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| data science bridging principles and practice: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science bridging principles and practice: Responsible Data Science Grant Fleming, Peter C. Bruce, 2021-04-21 Explore the most serious prevalent ethical issues in data science with this insightful new resource The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair. Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to: Improve model transparency, even for black box models Diagnose bias and unfairness within models using multiple metrics Audit projects to ensure fairness and minimize the possibility of unintended harm Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians. |
| data science bridging principles and practice: Responsible Data Science Peter C. Bruce, Grant Fleming, 2021-04-13 Explore the most serious prevalent ethical issues in data science with this insightful new resource The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair. Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to: Improve model transparency, even for black box models Diagnose bias and unfairness within models using multiple metrics Audit projects to ensure fairness and minimize the possibility of unintended harm Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians. |
| data science bridging principles and practice: 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 bridging principles and practice: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data science bridging principles and practice: Mathematical Problems in Data Science Li M. Chen, Zhixun Su, Bo Jiang, 2015-12-15 This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful. |
| data science bridging principles and practice: Aligning Business Strategies and Analytics Murugan Anandarajan, Teresa D. Harrison, 2018-09-27 This book examines issues related to the alignment of business strategies and analytics. Vast amounts of data are being generated, collected, stored, processed, analyzed, distributed and used at an ever-increasing rate by organizations. Simultaneously, managers must rapidly and thoroughly understand the factors driving their business. Business Analytics is an interactive process of analyzing and exploring enterprise data to find valuable insights that can be exploited for competitive advantage. However, to gain this advantage, organizations need to create a sophisticated analytical climate within which strategic decisions are made. As a result, there is a growing awareness that alignment among business strategies, business structures, and analytics are critical to effectively develop and deploy techniques to enhance an organization’s decision-making capability. In the past, the relevance and usefulness of academic research in the area of alignment is often questioned by practitioners, but this book seeks to bridge this gap. Aligning Business Strategies and Analytics: Bridging Between Theory and Practice is comprised of twelve chapters, divided into three sections. The book begins by introducing business analytics and the current gap between academic training and the needs within the business community. Chapters 2 - 5 examines how the use of cognitive computing improves financial advice, how technology is accelerating the growth of the financial advising industry, explores the application of advanced analytics to various facets of the industry and provides the context for analytics in practice. Chapters 6 - 9 offers real-world examples of how project management professionals tackle big-data challenges, explores the application of agile methodologies, discusses the operational benefits that can be gained by implementing real-time, and a case study on human capital analytics. Chapters 10 - 11 reviews the opportunities and potential shortfall and highlights how new media marketing and analytics fostered new insights. Finally the book concludes with a look at how data and analytics are playing a revolutionary role in strategy development in the chemical industry. |
| data science bridging principles and practice: Transforming Teaching and Learning Through Data-Driven Decision Making Ellen B. Mandinach, Sharnell S. Jackson, 2012-04-10 Gathering data and using it to inform instruction is a requirement for many schools, yet educators are not necessarily formally trained in how to do it. This book helps bridge the gap between classroom practice and the principles of educational psychology. Teachers will find cutting-edge advances in research and theory on human learning and teaching in an easily understood and transferable format. The text's integrated model shows teachers, school leaders, and district administrators how to establish a data culture and transform quantitative and qualitative data into actionable knowledge based on: assessment; statistics; instructional and differentiated psychology; classroom management.--Publisher's description. |
| data science bridging principles and practice: Elgar Encyclopedia of Law and Data Science Comandé, Giovanni, 2022-02-18 This Encyclopedia brings together jurists, computer scientists, and data analysts to map the emerging field of data science and law for the first time, uncovering the challenges, opportunities, and fault lines that arise as these groups are increasingly thrown together by expanding attempts to regulate and adapt to a data-driven world. It explains the concepts and tools at the crossroads of the many disciplines involved in data science and law, bridging scientific and applied domains. Entries span algorithmic fairness, consent, data protection, ethics, healthcare, machine learning, patents, surveillance, transparency and vulnerability. |
