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| data science eastern university: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data science eastern university: Data Science in Chemistry Thorsten Gressling, 2020-11-23 The ever-growing wealth of information has led to the emergence of a fourth paradigm of science. This new field of activity – data science – includes computer science, mathematics and a given specialist domain. This book focuses on chemistry, explaining how to use data science for deep insights and take chemical research and engineering to the next level. It covers modern aspects like Big Data, Artificial Intelligence and Quantum computing. |
| data science eastern university: Introduction to Probability for Data Science Stanley H. Chan, 2021 Probability is one of the most interesting subjects in electrical engineering and computer science. It bridges our favorite engineering principles to the practical reality, a world that is full of uncertainty. However, because probability is such a mature subject, the undergraduate textbooks alone might fill several rows of shelves in a library. When the literature is so rich, the challenge becomes how one can pierce through to the insight while diving into the details. For example, many of you have used a normal random variable before, but have you ever wondered where the 'bell shape' comes from? Every probability class will teach you about flipping a coin, but how can 'flipping a coin' ever be useful in machine learning today? Data scientists use the Poisson random variables to model the internet traffic, but where does the gorgeous Poisson equation come from? This book is designed to fill these gaps with knowledge that is essential to all data science students. -- Preface. |
| data science eastern university: Data Analytics in Cognitive Linguistics Dennis Tay, Molly Xie Pan, 2022-05-09 Contemporary data analytics involves extracting insights from data and translating them into action. With its turn towards empirical methods and convergent data sources, cognitive linguistics is a fertile context for data analytics. There are key differences between data analytics and statistical analysis as typically conceived. Though the former requires the latter, it emphasizes the role of domain-specific knowledge. Statistical analysis also tends to be associated with preconceived hypotheses and controlled data. Data analytics, on the other hand, can help explore unstructured datasets and inspire emergent questions. This volume addresses two key aspects in data analytics for cognitive linguistic work. Firstly, it elaborates the bottom-up guiding role of data analytics in the research trajectory, and how it helps to formulate and refine questions. Secondly, it shows how data analytics can suggest concrete courses of research-based action, which is crucial for cognitive linguistics to be truly applied. The papers in this volume impart various data analytic methods and report empirical studies across different areas of research and application. They aim to benefit new and experienced researchers alike. |
| data science eastern university: Data Mining for the Social Sciences Paul Attewell, David Monaghan, 2015-05 The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and uncovering patterns. These techniques go by many names - data mining, predictive analytics, machine learning - and they are being used by governments as they spy on citizens and by huge corporations are they fine-tune their advertising strategies. And yet social scientists continue mainly to employ a set of analytical tools developed in an earlier era when data was sparse and difficult to come by. In this timely book, Paul Attewell and David Monaghan provide a simple and accessible introduction to Data Mining geared towards social scientists. They discuss how the data mining approach differs substantially, and in some ways radically, from that of conventional statistical modeling familiar to most social scientists. They demystify data mining, describing the diverse set of techniques that the term covers and discussing the strengths and weaknesses of the various approaches. Finally they give practical demonstrations of how to carry out analyses using data mining tools in a number of statistical software packages. It is the hope of the authors that this book will empower social scientists to consider incorporating data mining methodologies in their analytical toolkits--Provided by publisher. |
| data science eastern university: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| data science eastern university: Deep Learning Architectures Ovidiu Calin, 2020-02-13 This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject. |
| data science eastern university: Discovering Knowledge in Data Daniel T. Larose, 2005-01-28 Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a white box methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online. |
