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| data science masters michigan: The Science of Success: What Researchers Know that You Should Know Paula J. Caproni, 2016-12-08 Short description. |
| data science masters michigan: Patching Development Rajesh Veeraraghavan, 2021-12-16 Diving into an original and unusually positive case study from India, Patching Development shows how development programs can be designed to work. How can development programs deliver benefits to marginalized citizens in ways that expand their rights and freedoms? Political will and good policy design are critical but often insufficient due to resistance from entrenched local power systems. In Patching Development, Rajesh Veeraraghavan presents an ethnography of one of the largest development programs in the world, the Indian National Rural Employment Guarantee Act (NREGA), and examines NREGA's implementation in the South Indian state of Andhra Pradesh. He finds that the local system of power is extremely difficult to transform, not because of inertia, but because of coercive counter strategy from actors at the last mile and their ability to exploit information asymmetries. Upper-level NREGA bureaucrats in Andhra Pradesh do not possess the capacity to change the power axis through direct confrontation with local elites, but instead have relied on a continuous series of responses that react to local implementation and information, a process of patching development. Patching development is a top-down, fine-grained, iterative socio-technical process that makes local information about implementation visible through technology and enlists participation from marginalized citizens through social audits. These processes are neither neat nor orderly and have led to a contentious sphere where the exercise of power over documents, institutions and technology is intricate, fluid and highly situated. A highly original account with global significance, this book casts new light on the challenges and benefits of using information and technology in novel ways to implement development programs. |
| data science masters michigan: 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 masters michigan: 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 masters michigan: Mathematical Epidemiology Fred Brauer, Pauline van den Driessche, J. Wu, 2008-04-30 Based on lecture notes of two summer schools with a mixed audience from mathematical sciences, epidemiology and public health, this volume offers a comprehensive introduction to basic ideas and techniques in modeling infectious diseases, for the comparison of strategies to plan for an anticipated epidemic or pandemic, and to deal with a disease outbreak in real time. It covers detailed case studies for diseases including pandemic influenza, West Nile virus, and childhood diseases. Models for other diseases including Severe Acute Respiratory Syndrome, fox rabies, and sexually transmitted infections are included as applications. Its chapters are coherent and complementary independent units. In order to accustom students to look at the current literature and to experience different perspectives, no attempt has been made to achieve united writing style or unified notation. Notes on some mathematical background (calculus, matrix algebra, differential equations, and probability) have been prepared and may be downloaded at the web site of the Centre for Disease Modeling (www.cdm.yorku.ca). |
| data science masters michigan: 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. |
| data science masters michigan: 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 masters michigan: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| data science masters michigan: Computational Catalysis Aravind Asthagiri, Michael Janik, 2014 This book presents a comprehensive review of the methods and approaches being adopted to push forward the boundaries of computational catalysis. |
| data science masters michigan: Actuarial Mathematics Harry H. Panjer, American Mathematical Society, 1986 These lecture notes from the 1985 AMS Short Course examine a variety of topics from the contemporary theory of actuarial mathematics. Recent clarification in the concepts of probability and statistics has laid a much richer foundation for this theory. Other factors that have shaped the theory include the continuing advances in computer science, the flourishing mathematical theory of risk, developments in stochastic processes, and recent growth in the theory of finance. In turn, actuarial concepts have been applied to other areas such as biostatistics, demography, economic, and reliability engineering. |
