| data science michigan state university: Purchasing and Supply Management Michiel Leenders, P. Fraser Johnson, Anna Flynn, 2010-07-13 The Fourteenth Edition of Purchasing and Supply Management provides a comprehensive introduction to the purchasing and supply chain management field, supported by over 40 case studies. Cases cover purchasing and supply chain issues in a variety of settings, from process industries to high tech manufacturing and services as well as public institutions. The text focuses on decision making throughout the supply chain. Based on the conviction that supply managers, in concert with suppliers and distributors, have to contribute to organizational goals and strategies, this edition continues to focus on how to make that mission a reality. |
| data science michigan state university: The Agricultural College Of The State Of Michigan East Lans Michigan State University, 2023-07-18 This book offers a unique perspective on the history of agricultural education in the United States, focusing on the founding and early years of the Agricultural College of the State of Michigan (now Michigan State University). Through firsthand accounts and historical records, readers can learn about the challenges and opportunities facing early agricultural educators and their students. This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. |
| data science michigan state university: Probability and Statistics for Data Science Norman Matloff, 2019-06-21 Probability and Statistics for Data Science: Math + R + Data covers math stat—distributions, expected value, estimation etc.—but takes the phrase Data Science in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the how and why of statistics, and to see the big picture. * Not theorem/proof-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award. |
| data science michigan state university: How Social Science Got Better Matt Grossmann, 2021-07-05 It seems like most of what we read about the academic social sciences in the mainstream media is negative. The field is facing mounting criticism, as canonical studies fail to replicate, questionable research practices abound, and researcher social and political biases come under fire. In response to these criticisms, Matt Grossmann, in How Social Science Got Better, provides a robust defense of the current state of the social sciences. Applying insights from the philosophy, history, and sociology of science and providing new data on research trends and scholarly views, he argues that, far from crisis, social science is undergoing an unparalleled renaissance of ever-broader understanding and application. According to Grossmann, social science research today has never been more relevant, rigorous, or self-reflective because scholars have a much better idea of their blind spots and biases. He highlights how scholars now closely analyze the impact of racial, gender, geographic, methodological, political, and ideological differences on research questions; how the incentives of academia influence our research practices; and how universal human desires to avoid uncomfortable truths and easily solve problems affect our conclusions. Though misaligned incentive structures of course remain, a messy, collective deliberation across the research community has shifted us into an unprecedented age of theoretical diversity, open and connected data, and public scholarship. Grossmann's wide-ranging account of current trends will necessarily force the academy's many critics to rethink their lazy critiques and instead acknowledge the path-breaking advances occurring in the social sciences today. |
| data science michigan state university: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| data science michigan state university: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science michigan state university: The U.S.–China Trade War Louisa Ha, Lars Willnat, 2022-04-01 Drawing on data from three national surveys, three content analyses, computational topic modeling, and rhetorical analysis, The U.S.–China Trade War sheds light on the twenty-first century’s most high-profile contest over global trade to date. Through diverse empirical studies, the contributors examine the effects of news framing and agenda-setting during the trade war in the Chinese and U.S. news media. Looking at the coverage of Chinese investment in the United States, the use of peace and war journalism frames, and the way media have portrayed the trade war to domestic audiences, the studies explore how media coverage of the trade war has affected public opinion in both countries, as well as how social media has interacted with traditional media in creating news. The authors also analyze the roles of traditional news media and social media in international relations and offer insights into the interactions between professional journalism and user-generated content—interactions that increasingly affect the creation and impact of global news. At a time when social media are being blamed for spreading misinformation and rumors, this book illustrates how professional and user-generated media can reduce international conflicts, foster mutual understanding, and transcend nationalism and ethnocentrism. |