| data science eastern university: The Practice of Reproducible Research Justin Kitzes, Daniel Turek, Fatma Deniz, 2018 The Practice of Reproducible Research presents concrete examples of how researchers in the data-intensive sciences are working to improve the reproducibility of their research projects. In each of the thirty-one case studies in this volume, the author or team describes the workflow that they used to complete a real-world research project. Authors highlight how they utilized particular tools, ideas, and practices to support reproducibility, emphasizing the very practical how, rather than the why or what, of conducting reproducible research. Part 1 provides an accessible introduction to reproducible research, a basic reproducible research project template, and a synthesis of lessons learned from across the thirty-one case studies. Parts 2 and 3 focus on the case studies themselves. The Practice of Reproducible Research is an invaluable resource for students and researchers who wish to better understand the practice of data-intensive sciences and learn how to make their own research more reproducible. |
| data science eastern university: Applied Predictive Modeling Max Kuhn, Kjell Johnson, 2013-05-17 Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. |
| data science eastern university: 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 eastern university: Science and Eastern Orthodoxy Efthymios Nicolaidis, 2011-12-15 People have pondered conflicts between science and religion since at least the time of Christ. The millennia-long debate is well documented in the literature in the history and philosophy of science and religion in Western civilization. Science and Eastern Orthodoxy is a departure from that vast body of work, providing the first general overview of the relationship between science and Christian Orthodoxy, the official church of the Oriental Roman Empire. This pioneering study traces a rich history over an impressive span of time, from Saint Basil’s Hexameron of the fourth century to the globalization of scientific debates in the twentieth century. Efthymios Nicolaidis argues that conflicts between science and Greek Orthodoxy—when they existed—were not science versus Christianity but rather ecclesiastical debates that traversed the whole of society. Nicolaidis explains that during the Byzantine period, the Greek fathers of the church and their Byzantine followers wrestled passionately with how to reconcile their religious beliefs with the pagan science of their ancient ancestors. What, they repeatedly asked, should be the church’s official attitude toward secular knowledge? From the rise of the Ottoman Empire in the fifteenth century to its dismantling in the nineteenth century, the patriarchate of Constantinople attempted to control the scientific education of its Christian subjects, an effort complicated by the introduction of European science in the seventeenth and eighteenth centuries. Science and Eastern Orthodoxy provides a wealth of new information concerning Orthodoxy and secular knowledge—and the reactions of the Orthodox Church to modern sciences. |
| data science eastern university: Design and Science Leslie Atzmon, 2023-01-12 Design and Science addresses the inter-relationship, in both historical and contemporary contexts, between design thinking and design processes and scientific and medical research methods. Contributors address the parallels between research methodologies in design and the sciences, both of which involve the recognition of an issue, conceptualisation of ways to resolve it, and then the modelling and implementation of a viable solution. Much research across various scientific disciplines follows a similar pattern. Thematic sections explore visualisation, visual narrative and visual metaphor; biodesign and biomimicry; makers and users in design and science, and data visualisation, discussing the role of data from nature as an ultimate source of design. |
| data science eastern university: Translating the Message Lamin Sanneh, 2015-02-25 |
| data science eastern university: The Northern Baptist , 1842 |
| data science eastern university: Multi-verb constructions in Eastern Indonesia Volker Unterladstetter , 2020 Constructions with multiple verbal elements have posed a long-standing challenge to linguistic analysis. Most studies of verb serialisation have been confined to single languages rather than looking at crosslinguistic patterns. This book provides the first in-depth account into the areal characteristics of multi-verb constructions (MVCs) in Eastern Indonesia. By collating published data as well as corpus data from 32 Austronesian and Papuan languages, the study traces commonalities as well as differences in MVC use across the area. Analysis takes place on two levels: first, the morpho-syntactic behaviour of MVCs is taken into account. As this plane of analysis arguably does not provide any meaningful insights into why MVCs are construed and used the way they are, a semantic account of MVCs is presented. One of the main hypotheses advanced in this book is that the crucial driving force behind multi-verb construals is semantic interaction between the verbs, leading to four principal techniques of event formation: merging, staging, modification, and free juxtaposition. The study aims at showing that while all four techniques are, to varying degrees, in use in Eastern Indonesian languages, the morpho-syntactic output does not necessarily mirror these underlying differences in event conception. Applying insights from Davidsonian event semantics as well as from predicate decomposition, the book provides a model of event interaction that helps to explain differences in MVC behaviour such as issues in constituent order or operator assignment. |
