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| drexel university ms in data science: Data Science Careers, Training, and Hiring Renata Rawlings-Goss, 2019-08-02 This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build. |
| drexel university ms in data science: Data Science and Digital Business Fausto Pedro García Márquez, Benjamin Lev, 2019-01-04 This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business. |
| drexel university ms in data science: Why Data Science Projects Fail Douglas Gray, Evan Shellshear, 2024-09-05 The field of artificial intelligence, data science, and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects, and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven. This book aims to fix this by countering the AI hype with a dose of realism. Written by two experts in the field, the authors firmly believe in the power of mathematics, computing, and analytics, but if false expectations are set and practitioners and leaders don’t fully understand everything that really goes into data science projects, then a stunning 80% (or more) of analytics projects will continue to fail, costing enterprises and society hundreds of billions of dollars, and leading to non-experts abandoning one of the most important data-driven decision-making capabilities altogether. For the first time, business leaders, practitioners, students, and interested laypeople will learn what really makes a data science project successful. By illustrating with many personal stories, the authors reveal the harsh realities of implementing AI and analytics. |
| drexel university ms in data science: CiteSpace Chaomei Chen, 2016 CiteSpace is a freely available computer program written in Java for visualizing and analyzing literature of a scientific domain. A knowledge domain is broadly defined in order to capture the notion of a logically and cohesively organized body of knowledge. It may range from specific topics such as post-traumatic stress disorder to fields of study lacking clear-cut boundaries, such as research on terrorism or regenerative medicine. CiteSpace takes bibliographic information, especially citation information from the Web of Science, and generates interactive visualizations. Users can explore various patterns and trends uncovered from scientific publications, and develop a good understanding of scientific literature much more efficiently than they would from an unguided search through literature. The full text of many scientific publications can be accessed with a single click through the interactive visualization in CiteSpace. At the end of a session, CiteSpace can generate a summary report to summarize key information about the literature analyzed. This book is a practical guide not only on how to operate the tool but also on why the tool is designed and what implications of various patterns that require special attention. This book is written with a minimum amount of jargon. It uses everyday language to explain what people may learn from the writings of scholars of all kinds. |
| drexel university ms in data science: Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on the Growth of Computer Science Undergraduate Enrollments, 2018-04-28 The field of computer science (CS) is currently experiencing a surge in undergraduate degree production and course enrollments, which is straining program resources at many institutions and causing concern among faculty and administrators about how best to respond to the rapidly growing demand. There is also significant interest about what this growth will mean for the future of CS programs, the role of computer science in academic institutions, the field as a whole, and U.S. society more broadly. Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments seeks to provide a better understanding of the current trends in computing enrollments in the context of past trends. It examines drivers of the current enrollment surge, relationships between the surge and current and potential gains in diversity in the field, and the potential impacts of responses to the increased demand for computing in higher education, and it considers the likely effects of those responses on students, faculty, and institutions. This report provides recommendations for what institutions of higher education, government agencies, and the private sector can do to respond to the surge and plan for a strong and sustainable future for the field of CS in general, the health of the institutions of higher education, and the prosperity of the nation. |
| drexel university ms in data science: Cognitive Electronic Warfare: An Artificial Intelligence Approach Karen Haigh, Julia Andrusenko, 2021-07-31 This comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high priority on EW systems that can characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time. The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization. |