| data science michigan state university: Service-Learning as a New Paradigm in Higher Education of China Carol Ma Hok-ka, Alfred Chan Cheung-ming, Alice Liu Cheng, Fanny Mak Mui-fong, 2018-05-01 The first reference book to introduce the concept and development of service-learning in China, Service-Learning as a New Paradigm in Higher Education of China provides a full picture of the infusion of service-learning into the Chinese educational system and describes this new teaching experience using case studies, empirical data, and educational and institutional policies within Chinese context. The text demonstrates how students learn outside the classroom through service-learning with valuable feedback and reflection from faculty members and fellow students about the meaning of education in China. Though service-learning was initially developed in the United States, the concept is rooted in Chinese literatures and values. This book will help readers understand how service-learning is being used as a pedagogy with Chinese values and philosophy in Chinese education, filling a niche within the worldwide literature of service-learning. |
| data science michigan state university: Fighting the Death Penalty Eugene G. Wanger, 2017-04-01 Michigan is the only state in the country that has a death penalty prohibition in its constitution—Eugene G. Wanger’s compelling arguments against capital punishment is a large reason it is there. The forty pieces in this volume are writings created or used by the author, who penned the prohibition clause, during his fifty years as a death penalty abolitionist. His extraordinary background in forensics, law, and political activity as constitutional convention delegate and co-chairman of the Michigan Committee Against Capital Punishment has produced a remarkable collection. It is not only a fifty-year history of the anti–death penalty argument in America, it also is a detailed and challenging example of how the argument against capital punishment may be successfully made. |
| data science michigan state university: The Toolbox Dialogue Initiative Graham Hubbs, Michael O'Rourke, Steven Hecht Orzack, 2020-09-10 Cross-disciplinary scientific collaboration is emerging as standard operating procedure for many scholarly research enterprises. And yet, the skill set needed for effective collaboration is neither taught nor mentored. The goal of the Toolbox Dialogue Initiative is to facilitate cross-disciplinary collaboration. This book, inspired by this initiative, presents dialogue-based methods designed to increase mutual understanding among collaborators so as to enhance the quality and productivity of cross-disciplinary collaboration. It provides a theoretical context, principal activities, and evidence for effectiveness that will assist readers in honing their collaborative skills. Key Features Introduces the Toolbox Dialogue method for improving cross-disciplinary collaboration Reviews the theoretical background of cross-disciplinary collaboration and considers the communication and integration challenges associated with such collaboration Presents methods employed in workshop development and implementation Uses various means to examine the effectiveness of team-building exercises Related Titles Fam, D., J. Palmer, C. Riedy, and C. Mitchell. Transdisciplinary Research and Practice for Sustainability Outcomes (ISBN: 978-1-138-62573-0) Holland, D. Integrating Knowledge through Interdisciplinary Research: Problems of Theory and Practice (ISBN: 978-1-138-91941-9) Padmanabhan, M. Transdisciplinary Research and Sustainability: Collaboration, Innovation and Transformation (ISBN: 978-1-138-21640-2) |
| data science michigan state university: Deep Learning on Graphs Yao Ma, Jiliang Tang, 2021-09-23 A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare. |
| data science michigan state university: Data Science Pinle Qin, Hongzhi Wang, Guanglu Sun, Zeguang Lu, 2020-08-20 This two volume set (CCIS 1257 and 1258) constitutes the refereed proceedings of the 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 held in Taiyuan, China, in September 2020. The 98 papers presented in these two volumes were carefully reviewed and selected from 392 submissions. The papers are organized in topical sections: database, machine learning, network, graphic images, system, natural language processing, security, algorithm, application, and education. |
| data science michigan state university: Data Visualization Kieran Healy, 2018-12-18 An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions |
| data science michigan state university: Python for Everybody Charles R. Severance, 2016-04-09 Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled Python for Informatics: Exploring Information.There are free downloadable electronic copies of this book in various formats and supporting materials for the book at www.pythonlearn.com. The course materials are available to you under a Creative Commons License so you can adapt them to teach your own Python course. |
| data science michigan state university: Theater as Data Miguel Escobar Varela, 2021-08-02 In Theater as Data, Miguel Escobar Varela explores the use of computational methods and digital data in theater research. He considers the implications of these new approaches, and explains the roles that statistics and visualizations play. Reflecting on recent debates in the humanities, the author suggests that there are two ways of using data, both of which have a place in theater research. Data-driven methods are closer to the pursuit of verifiable results common in the sciences; and data-assisted methods are closer to the interpretive traditions of the humanities. The book surveys four major areas within theater scholarship: texts (not only playscripts but also theater reviews and program booklets); relationships (both the links between fictional characters and the collaborative networks of artists and producers); motion (the movement of performers and objects on stage); and locations (the coordinates of performance events, venues, and touring circuits). Theater as Data examines important contributions to theater studies from similar computational research, including in classical French drama, collaboration networks in Australian theater, contemporary Portuguese choreography, and global productions of Ibsen. This overview is complemented by short descriptions of the author’s own work in the computational analysis of theater practices in Singapore and Indonesia. The author ends by considering the future of computational theater research, underlining the importance of open data and digital sustainability practices, and encouraging readers to consider the benefits of learning to code. A web companion offers illustrative data, programming tutorials, and videos. |