| data science eastern university: Data Science and Computational Intelligence K. R. Venugopal, P. Deepa Shenoy, Rajkumar Buyya, L. M. Patnaik, Sitharama S. Iyengar, 2022-01-01 This book constitutes revised and selected papers from the Sixteenth International Conference on Information Processing, ICInPro 2021, held in Bangaluru, India in October 2021. The 33 full and 9 short papers presented in this volume were carefully reviewed and selected from a total of 177 submissions. The papers are organized in the following thematic blocks: Computing & Network Security; Data Science; Intelligence & IoT. |
| data science eastern university: Transparent and Reproducible Social Science Research Garret Christensen, Jeremy Freese, Edward Miguel, 2019-07-23 Recently, social science has had numerous episodes of influential research that was found invalid when placed under rigorous scrutiny. The growing sense that many published results are potentially erroneous has made those conducting social science research more determined to ensure the underlying research is sound. Transparent and Reproducible Social Science Research is the first book to summarize and synthesize new approaches to combat false positives and non-reproducible findings in social science research, document the underlying problems in research practices, and teach a new generation of students and scholars how to overcome them. Understanding that social science research has real consequences for individuals when used by professionals in public policy, health, law enforcement, and other fields, the book crystallizes new insights, practices, and methods that help ensure greater research transparency, openness, and reproducibility. Readers are guided through well-known problems and are encouraged to work through new solutions and practices to improve the openness of their research. Created with both experienced and novice researchers in mind, Transparent and Reproducible Social Science Research serves as an indispensable resource for the production of high quality social science research. |
| data science eastern university: Outsmarting AI Brennan Pursell, Joshua Walker, 2020-08-15 From factories to smartphones, Artificial Intelligence is already taking over. Outsmarting AI is not a how-to guide on making AI work, but making it work for YOU to boost profits and productivity. Each development in Artificial Intelligence (AI) technology brings about apprehension and panic for the future of society and for business. We’re bombarded with stories about the impending human-less workplace; it is no longer a question if man can be replaced by machine in certain tasks, but when. However, AI was not manufactured to destroy life as we know it. These emerging technologies were developed and are constantly updating with a particular goal in mind: optimization. AI feeds on data and information to improve outputs and increase potential. With this enhanced productivity, profit and productivity will be sure to follow. Written by Brennan Pursell, a business consultant and professor who hates jargon, and Joshua Walker, an AI pioneer with 18 years of experience in solutions and applications, Outsmarting AI is the first plain-English how-to guide on adapting AI for the non-coding proficient business leader. This book will help readers to Cut through the fog of AI hype See exactly what AI can actually do for people in business Identify the areas of their organization in most need of AI tools Prepare and control their data – AI is useless without it Adopt AI and develop the right culture to support it Track the productivity boost, cost savings, and increased profits Manage and minimize the threat of crippling lawsuits |
| data science eastern university: Complex Networks XV Federico Botta, |
| data science eastern university: Machine Learning for Financial Risk Management with Python Abdullah Karasan, 2021-12-07 Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models |
| data science eastern university: Data Science in Engineering Vol. 10 Thomas Matarazzo, |
| data science eastern university: Rich Christians in an Age of Hunger Ronald J. Sider, 1978 |
