| data science major northeastern: 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 major northeastern: Robot-Proof, revised and updated edition Joseph E. Aoun, 2024-10-15 A fresh look at a “robot-proof” education in the new age of generative AI. In 2017, Robot-Proof, the first edition, foresaw the advent of the AI economy and called for a new model of higher education designed to help human beings flourish alongside smart machines. That economy has arrived. Creative tasks that, seven years ago, seemed resistant to automation can now be performed with a simple prompt. As a result, we must now learn not only to be conversant with these technologies, but also to comprehend and deploy their outputs. In this revised and updated edition, Joseph Aoun rethinks the university’s mission for a world transformed by AI, advocating for the lifelong endeavor of a “robot-proof” education. Aoun puts forth a framework for a new curriculum, humanics, which integrates technological, data, and human literacies in an experiential setting, and he renews the call for universities to embrace lifelong learning through a social compact with government, employers, and learners themselves. Drawing on the latest developments and debates around generative AI, Robot-Proof is a blueprint for the university as a force for human reinvention in an era of technological change—an era in which we must constantly renegotiate the shifting boundaries between artificial intelligence and the capacities that remain uniquely human. |
| data science major northeastern: Comparative Textual Media N. Katherine Hayles, Jessica Pressman, 2013-12-01 For the past few hundred years, Western cultures have relied on print. When writing was accomplished by a quill pen, inkpot, and paper, it was easy to imagine that writing was nothing more than a means by which writers could transfer their thoughts to readers. The proliferation of technical media in the latter half of the twentieth century has revealed that the relationship between writer and reader is not so simple. From telegraphs and typewriters to wire recorders and a sweeping array of digital computing devices, the complexities of communications technology have made mediality a central concern of the twenty-first century. Despite the attention given to the development of the media landscape, relatively little is being done in our academic institutions to adjust. In Comparative Textual Media, editors N. Katherine Hayles and Jessica Pressman bring together an impressive range of essays from leading scholars to address the issue, among them Matthew Kirschenbaum on archiving in the digital era, Patricia Crain on the connection between a child’s formation of self and the possession of a book, and Mark Marino exploring how to read a digital text not for content but for traces of its underlying code. Primarily arguing for seeing print as a medium along with the scroll, electronic literature, and computer games, this volume examines the potential transformations if academic departments embraced a media framework. Ultimately, Comparative Textual Media offers new insights that allow us to understand more deeply the implications of the choices we, and our institutions, are making. Contributors: Stephanie Boluk, Vassar College; Jessica Brantley, Yale U; Patricia Crain, NYU; Adriana de Souza e Silva, North Carolina State U; Johanna Drucker, UCLA; Thomas Fulton, Rutgers U; Lisa Gitelman, New York U; William A. Johnson, Duke U; Matthew G. Kirschenbaum, U of Maryland; Patrick LeMieux; Mark C. Marino, U of Southern California; Rita Raley, U of California, Santa Barbara; John David Zuern, U of Hawai‘i at Mānoa. |
| data science major northeastern: Topics in Topology. (AM-10), Volume 10 Solomon Lefschetz, 2016-03-02 Solomon Lefschetz pioneered the field of topology--the study of the properties of manysided figures and their ability to deform, twist, and stretch without changing their shape. According to Lefschetz, If it's just turning the crank, it's algebra, but if it's got an idea in it, it's topology. The very word topology comes from the title of an earlier Lefschetz monograph published in 1920. In Topics in Topology Lefschetz developed a more in-depth introduction to the field, providing authoritative explanations of what would today be considered the basic tools of algebraic topology. Lefschetz moved to the United States from France in 1905 at the age of twenty-one to find employment opportunities not available to him as a Jew in France. He worked at Westinghouse Electric Company in Pittsburgh and there suffered a horrible laboratory accident, losing both hands and forearms. He continued to work for Westinghouse, teaching mathematics, and went on to earn a Ph.D. and to pursue an academic career in mathematics. When he joined the mathematics faculty at Princeton University, he became one of its first Jewish faculty members in any discipline. He was immensely popular, and his memory continues to elicit admiring anecdotes. Editor of Princeton University Press's Annals of Mathematics from 1928 to 1958, Lefschetz built it into a world-class scholarly journal. He published another book, Lectures on Differential Equations, with Princeton in 1946. |
| data science major northeastern: Deep Learning Essentials Anurag Bhardwaj, Wei Di, Jianing Wei, 2018-01-30 Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python. |