| data science bridging principles and practice: Data Science Projects with Python Stephen Klosterman, 2021-07-29 Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost Key FeaturesThink critically about data and use it to form and test a hypothesisChoose an appropriate machine learning model and train it on your dataCommunicate data-driven insights with confidence and clarityBook Description If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable. In this book, you'll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you'll experience in real-world data science projects. You'll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest. Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. By the end of this data science book, you'll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data. What you will learnLoad, explore, and process data using the pandas Python packageUse Matplotlib to create compelling data visualizationsImplement predictive machine learning models with scikit-learnUse lasso and ridge regression to reduce model overfittingEvaluate random forest and logistic regression model performanceDeliver business insights by presenting clear, convincing conclusionsWho this book is for Data Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you're keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics. |
| data science bridging principles and practice: Facework Kathy Domenici, Stephen W. Littlejohn, 2006-04-27 Written in a clear, engaging style Facework: Bridging Theory and Practice introduces a new paradigm that identifies facework as the key to communication within the management of difference. Authors Kathy Domenici and Stephen W. Littlejohn illustrate how facework is a central process in the social construction of both identity and community. |
| data science bridging principles and practice: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano, 2021-09-09 The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media. |
| data science bridging principles and practice: Toward a Science of Consciousness II Stuart R. Hameroff, Alfred W. Kaszniak, Alwyn Scott, 1998 This text originates from the second of two conferences discussing the concept of consciousness. In 15 sections, this book demonstrates the broad range of fields now focusing on consciousness. |
| data science bridging principles and practice: Business and Consumer Analytics: New Ideas Pablo Moscato, Natalie Jane de Vries, 2019-05-30 This two-volume handbook presents a collection of novel methodologies with applications and illustrative examples in the areas of data-driven computational social sciences. Throughout this handbook, the focus is kept specifically on business and consumer-oriented applications with interesting sections ranging from clustering and network analysis, meta-analytics, memetic algorithms, machine learning, recommender systems methodologies, parallel pattern mining and data mining to specific applications in market segmentation, travel, fashion or entertainment analytics. A must-read for anyone in data-analytics, marketing, behavior modelling and computational social science, interested in the latest applications of new computer science methodologies. The chapters are contributed by leading experts in the associated fields.The chapters cover technical aspects at different levels, some of which are introductory and could be used for teaching. Some chapters aim at building a common understanding of the methodologies and recent application areas including the introduction of new theoretical results in the complexity of core problems. Business and marketing professionals may use the book to familiarize themselves with some important foundations of data science. The work is a good starting point to establish an open dialogue of communication between professionals and researchers from different fields. Together, the two volumes present a number of different new directions in Business and Customer Analytics with an emphasis in personalization of services, the development of new mathematical models and new algorithms, heuristics and metaheuristics applied to the challenging problems in the field. Sections of the book have introductory material to more specific and advanced themes in some of the chapters, allowing the volumes to be used as an advanced textbook. Clustering, Proximity Graphs, Pattern Mining, Frequent Itemset Mining, Feature Engineering, Network and Community Detection, Network-based Recommending Systems and Visualization, are some of the topics in the first volume. Techniques on Memetic Algorithms and their applications to Business Analytics and Data Science are surveyed in the second volume; applications in Team Orienteering, Competitive Facility-location, and Visualization of Products and Consumers are also discussed. The second volume also includes an introduction to Meta-Analytics, and to the application areas of Fashion and Travel Analytics. Overall, the two-volume set helps to describe some fundamentals, acts as a bridge between different disciplines, and presents important results in a rapidly moving field combining powerful optimization techniques allied to new mathematical models critical for personalization of services. Academics and professionals working in the area of business anyalytics, data science, operations research and marketing will find this handbook valuable as a reference. Students studying these fields will find this handbook useful and helpful as a secondary textbook. |