| data science eastern university: Game Analytics Magy Seif El-Nasr, Anders Drachen, Alessandro Canossa, 2013-03-30 Developing a successful game in today’s market is a challenging endeavor. Thousands of titles are published yearly, all competing for players’ time and attention. Game analytics has emerged in the past few years as one of the main resources for ensuring game quality, maximizing success, understanding player behavior and enhancing the quality of the player experience. It has led to a paradigm shift in the development and design strategies of digital games, bringing data-driven intelligence practices into the fray for informing decision making at operational, tactical and strategic levels. Game Analytics - Maximizing the Value of Player Data is the first book on the topic of game analytics; the process of discovering and communicating patterns in data towards evaluating and driving action, improving performance and solving problems in game development and game research. Written by over 50 international experts from industry and research, it covers a comprehensive range of topics across more than 30 chapters, providing an in-depth discussion of game analytics and its practical applications. Topics covered include monetization strategies, design of telemetry systems, analytics for iterative production, game data mining and big data in game development, spatial analytics, visualization and reporting of analysis, player behavior analysis, quantitative user testing and game user research. This state-of-the-art volume is an essential source of reference for game developers and researchers. Key takeaways include: Thorough introduction to game analytics; covering analytics applied to data on players, processes and performance throughout the game lifecycle. In-depth coverage and advice on setting up analytics systems and developing good practices for integrating analytics in game-development and -management. Contributions by leading researchers and experienced professionals from the industry, including Ubisoft, Sony, EA, Bioware, Square Enix, THQ, Volition, and PlayableGames. Interviews with experienced industry professionals on how they use analytics to create hit games. |
| data science eastern university: Big Data Is Not a Monolith Cassidy R. Sugimoto, Hamid R. Ekbia, Michael Mattioli, 2016-10-21 Perspectives on the varied challenges posed by big data for health, science, law, commerce, and politics. Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies. The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control through monitoring, mining, and manipulation; big data and society, examining both its empowering and its constraining effects; big data and science, considering issues of data governance, provenance, reuse, and trust; and big data and organizations, discussing data responsibility, “data harm,” and decision making. Contributors Ryan Abbott, Cristina Alaimo, Kent R. Anderson, Mark Andrejevic, Diane E. Bailey, Mike Bailey, Mark Burdon, Fred H. Cate, Jorge L. Contreras, Simon DeDeo, Hamid R. Ekbia, Allison Goodwell, Jannis Kallinikos, Inna Kouper, M. Lynne Markus, Michael Mattioli, Paul Ohm, Scott Peppet, Beth Plale, Jason Portenoy, Julie Rennecker, Katie Shilton, Dan Sholler, Cassidy R. Sugimoto, Isuru Suriarachchi, Jevin D. West |
| data science eastern university: Cross Reality and Data Science in Engineering Michael E. Auer, Dominik May, 2020-08-20 Today, online technologies are at the core of most fields of engineering and society as a whole . This book discusses the fundamentals, applications and lessons learned in the field of online and remote engineering, virtual instrumentation, and other related technologies like Cross Reality, Data Science & Big Data, Internet of Things & Industrial Internet of Things, Industry 4.0, Cyber Security, and M2M & Smart Objects. Since the first Remote Engineering and Virtual Instrumentation (REV) conference in 2004, the event has focused on the use of the Internet for engineering tasks, as well as the related opportunities and challenges. In a globally connected world, interest in online collaboration, teleworking, remote services, and other digital working environments is rapidly increasing. In this context, the REV conferences discuss fundamentals, applications and experiences in the field of Online and Remote Engineering as well as Virtual Instrumentation. Furthermore, the conferences focus on guidelines and new concepts for engineering education in higher and vocational education institutions, including emerging technologies in learning, MOOCs & MOOLs, and open resources. This book presents the proceedings of REV2020 on “Cross Reality and Data Science in Engineering” which was held as the 17th in series of annual events. It was organized in cooperation with the Engineering Education Transformations Institute and the Georgia Informatics Institutes for Research and Education and was held at the College of Engineering at the University of Georgia in Athens (GA), USA, from February 26 to 28, 2020. |