| data science michigan state university: At the Core and in the Margins Julia Albarracín, 2016-05-01 Beardstown and Monmouth, Illinois, two rural Midwestern towns, have been transformed by immigration in the last three decades. This book examines how Mexican immigrants who have made these towns their homes have integrated legally, culturally, and institutionally. What accounts for the massive growth in the Mexican immigrant populations in these two small towns, and what does the future hold for them? Based on 260 surveys and 47 in-depth interviews, this study combines quantitative and qualitative research to explore the level and characteristics of immigrant incorporation in Beardstown and Monmouth. It assesses the advancement of immigrants in the immigration/ residency/citizenship process, the immigrants’ level of cultural integration (via language, their connectedness with other members of society, and their relationships with neighbors), the degree and characteristics of discrimination against immigrants in these two towns, and the extent to which immigrants participate in different social and political activities and trust government institutions. Immigrants in new destinations are likely to be poorer, to be less educated, and to have weaker English-language skills than immigrants in traditional destinations. Studying how this population negotiates the obstacles to and opportunities for incorporation is crucial. |
| data science michigan state university: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science michigan state university: Data Science Xiaohui Cheng, Weipeng Jing, Xianhua Song, Zeguang Lu, 2019-09-13 This two volume set (CCIS 1058 and 1059) constitutes the refereed proceedings of the 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019 held in Guilin, China, in September 2019. The 104 revised full papers presented in these two volumes were carefully reviewed and selected from 395 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including data mining; data base; net work; security; machine learning; bioinformatics; natural language processing; software engineering; graphic images; system; education; application. |
| data science michigan state university: Data Science Zhiwen Yu, Qilong Han, Hongzhi Wang, Bin Guo, Xiaokang Zhou, Xianhua Song, Zeguang Lu, 2023-09-14 This two-volume set (CCIS 1879 and 1880) constitutes the refereed proceedings of the 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023 held in Harbin, China, during September 22–24, 2023. The 52 full papers and 14 short papers presented in these two volumes were carefully reviewed and selected from 244 submissions. The papers are organized in the following topical sections: Part I: Applications of Data Science, Big Data Management and Applications, Big Data Mining and Knowledge Management, Data Visualization, Data-driven Security, Infrastructure for Data Science, Machine Learning for Data Science and Multimedia Data Management and Analysis. Part II: Data-driven Healthcare, Data-driven Smart City/Planet, Social Media and Recommendation Systems and Education using big data, intelligent computing or data mining, etc. |
| data science michigan state university: Data Science Jianchao Zeng, Weipeng Jing, Xianhua Song, Zeguang Lu, 2020-08-20 This two volume set (CCIS 1257 and 1258) constitutes the refereed proceedings of the 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 held in Taiyuan, China, in September 2020. The 98 papers presented in these two volumes were carefully reviewed and selected from 392 submissions. The papers are organized in topical sections: database, machine learning, network, graphic images, system, natural language processing, security, algorithm, application, and education. The chapter “Highly Parallel SPARQL Engine for RDF” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. |
| data science michigan state university: Modeling Archaeological Site Burial in Southern Michigan G. William Monaghan, William A. Lovis, 2005 Modeling Archaeological Site Burial in Southern Michigan is the first volume in the Environmental Research Series. The product of more than two decades of research, it examines relationships between regional and local scale fluvial system evolution and the processes that result in the deep burial of archaeological sites--primarily in floodplain and coastal contexts. This multidisciplinary study incorporates findings from earth and social sciences, discussing regional scale processes of environmental change that are necessary to understand relationships between human economic needs, social adaptation, and changing paleoenvironment. Monaghan and Lovis have compiled and synthesized available data on deeply buried archaeological sites in southern Lower Michigan; the result is the most comprehensive single compendium of such data available for any region of the Great Lakes. Since the processes and contexts present in southern Lower Michigan are comparable to those in the larger region, research modes presented here also have applicability across northeastern North America. This is one of the most important pieces of research to be produced on Michigan archeology. |