| data science bridging principles and practice: Intelligent Virtual Agents Themis Panayiotopoulos, Jonathan Gratch, Ruth Aylett, Daniel Ballin, Patrick Olivier, Thomas Rist, 2005-09-22 The origin of the Intelligent Virtual Agents conference dates from a successful workshop on Intelligent Virtual Environments held in Brighton at the 13th European Conference on Artificial Intelligence (ECAI'98). This workshop was followed by a second one held in Salford in Manchester in 1999. Subsequent events took place in Madrid, Spain in 2001 and Irsee, Germany in 2003 and attracted participants from both sides of the Atlantic as well as Asia. th This volume contains the proceedings of the 5 International Working Conference on Intelligent Virtual Agents, IVA 2005, held on Kos Island, Greece, September 12–14, 2005, which highlighted once again the importance and vigor of the research field. A half-day workshop under the title “Socially Competent IVA’s: We are not alone in this (virtual) world!” also took place as part of this event. IVA 2005 received 69 submissions from Europe, North and South America, Africa and Asia. The papers published here are the 26 full papers and 14 short papers presented at the conference, as well as one-page descriptions of the 15 posters and the descriptions of the featured invited talks by Prof. Justine Cassell, of Northwestern University and Prof. Kerstin Dautenhahn, of the University of Hertfordshire. We would like to thank a number of people that have contributed to the success of this conference. First of all, we thank the authors for their high-quality work and their willingness to share their ideas. |
| data science bridging principles and practice: Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track Yuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke, 2021-02-24 The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track. |
| data science bridging principles and practice: Mind, Brain and the Path to Happiness Dusana Dorjee, 2013-09-11 Mind, Brain and the Path to Happiness presents a contemporary account of traditional Buddhist mind training and the pursuit of wellbeing and happiness in the context of the latest research in psychology and the neuroscience of meditation. Following the Tibetan Buddhist tradition of Dzogchen, the book guides the reader through the gradual steps in transformation of the practitioner’s mind and brain on the path to advanced states of balance, genuine happiness and wellbeing. Dusana Dorjee explains how the mind training is grounded in philosophical and experiential exploration of the notions of happiness and human potential, and how it refines attention skills and cultivates emotional balance in training of mindfulness, meta-awareness and development of healthy emotions. The book outlines how the practitioner can explore subtle aspects of conscious experience in order to recognize the nature of the mind and reality. At each of the steps on the path the book provides novel insights into similarities and differences between Buddhist accounts and current psychological and neuroscientific theories and evidence. Throughout the book the author skilfully combines Buddhist psychology and Western scientific research with examples of meditation practices, highlighting the ultimately practical nature of Buddhist mind training. Mind, Brain and the Path to Happiness is an important book for health professionals and educators who teach or apply mindfulness and meditation-based techniques in their work, as well as for researchers and students investigating these techniques both in a clinical context and in the emerging field of contemplative science. |
| data science bridging principles and practice: Programming Multi-Agent Systems Rafael H. Bordini, Mehdi Dastani, Amal El Fallah Seghrouchni, 2005-02-23 This book constitutes the thoroughly refereed postproceedings of the Second International Workshop on Programming Multi-Agent Systems, ProMAS 2004, held in New York, NY, USA in July 2004 as a satellite workshop of AAMAS 2004. The 10 revised full papers presented together with two invited articles were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on agent-oriented programming, agent platforms and tools, agent languages, and multi-agent systems techniques. |
| data science bridging principles and practice: Practical Data Science Cookbook Prabhanjan Tattar, Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta, 2017-06-29 Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization |
| data science bridging principles and practice: Ultimate MLOps for Machine Learning Models Saurabh Dorle, 2024-08-30 TAGLINE The only MLOps guide you'll ever need KEY FEATURES ● Acquire a comprehensive understanding of the entire MLOps lifecycle, from model development to monitoring and governance. ● Gain expertise in building efficient MLOps pipelines with the help of practical guidance with real-world examples and case studies. ● Develop advanced skills to implement scalable solutions by understanding the latest trends/tools and best practices. DESCRIPTION This book is an essential resource for professionals aiming to streamline and optimize their machine learning operations. This comprehensive guide provides a thorough understanding of the MLOps life cycle, from model development and training to deployment and monitoring. By delving into the intricacies of each phase, the book equips readers with the knowledge and tools needed to create robust, scalable, and efficient machine learning workflows. Key chapters include a deep dive into essential MLOps tools and technologies, effective data pipeline management, and advanced model optimization techniques. The book also addresses critical aspects such as scalability challenges, data and model governance, and security in machine learning operations. Each topic is presented with practical insights and real-world case studies, enabling readers to apply best practices in their job roles. Whether you are a data scientist, ML engineer, or IT professional, this book empowers you to take your machine learning projects from concept to production with confidence. It equips you with the practical skills to ensure your models are reliable, secure, and compliant with regulations. By the end, you will be well-positioned to navigate the ever-evolving landscape of MLOps and unlock the true potential of your machine learning initiatives. WHAT