| data science eastern university: So You Want to Be a Social Worker (2nd Edition) Alan Keith-Lucas, Leslie Gregory, Sandra Bauer, 2021-06 So You Want to Be a Social Worker is an invaluable, concise resource for both students and practitioners who are concerned about the thoughtful integration of their Christian faith and competent, ethical social work practice. This text is a clear distillation of practice wisdom and responsible guidelines regarding perennial questions which arise when Christians in social work take both their Christian faith and their professional social work practice seriously.Especially useful in the classroom or social work trainings, the 2nd edition of So You Want to Be a Social Worker includes most of the chapters from the original edition, though with updated language, case examples and references - plus two new chapters, Hope and Resilience, and Settings for Practice. |
| data science eastern university: Machine Learning In Pure Mathematics And Theoretical Physics Yang-hui He, 2023-06-21 The juxtaposition of 'machine learning' and 'pure mathematics and theoretical physics' may first appear as contradictory in terms. The rigours of proofs and derivations in the latter seem to reside in a different world from the randomness of data and statistics in the former. Yet, an often under-appreciated component of mathematical discovery, typically not presented in a final draft, is experimentation: both with ideas and with mathematical data. Think of the teenage Gauss, who conjectured the Prime Number Theorem by plotting the prime-counting function, many decades before complex analysis was formalized to offer a proof.Can modern technology in part mimic Gauss's intuition? The past five years saw an explosion of activity in using AI to assist the human mind in uncovering new mathematics: finding patterns, accelerating computations, and raising conjectures via the machine learning of pure, noiseless data. The aim of this book, a first of its kind, is to collect research and survey articles from experts in this emerging dialogue between theoretical mathematics and machine learning. It does not dwell on the well-known multitude of mathematical techniques in deep learning, but focuses on the reverse relationship: how machine learning helps with mathematics. Taking a panoramic approach, the topics range from combinatorics to number theory, and from geometry to quantum field theory and string theory. Aimed at PhD students as well as seasoned researchers, each self-contained chapter offers a glimpse of an exciting future of this symbiosis. |
| data science eastern university: Ultrasound Edward I. Bluth, Carol B. Benson, Philip W. Ralls, 2011-01-01 Based on a popular course taught at the Radiological Society of North America's Annual Meeting, this book provides all the essential information for choosing the appropriate imaging examination and completing the imaging workup of a patient. Chapters are organized into parts according to the anatomical location of the clinical problems addressed. The authors guide the reader through the diagnostic evaluation, reviewing the indications for and the strengths and limitations of ultrasound imaging.Features: Practical information on the usefulness of ultrasound, nonimaging tests, or other imaging modalities, such as CT and MR, for evaluating each clinical situation Clear descriptions of symptoms and differential diagnosis Nearly 1,300 images and photographs demonstrating key points A new chapter on neonatal spinal cord anomalies Comprehensive and up-to-date, this edition is essential for ultrasonographers, radiologists, residents, physicians, nurses, and radiology assistants seeking the latest recommendations for the effective use of ultrasonography. |
| data science eastern university: Big Data Analytics: From Data to Discovery Dr.K.Varada Rajkumar, Vikram Pasupuleti, Bharadwaj Thuraka, Dr.Saiteja Malisetty, Chandra Shikhi Kodete, 2024-07-13 Dr.K.Varada Rajkumar, Associate Professor, Department of Computer Science and Engineering (AIML), MLR Institute of Technology, Hyderabad, Telangana, India. Vikram Pasupuleti, Software Developer, MS in Computer Technology, School of Technology, Eastern Illinois University, Charleston, Illinois, USA. Bharadwaj Thuraka, Software Developer, Master of Science, Information Systems, Northwest Missouri State University, Maryville, Missouri, USA. Dr.Saiteja Malisetty, Ph.D in Computing & Information Science, College of Information Science and Technology, University of Nebraska, Omaha, Nebraska, USA. Chandra Shikhi Kodete, Software Engineer, MS in Computer Technology, School of Technology, Eastern Illinois University, Charleston, Illinois, USA. |
| data science eastern university: Real Sex Lauren F. Winner, 2006-07-01 SEX. Splashed across magazine covers, billboards, and computer screens--sex is casual, aggressive, and absolutely everywhere. And everybody's doing it, right? In Real Sex, heralded young author Lauren F. Winner speaks candidly to Christians about the difficulty--and the importance--of sexual chastity. With honesty and wit, she talks about her struggle to live a celibate life. Never dodging tough terms like confession and sin, Winner grounds her discussion of chastity first and foremost in Scripture. She confronts cultural lies about sex and challenges how we talk about sex in church. Her biblically grounded observations and suggestions will be especially valuable to unmarried Christians struggling with the sexual mania of today's culture. Real Sex is essential reading for Christians grappling with chastity and a valuable tool for pastors. |