| data science masters michigan: Coronavirus Politics Scott L Greer, Elizabeth King, Elize Massard da Fonseca, Andre Peralta-Santos, 2021-04-19 COVID-19 is the most significant global crisis of any of our lifetimes. The numbers have been stupefying, whether of infection and mortality, the scale of public health measures, or the economic consequences of shutdown. Coronavirus Politics identifies key threads in the global comparative discussion that continue to shed light on COVID-19 and shape debates about what it means for scholarship in health and comparative politics. Editors Scott L. Greer, Elizabeth J. King, Elize Massard da Fonseca, and André Peralta-Santos bring together over 30 authors versed in politics and the health issues in order to understand the health policy decisions, the public health interventions, the social policy decisions, their interactions, and the reasons. The book’s coverage is global, with a wide range of key and exemplary countries, and contains a mixture of comparative, thematic, and templated country studies. All go beyond reporting and monitoring to develop explanations that draw on the authors' expertise while engaging in structured conversations across the book. |
| data science masters michigan: The Information Master Jacob Soll, 2011-08-08 Colbert has long been celebrated as Louis XIV's minister of finance, trade, and industry. More recently, he has been viewed as his minister of culture and propaganda. In this lively and persuasive book, Jake Soll has given us a third Colbert, the information manager. ---Peter Burke, University of Cambridge Jacob Soll gives us a road map drawn from the French state under Colbert. With a stunning attention to detail Colbert used knowledge in the service of enhancing royal power. Jacob Soll's scholarship is impeccable and his story long overdue and compelling. ---Margaret Jacob, University of California, Los Angeles Nowadays we all know that information is the key to power, and that the masters of information rule the world. Jacob Soll teaches us that Jean-Baptiste Colbert had grasped this principle three and a half centuries ago, and used it to construct a new kind of state. This imaginative, erudite, and powerfully written book re-creates the history of libraries and archives in early modern Europe, and ties them in a novel and convincing way to the new statecraft of Europe's absolute monarchs. ---Anthony Grafton, Princeton University Brilliantly researched, superbly told, and timely, Soll's story is crucial for the history of the modern state. ---Keith Baker, Stanford University When Louis XIV asked his minister Jean-Baptiste Colbert---the man who was to oversee the building of Versailles and the Royal Academy of Sciences, as well as the navy, the Paris police force, and French industry---to build a large-scale administrative government, Colbert created an unprecedented information system for political power. In The Information Master, Jacob Soll shows how the legacy of Colbert's encyclopedic tradition lies at the very center of the rise of the modern state and was a precursor to industrial intelligence and Internet search engines. Soll's innovative look at Colbert's rise to power argues that his practice of collecting knowledge originated from techniques of church scholarship and from Renaissance Italy, where merchants recognized the power to be gained from merging scholarship, finance, and library science. With his connection of interdisciplinary approaches---regarding accounting, state administration, archives, libraries, merchant techniques, ecclesiastical culture, policing, and humanist pedagogy---Soll has written an innovative book that will redefine not only the history of the reign of Louis XIV and information science but also the study of political and economic history. Jacket illustration: Jean Baptiste Colbert (1619–1683), Philippe de Champaigne, 1655, The Metropolitan Museum of Art, Gift of the Wildenstein Foundation, Inc., 1951 (51.34). Photograph © 2003 The Metropolitan Museum of Art. |
| data science masters michigan: Graduate STEM Education for the 21st Century National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on Revitalizing Graduate STEM Education for the 21st Century, 2018-09-21 The U.S. system of graduate education in science, technology, engineering, and mathematics (STEM) has served the nation and its science and engineering enterprise extremely well. Over the course of their education, graduate students become involved in advancing the frontiers of discovery, as well as in making significant contributions to the growth of the U.S. economy, its national security, and the health and well-being of its people. However, continuous, dramatic innovations in research methods and technologies, changes in the nature and availability of work, shifts in demographics, and expansions in the scope of occupations needing STEM expertise raise questions about how well the current STEM graduate education system is meeting the full array of 21st century needs. Indeed, recent surveys of employers and graduates and studies of graduate education suggest that many graduate programs do not adequately prepare students to translate their knowledge into impact in multiple careers. Graduate STEM Education for the 21st Century examines the current state of U.S. graduate STEM education. This report explores how the system might best respond to ongoing developments in the conduct of research on evidence-based teaching practices and in the needs and interests of its students and the broader society it seeks to serve. This will be an essential resource for the primary stakeholders in the U.S. STEM enterprise, including federal and state policymakers, public and private funders, institutions of higher education, their administrators and faculty, leaders in business and industry, and the students the system is intended to educate. |