WILL YOU LEARN ● Implement and manage end-to-end machine learning lifecycles. ● Utilize essential tools and technologies for MLOps effectively. ● Design and optimize data pipelines for efficient model training. ● Develop and train machine learning models with best practices. ● Deploy, monitor, and maintain models in production environments. ● Address scalability challenges and solutions in MLOps. ● Implement robust security practices to protect your ML systems. ● Ensure data governance, model compliance, and security in ML operations. ● Understand emerging trends in MLOps and stay ahead of the curve. WHO IS THIS BOOK FOR? This book is for data scientists, machine learning engineers, and data engineers aiming to master MLOps for effective model management in production. It’s also ideal for researchers and stakeholders seeking insights into how MLOps drives business strategy and scalability, as well as anyone with a basic grasp of Python and machine learning looking to enter the field of data science in production. TABLE OF CONTENTS 1. Introduction to MLOps 2. Understanding Machine Learning Lifecycle 3. Essential Tools and Technologies in MLOps 4. Data Pipelines and Management in MLOps 5. Model Development and Training 6. Model Optimization Techniques for Performance 7. Efficient Model Deployment and Monitoring Strategies 8. Scalability Challenges and Solutions in MLOps 9. Data, Model Governance, and Compliance in Production Environments 10. Security in Machine Learning Operations 11. Case Studies and Future Trends in MLOps Index |
| data science bridging principles and practice: Organizational Behavior Talya Bauer, Berrin Erdogan, 2021 |
| data science bridging principles and practice: How Learning Works Susan A. Ambrose, Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, Marie K. Norman, 2010-04-16 Praise for How Learning Works How Learning Works is the perfect title for this excellent book. Drawing upon new research in psychology, education, and cognitive science, the authors have demystified a complex topic into clear explanations of seven powerful learning principles. Full of great ideas and practical suggestions, all based on solid research evidence, this book is essential reading for instructors at all levels who wish to improve their students' learning. —Barbara Gross Davis, assistant vice chancellor for educational development, University of California, Berkeley, and author, Tools for Teaching This book is a must-read for every instructor, new or experienced. Although I have been teaching for almost thirty years, as I read this book I found myself resonating with many of its ideas, and I discovered new ways of thinking about teaching. —Eugenia T. Paulus, professor of chemistry, North Hennepin Community College, and 2008 U.S. Community Colleges Professor of the Year from The Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education Thank you Carnegie Mellon for making accessible what has previously been inaccessible to those of us who are not learning scientists. Your focus on the essence of learning combined with concrete examples of the daily challenges of teaching and clear tactical strategies for faculty to consider is a welcome work. I will recommend this book to all my colleagues. —Catherine M. Casserly, senior partner, The Carnegie Foundation for the Advancement of Teaching As you read about each of the seven basic learning principles in this book, you will find advice that is grounded in learning theory, based on research evidence, relevant to college teaching, and easy to understand. The authors have extensive knowledge and experience in applying the science of learning to college teaching, and they graciously share it with you in this organized and readable book. —From the Foreword by Richard E. Mayer, professor of psychology, University of California, Santa Barbara; coauthor, e-Learning and the Science of Instruction; and author, Multimedia Learning |
| data science bridging principles and practice: Data Science and Human-Environment Systems Steven M. Manson, 2023-01-31 Transformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences. |
| data science bridging principles and practice: Open Access and the Library Anja Oberländer, Torsten Reimer, 2019-04-04 Libraries are places of learning and knowledge creation. Over the last two decades, digital technology—and the changes that came with it—have accelerated this transformation to a point where evolution starts to become a revolution. The wider Open Science movement, and Open Access in particular, is one of these changes and is already having a profound impact. Under the subscription model, the role of libraries was to buy or license content on behalf of their users and then act as gatekeepers to regulate access on behalf of rights holders. In a world where all research is open, the role of the library is shifting from licensing and disseminating to facilitating and supporting the publishing process itself. This requires a fundamental shift in terms of structures, tasks, and skills. It also changes the idea of a library’s collection. Under the subscription model, contemporary collections largely equal content bought from publishers. Under an open model, the collection is more likely to be the content created by the users of the library (researchers, staff, students, etc.), content that is now curated by the library. Instead of selecting external content, libraries have to understand the content created by their own users and help them to make it publicly available—be it through a local repository, payment of article processing charges, or through advice and guidance. Arguably, this is an overly simplified model that leaves aside special collections and other areas. Even so, it highlights the changes that research libraries are undergoing, changes that are likely to accelerate as a result of initiatives such as Plan S. This Special Issue investigates some of the changes in today’s library services that relate to open access. |