| data science eastern university: The Crossley ID Guide Richard Crossley, 2011-01-31 The acclaimed book that has revolutionized birding and field guides [The Crossley ID Guide] has turned the traditional field guide on its ear.—Wall Street Journal Deserves to be your essential resource for definitive species identification.—Richmond Times–Dispatch The perfect book for beginning birders, and even experts will marvel at its thoroughness.—Pittsburgh Post–Gazette A different kind of ID book, a book much more useful and helpful.—Minneapolis Star Tribune This stunningly illustrated book from acclaimed birder and photographer Richard Crossley revolutionizes field guide design by providing the first real-life approach to identification. Whether you are a beginner, expert, or anywhere in between, The Crossley ID Guide will vastly improve your ability to identify birds. Unlike other guides, which provide isolated individual photographs or illustrations, this is the first book to feature large, lifelike scenes for each species. These scenes—640 in all—are composed from more than 10,000 of the author's images showing birds in a wide range of views—near and far, from different angles, in various plumages and behaviors, including flight, and in the habitat in which they live. These beautiful compositions show how a bird's appearance changes with distance, and give equal emphasis to characteristics experts use to identify birds: size, structure and shape, behavior, probability, and color. This is the first book to convey all of these features visually—in a single image—and to reinforce them with accurate, concise text. Each scene provides a wealth of detailed visual information that invites and rewards careful study, but the most important identification features can be grasped instantly by anyone. By making identification easier, more accurate, and more fun than ever before, The Crossley ID Guide will completely redefine how its users look at birds. Essential for all birders, it also promises to make new birders of many people who have despaired of using traditional guides. Revolutionary. This book changes field guide design to make you a better birder A picture says a thousand words. The most comprehensive guide: 640 stunning scenes created from 10,000 of the author's photographs Reality birding. Lifelike in-focus scenes show birds in their habitats, from near and far, and in all plumages and behaviors Teaching and reference. The first book to accurately portray all the key identification characteristics: size, shape, behavior, probability, and color Practice makes perfect. An interactive learning experience to sharpen and test field identification skills Bird like the experts. The first book to simplify birding and help you understand how to bird like the best An interactive website—www.crossleybirds.com—includes expanded captions for the plates and species updates |
| data science eastern university: Career Development David H. Montross, Christopher J. Shinkman, 1992 |
| data science eastern university: Agents and Artificial Intelligence Ana Paula Rocha, Luc Steels, Jaap van den Herik, 2022-07-18 This book constitutes selected papers from the refereed proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, which was held online during February 4–6, 2021. A total of 72 full and 99 short papers were carefully reviewed and selected for the conference from a total of 298 submissions; 17 selected full papers are included in this book. They were organized in topical sections named agents and artificial intelligence. |
| data science eastern university: Data Science for Librarians Yunfei Du, Hammad Rauf Khan, 2020-03-26 More data, more problems -- A new strand of librarianship -- Data creation and collection -- Data for the academic librarian -- Research data services and the library ecosystem -- Data sources -- Data curation (archiving/preservation) -- Data storage, management, and retrieval -- Data analysis and visualization -- Data ethics and policies -- Data for public libraries and special libraries -- Conclusion: library, information, and data science. |
| data science eastern university: Categorical Data Analysis and Multilevel Modeling Using R Xing Liu, 2022-02-24 Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal, nominal, and count response variables using the R software. Author Xing Liu offers a unified framework for both single-level and multilevel modeling of categorical and count response variables with both frequentist and Bayesian approaches. Each chapter demonstrates how to conduct the analysis using R, how to interpret the models, and how to present the results for publication. A companion website for this book contains datasets and R commands used in the book for students, and solutions for the end-of-chapter exercises on the instructor site. |