| data science masters michigan: Academic Writing for Graduate Students John M. Swales, Christine B. Feak, 2004 New material featured in this edition includes updates and replacements of older data sets, a broader range of disciplines represented in models and examples, a discussion of discourse analysis, and tips for Internet communication. |
| data science masters michigan: Linear Algebra and Learning from Data Gilbert Strang, 2019-01-31 Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation. |
| data science masters michigan: Linear Mixed Models Brady T. West, Kathleen B. Welch, Andrzej T Galecki, 2006-11-22 Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-nav |
| data science masters michigan: The Soar Cognitive Architecture John E. Laird, 2019-08-20 The definitive presentation of Soar, one AI's most enduring architectures, offering comprehensive descriptions of fundamental aspects and new components. In development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements. |
| data science masters michigan: The Smart Enough City Ben Green, 2019-04-09 Why technology is not an end in itself, and how cities can be “smart enough,” using technology to promote democracy and equity. Smart cities, where technology is used to solve every problem, are hailed as futuristic urban utopias. We are promised that apps, algorithms, and artificial intelligence will relieve congestion, restore democracy, prevent crime, and improve public services. In The Smart Enough City, Ben Green warns against seeing the city only through the lens of technology; taking an exclusively technical view of urban life will lead to cities that appear smart but under the surface are rife with injustice and inequality. He proposes instead that cities strive to be “smart enough”: to embrace technology as a powerful tool when used in conjunction with other forms of social change—but not to value technology as an end in itself. In a technology-centric smart city, self-driving cars have the run of downtown and force out pedestrians, civic engagement is limited to requesting services through an app, police use algorithms to justify and perpetuate racist practices, and governments and private companies surveil public space to control behavior. Green describes smart city efforts gone wrong but also smart enough alternatives, attainable with the help of technology but not reducible to technology: a livable city, a democratic city, a just city, a responsible city, and an innovative city. By recognizing the complexity of urban life rather than merely seeing the city as something to optimize, these Smart Enough Cities successfully incorporate technology into a holistic vision of justice and equity. |
| data science masters michigan: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| data science masters michigan: Learning How to Learn Barbara Oakley, PhD, Terrence Sejnowski, PhD, Alistair McConville, 2018-08-07 A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course Learning How to Learn have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid rut think in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun. |
| data science masters michigan: Clinical Research Informatics Rachel Richesson, James Andrews, 2012-02-15 The purpose of the book is to provide an overview of clinical research (types), activities, and areas where informatics and IT could fit into various activities and business practices. This book will introduce and apply informatics concepts only as they have particular relevance to clinical research settings. |
| data science masters michigan: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data science masters michigan: Applied Survey Data Analysis Steven G. Heeringa, Brady West, Steve G. Heeringa, Patricia A. Berglund, Patricia Berglund, 2017-07-12 Highly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software. Designed for readers working in a wide array of disciplines who use survey data in their work, this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data, the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s updated website. |
| data science masters michigan: Race And The Incidence Of Environmental Hazards Bunyan Bryant, Paul Mohai, 2019-06-26 This book discusses the poor and people of color and their struggle to take control of one of the most basic aspects of their lives: the quality of their environment. It exposes the fact of environmental inequity and its consequences in face of general neglect by policymakers and social scientists. |