| drexel university ms in data science: Proceedings of the 7th International Conference on Emerging Databases Wookey Lee, Wonik Choi, Sungwon Jung, Min Song, 2017-10-13 This proceedings volume presents selected papers from the 7th International Conference on Emerging Databases: Technologies, Applications, and Theory (EDB 2017), which was held in Busan, Korea from 7 to 9 August, 2017. This conference series was launched by the Korean Institute of Information Scientists and Engineers (KIISE) Database Society of Korea as an annual forum for exploring novel technologies, applications, and research advances in the field of emerging databases. This forum has evolved into the premier international venue for researchers and practitioners to discuss current research issues, challenges, new technologies, and solutions. |
| drexel university ms in data science: Big Data Management Fausto Pedro García Márquez, Benjamin Lev, 2016-11-15 This book focuses on the analytic principles of business practice and big data. Specifically, it provides an interface between the main disciplines of engineering/technology and the organizational and administrative aspects of management, serving as a complement to books in other disciplines such as economics, finance, marketing and risk analysis. The contributors present their areas of expertise, together with essential case studies that illustrate the successful application of engineering management theories in real-life examples. |
| drexel university ms in data science: Disability and Equity in Higher Education Accessibility Alphin, Jr., Henry C., Lavine, Jennie, Chan, Roy Y., 2017-03-24 Education is the foundation to almost all successful lives. It is vital that learning opportunities are available on a global scale, regardless of individual disabilities or differences, and to create more inclusive educational practices. Disability and Equity in Higher Education Accessibility is a comprehensive reference source for the latest scholarly material on emerging methods and trends in disseminating knowledge in higher education, despite traditional hindrances. Featuring extensive coverage on relevant topics such as higher education policies, electronic resources, and inclusion barriers, this publication is ideally designed for educators, academics, students, and researchers interested in expanding their knowledge of disability-inclusive global education. |
| drexel university ms in data science: Internet of Things Fausto Pedro García Márquez, 2021-08-18 The Internet of Things (IoT) is a closed-loop system in which a set of sensors is connected to servers via a network. The data from sensors are stored in a database and then analysed by IoT analytics. The results are usually employed by either humans, machines, or software to make decisions about the operation of the system. This book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning capabilities of managing the IoT. |
| drexel university ms in data science: Building Drexel Richardson Dilworth, Scott Gabriel Knowles, 2016-12-12 Published in conjunction with Drexel University’s 125th anniversary, Building Drexel chronicles the founding of the university by Anthony J. Drexel through to the present day. The editors and contributors create a prismatic discussion of the university and its evolution. Richly illustrated chapters cover the architectural history of notable Drexel buildings; the role of Drexel in Philadelphia’s modern history; its Greek life; sports—particularly Drexel’s history in the Big 5; and each of the university’s schools and colleges. There is a history of the medical college and law school, plus the creation of new schools such as those of biomedical engineering, science and health systems. Building Drexel also documents the civil rights history of Drexel and its urban planning history in relation to the racially diverse Powelton Village and Mantua neighborhoods it borders. This commemorative volume shows the development of the university both in the city and in the world. Contributors include: Lloyd Ackert, Cordelia Frances Biddle, Paula Marantz Cohen, Donna Marie De Carolis, Roger Dennis, Gloria Donnelly, Kevin D. Egan, Alissa Falcone, David Fenske, John A. Fry, Stephen F. Gambescia, Marla J. Gold, Charles Haas, Kathy Harvatt, Daniel Johnson, Jeannine Keefer, Larry Keiser, Michael Kelley, Jason Ludwig, Jonson Miller, Julie Mostov, Danuta A. Nitecki, Anthony M. Noce, Steven J. Peitzman, David Raizman, Tiago Saraiva, Amy E. Slaton, Nathaniel Stanton, Virginia Theerman, Laura Valenti, James Wolfinger, Eric A. Zillmer, and the editors. |
| drexel university ms in data science: Security Informatics Christopher C. Yang, Michael Chau, Jau-Hwang Wang, Hsinchun Chen, 2010-01-08 Intelligence and Security Informatics (ISI) is defined as the study of the development and use of advanced information systems and technologies for national, international, and societal security-related applications. With the rise of global terrorism, the field has been given an increasing amount of attention from academic researchers, law enforcement, intelligent experts, information technology consultants and practitioners. SECURITY INFORMATICS is global in scope and perspective. Leading experts will be invited as contributing authors from the US, UK, Denmark, Israel, Singapore, Hong Kong, Taiwan, Europe, etc. It is the first systematic, archival volume treatment of the field and will cover the very latest advances in ISI research and practice. It is organized in four major subject areas: (1) Information and Systems Security, (2) Information Sharing and Analysis in Security Informatics, (3) Infrastructure Protection and Emergency Responses, and (4) National Security and Terrorism Informatics. |