| data science eastern university: The Ultimate Guide to Snowpark Shankar Narayanan SGS, Vivekanandan SS, 2024-05-30 Develop robust data pipelines, deploy mature machine learning models, and build secure data apps with Snowflake Snowpark using Python Key Features Get to grips with Snowflake Snowpark’s basic and advanced features Implement workloads in domains like data engineering, data science, and data applications using Snowpark with Python Deploy Snowpark in production with practical examples and best practices Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionSnowpark is a powerful framework that helps you unlock numerous possibilities within the Snowflake Data Cloud. However, without proper guidance, leveraging the full potential of Snowpark with Python can be challenging. Packed with practical examples and code snippets, this book will be your go-to guide to using Snowpark with Python successfully. The Ultimate Guide to Snowpark helps you develop an understanding of Snowflake Snowpark and how it enables you to implement workloads in data engineering, data science, and data applications within the Data Cloud. From configuration and coding styles to workloads such as data manipulation, collection, preparation, transformation, aggregation, and analysis, this guide will equip you with the right knowledge to make the most of this framework. You'll discover how to build, test, and deploy data pipelines and data science models. As you progress, you’ll deploy data applications natively in Snowflake and operate large language models (LLMs) using Snowpark container services. By the end of this book, you'll be able to leverage Snowpark's capabilities and propel your career as a Snowflake developer to new heights.What you will learn Harness Snowpark with Python for diverse workloads Develop robust data pipelines with Snowpark using Python Deploy mature machine learning models Explore the process of developing, deploying, and monetizing native apps using Snowpark Deploy and operate containers in Snowpark Discover the pathway to adopting Snowpark effectively in production Who this book is for This book is for data engineers, data scientists, developers, and data practitioners seeking an in-depth understanding of Snowpark’s features and best practices for deploying various workloads in Snowpark using the Python programming language. Basic knowledge of SQL, proficiency in Python, an understanding of data engineering and data science basics, and familiarity with the Snowflake Data Cloud platform are required to get the most out of this book. |
| data science eastern university: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track Albert Bifet, |
| data science eastern university: The Sabermetric Revolution Benjamin Baumer, Andrew Zimbalist, 2014-01-23 The authors look at the history of statistical analysis in baseball, how it can best be used today and how its it must evolve for the future. |
| data science eastern university: Industry 5.0 for Smart Healthcare Technologies Sherin Zafar, S. N. Kumar, A. Ahilan, Gulsun Kurubacak Cakir, 2024-08-13 In this book, the role of Artificial Intelligence (AI), Internet of Things (IoT) and Blockchain in smart healthcare is explained through a detailed study of Artificial Neural Network, Fuzzy Set Theory, Intuitionistic Fuzzy Set, Machine Learning and Big Data technology. Industry 5.0 for Smart Healthcare Technologies: Utilizing Artificial Intelligence, Internet of Medical Things and Blockchain focuses on interesting applications of AI, promising advancements in IoT and important findings in Blockchain technology. When applied to smart healthcare technologies, Industry 5.0 offers numerous benefits that can revolutionize the healthcare industry. This book provides readers with insights and tools for enhanced patient care, remote patient monitoring, predictive analytics and early intervention of diseases, seamless data sharing and interoperability, telemedicine and virtual care, and a safer and more secure healthcare ecosystem. The authors examine novel computational algorithms for the processing of medical images, as well as novel algorithms for the processing of biosignals in detection of diseases. This book also explores systems for processing physiological parameters and discusses applications of AI techniques in the broader healthcare industry. The authors also investigate the importance of Augment Reality/Virtual Relatity (AR/VR) in the healthcare sector and examine the futuristic applications of Industry 5.0 in the healthcare sector. This book is intended for researchers and professionals working in interdisciplinary fields of computer engineering/science and healthcare. It will provide them with the tools to enhance diagnostics, optimize treatment plans, and empower patients to actively participate in their healthcare journey. |
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 …