| data science major northeastern: Game Data Science Magy Seif El-Nasr, Truong-Huy D. Nguyen, Alessandro Canossa, Anders Drachen, 2021-09-30 Game data science, defined as the practice of deriving insights from game data, has created a revolution in the multibillion-dollar games industry - informing and enhancing production, design, and development processes. Almost all game companies and academics have now adopted some type of game data science, every tool utilized by game developers allows collecting data from games, yet there has been no definitive resource for academics and professionals in this rapidly developing sector until now. Games Data Science delivers an excellent introduction to this new domain and provides the definitive guide to methods and practices of computer science, analytics, and data science as applied to video games. It is the ideal resource for academic students and professional learners seeking to understand how data science is used within the game development and production cycle, as well as within the interdisciplinary field of games research. Organized into chapters that integrate laboratory and game data examples, this book provides a unique resource to train and educate both industry professionals and academics about the use of game data science, with practical exercises and examples on how such processes are implemented and used in academia and industry, interweaving theoretical learning with practical application throughout. |
| data science major northeastern: Field Guide to the Flower Flies of Northeastern North America Jeffrey H. Skevington, Michelle M. Locke, Andrew D. Young, Kevin Moran, William J. Crins, Stephen A. Marshall, 2019-05-14 Covers all 416 species of flower flies that occur north of Tennessee and east of the Dakotas, including the high Arctic and Greenland--Page [4] of cover. |
| data science major northeastern: 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 major northeastern: Atmospheric Rivers F. Martin Ralph, Michael D. Dettinger, Jonathan J. Rutz, Duane E. Waliser, 2020-07-10 This book is the standard reference based on roughly 20 years of research on atmospheric rivers, emphasizing progress made on key research and applications questions and remaining knowledge gaps. The book presents the history of atmospheric-rivers research, the current state of scientific knowledge, tools, and policy-relevant (science-informed) problems that lend themselves to real-world application of the research—and how the topic fits into larger national and global contexts. This book is written by a global team of authors who have conducted and published the majority of critical research on atmospheric rivers over the past years. The book is intended to benefit practitioners in the fields of meteorology, hydrology and related disciplines, including students as well as senior researchers. |
| data science major northeastern: Probabilistic Databases Dan Suciu, Dan Olteanu, Christoph Koch, 2011 Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques |
| data science major northeastern: A Field Guide to Ferns Boughton Cobb, 1999-03 Identifies over five hundred species. |
| data science major northeastern: Homelands and Empires Jeffers Lennox, 2017-01-01 In this deeply researched and engagingly argued work, Jeffers Lennox reconfigures our general understanding of how Indigenous peoples, imperial forces, and settlers competed for space in northeastern North America before the British conquest in 1763. |
| data science major northeastern: Amphibians and Reptiles of Pennsylvania and the Northeast Arthur C. Hulse, Clarence J. McCoy, Ellen Joan Censky, 2001 Though Pennsylvania receives the most emphasis here, readers will find references to all of the northeastern US. A wide audience will find this a useful resource, including professional and amateur herpetologists, students, and amateur naturalists. As in a field guide, the amphibians and reptiles are described in detail to aid identification. The text goes far beyond a simple field guide, however, containing substantial treatment of the habitat, habits, reproduction, and distribution of each species with additional remarks on taxonomic status, behavior, appearance and conservation. Color photos of each species are included. Two of the authors are curators for museums of natural history, the third teaches biology at Indiana U. of Pennsylvania. c. Book News Inc. |
| data science major northeastern: Intellectual Privacy Neil Richards, 2015 How should we think about the problems of privacy and free speech? Neil Richards argues that when privacy and free speech truly conflict, free speech should almost always win, but contends that, contrary to conventional wisdom, speech and privacy are only rarely in conflict. |