| drexel university ms in data science: Teaching Data Analytics Susan Vowels, Katherine Leaming Goldberg, 2019-06-17 The need for analytics skills is a source of the burgeoning growth in the number of analytics and decision science programs in higher education developed to feed the need for capable employees in this area. The very size and continuing growth of this need means that there is still space for new program development. Schools wishing to pursue business analytics programs intentionally assess the maturity level of their programs and take steps to close the gap. Teaching Data Analytics: Pedagogy and Program Design is a reference for faculty and administrators seeking direction about adding or enhancing analytics offerings at their institutions. It provides guidance by examining best practices from the perspectives of faculty and practitioners. By emphasizing the connection of data analytics to organizational success, it reviews the position of analytics and decision science programs in higher education, and to review the critical connection between this area of study and career opportunities. The book features: A variety of perspectives ranging from the scholarly theoretical to the practitioner applied An in-depth look into a wide breadth of skills from closely technology-focused to robustly soft human connection skills Resources for existing faculty to acquire and maintain additional analytics-relevant skills that can enrich their current course offerings. Acknowledging the dichotomy between data analytics and data science, this book emphasizes data analytics rather than data science, although the book does touch upon the data science realm. Starting with industry perspectives, the book covers the applied world of data analytics, covering necessary skills and applications, as well as developing compelling visualizations. It then dives into pedagogical and program design approaches in data analytics education and concludes with ideas for program design tactics. This reference is a launching point for discussions about how to connect industry’s need for skilled data analysts to higher education’s need to design a rigorous curriculum that promotes student critical thinking, communication, and ethical skills. It also provides insight into adding new elements to existing data analytics courses and for taking the next step in adding data analytics offerings, whether it be incorporating additional analytics assignments into existing courses, offering one course designed for undergraduates, or an integrated program designed for graduate students. |
| drexel university ms in data science: Learning in Information-Rich Environments Delia Neuman, Mary Jean Tecce DeCarlo, Vera J. Lee, Stacey Greenwell, Allen Grant, 2019-12-31 The amount and range of information available to today’s students—and indeed to all learners—is unprecedented. If the characteristics of “the information age” demand new conceptions of commerce, national security, and publishing—among other things—it is logical to assume that they carry implications for education as well. Little has been written, however, about how the specific affordances of these technologies—and the kinds of information they allow students to access and create—relate to the central purpose of education: learning. What does “learning” mean in an information-rich environment? What are its characteristics? What kinds of tasks should it involve? What concepts, strategies, attitudes, and skills do educators and students need to master if they are to learn effectively and efficiently in such an environment? How can researchers, theorists, and practitioners foster the well-founded and widespread development of such key elements of the learning process? This second edition continues these discussions and suggests some tentative answers. Drawing primarily from research and theory in three distinct but related fields—learning theory, instructional systems design, and information studies—it presents a way to think about learning that responds directly to the actualities of a world brimming with information. The second edition also includes insights from digital and critical literacies and provides a combination of an updated research-and-theory base and a collection of instructional scenarios for helping teachers and librarians implement each step of the I-LEARN model. The book could be used in courses in teacher preparation, academic-librarian preparation, and school-librarian preparation. |
| drexel university ms in data science: Practical Text Analytics Murugan Anandarajan, Chelsey Hill, Thomas Nolan, 2018-10-19 This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information. |