| data science masters michigan: Good with Words Patrick Barry, 2019-05-31 If your success at work or in school depends on your ability to communicate persuasively in writing, you'll want to get Good with Words. Based on a course that law students at the University of Michigan and the University of Chicago have called outstanding, A-M-A-Z-I-N-G, and the best course I have ever taken, the book brings together a collection of concepts, exercises, and examples that have also helped improve the advocacy skills of people pursuing careers in many other fields--from marketing, to management, to medicine. There is nobody better than Patrick Barry when it comes to breaking down how to write and edit. His techniques don't just make you sound better. They make you think better. I'm jealous of the people who get to take his classes. --Professor Lisa Bernstein, University of Chicago Law School and Oxford University Center for Corporate Regulation Whenever I use Patrick Barry's materials in my class, the student reaction is the same: 'We want more of them.' --Professor Dave Babbe, UCLA School of Law Working one-on-one with Patrick Barry should be mandatory for all lawyers, regardless of seniority. This book is the next best thing. --Purvi Patel, Partner at Morrison Foerster LLP I am proud to say that, when it comes to writing, I speak Patrick Barry. What I mean is that I use, pretty much every day, the writing vocabulary and techniques he offers in this great book. So read it. Share it. And then, if you can, teach it. There are a lot of good causes in the world that could use a new generation of great advocates. --Professor Bridgette Carr, Assistant Dean of Strategic Initiatives and Director of the Human Trafficking Clinic at the University of Michigan Law School Patrick Barry is my secret weapon. I use his techniques every time I write, and I also teach them to all my students. --Professor Shai Dothan, Copenhagen Faculty of Law I know the materials in this book were originally created for lawyers and law students. But I actually find them really helpful for doctors as well, given that a lot of what I do every day depends on effective communication. There is a tremendous upside to becoming 'Good with Words. --Dr. Ramzi Abboud, Washington University School of Medicine in St. Louis. |
| data science masters michigan: Data Science Thinking Longbing Cao, 2018-08-17 This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. |
| data science masters michigan: Prototype Nation Silvia M. Lindtner, 2020-09-15 A vivid look at China’s shifting place in the global political economy of technology production How did China’s mass manufacturing and “copycat” production become transformed, in the global tech imagination, from something holding the nation back to one of its key assets? Prototype Nation offers a rich transnational analysis of how the promise of democratized innovation and entrepreneurial life has shaped China’s governance and global image. With historical precision and ethnographic detail, Silvia Lindtner reveals how a growing distrust in Western models of progress and development, including Silicon Valley and the tech industry after the financial crisis of 2007–8, shaped the rise of the global maker movement and the vision of China as a “new frontier” of innovation. Lindtner’s investigations draw on more than a decade of research in experimental work spaces—makerspaces, coworking spaces, innovation hubs, hackathons, and startup weekends—in China, the United States, Africa, Europe, Taiwan, and Singapore, as well as in key sites of technology investment and industrial production—tech incubators, corporate offices, and factories. She examines how the ideals of the maker movement, to intervene in social and economic structures, served the technopolitical project of prototyping a “new” optimistic, assertive, and global China. In doing so, Lindtner demonstrates that entrepreneurial living influences governance, education, policy, investment, and urban redesign in ways that normalize the persistence of sexism, racism, colonialism, and labor exploitation. Prototype Nation shows that by attending to the bodies and sites that nurture entrepreneurial life, technology can be extricated from the seemingly endless cycle of promise and violence. Cover image: Courtesy of Cao Fei, Vitamin Creative Space and Sprüth Magers |
| data science masters michigan: 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 masters michigan: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data science masters michigan: Building Successful Online Communities Robert E. Kraut, Paul Resnick, 2016-02-12 How insights from the social sciences, including social psychology and economics, can improve the design of online communities. Online communities are among the most popular destinations on the Internet, but not all online communities are equally successful. For every flourishing Facebook, there is a moribund Friendster—not to mention the scores of smaller social networking sites that never attracted enough members to be viable. This book offers lessons from theory and empirical research in the social sciences that can help improve the design of online communities. The authors draw on the literature in psychology, economics, and other social sciences, as well as their own research, translating general findings into useful design claims. They explain, for example, how to encourage information contributions based on the theory of public goods, and how to build members' commitment based on theories of interpersonal bond formation. For each design claim, they offer supporting evidence from theory, experiments, or observational studies. |