| data science major northeastern: Nazis of Copley Square Charles Gallagher, 2021-09-28 The forgotten history of American terrorists who, in the name of God, conspired to overthrow the government and formed an alliance with Hitler. On January 13, 1940, FBI agents burst into the homes and offices of seventeen members of the Christian Front, seizing guns, ammunition, and homemade bombs. J. Edgar HooverÕs charges were incendiary: the group, he alleged, was planning to incite a revolution and install a Òtemporary dictatorshipÓ in order to stamp out Jewish and communist influence in the United States. Interviewed in his jail cell, the frontÕs ringleader was unbowed: ÒAll I can say isÑlong live Christ the King! Down with communism!Ó In Nazis of Copley Square, Charles Gallagher provides a crucial missing chapter in the history of the American far right. The men of the Christian Front imagined themselves as crusaders fighting for the spiritual purification of the nation, under assault from godless communism, and they were hardly alone in their beliefs. The front traced its origins to vibrant global Catholic theological movements of the early twentieth century, such as the Mystical Body of Christ and Catholic Action. The frontÕs anti-Semitism was inspired by Sunday sermons and by lay leaders openly espousing fascist and Nazi beliefs. Gallagher chronicles the evolution of the front, the transatlantic cloak-and-dagger intelligence operations that subverted it, and the mainstream political and religious leaders who shielded the frontÕs activities from scrutiny. Nazis of Copley Square offers a grim tale of faith perverted to violent ends, and its lessons provide a warning for those who hope to stop the spread of far-right violence today. |
| data science major northeastern: Algorithms Robert Sedgewick, Kevin Wayne, 2014-02-01 This book is Part I of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms, the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part I contains Chapters 1 through 3 of the book. The fourth edition of Algorithms surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the Online Course link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience. |
| data science major northeastern: Listening to Reading Stephen Ratcliffe, 2000-03-30 Contends that experimental writing--from Mallarme, Stein, and Cage to contemporary poets of the eighties and nineties--can teach us much about how we write and read both poetry and criticism. |
| data science major northeastern: The Math Olympian Richard Hoshino, 2015-01-26 BETHANY MACDONALD HAS TRAINED SIX LONG YEARS FOR THIS MOMENT. SHE'LL TRY TO SOLVE FIVE QUESTIONS IN THREE HOURS, FOR ONE IMPROBABLE DREAM. THE DREAM OF REPRESENTING HER COUNTRY, AND BECOMING A MATH OLYMPIAN. As a small-town girl in Nova Scotia bullied for liking numbers more than boys, and lacking the encouragement of her unsupportive single mother who frowns at her daughter's unrealistic ambition, Bethany's road to the International Math Olympiad has been marked by numerous challenges. Through persistence, perseverance, and the support of innovative mentors who inspire her with a love of learning, Bethany confronts these challenges and develops the creativity and confidence to reach her potential. In training to become a world-champion mathlete, Bethany discovers the heart of mathematics - a subject that's not about memorizing formulas, but rather about problem-solving and detecting patterns to uncover truth, as well as learning how to apply the deep and unexpected connections of mathematics to every aspect of her life, including athletics, spirituality, and environmental sustainability. As Bethany reflects on her long journey and envisions her exciting future, she realizes that she has shattered the misguided stereotype that only boys can excel in math, and discovers a sense of purpose that through mathematics, she can and she will make an extraordinary contribution to society. |
| data science major northeastern: Quantum Computation and Information Hiroshi Imai, Masahito Hayashi, 2008-09-12 This book reviews selected topics charterized by great progress and covers the field from theoretical areas to experimental ones. It contains fundamental areas, quantum query complexity, quantum statistical inference, quantum cloning, quantum entanglement, additivity. It treats three types of quantum security system, quantum public key cryptography, quantum key distribution, and quantum steganography. A photonic system is highlighted for the realization of quantum information processing. |
| data science major northeastern: Colleges That Create Futures Princeton Review, 2016-05-10 KICK-START YOUR CAREER WITH THE RIGHT ON-CAMPUS EXPERIENCE! When it comes to getting the most out of college, the experiences you have outside the classroom are just as important as what you study. Colleges That Create Futures looks beyond the usual “best of” college lists to highlight 50 schools that empower students to discover practical, real-world applications for their talents and interests. The schools in this book feature distinctive research, internship, and hands-on learning programs—all the info you need to help find a college where you can parlay your passion into a successful post-college career. Inside, You'll Find: • In-depth profiles covering career services, internship support, student group activity, alumni satisfaction, noteworthy facilities and programs, and more • Candid assessments of each school’s academics from students, current faculty, and alumni • Unique hands-on learning opportunities for students across majors • Testimonials on career prep from alumni in business, education, law, and much more *************************** What makes Colleges That Create Futures important? You've seen the headlines—lately the news has been full of horror stories about how the college educational system has failed many recent grads who leave school with huge debt, no job prospects, and no experience in the working world. Colleges That Create Futures identifies schools that don't fall into this trap but instead prepare students for successful careers! How are the colleges selected? Schools are selected based on survey results on career services, grad school matriculation, internship support, student group and government activity, alumni activity and salaries, and noteworthy facilities and programs. |