| data science bridging principles and practice: Bridging Science and Policy for Surveillance, Economics and Social Sciences: ICAHS & ISESSAH 2020 Carola Sauter-Louis, Lis Alban, Victoria J. Brookes, Victor Javier Del Rio Vilas, Salome Dürr, Chris J. M. Bartels, Bouda Vosough Ahmadi, Jonathan Rushton, 2022-01-10 Topic Editor Lis Alban works for an organization that gives advice to farmers and abattoirs. All other Topic Editors declare no competing interests with regard to the Research Topic subject. |
| data science bridging principles and practice: Data Analytics Initiatives Ondřej Bothe, Ondřej Kubera, David Bednář, Martin Potančok, Ota Novotný, 2022-04-20 The categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives. But how can this complex work be categorized? What makes it so complex? Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different. At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects. Describing these commonalities helps to develop a conceptual understanding of analytical work. However, features specific to each initiative affects the entire analytics project lifecycle. Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges. The book also presents examples of questions that could be asked of relevant people to analyse an analytics project. These questions help to position properly the project and to find commonalities and general project challenges. |
| data science bridging principles and practice: GIS and Machine Learning for Small Area Classifications in Developing Countries Adegbola Ojo, 2020-12-30 Since the emergence of contemporary area classifications, population geography has witnessed a renaissance in the area of policy related spatial analysis. Area classifications subsume geodemographic systems which often use data mining techniques and machine learning algorithms to simplify large and complex bodies of information about people and the places in which they live, work and undertake other social activities. Outputs developed from the grouping of small geographical areas on the basis of multi- dimensional data have proved beneficial particularly for decision-making in the commercial sectors of a vast number of countries in the northern hemisphere. This book argues that small area classifications offer countries in the Global South a distinct opportunity to address human population policy related challenges in novel ways using area-based initiatives and evidence-based methods. This book exposes researchers, practitioners, and students to small area segmentation techniques for understanding, interpreting, and visualizing the configuration, dynamics, and correlates of development policy challenges at small spatial scales. It presents strategic and operational responses to these challenges in cost effective ways. Using two developing countries as case studies, the book connects new transdisciplinary ways of thinking about social and spatial inequalities from a scientific perspective with GIS and Data Science. This offers all stakeholders a framework for engaging in practical dialogue on development policy within urban and rural settings, based on real-world examples. Features: The first book to address the huge potential of small area segmentation for sustainable development, combining explanations of concepts, a range of techniques, and current applications. Includes case studies focused on core challenges that confront developing countries and provides thorough analytical appraisal of issues that resonate with audiences from the Global South. Combines GIS and machine learning methods for studying interrelated disciplines such as Demography, Urban Science, Sociology, Statistics, Sustainable Development and Public Policy. Uses a multi-method approach and analytical techniques of primary and secondary data. Embraces a balanced, chronological, and well sequenced presentation of information, which is very practical for readers. |
| data science bridging principles and practice: How To Become A Data Scientist With ChatGPT: A Beginner's Guide to ChatGPT-Assisted Programming Rafiq Muhammad, 2024-01-13 Are you aspiring to become a data scientist but feeling overwhelmed by the challenges of coding in programming languages? Are you new to data science and don't know how to code in any programming language? Look no further; this book is your comprehensive solution. Master the fundamentals of code generation with ChatGPT, learn to craft effective prompts, and navigate the DOs and DON'Ts of this invaluable tool. This book tackles the problem many aspiring data scientists face: the lack of programming skills. It's a step-by-step guide that utilizes the transformative potential of ChatGPT to empower you to code efficiently, streamline complex data analytics, and become a successful data scientist. The book contains: The role of ChatGPT in Data Science ChatGPT for Data Analytics ChatGPT-assisted programming Step-by-step approach to code generation in ChatGPT for data science Case Studies to Demonstrate Data Analysis with ChatGPT Whether you are an experienced data scientist or just starting, this book will be your trusted ally in the journey. It explores real-world applications, deepens your understanding of predictive analytics, and supercharges your data science projects. Don't let programming hurdles hold you back. Let ChatGPT assist you on your path to becoming a data scientist. Are you ready to become a data scientist without a programming background? This book is your definitive guide to a future where ChatGPT empowers your journey to become a data scientist. |