| data science michigan state university: Data Science Yang Wang, Guobin Zhu, Qilong Han, Hongzhi Wang, Xianhua Song, Zeguang Lu, 2022-08-10 This two volume set (CCIS 1628 and 1629) constitutes the refereed proceedings of the 8th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2022 held in Chengdu, China, in August, 2022. The 65 full papers and 26 short papers presented in these two volumes were carefully reviewed and selected from 261 submissions. The papers are organized in topical sections on: Big Data Mining and Knowledge Management; Machine Learning for Data Science; Multimedia Data Management and Analysis. |
| data science michigan state university: Introduction to Probability for Data Science Stanley H. Chan, 2021 Probability is one of the most interesting subjects in electrical engineering and computer science. It bridges our favorite engineering principles to the practical reality, a world that is full of uncertainty. However, because probability is such a mature subject, the undergraduate textbooks alone might fill several rows of shelves in a library. When the literature is so rich, the challenge becomes how one can pierce through to the insight while diving into the details. For example, many of you have used a normal random variable before, but have you ever wondered where the 'bell shape' comes from? Every probability class will teach you about flipping a coin, but how can 'flipping a coin' ever be useful in machine learning today? Data scientists use the Poisson random variables to model the internet traffic, but where does the gorgeous Poisson equation come from? This book is designed to fill these gaps with knowledge that is essential to all data science students. -- Preface. |
| data science michigan state university: Data Science Rui Mao, Hongzhi Wang, Xiaolan Xie, Zeguang Lu, 2019-09-13 This two volume set (CCIS 1058 and 1059) constitutes the refereed proceedings of the 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019 held in Guilin, China, in September 2019. The 104 revised full papers presented in these two volumes were carefully reviewed and selected from 395 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including data mining; data base; net work; security; machine learning; bioinformatics; natural language processing; software engineering; graphic images; system; education; application. |
| data science michigan state university: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| data science michigan state university: Data Science Qinglei Zhou, Yong Gan, Weipeng Jing, Xianhua Song, Yan Wang, Zeguang Lu, 2018-09-10 This two volume set (CCIS 901 and 902) constitutes the refereed proceedings of the 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018 (originally ICYCSEE) held in Zhengzhou, China, in September 2018. The 125 revised full papers presented in these two volumes were carefully reviewed and selected from 1057 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including mathematical issues in data science, computational theory for data science, big data management and applications, data quality and data preparation, evaluation and measurement in data science, data visualization, big data mining and knowledge management, infrastructure for data science, machine learning for data science, data security and privacy, applications of data science, case study of data science, multimedia data management and analysis, data-driven scientific research, data-driven bioinformatics, data-driven healthcare, data-driven management, data-driven eGovernment, data-driven smart city/planet, data marketing and economics, social media and recommendation systems, data-driven security, data-driven business model innovation, social and/or organizational impacts of data science. |
| data science michigan state university: Health Care Policy and Practice Cynthia D. Moniz, Stephen H. Gorin, 2014-01-10 In Health Care Policy and Practice: A Biopsychosocial Perspective, Moniz and Gorin have updated their text to incorporate health care reform. The authors have also restructured the book to guide students through the development of the American health care system: what it is, what the policies are, and how students can influence them. The first section focuses on recent history and reforms during the Obama Administration to describe the health care system; section two examines the system’s structure and policies; and the third section explores policy analysis and advocacy, and disparities in health based on demographics and inequities in access to care. It concludes with a discussion of the impact of social factors on health and health status. The new edition incorporates the CSWE EPAS competencies; it is for social work courses in health care, health care policy, and health and mental health care policy. |
| data science michigan state university: Data Science Chengzhong Xu, |
| data science michigan state university: Practice of Computing Using Python, The: Pearson New International Edition William F. Punch, Richard Enbody, 2013-08-29 For CS1 courses in Python Programming (including majors and non-majors). A problem-solving approach to programming with Python. The Practice of Computing Using Python introduces CS1 students (majors and non-majors) to computational thinking using Python.With data-manipulation as a theme, students quickly see the value in what they’re learning and leave the course with a set of immediately useful computational skills that can be applied to problems they encounter in future pursuits. The book takes an “object-use-first” approach–writing classes is covered only after students have mastered using objects. This edition is available with MyProgrammingLab, an innovative online homework and assessment tool. Through the power of practice and immediate personalized feedback, MyProgrammingLab helps students fully grasp the logic, semantics, and syntax of programming. Note: If you are purchasing the standalone text or electronic version, MyProgrammingLab does not come automatically packaged with the text. To purchase MyProgrammingLab, please visit: myprogramminglab.com or you can purchase a package of the physical text + MyProgrammingLab by searching for ISBN 10: 0132992833 / ISBN 13: 9780132992831.MyProgrammingLab is not a self-paced technology and should only be purchased when required by an instructor |