| data science major northeastern: How to Design Programs, second edition Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, Shriram Krishnamurthi, 2018-05-25 A completely revised edition, offering new design recipes for interactive programs and support for images as plain values, testing, event-driven programming, and even distributed programming. This introduction to programming places computer science at the core of a liberal arts education. Unlike other introductory books, it focuses on the program design process, presenting program design guidelines that show the reader how to analyze a problem statement, how to formulate concise goals, how to make up examples, how to develop an outline of the solution, how to finish the program, and how to test it. Because learning to design programs is about the study of principles and the acquisition of transferable skills, the text does not use an off-the-shelf industrial language but presents a tailor-made teaching language. For the same reason, it offers DrRacket, a programming environment for novices that supports playful, feedback-oriented learning. The environment grows with readers as they master the material in the book until it supports a full-fledged language for the whole spectrum of programming tasks. This second edition has been completely revised. While the book continues to teach a systematic approach to program design, the second edition introduces different design recipes for interactive programs with graphical interfaces and batch programs. It also enriches its design recipes for functions with numerous new hints. Finally, the teaching languages and their IDE now come with support for images as plain values, testing, event-driven programming, and even distributed programming. |
| data science major northeastern: Game Research Methods: An Overview Patri Lankoski, Staffan Björk, et al., 2015 Games are increasingly becoming the focus for research due to their cultural and economic impact on modern society. However, there are many different types of approaches and methods than can be applied to understanding games or those that play games. This book provides an introduction to various game research methods that are useful to students in all levels of higher education covering both quantitative, qualitative and mixed methods. In addition, approaches using game development for research is described. Each method is described in its own chapter by a researcher with practical experience of applying the method to topic of games. Through this, the book provides an overview of research methods that enable us to better our understanding on games.--Provided by publisher. |
| data science major northeastern: Voice Preservation Emma Selle, 1926 |
| data science major northeastern: A Field Guide to Wildflowers Roger Tory Peterson, Margaret McKenny, 1968 This book is a guide to the wildflowers in the Northcentral and Eastern regions of the United States. |
| data science major northeastern: Biological Kinetics Elena Burlakova, Sergey Dmitrievich Varfolomeev, 2005-10-28 This volume discusses questions of free-radical biology and new, modern directions in molecular cytobiology; proteomics and genomics. The book presents articles and reviews on bioantioxidants, synthesis of new compounds, mechanisms of their action and areas of application. Studies on free radical states using ESR technique, biochemistry of regulato |
| data science major northeastern: Digital Workplace Learning Dirk Ifenthaler, 2018-02-01 This book aims to provide insight into how digital technologies may bridge and enhance formal and informal workplace learning. It features four major themes: 1. Current research exploring the theoretical underpinnings of digital workplace learning. 2. Insights into available digital technologies as well as organizational requirements for technology-enhanced learning in the workplace. 3. Issues and challenges for designing and implementing digital workplace learning as well as strategies for assessments of learning in the workplace. 4. Case studies, empirical research findings, and innovative examples from organizations which successfully adopted digital workplace learning. |
| data science major northeastern: Mentalligence Kristen Lee, 2018-02-06 As the headlines warn of a world seemingly taking steps backward, behavioral scientist Kristen Lee shares a groundbreaking new psychology of thinking that will propel you forward with a fresh mind-set that inspires connection, collaboration, and creativity. Based on twenty-two years of clinical practice and neuro-scientific research. Dr. Kristen Lee teaches us how to rip up the script society hands us and to see the world through a series of different lenses: The Reflective Lens, The Mindful Lens, the Global Lens, and the Imagineering Lens. Through exercises, worksheets, and thought-provoking anecdotes and case studies, Lee teaches how to cultivate Upward Spiral Habits that are less I-focused and more we-focused, and that will make a positive difference in our circles and beyond. Rather than striving for preconceived notions of success that leave us boxed in, depleted, and oblivious to ways we can work together, Mentalligence helps us break out of our comfort zones, elevate our thinking, and develop the behavioral agility to work toward what Positive Psychologists call The Good Life, one characterized by authentic connections and impact. |