| data science bridging principles and practice: New Technologies for Human Rights Law and Practice Molly K. Land, Jay D. Aronson, 2018-04-19 New technological innovations offer significant opportunities to promote and protect human rights. At the same time, they also pose undeniable risks. In some areas, they may even be changing what we mean by human rights. The fact that new technologies are often privately controlled raises further questions about accountability and transparency and the role of human rights in regulating these actors. This volume - edited by Molly K. Land and Jay D. Aronson - provides an essential roadmap for understanding the relationship between technology and human rights law and practice. It offers cutting-edge analysis and practical strategies in contexts as diverse as autonomous lethal weapons, climate change technology, the Internet and social media, and water meters. This title is also available as Open Access. |
| data science bridging principles and practice: Handbook of STEM Faculty Development Sandra M. Linder, Cindy M. Lee, Shannon K Stefl, Karen A. High, 2022-12-01 Faculty in the science, technology, engineering, and mathematics (STEM) disciplines face intensifying pressures in the 21st century, including multiple roles as educator, researcher, and entrepreneur. In addition to continuously increasing teaching and service expectations, faculty are engaged in substantive research that requires securing external funding, mentoring other faculty and graduate students, and disseminating this work in a broad range of scholarly outlets. Societal needs of their expertise include discovery, innovation, and workforce development. It is critical to provide STEM faculty with the professional development to support their complex roles and to base this development on evidence derived from research. This edited handbook provides STEM stakeholders with an opportunity to share studies and/or experiences that explore STEM faculty development (FD) in higher education settings. More specifically, we include work that examines faculty development planning, techniques/models, experiences, and outcomes focused on supporting the teaching, research, service, and leadership responsibilities of STEM faculty. The Handbook is suited for researchers and practitioners in STEM, STEM Education, Mathematics, Science, Technology, and Engineering disciplines. It is also suited towards faculty developers, higher education administrators, funding agencies, industry leaders, and the STEM community at large. This handbook is organized around three constructs (INPUTS, MECHANISMS, and OUTPUTS). The STEM faculty development inputs construct focuses on topics related to the characteristics of faculty members and institutions that serve as barriers or supports to the adoption and implementation of holistic STEM faculty development programs. Questions addressed in the handbook around this topic include: What barriers/supports exist for STEM faculty? How are these barriers/supports being addressed through STEM FD? How do contexts (e.g., economic, political, historical) influence faculty/administrative needs related to STEM FD? How do demographics (e.g., gender, ethnicity, age, family background) influence faculty/administrative needs related to STEM FD? The STEM faculty development mechanisms construct focuses on topics related to the actual implementation of STEM faculty development and we consider the potential models or structures of STEM faculty development that are currently in place or conceptualized in theory. Questions addressed in the handbook around this topic include: What are the processes for developing models of STEM FD? What are effective models of STEM FD? How is effectiveness determined? What roles do stakeholders (e.g., faculty, administration, consultants) play within STEM FD mechanisms? The STEM faculty development outputs construct focuses on how to best understand the influence of STEM faculty development on outcomes such as productivity, teacher quality, and identity in relation to faculty development. Questions addressed in the handbook around this topic include: How has STEM FD influenced higher education practices and settings? What are appropriate output measures and how are they used in practice? What collaborations emerge from STEM FD? How does STEM FD affect other STEM stakeholders (e.g. students, administration, business, community)? The aim for this handbook was to examine the multifaceted demands of faculty roles, and together with members of the STEM education community, envision pathways through which universities and individuals may support STEM colleagues, regardless of their experience or rank, to enjoy long and satisfying careers. Our hope is for these chapters to aid readers in deep reflection on challenges faculty face, to contemplate adaptations of models presented, and to draw inspiration for creating or engaging in new professional development programs. Chapters across this handbook highlight a variety of institutional contexts from 2-year technical colleges, to teaching-focused institutions, in addition to research-centric settings. Some chapters focus primarily on teaching and learning practices and offer models for improving STEM instruction. Others focus on barriers that emerge for STEM faculty when trying to engage in development experiences. There are chapters that examine tenure structures in relation to faculty development and how STEM FD efforts could support research endeavors. Mentorship and leadership models are also addressed along with a focus on equity issues that permeate higher education and impact STEM FD. It is our sincere hope that this Handbook sparks increased discourse and continued explorations related to STEM FD, and in particular, the intentional focus of faculty development initiatives to extend to the many facets of academic life. |
| data science bridging principles and practice: Artificial Intelligence in HCI Helmut Degen, |
