr. Kirk Borne, Booz Allen Hamilton, USA - Dr. Bülent Kızıltan, Harvard University, USA - Kate Strachnyi, Story by Data, USA - Kristen Kehrer, Data Moves Me, USA - Marie Wallace, IBM Watson Health, Ireland - Timothy Kooi, DHL, Singapore - Jesse Anderson, Big Data Institute, USA - Charles Givre, JPMorgan Chase & Co, USA - Anne Buff, Centene Corporation, USA - Bala Venkatesh, AIBOTS, Malaysia - Mauro Damo, Hitachi Vantara, USA - Dr. Rajkumar Bondugula, Equifax, USA - Waldinei Guimaraes, Experian, Brazil - Michael Ferrari, Atlas Research Innovations, USA - Dr. Aviv Gruber, Tel-Aviv University, Israel - Amit Agarwal, NVIDIA, India This book is part of the CRM and Customer Experience Trilogy called CX Trilogy which aims to unite the worldwide community of CX, Customer Service, Data Science and CRM professionals. I believe that this union would facilitate the contracting of our sector and profession, as well as identifying the best professionals in the market. The CX Trilogy consists of 3 books and a dictionary: 1st) 30 Advice from 30 greatest professionals in CRM and customer service in the world; 2nd) The Book of all Methodologies and Tools to Improve and Profit from Customer Experience and Service; 3rd) Data Science and Business Intelligence - Advice from reputable Data Scientists around the world; and plus, the book: The Official Dictionary for Internet, Computer, ERP, CRM, UX, Analytics, Big Data, Customer Experience, Call Center, Digital Marketing and Telecommunication: The Vocabulary of One New Digital World |
| collaboration in data science: Machine Learning and Data Science Basics Cybellium Ltd, Your Essential Guide to Understanding Data-driven Technologies In a world inundated with data, the ability to harness its power through machine learning and data science is a vital skill. Machine Learning and Data Science Basics is your gateway to unraveling the complexities of these transformative technologies, offering a comprehensive introduction to the fundamental concepts that drive data-driven decision-making. About the Book: In an era where data has become the driving force behind innovation and growth, understanding the principles of machine learning and data science is no longer optional—it's essential. Machine Learning and Data Science Basics demystifies these disciplines, making them accessible to beginners while providing valuable insights for those looking to expand their knowledge. Key Features: Foundation Building: Start your journey by grasping the core concepts of data science, machine learning, and their intersection. Understand how data drives insights and empowers informed decisions. Data Exploration: Dive into data exploration techniques, learning how to clean, transform, and prepare data for analysis. Discover the crucial role data quality plays in obtaining accurate results. Machine Learning Essentials: Uncover the basics of machine learning algorithms, including supervised and unsupervised learning. Explore how algorithms learn patterns from data and make predictions or classifications. Feature Engineering: Learn the art of feature engineering—the process of selecting and transforming relevant data attributes to improve model performance and accuracy. Model Evaluation: Delve into model evaluation techniques to assess the performance of machine learning models. Understand metrics such as accuracy, precision, recall, and F1 score. Introduction to Data Science Tools: Familiarize yourself with essential data science tools and libraries, such as Python, NumPy, pandas, and scikit-learn. Gain hands-on experience with practical examples. Real-World Applications: Explore case studies showcasing how machine learning and data science are applied across industries. From recommendation systems to fraud detection, understand their impact on diverse domains. Why This Book Matters: In a landscape driven by data, proficiency in machine learning and data science is a competitive advantage. Machine Learning and Data Science Basics empowers individuals, students, and professionals to build a strong foundation in these fields, enabling them to contribute meaningfully to data-driven projects. Who Should Read This Book: Students and Beginners: Build a solid understanding of the principles underlying machine learning and data science. Professionals Seeking Knowledge: Enhance your expertise by familiarizing yourself with foundational concepts. Business Leaders: Grasp the potential of data-driven technologies to make informed strategic decisions. Embark on Your Data Journey: The era of data-driven decision-making is here to stay. Machine Learning and Data Science Basics equips you with the knowledge needed to embark on this exciting journey. Whether you're a novice eager to understand the basics or a professional looking to enhance your skill set, this book will guide you through the transformative landscape of machine learning and data science, setting the stage for continued learning and growth. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| collaboration in data science: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-10-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
| collaboration in data science: Recent Advancement in Geoinformatics and Data Science Xiaogang Ma, Matty Mookerjee, Leslie Hsu, Denise Hills, 2023-04-11 |
| collaboration in data science: Towards a Collaborative Society Through Creative Learning Therese Keane, Cathy Lewin, Torsten Brinda, Rosa Bottino, 2023-09-27 This book contains the revised selected, refereed papers from the IFIP World Conference on Computers in Education on Towards a Collaborative Society through Creative Learning, WCCE 2022, Hiroshima, Japan, August 20-24, 2022. A total of 61 papers (54 full papers and 7 short papers) were carefully reviewed and selected from 131 submissions. They were organized in topical sections as follows: Digital Education and Computing in Schools, Digital Education and Computing in Higher Education, National Policies and Plans for Digital Competence. |
Collaboration and teams - HBR - Harvard Business Review
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Feb 13, 2024 · Jenny Fernandez is an executive and team coach who helps senior leaders and teams boost …
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Companies that excel at collaboration, in contrast, realize it involves instilling the right mindset: widespread …
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Gratton, a London Business School professor, and Erickson, president of the Concours Institute, studied 55 …
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Collaboration and teams - HBR - Harvard Business Review
4 days ago · The HBR Executive Playbook on fostering collaboration—and avoiding power …
Why Collaboration Is Critical in Uncertain Times - Harvard Bu…
Feb 13, 2024 · Jenny Fernandez is an executive and team coach who helps senior leaders and teams boost …
Cracking the Code of Sustained Collaboration - Harvard Busin…
Companies that excel at collaboration, in contrast, realize it involves instilling the right mindset: widespread …
Eight Ways to Build Collaborative Teams - Harvar…
Gratton, a London Business School professor, and Erickson, president of the Concours Institute, studied 55 …
4 Tips for Effective Virtual Collaboration - Harvard Busin…
Oct 13, 2020 · Team collaboration done right is a powerful force to align a group of individuals to accomplish a …