| data science major northeastern: Adaptive Object-oriented Software Karl J. Lieberherr, 1996 This ground-breaking book presents a complete methodology for adaptive programming in any object-oriented programming language. Lieberherr's adaptive method signals a new approach to object-oriented program design that goes beyond object encapsulation and hard-coded navigation paths to achieve more flexible interactions among objects. Programmers using this method work at a higher, schematic level of abstraction; graph notation represents the class structure and a propagation pattern language tells how to distribute meaningful methods - including navigation - across the structure. Using this method, programmers can easily adapt and modify programs as they evolve. This book can be used with any object-oriented programming environment, or with the Demeter Tools Version 5.5, a complete, professional software system for creating and maintaining adaptive programs. |
| data science major northeastern: The Successful Nurse Faculty Member Denise Korniewicz, 2020-02-20 This concise guide is designed for novice faculty and nursing education students who are teaching or will be teaching in higher education. The text provides the skills necessary to teach courses for classroom and online courses. Information is provided in a practical and accessible way and includes best practices for setting up a syllabus and curriculum, test item writing, grading, mentoring students, and personal advancement as a faculty member. Exercises, rubrics, and self-test items are furnished to give readers hands-on practice in defining and carrying out an effective instructional agenda. |
| data science major northeastern: Network Science Albert-László Barabási, Márton PÃ3sfai, 2016-07-21 Illustrated throughout in full colour, this pioneering text is the only book you need for an introduction to network science. |
| data science major northeastern: The Structure and Dynamics of Networks Mark Newman, Albert-László Barabási, Duncan J. Watts, 2011-10-23 From the Internet to networks of friendship, disease transmission, and even terrorism, the concept--and the reality--of networks has come to pervade modern society. But what exactly is a network? What different types of networks are there? Why are they interesting, and what can they tell us? In recent years, scientists from a range of fields--including mathematics, physics, computer science, sociology, and biology--have been pursuing these questions and building a new science of networks. This book brings together for the first time a set of seminal articles representing research from across these disciplines. It is an ideal sourcebook for the key research in this fast-growing field. The book is organized into four sections, each preceded by an editors' introduction summarizing its contents and general theme. The first section sets the stage by discussing some of the historical antecedents of contemporary research in the area. From there the book moves to the empirical side of the science of networks before turning to the foundational modeling ideas that have been the focus of much subsequent activity. The book closes by taking the reader to the cutting edge of network science--the relationship between network structure and system dynamics. From network robustness to the spread of disease, this section offers a potpourri of topics on this rapidly expanding frontier of the new science. |
| data science major northeastern: Higher Education and Silicon Valley W. Richard Scott, Michael W. Kirst, 2017-09 Universities and colleges often operate between two worlds: higher education and economic systems. It is impossible to understand how current developments are affecting colleges without attending to the changes in both the higher education system and in the economic communities in which they exist. W. Richard Scott, Michael W. Kirst, and colleagues focus on the changing relations between colleges and companies in one vibrant economic region: the San Francisco Bay Area. Colleges and tech companies, they argue, have a common interest in knowledge generation and human capital, but they operate in social worlds that substantially differ, making them uneasy partners. Colleges are a part of a long tradition that stresses the importance of precedent, academic values, and liberal education. High-tech companies, by contrast, value innovation and know-how, and they operate under conditions that reward rapid response to changing opportunities. The economy is changing faster than the postsecondary education system. -- From the cover. |