| data science bridging principles and practice: Mobile Services Industries, Technologies, and Applications in the Global Economy Lee, In, 2012-08-31 As business paradigms shift from desktop-centric environments to data-centric mobile environments, mobile services create numerous new business opportunities. At the same time, these advances may also challenge many of the basic premises of existing business models. Mobile Services Industries, Technologies, and Applications in the Global Economy fosters a scientific understanding of mobile services, provides a timely publication of current research efforts, and forecasts future trends in the mobile services industry and its important role in the world economy. Written for academics, researchers, government policymakers, and corporate managers, this comprehensive volume will outline the great potential for new business models and applications in mobile commerce. |
| data science bridging principles and practice: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| data science bridging principles and practice: Encyclopedia of Sport Management Paul M Pedersen, 2024-09-06 This thoroughly updated second edition of the Encyclopedia of Sport Management is an authoritative reference work that provides detailed explanations of critical concepts within the field. |
| data science bridging principles and practice: The Character of Consciousness David J. Chalmers, 2010-10-28 In this book David Chalmers follows up and extends his thoughts and arguments on the nature of consciousness that he first set forth in his groundbreaking 1996 book, The Conscious Mind. |
| data science bridging principles and practice: Social Science Research Anol Bhattacherjee, 2012-04-01 This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages. |
| data science bridging principles and practice: Information Modelling and Knowledge Bases XXXV M. Tropmann-Frick, H. Jaakkola, B. Thalheim, 2024-02 The volume and complexity of information, together with the number of abstraction levels and the size of data and knowledge bases, grow continually. Data originating from diverse sources involves a combination of data from traditional legacy sources and unstructured data requiring backwards modeling, meanwhile, information modeling and knowledge bases have become important contributors to 21st-century academic and industrial research. This book presents the proceedings of EJC 2023, the 33rd International Conference on Information Modeling and Knowledge Bases, held from 5 to 9 June 2023 in Maribor, Slovenia. The aim of the EJC conferences is to bring together experts from different areas of computer science and from other disciplines that share the common interest of understanding and solving the problems of information modeling and knowledge bases and applying the results of research to practice. The conference constitutes a research forum for the exchange of results and experiences by academics and practitioners dealing with information and knowledge bases. The topics covered at EJC 2023 encompass a wide range of themes including conceptual modeling; knowledge and information modeling and discovery; linguistic modeling; cross-cultural communication and social computing; environmental modeling and engineering; and multimedia data modeling and systems. In the spirit of adapting to the changes taking place in these areas of research, the conference was also open to new topics related to its main themes. Providing a current overview of progress in the field, this book will be of interest to all those whose work involves the use of information modeling and knowledge bases. |
| data science bridging principles and practice: The Cognitive Neurosciences Michael S. Gazzaniga, 2004 The third edition of a work that defines the field of cognitive neuroscience, with extensive new material including new chapters and new contributors. |
| data science bridging principles and practice: Ethical Dimensions of AI Development Bhattacharya, Pronaya, Hassan, Ahdi, Liu, Haipeng, Bhushan, Bharat, 2024-10-17 The digital age has witnessed the meteoric rise of artificial intelligence (AI), a paradigm-shifting technology that has redefined the boundaries of computation and decision-making. Initially, AI's journey began with basic rule-based systems, evolving into the current digital age is dominated by complex machine learning and deep learning models. The digital AI presence and progression has brought with it a myriad of ethical challenges, necessitating a rigorous examination of AI's role in complex and interconnected systems. Ethical Dimensions of AI Development notes that the core of these challenges are issues of privacy, transparency, and validity. AI's ability to process vast datasets can intrude on individual privacy, while opaque algorithmic decision-making processes can obscure transparency. Addressing these ethical concerns is crucial to fostering trust and ensuring the responsible use of AI technologies in society. Covering topics such as accountability, discrimination, and privacy and security, this book is an essential resource for AI researchers and developers, data scientists, ethicists, policy makers, legal professionals, technology industry leaders, and more. |
| data science bridging principles and practice: Machine Learning and Knowledge Discovery in Databases Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet, 2020-04-30 The three volume proceedings LNAI 11906 – 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019. The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. The contributions were organized in topical sections named as follows: Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy and security; optimization. Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models; natural language processing. Part III: reinforcement learning and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