| data science masters michigan: Data Science Applied to Sustainability Analysis Jennifer Dunn, Prasanna Balaprakash, 2021-05-11 Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. - Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery - Includes considerations sustainability analysts must evaluate when applying big data - Features case studies illustrating the application of data science in sustainability analyses |
| data science masters michigan: Artificial Intelligence for Business Ana Landeta Echeberria, 2022-01-22 This book seeks to build a shared understanding of Artificial Intelligence (AI) within the global business scenario today and in the near future. Drawing on academic theory and real-world case studies, it examines AI’s development and application across a number of business contexts. Taking current scholarship forward in its engagement with AI theory and practice for enterprises and applied research and innovation, it outlines international practices for the promotion of reliable AI systems, trends, research and development, fostering a digital ecosystem for AI and preparing companies for job transformation and building skills. This book will be of great interest to academics studying Digital Business, Digital Strategy, Innovation Management, and Information Technology. |
| data science masters michigan: Hands-On Deep Learning with R Michael Pawlus, Rodger Devine, 2020-04-24 Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and Deepnet Key FeaturesUnderstand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problemImprove models using parameter tuning, feature engineering, and ensemblingApply advanced neural network models such as deep autoencoders and generative adversarial networks (GANs) across different domainsBook Description Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You’ll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms. What you will learnDesign a feedforward neural network to see how the activation function computes an outputCreate an image recognition model using convolutional neural networks (CNNs)Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithmApply text cleaning techniques to remove uninformative text using NLPBuild, train, and evaluate a GAN model for face generationUnderstand the concept and implementation of reinforcement learning in RWho this book is for This book is for data scientists, machine learning engineers, and deep learning developers who are familiar with machine learning and are looking to enhance their knowledge of deep learning using practical examples. Anyone interested in increasing the efficiency of their machine learning applications and exploring various options in R will also find this book useful. Basic knowledge of machine learning techniques and working knowledge of the R programming language is expected. |
| data science masters michigan: Developing Analytic Talent Vincent Granville, 2014-03-24 Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. |
| data science masters michigan: Pandas for Everyone Daniel Y. Chen, 2017-12-15 The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning |
| data science masters michigan: Masters Abstracts , 1984 |
| data science masters michigan: Colleges That Create Futures Princeton Review, 2016-05-10 KICK-START YOUR CAREER WITH THE RIGHT ON-CAMPUS EXPERIENCE! When it comes to getting the most out of college, the experiences you have outside the classroom are just as important as what you study. Colleges That Create Futures looks beyond the usual “best of” college lists to highlight 50 schools that empower students to discover practical, real-world applications for their talents and interests. The schools in this book feature distinctive research, internship, and hands-on learning programs—all the info you need to help find a college where you can parlay your passion into a successful post-college career. Inside, You'll Find: • In-depth profiles covering career services, internship support, student group activity, alumni satisfaction, noteworthy facilities and programs, and more • Candid assessments of each school’s academics from students, current faculty, and alumni • Unique hands-on learning opportunities for students across majors • Testimonials on career prep from alumni in business, education, law, and much more *************************** What makes Colleges That Create Futures important? You've seen the headlines—lately the news has been full of horror stories about how the college educational system has failed many recent grads who leave school with huge debt, no job prospects, and no experience in the working world. Colleges That Create Futures identifies schools that don't fall into this trap but instead prepare students for successful careers! How are the colleges selected? Schools are selected based on survey results on career services, grad school matriculation, internship support, student group and government activity, alumni activity and salaries, and noteworthy facilities and programs. |