| data science major northeastern: Managing Diversity in Organizations Barbara Beham, Caroline Straub, Joachim Schwalbach, 2013-07-10 Diversity management has recently attracted a lot of attention in both academia and practice. Globalization, migration, demographic changes, low fertility rates, a scarce pool of qualified labor, and women entering the workforce in large scales have led to an increasingly heterogeneous workforce in the past twenty years. In response to those ongoing changes, organizations have started to create work environments which address the needs and respond to the opportunities of a diverse workforce. The implementation of diversity policies and practices and the creation of an organizational culture that values heterogeneity have been the focus of recent organizational initiatives. This special issue aims at shedding light on some of open research questions by including both theoretical and empirical contributions. |
| data science major northeastern: Introduction to Machine and Assembly Language Programming Philip J. Gust, 1986 |
| data science major northeastern: Where We Live and what We Do , 1909 |
| data science major northeastern: Blackass A. Igoni Barrett, 2016-03-01 Furo Wariboko, a young Nigerian, awakes the morning before a job interview to find that he's been transformed into a white man. In this condition he plunges into the bustle of Lagos to make his fortune. With his red hair, green eyes, and pale skin, it seems he's been completely changed. Well, almost. There is the matter of his family, his accent, his name. Oh, and his black ass. Furo must quickly learn to navigate a world made unfamiliar and deal with those who would use him for their own purposes. Taken in by a young woman called Syreeta and pursued by a writer named Igoni, Furo lands his first-ever job, adopts a new name, and soon finds himself evolving in unanticipated ways. A. Igoni Barrett's Blackass is a fierce comic satire that touches on everything from race to social media while at the same time questioning the values society places on us simply by virtue of the way we look. As he did in Love Is Power, or Something Like That, Barrett brilliantly depicts life in contemporary Nigeria and details the double-dealing and code-switching that are implicit in everyday business. But it's Furo's search for an identity--one deeper than skin--that leads to the final unraveling of his own carefully constructed story. |
| data science major northeastern: The Science of Science Dashun Wang, Albert-László Barabási, 2021-03-25 This is the first comprehensive overview of the exciting field of the 'science of science'. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists, policy makers, and administrators with an interest in the wider scientific enterprise. |
| data science major northeastern: Computer Science and its Applications Sang-Soo Yeo, Yi Pan, Yang Sun Lee, Hang Bae Chang, 2012-10-19 The 4th FTRA International Conference on Computer Science and its Applications (CSA-12) will be held in Jeju, Korea on November 22~25, 2012. CSA-12 will be the most comprehensive conference focused on the various aspects of advances in computer science and its applications. CSA-12 will provide an opportunity for academic and industry professionals to discuss the latest issues and progress in the area of CSA. In addition, the conference will publish high quality papers which are closely related to the various theories and practical applications in CSA. Furthermore, we expect that the conference and its publications will be a trigger for further related research and technology improvements in this important subject. CSA-12 is the next event in a series of highly successful International Conference on Computer Science and its Applications, previously held as CSA-11 (3rd Edition: Jeju, December, 2011), CSA-09 (2nd Edition: Jeju, December, 2009), and CSA-08 (1st Edition: Australia, October, 2008). |
| data science major northeastern: Exercise in Health and Disease Francis J. Nagle, Henry Joseph Montoye, 1981 |
| data science major northeastern: Landscapes from Antiquity Simon Stoddart, 2001 This is the first volume of an exciting new project; Antiquity , drawing on its 75-year tradition of publishing articles of enduring value, has brought together twenty-four classic papers on a central archaeological theme. The papers have been selected to represent ancient and modern landscape approaches, organized into thematic sections: Early studies of Fox and Curwen, aerial photography of Bradford, Crawford and St Joseph, survey method, integrated regional landscapes, physical, industrial, contested and experienced landscapes. Each section is introduced with an overview and personal perspective by Simon Stoddart, the current editor of Antiquity . As he points out in the introduction, the editor of Antiquity has always drawn on the most exciting and relevant of current research. Consequently the frequency and content of landscape in Antiquity provides illuminating commentary on the definition and prominence of the theme landscape in archaeological research. Contents: Early studies of landscape: Prehistoric Cart-tracks in Malta ( T. Zammit ); Dykes ( Cyril Fox ); The Hebrides: a Cultural Backwater ( E. Cecil Curwen ); Native Settlements of Northumberland ( A. H. A. Hogg ). The impact of aerial photography: Woodbury. Two marvellous air-photographs ( O. G. S. Crawford ); Iron Age square enclosures in Rhineland ( K. V. Decker and I. Scollar ); Aerial reconnaissance in Picardy ( R. Agache ); Air reconnaissance: recent results ( J. K. St Joseph ). Survey method and analysis: Understanding early medieval pottery distributions ( A. J. Schofield ); Exploring the topography of the mind: GIS, social space and archaeology ( Marcos Llobera ). Integrated landscape archaeology: Neolithic settlement patterns at Avebury, Wiltshire ( Robin Holgate ); Stonehenge for the ancestors: the stones pass on the message ( M. Parker Pearson and Ramilisonina ); Aerial reconnaissance of the Fen Basin ( D. N. Riley ); The Fenland Project: from survey management and beyond ( John Coles and David Hall ); Siticulosa Apulia ( John Bradford and P. R. Williams-Hunt ); Archaeology and the Etruscan countryside ( Graeme Barker ). Physical landscapes: Active tectonics and land-use strategies: a Palaeolithic example from northwest Greece ( Geoff Bailey, Geoff King and Derek Sturdy ); A guide for archaeologists investigating Holocene landscapes ( A. J. Howard and M. G. Macklin ). Industrial landscapes: Trouble at t'mill: industrial archaeology in the 1980s ( C. M. Clark ); Towards an archaeology of navvy huts and settlements of the industrial revolution ( Michael Morris ). Contested landscapes: The Berlin Wall: production, preservation and consumption of a 20th-century monument ( Frederick Baker ); Seeing stars: character and identity in the landscapes of modern Macedonia ( Keith Brown ). Experienced landscapes: Forms of power: dimensions of an Irish megalithic landscape ( Jean McMann ); Late woodland landscapes of Wisconsin: ridges, fields, effigy mounds and territoriality ( William Gustav Gartner ). |
Northeastern University Oakland Fall 2024 and Spring 2025 …
Northeastern University Oakland students can pursue a variety of interdisciplinary degree programs positioned at the intersection of technology, healthcare, science, business, and the …
INFO 6105 Data Science Engineering Methods and Tools
Introduces the fundamental techniques for machine learning and data science engineering. Discusses a variety of machine learning algorithms, along with examples of their …
Northeastern Data Science Major [PDF] - interactive.cornish.edu
The book delves into Northeastern Data Science Major. Northeastern Data Science Major is a vital topic that must be grasped by everyone, from students and scholars to the general public.
Fall 2025 Course Registration Information for MS Data …
Congratulations on your acceptance to the MS Data Analytics Engineering program at Northeastern University! At this time, we suggest that you register for Fall 2025 courses.
Data Science and Mathematics, BS - catalog.northeastern.edu
The data science and mathematics combined major combines computer science, data science, and mathematics into an integrated curriculum. The program provides the rigorous theoretical …
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of 2021, students at Northeastern can choose among three comput-ing majors (computer science, data science or cybersecurity) and 42 combined majors, which combine one of the three …
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BSc (Hons) in Data Science CAREERS Data Scientist Informatics Specialist Data Engineer An experience-driven degree program uniquely designed to help employers who are leading data …
Facts and Figures - facts.northeastern.edu
Northeastern’s research enterprise is strategically focused on building interdisciplinary teams to solve global challenges in health, security, and sustainability. Institutes in mission-critical fields …
Data Science an - Northeastern University Academic Catalog
The Data Science and Biochemistry Major combines computer science, biochemistry, biology, information science, mathematics, and statistics into an integrated curriculum. The program …
Northeastern Data Science Major - interactive.cornish.edu
Students will need data literacy to manage the flow of big data, and technological literacy to know how their machines work, but human literacy—the humanities, communication, and design—to …
Data Science and Business Administration, BS
The data science and business administration combined major integrates a technical degree with the theory and practice of management through active learning, problem-driven research, …
Master of Science in Artificial Intelligence-4
AI intersects with Data Science (DS) and Robotics (ROB), with techniques from Machine Learning (ML) playing a major role in all three areas. AI includes subareas such as knowledge
MS Bioinformatics - Northeastern University College of Science
BINF Courses are offered in Fall and Spring semesters only. Some graduate classes, research or thesis credits could be completed in the summer, depending on the availability of courses and …
Data Science and Physics, BS - catalog.northeastern.edu
The data science and physics combined major brings together computer and data science, physics, and mathematics. The computer science and mathematics requirements serve as a …
Master of Science in Business Analytics Curriculum - D'Amore …
Covers the leading data practices from early adopters, focusing on innovative information design, data quality, data sharing, and data integration perspectives and methods for managing data …
Data Science and Design, BS - Northeastern University …
Students study the collection, manipulation, storage, retrieval, and computational analysis of data in its various forms including numeric, textual, image, and video data. Information designers …
Applied Mathematics - connect.northeastern.edu