| drexel university ms in data science: Programmed Inequality Mar Hicks, 2018-02-23 This “sobering tale of the real consequences of gender bias” explores how Britain lost its early dominance in computing by systematically discriminating against its most qualified workers: women (Harvard Magazine) In 1944, Britain led the world in electronic computing. By 1974, the British computer industry was all but extinct. What happened in the intervening thirty years holds lessons for all postindustrial superpowers. As Britain struggled to use technology to retain its global power, the nation’s inability to manage its technical labor force hobbled its transition into the information age. In Programmed Inequality, Mar Hicks explores the story of labor feminization and gendered technocracy that undercut British efforts to computerize. That failure sprang from the government’s systematic neglect of its largest trained technical workforce simply because they were women. Women were a hidden engine of growth in high technology from World War II to the 1960s. As computing experienced a gender flip, becoming male-identified in the 1960s and 1970s, labor problems grew into structural ones and gender discrimination caused the nation’s largest computer user—the civil service and sprawling public sector—to make decisions that were disastrous for the British computer industry and the nation as a whole. Drawing on recently opened government files, personal interviews, and the archives of major British computer companies, Programmed Inequality takes aim at the fiction of technological meritocracy. Hicks explains why, even today, possessing technical skill is not enough to ensure that women will rise to the top in science and technology fields. Programmed Inequality shows how the disappearance of women from the field had grave macroeconomic consequences for Britain, and why the United States risks repeating those errors in the twenty-first century. |
| drexel university ms in data science: The Integration of the Humanities and Arts with Sciences, Engineering, and Medicine in Higher Education National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on Integrating Higher Education in the Arts, Humanities, Sciences, Engineering, and Medicine, 2018-06-21 In the United States, broad study in an array of different disciplines â€arts, humanities, science, mathematics, engineering†as well as an in-depth study within a special area of interest, have been defining characteristics of a higher education. But over time, in-depth study in a major discipline has come to dominate the curricula at many institutions. This evolution of the curriculum has been driven, in part, by increasing specialization in the academic disciplines. There is little doubt that disciplinary specialization has helped produce many of the achievement of the past century. Researchers in all academic disciplines have been able to delve more deeply into their areas of expertise, grappling with ever more specialized and fundamental problems. Yet today, many leaders, scholars, parents, and students are asking whether higher education has moved too far from its integrative tradition towards an approach heavily rooted in disciplinary silos. These silos represent what many see as an artificial separation of academic disciplines. This study reflects a growing concern that the approach to higher education that favors disciplinary specialization is poorly calibrated to the challenges and opportunities of our time. The Integration of the Humanities and Arts with Sciences, Engineering, and Medicine in Higher Education examines the evidence behind the assertion that educational programs that mutually integrate learning experiences in the humanities and arts with science, technology, engineering, mathematics, and medicine (STEMM) lead to improved educational and career outcomes for undergraduate and graduate students. It explores evidence regarding the value of integrating more STEMM curricula and labs into the academic programs of students majoring in the humanities and arts and evidence regarding the value of integrating curricula and experiences in the arts and humanities into college and university STEMM education programs. |
| drexel university ms in data science: The Food Babe Way Vani Hari, 2015-02-10 Eliminate toxins from your diet and transform the way you feel in just 21 days with this national bestseller full of shopping lists, meal plans, and mouth-watering recipes. Did you know that your fast food fries contain a chemical used in Silly Putty? Or that a juicy peach sprayed heavily with pesticides could be triggering your body to store fat? When we go to the supermarket, we trust that all our groceries are safe to eat. But much of what we're putting into our bodies is either tainted with chemicals or processed in a way that makes us gain weight, feel sick, and age before our time. Luckily, Vani Hari -- aka the Food Babe -- has got your back. A food activist who has courageously put the heat on big food companies to disclose ingredients and remove toxic additives from their products, Hari has made it her life's mission to educate the world about how to live a clean, organic, healthy lifestyle in an overprocessed, contaminated-food world, and how to look and feel fabulous while doing it. In The Food Babe Way, Hari invites you to follow an easy and accessible plan that will transform the way you feel in three weeks. Learn how to: Remove unnatural chemicals from your diet Rid your body of toxins Lose weight without counting calories Restore your natural glow Including anecdotes of her own transformation along with easy-to-follow shopping lists, meal plans, and tantalizing recipes, The Food Babe Way will empower you to change your food, change your body, and change the world. |