| data science michigan state university: Mastering Finance Financial Times Limited, 1998 FT Mastering Finance brings together the latest in financial expertise from three of the best finance schools in the world. Built around three core modules - corporate finance, investment and financial markets - it provides an understanding and application of financial skills, covering such areas as capital structure, floors, random walk and risk management. |
| data science michigan state university: The New Library Building Boston College. Library, 1925 |
| data science michigan state university: Negotiating for Success: Essential Strategies and Skills George J. Siedel, 2014-10-04 We all negotiate on a daily basis. We negotiate with our spouses, children, parents, and friends. We negotiate when we rent an apartment, buy a car, purchase a house, and apply for a job. Your ability to negotiate might even be the most important factor in your career advancement. Negotiation is also the key to business success. No organization can survive without contracts that produce profits. At a strategic level, businesses are concerned with value creation and achieving competitive advantage. But the success of high-level business strategies depends on contracts made with suppliers, customers, and other stakeholders. Contracting capability—the ability to negotiate and perform successful contracts—is the most important function in any organization. This book is designed to help you achieve success in your personal negotiations and in your business transactions. The book is unique in two ways. First, the book not only covers negotiation concepts, but also provides practical actions you can take in future negotiations. This includes a Negotiation Planning Checklist and a completed example of the checklist for your use in future negotiations. The book also includes (1) a tool you can use to assess your negotiation style; (2) examples of “decision trees,” which are useful in calculating your alternatives if your negotiation is unsuccessful; (3) a three-part strategy for increasing your power during negotiations; (4) a practical plan for analyzing your negotiations based on your reservation price, stretch goal, most-likely target, and zone of potential agreement; (5) clear guidelines on ethical standards that apply to negotiations; (6) factors to consider when deciding whether you should negotiate through an agent; (7) psychological tools you can use in negotiations—and traps to avoid when the other side uses them; (8) key elements of contract law that arise during negotiations; and (9) a checklist of factors to use when you evaluate your performance as a negotiator. Second, the book is unique in its holistic approach to the negotiation process. Other books often focus narrowly either on negotiation or on contract law. Furthermore, the books on negotiation tend to focus on what happens at the bargaining table without addressing the performance of an agreement. These books make the mistaken assumption that success is determined by evaluating the negotiation rather than evaluating performance of the agreement. Similarly, the books on contract law tend to focus on the legal requirements for a contract to be valid, thus giving short shrift to the negotiation process that precedes the contract and to the performance that follows. In the real world, the contracting process is not divided into independent phases. What happens during a negotiation has a profound impact on the contract and on the performance that follows. The contract’s legal content should reflect the realities of what happened at the bargaining table and the performance that is to follow. This book, in contrast to others, covers the entire negotiation process in chronological order beginning with your decision to negotiate and continuing through the evaluation of your performance as a negotiator. A business executive in one of the negotiation seminars the author teaches as a University of Michigan professor summarized negotiation as follows: “Life is negotiation!” No one ever stated it better. As a mother with young children and as a company leader, the executive realized that negotiations are pervasive in our personal and business lives. With its emphasis on practical action, and with its chronological, holistic approach, this book provides a roadmap you can use when navigating through your life as a negotiator. |