The data science concentration provides you with solid knowledge of mathematical modeling, probability, statistics, machine learning, and data analysis. Plus, you’ll gain excellent …
Data Science and Public Health, BS - Northeastern University …
The data science and public health combined major offers students a strong foundation and experiential education integrating studies in public health and data science. The program …
Bioinformatics - connect.northeastern.edu
computer science, and data analytics—along with real-world experience. The program prepares graduates to play pivotal roles in today’s cutting-edge life science, biotechnology, and …
Data Science, BS - Northeastern University Academic Catalog
The Bachelor of Science in Data Science studies the collection, manipulation, storage, retrieval, and computational analysis of data in its various forms, including numeric, textual, image, and …
Northeastern University Oakland Fall 2024 and Spring 2025 …
Northeastern University Oakland students can pursue a variety of interdisciplinary degree programs positioned at the intersection of technology, healthcare, science, business, and the …
INFO 6105 Data Science Engineering Methods and Tools
Introduces the fundamental techniques for machine learning and data science engineering. Discusses a variety of machine learning algorithms, along with examples of their …
Northeastern Data Science Major [PDF]
The book delves into Northeastern Data Science Major. Northeastern Data Science Major is a vital topic that must be grasped by everyone, from students and scholars to the general public.
Fall 2025 Course Registration Information for MS Data …
Congratulations on your acceptance to the MS Data Analytics Engineering program at Northeastern University! At this time, we suggest that you register for Fall 2025 courses.
Data Science and Mathematics, BS
The data science and mathematics combined major combines computer science, data science, and mathematics into an integrated curriculum. The program provides the rigorous theoretical …
Broadening Participation in Computing via Ubiquitous …
of 2021, students at Northeastern can choose among three comput-ing majors (computer science, data science or cybersecurity) and 42 combined majors, which combine one of the three …
An experience-driven degree program Data Scientist
BSc (Hons) in Data Science CAREERS Data Scientist Informatics Specialist Data Engineer An experience-driven degree program uniquely designed to help employers who are leading data …
Facts and Figures - facts.northeastern.edu
Northeastern’s research enterprise is strategically focused on building interdisciplinary teams to solve global challenges in health, security, and sustainability. Institutes in mission-critical fields …
Data Science an - Northeastern University Academic Catalog
The Data Science and Biochemistry Major combines computer science, biochemistry, biology, information science, mathematics, and statistics into an integrated curriculum. The program …
Northeastern Data Science Major - interactive.cornish.edu
Students will need data literacy to manage the flow of big data, and technological literacy to know how their machines work, but human literacy—the humanities, communication, and design—to …
Data Science and Business Administration, BS
The data science and business administration combined major integrates a technical degree with the theory and practice of management through active learning, problem-driven research, …
Master of Science in Artificial Intelligence-4
AI intersects with Data Science (DS) and Robotics (ROB), with techniques from Machine Learning (ML) playing a major role in all three areas. AI includes subareas such as knowledge
MS Bioinformatics - Northeastern University College of Science
BINF Courses are offered in Fall and Spring semesters only. Some graduate classes, research or thesis credits could be completed in the summer, depending on the availability of courses and …
Data Science and Physics, BS - catalog.northeastern.edu
The data science and physics combined major brings together computer and data science, physics, and mathematics. The computer science and mathematics requirements serve as a …
Master of Science in Business Analytics Curriculum - D'Amore …
Covers the leading data practices from early adopters, focusing on innovative information design, data quality, data sharing, and data integration perspectives and methods for managing data …
Data Science and Design, BS - Northeastern University …
Students study the collection, manipulation, storage, retrieval, and computational analysis of data in its various forms including numeric, textual, image, and video data. Information designers …
Applied Mathematics - connect.northeastern.edu
The data science concentration provides you with solid knowledge of mathematical modeling, probability, statistics, machine learning, and data analysis. Plus, you’ll gain excellent …
Data Science and Public Health, BS - Northeastern University …
The data science and public health combined major offers students a strong foundation and experiential education integrating studies in public health and data science. The program …
Bioinformatics - connect.northeastern.edu
computer science, and data analytics—along with real-world experience. The program prepares graduates to play pivotal roles in today’s cutting-edge life science, biotechnology, and …