| drexel university ms in data science: Cloth Modeling and Animation Donald House, David Breen, 2000-07-13 Written by leaders in the field of computer clothing design and simulation, Cloth Modeling and Animation is a vital resource for researchers and developers of cloth simulation software as well as computer animators and graphics programmers. Readers will learn about cloth's nature and structure, and scientific approaches to understanding its behavior. |
| drexel university ms in data science: Cognitive Computing and Big Data Analytics Judith S. Hurwitz, Marcia Kaufman, Adrian Bowles, 2015-02-12 A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data. This book helps technologists understand cognitive computing's underlying technologies, from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches based on accumulated evidence, rather than reprogramming. Detailed case examples from the financial, healthcare, and manufacturing walk readers step-by-step through the design and testing of cognitive systems, and expert perspectives from organizations such as Cleveland Clinic, Memorial Sloan-Kettering, as well as commercial vendors that are creating solutions. These organizations provide insight into the real-world implementation of cognitive computing systems. The IBM Watson cognitive computing platform is described in a detailed chapter because of its significance in helping to define this emerging market. In addition, the book includes implementations of emerging projects from Qualcomm, Hitachi, Google and Amazon. Today's cognitive computing solutions build on established concepts from artificial intelligence, natural language processing, ontologies, and leverage advances in big data management and analytics. They foreshadow an intelligent infrastructure that enables a new generation of customer and context-aware smart applications in all industries. Cognitive Computing is a comprehensive guide to the subject, providing both the theoretical and practical guidance technologists need. Discover how cognitive computing evolved from promise to reality Learn the elements that make up a cognitive computing system Understand the groundbreaking hardware and software technologies behind cognitive computing Learn to evaluate your own application portfolio to find the best candidates for pilot projects Leverage cognitive computing capabilities to transform the organization Cognitive systems are rightly being hailed as the new era of computing. Learn how these technologies enable emerging firms to compete with entrenched giants, and forward-thinking established firms to disrupt their industries. Professionals who currently work with big data and analytics will see how cognitive computing builds on their foundation, and creates new opportunities. Cognitive Computing provides complete guidance to this new level of human-machine interaction. |
| drexel university ms in data science: Fascist Pigs Tiago Saraiva, 2016-10-07 How the breeding of new animals and plants was central to fascist regimes in Italy, Portugal, and Germany and to their imperial expansion. In the fascist regimes of Mussolini's Italy, Salazar's Portugal, and Hitler's Germany, the first mass mobilizations involved wheat engineered to take advantage of chemical fertilizers, potatoes resistant to late blight, and pigs that thrived on national produce. Food independence was an early goal of fascism; indeed, as Tiago Saraiva writes in Fascist Pigs, fascists were obsessed with projects to feed the national body from the national soil. Saraiva shows how such technoscientific organisms as specially bred wheat and pigs became important elements in the institutionalization and expansion of fascist regimes. The pigs, the potatoes, and the wheat embodied fascism. In Nazi Germany, only plants and animals conforming to the new national standards would be allowed to reproduce. Pigs that didn't efficiently convert German-grown potatoes into pork and lard were eliminated. Saraiva describes national campaigns that intertwined the work of geneticists with new state bureaucracies; discusses fascist empires, considering forced labor on coffee, rubber, and cotton in Ethiopia, Mozambique, and Eastern Europe; and explores fascist genocides, following Karakul sheep from a laboratory in Germany to Eastern Europe, Libya, Ethiopia, and Angola. Saraiva's highly original account—the first systematic study of the relation between science and fascism—argues that the “back to the land” aspect of fascism should be understood as a modernist experiment involving geneticists and their organisms, mass propaganda, overgrown bureaucracy, and violent colonialism. |
| drexel university ms in data science: Information Experience in Theory and Design Tim Gorichanaz, 2020-10-01 SI 14 provides a rigorous theoretical foundation for the study of information experience, an emerging field within Information Science. With particular focus on information behavior and literacy, it explores the importance and implications of individual user experience through the themes of understanding, meaning, and self. |