| data science michigan state university: Philosophy of Population Health Sean Valles, 2018-05-11 Population health has recently grown from a series of loosely connected critiques of twentieth-century public health and medicine into a theoretical framework with a corresponding field of research—population health science. Its approach is to promote the public’s health through improving everyday human life: afford-able nutritious food, clean air, safe places where children can play, living wages, etc. It recognizes that addressing contemporary health challenges such as the prevalence of type 2 diabetes will take much more than good hospitals and public health departments. Blending philosophy of science/medicine, public health ethics and history, this book offers a framework that explains, analyses and largely endorses the features that define this relatively new field. Presenting a philosophical perspective, Valles helps to clarify what these features are and why they matter, including: searching for health’s upstream causes in social life, embracing a professional commitment to studying and ameliorating the staggering health inequities in and between populations; and reforming scientific practices to foster humility and respect among the many scientists and non- scientists who must work collaboratively to promote health. Featuring illustrative case studies from around the globe at the end of all main chapters, this radical monograph is written to be accessible to all scholars and advanced students who have an interest in health—from public health students to professional philosophers. |
| data science michigan state university: Structured Analysis and Design of Information Systems A. Ziya Aktaş, 1987 |
| data science michigan state university: The Cassava Transformation Felix I. Nweke, John K. Lynam, Dunstan S. C. Spencer, 2002 Cassava is Africa's second most important food crop. The cassava transformation that is now underway in West Africa is fueled by new high yielding TMS varieties that have transformed cassava from a low-yielding, famine-reserve crop to a high-yielding cash crop for both rural and urban consumers. The book highlights the role of cassava as a poverty fighter by increasing cassava productivity and driving down the cost of cassava in rural and urban diets. |
| data science michigan state university: Hospitality Financial Management Agnes L. DeFranco, Thomas W. Lattin, 2006-10-20 Real-world advice for quick retention of the most important business concepts and skills of hospitality finance Hospitality Financial Management provides a straightforward, practical approach to help the hospitality manager effectively analyze hospitality industry management reports and financial statements; prepare accurate business forecasts, strategic pricing models, and effective cost-control systems; manage working capital; develop and finance growth strategies; perform investment analysis; prepare investment packages; negotiate and structure business deals; and ultimately increase shareholder value and personal wealth. This comprehensive how-to book includes: Feature Stories--brief histories of famous hospitality leaders highlighting how they have used financial management skills to attain success for their companies and significant financial rewards for themselves Learning Outcomes--a summary of key topics covered in each chapter Finance in Action--scenarios that apply the concepts, skills, and techniques presented in the chapter to real-world situations. A step-by-step solution is provided for each problem to walk the reader through the necessary financial calculations The Real Deal--boxed inserts that emphasize the relevance of the book by linking financial concepts to fun facts associated with situations students either have or will encounter in their everyday lives Concept Checks--case studies that reinforce the materials presented and enable students to practice their analytic and problem-solving skills Hospitality Financial Management is the perfect book for undergraduate and graduate hospitality management students, hospitality industry managers, and owners of small hospitality businesses. |
| data science michigan state university: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-10-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science michigan state university: Learning Science - the Value of Crafting Engagement in Science Environments Barbara Schneider, Joseph Krajcik, Jari Lavonen, Katariina Salmela-Aro, 2020-01-01 An innovative, internationally developed system to help advance science learning and instruction for high school students This book tells the story of a $3.6 million research project funded by the National Science Foundation aimed at increasing scientific literacy and addressing global concerns of declining science engagement. Studying dozens of classrooms across the United States and Finland, this international team combines large-scale studies with intensive interviews from teachers and students to examine how to transform science education. Written for teachers, parents, policymakers, and researchers, this book offers solutions for matching science learning and instruction with newly recommended twenty-first-century standards. Included are science activities that engage and inspire students; sample lesson plans; and approaches for measuring science engagement and encouragement of three-dimensional learning. |
| data science michigan state university: Papers of the Forty-Seventh Algonquian Conference Monica Macaulay, Margaret Noodin, 2018 This series is a collection of peer-reviewed papers presented at the annual Algonquian Conference, an international forum that focuses on topics related to the languages and cultures of Algonquian peoples. Contributors often cite never-before-published data in their research, giving the reader a fresh and unique insight into the Algonquian peoples and rendering these papers essential reading for those interested in studying Algonquian society. |
| data science michigan state university: Sexual and Gender Minority Health Brea L. Perry, Allen J. LeBlanc, 2021-01-15 This volume of Advances in Medical Sociology showcases rich theoretical and empirical contributions on SGM health and wellbeing. The chapters address a variety of topics, drawing from classic and contemporary sociological frameworks and constructs, and reflecting intersecting interdisciplinary approaches to SGM health. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