| drexel university ms in data science: Foundations of Statistics for Data Scientists Alan Agresti, Maria Kateri, 2021-11-22 Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on why it works as well as how to do it. Compared to traditional mathematical statistics textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into Data Analysis and Applications and Methods and Concepts. Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises. |
| drexel university ms in data science: Closing the Analytics Talent Gap Jennifer Priestley, Robert McGrath, 2021-05-03 How can we recruit out of your program? We have a project – how do we reach out to your students? If we do research together who owns it? We have employees who need to upskill in analytics – can you help me with that? How much does all of this cost? Managers and executives are increasingly asking university professors such questions as they deal with a critical shortage of skilled data analysts. At the same time, academics are asking such questions as: How can I bring a real analytical project in the classroom? How can I get real data to help my students develop the skills necessary to be a data scientist? Is what I am teaching in the classroom aligned with the demands of the market for analytical talent? After spending several years answering almost daily e-mails and telephone calls from business managers asking for staffing help and aiding fellow academics with their analytics teaching needs, Dr. Jennifer Priestley of Kennesaw State University and Dr. Robert McGrath of the University of New Hampshire wrote Closing the Analytics Talent Gap: An Executive’s Guide to Working with Universities. The book builds a bridge between university analytics programs and business organizations. It promotes a dialog that enables executives to learn how universities can help them find strategically important personnel and universities to learn how they can develop and educate this personnel. Organizations are facing previously unforeseen challenges related to the translation of massive amounts of data – structured and unstructured, static and in-motion, voice, text, and image – into information to solve current challenges and anticipate new ones. The advent of analytics and data science also presents universities with unforeseen challenges of providing learning through application. This book helps both organizations with finding data natives and universities with educating students to develop the facility to work in a multi-faceted and complex data environment. . |
| drexel university ms in data science: Coco's Fire Jeremy Wortzel, Lena Champlin, Group for the Advancement of Psychiatry - Climate Committee, 2021-09-30 |
| drexel university ms in data science: Creating Chaos Online Asta Zelenkauskaite, 2022-10-10 With the prevalence of disinformation geared to instill doubt rather than clarity, Creating Chaos Online unmasks disinformation when it attempts to pass as deliberation in the public sphere and distorts the democratic processes. Asta Zelenkauskaitė finds that repeated tropes justifying Russian trolling were found to circulate across not only all analyzed media platforms’ comments but also across two analyzed sociopolitical contexts suggesting the orchestrated efforts behind messaging. Through a dystopian vision of publics that are expected to navigate in the sea of uncertain both authentic and orchestrated content, pushed by human and nonhuman actors, Creating Chaos Online offers a concept of post-publics. The idea of post-publics is reflected within the continuum of treatment of public, counter public, and anti-public. This book argues that affect-instilled arguments used in public deliberation in times of uncertainty, along with whataboutism constitute a playbook for chaos online. |
| drexel university ms in data science: Principles and Practice of Clinical Research John I. Gallin, Frederick P Ognibene, 2011-04-28 The second edition of this innovative work again provides a unique perspective on the clinical discovery process by providing input from experts within the NIH on the principles and practice of clinical research. Molecular medicine, genomics, and proteomics have opened vast opportunities for translation of basic science observations to the bedside through clinical research. As an introductory reference it gives clinical investigators in all fields an awareness of the tools required to ensure research protocols are well designed and comply with the rigorous regulatory requirements necessary to maximize the safety of research subjects. Complete with sections on the history of clinical research and ethics, copious figures and charts, and sample documents it serves as an excellent companion text for any course on clinical research and as a must-have reference for seasoned researchers.*Incorporates new chapters on Managing Conflicts of Interest in Human Subjects Research, Clinical Research from the Patient's Perspective, The Clinical Researcher and the Media, Data Management in Clinical Research, Evaluation of a Protocol Budget, Clinical Research from the Industry Perspective, and Genetics in Clinical Research *Addresses the vast opportunities for translation of basic science observations to the bedside through clinical research*Delves into data management and addresses how to collect data and use it for discovery*Contains valuable, up-to-date information on how to obtain funding from the federal government |
| drexel university ms in data science: How to Read a Paper Trisha Greenhalgh, 2014-02-26 The best-selling introduction to evidence-based medicine In a clear and engaging style, How to Read a Paper demystifies evidence-based medicine and explains how to critically appraise published research and also put the findings into practice. An ideal introduction to evidence-based medicine, How to Read a Paper explains what to look for in different types of papers and how best to evaluate the literature and then implement the findings in an evidence-based, patient-centred way. Helpful checklist summaries of the key points in each chapter provide a useful framework for applying the principles of evidence-based medicine in everyday practice. This fifth edition has been fully updated with new examples and references to reflect recent developments and current practice. It also includes two new chapters on applying evidence-based medicine with patients and on the common criticisms of evidence-based medicine and responses. How to Read a Paper is a standard text for medical and nursing schools as well as a friendly guide for everyone wanting to teach or learn the basics of evidence-based medicine. |
| drexel university ms in data science: Big Data Analytics in Bioinformatics and Healthcare Wang, Baoying, 2014-10-31 As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information. |
| drexel university ms in data science: The Oxford Handbook of Environmental Political Theory Teena Gabrielson, Cheryl Hall, John M. Meyer, David Schlosberg, 2016-01-07 Set at the intersection of political theory and environmental politics, yet with broad engagement across the environmental social sciences and humanities, The Oxford Handbook of Environmental Political Theory, defines, illustrates, and challenges the field of environmental political theory (EPT). Featuring contributions from distinguished political scientists working in this field, this volume addresses canonical theorists and contemporary environmental problems with a diversity of theoretical approaches. The initial volume focuses on EPT as a field of inquiry, engaging both traditions of political thought and the academy. In the second section, the handbook explores conceptualizations of nature and the environment, as well as the nature of political subjects, communities, and boundaries within our environments. A third section addresses the values that motivate environmental theorists—including justice, responsibility, rights, limits, and flourishing—and the potential conflicts that can emerge within, between, and against these ideals. The final section examines the primary structures that constrain or enable the achievement of environmental ends, as well as theorizations of environmental movements, citizenship, and the potential for on-going environmental action and change. |
| drexel university ms in data science: Fundamentals and Methods of Machine and Deep Learning Pradeep Singh, 2022-02-01 FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers. |
| drexel university ms in data science: Big Data Analytics and Knowledge Discovery Min Song, Il-Yeol Song, Gabriele Kotsis, A Min Tjoa, Ismail Khalil, 2020-09-10 The volume LNCS 12393 constitutes the papers of the 22nd International Conference Big Data Analytics and Knowledge Discovery which will be held online in September 2020. The 15 full papers presented together with 14 short papers plus 1 position paper in this volume were carefully reviewed and selected from a total of 77 submissions. This volume offers a wide range to following subjects on theoretical and practical aspects of big data analytics and knowledge discovery as a new generation of big data repository, data pre-processing, data mining, text mining, sequences, graph mining, and parallel processing. |
| drexel university ms in data science: Applications of Artificial Intelligence and Machine Learning Ankur Choudhary, Arun Prakash Agrawal, Rajasvaran Logeswaran, Bhuvan Unhelkar, 2021-07-27 The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications. |
| drexel university ms in data science: Feeling Medicine Kelly Underman, 2020-08-18 Honorable Mention, Sociology of the Body and Embodiment Best Publication Award, given by the Body and Embodiment Section of the American Sociological Association The emotional and social components of teaching medical students to be good doctors The pelvic exam is considered a fundamental procedure for medical students to learn; it is also often the one of the first times where medical students are required to touch a real human being in a professional manner. In Feeling Medicine, Kelly Underman gives us a look inside these gynecological teaching programs, showing how they embody the tension between scientific thought and human emotion in medical education. Drawing on interviews with medical students, faculty, and the people who use their own bodies to teach this exam, Underman offers the first in-depth examination of this essential, but seldom discussed, aspect of medical education. Through studying, teaching, and learning about the pelvic exam, she contrasts the technical and emotional dimensions of learning to be a physician. Ultimately, Feeling Medicine explores what it means to be a good doctor in the twenty-first century, particularly in an era of corporatized healthcare. |
| drexel university ms in data science: Learning in Information-Rich Environments Delia Neuman, 2014-08-23 The amount and range of information available to today’s students—and indeed to all learners—is unprecedented. Phrases like “the information revolution”, “the information (or knowledge) society”, and “the knowledge economy” underscore the truism that our society has been transformed by virtually instantaneous access to virtually unlimited information. Thomas Friedman tells us that “The World Is Flat” and that we must devise new political and economic understandings based on the ceaseless communication of information from all corners of the world. The Bush administration tells us that information relating to the “war on terrorism” is so critical that we must allow new kinds of surveillance to keep society safe. Teenage subscribers to social-computing networks not only access information but enter text and video images and publish them widely—becoming the first adolescents in history to be creators as well as consumers of vast quantities of information. If the characteristics of “the information age” demand new conceptions of commerce, national security, and publishing—among other things—it is logical to assume that they carry implications for education as well. In fact, a good deal has been written over the last several decades about how education as a whole must transform its structure and curriculum to accommodate the possibilities offered by new technologies. Far less has been written, however, about how the specific affordances of these technologies—and the kinds of information they allow students to access and create—relate to the central purpose of education: learning. What does “learning” mean in an information-rich environment? What are its characteristics? What kinds of tasks should it involve? What concepts, strategies, attitudes, and skills do educators and students need to master if they are to learn effectively and efficiently in such an environment? How can researchers, theorists, and practitioners foster the well-founded and widespread development of such key elements of the learning process? This book explores these questions and suggests some tentative answers. Drawing from research and theory in three distinct but related fields—learning theory, instructional systems design, and information studies—it presents a way to think about learning that responds directly to the actualities of a world brimming with information. The book is grounded in the work of such key figures in learning theory as Bransford and Anderson & Krathwohl. It draws on such theorists of instructional design as Gagne, Mayer, and Merrill. From information studies, it uses ideas from Buckland, Marchionini, and Wilson (who is known for his pioneering work in “information behavior”—that is, the full range of information seeking and use). The book breaks new ground in bringing together ideas that have run in parallel for years but whose relationship has not been fully explored. |
| drexel university ms in data science: Ebony and Ivy Craig Steven Wilder, 2014-09-02 A leading African-American historian of race in America exposes the uncomfortable truths about race, slavery and the American academy, revealing that our leading universities, dependent on human bondage, became breeding grounds for the racist ideas that sustained it. |
| drexel university ms in data science: Envisioning the Data Science Discipline National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-03-05 The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation's ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses. The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction. This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education. |
| drexel university ms in data science: Learning pandas Michael Heydt, 2015-04-16 If you are a Python programmer who wants to get started with performing data analysis using pandas and Python, this is the book for you. Some experience with statistical analysis would be helpful but is not mandatory. |
| drexel university ms in data science: Quicksort Robert Sedgewick, 1980 |
| drexel university ms in data science: Mastering pandas for Finance Michael Heydt, 2015-05-25 If you are interested in quantitative finance, financial modeling, and trading, or simply want to learn how Python and pandas can be applied to finance, then this book is ideal for you. Some knowledge of Python and pandas is assumed. Interest in financial concepts is helpful, but no prior knowledge is expected. |
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Drexel University is a private research university with its main campus in Philadelphia, Pennsylvania, United States. Drexel's undergraduate school was founded in 1891 by Anthony J. Drexel, a …
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