| data science masters michigan: National Educational Technology Standards for Students International Society for Technology in Education, 2007 This booklet includes the full text of the ISTE Standards for Students, along with the Essential Conditions, profiles and scenarios. |
| data science masters michigan: Analytics and Knowledge Management Suliman Hawamdeh, Hsia-Ching Chang, 2018-08-06 The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms. |
| data science masters michigan: Introduction to Biomedical Data Science Robert Hoyt, Robert Muenchen, 2019-11-24 Overview of biomedical data science -- Spreadsheet tools and tips -- Biostatistics primer -- Data visualization -- Introduction to databases -- Big data -- Bioinformatics and precision medicine -- Programming languages for data analysis -- Machine learning -- Artificial intelligence -- Biomedical data science resources -- Appendix A: Glossary -- Appendix B: Using data.world -- Appendix C: Chapter exercises. |
Data Science Graduate Program Student Handbook
Welcome to the Data Science Graduate Program at Michigan Technological University. The Data Science program is an interdisciplinary program, meaning the faculty and courses for the …
MASTER OF APPLIED D ATA SCIENCE EMPLOYMENT …
The University of Michigan School of Information (UMSI) Career Development Office surveys our graduates to identify their post-graduation plans and outcomes. This report summarizes …
Master of Science Program in Health Data Science (HDS)
Health Data Science in U-M Biostatistics •A pilot HDS sub-plan in U-M Biostat MS was launched in Fall 2019. •Focused on enhanced training of computational and data analytic skills for …
Handbook for Graduate Students - Michigan State University
Aug 12, 2021 · MSU students have a wide variety of needs with regards to computational and data sci-ence, which may range from taking a single course on computational modeling or …
Data Science Program Guide (Effective for declarations WN …
Students majoring in Data Science will learn computer programming, data analysis, and database systems, and will learn to think critically about the process of understanding data.
SUGS Program in Computer Science and Engineering
Electrical Engineering, Data Science, and those with a minor in Computer Science are eligible for application to a SUGS program in Computer Science and Engineering. MS/MSE Computer …
Data Science Accelerated Masters Policy - www-cf.mtu.edu
The accelerated Masters in Data Science program is open to all high achieving undergraduate students at Michigan Tech. It allows students to double count up to six courses toward both …
Quantitative Finance and Risk Management Program Overview
Feb 3, 2024 · Data): introduces the basic statistical tools for financial data, including regression and time series models, as well as various inference techniques. For each sequence, the …
Data Science Graduate Program Handbook.Aug
Welcome to the Data Science Graduate Program at Michigan Technological University. The Data Science program is an interdisciplinary program, meaning the faculty and courses for the …
University of Michigan - Masters of Applied Data Science …
*Please note that the add/drop deadline for Month 1 in Fall 2024 has been moved back one day, as the University is closed in observence of Labor Day.
MASTER OF BUSINESS ANALYTICS - Ross School of …
The Michigan Ross Master of Business Analytics (MBAn) Program readies you to be the impactful, data-driven leader today’s businesses demand. Our action-based curriculum …
Michigan Institutes for Data Science: Successes and …
The Michigan Institute for Data Science (MIDAS) is a virtual institute that is “the gathering place” for the University of Michigan (U-M) data science community, with ~350 affiliate faculty …
Master of Science in Data Science and Analytics (MSc DSA)
Consider applying for Strathmore Univer-sity’s MSc in Data Science and Analytics today! The course work will cover core areas, such as: Data Mining, Storage and Retrieval, Time Series …
MASTER OF APPLIED D ATA SCIENCE EMPLOYMENT …
The University of Michigan School of Information (UMSI) Career Development Office surveys our graduates to identify their post-graduation plans and outcomes. This report summarizes …
MASTER OF BUSINESS ANALYTICS
The Michigan Ross Master of Business Analytics (MBAn) Program readies you to be the impactful, data-driven leader today’s businesses demand. Our action-based curriculum …
University of Michigan - Masters of Applied Data Science …
*Please note that the add/drop deadline for Month 1 has been moved back one day, as the University is closed in observence of Labor Day.
University of Michigan - Masters of Applied Data Science …
University of Michigan - Masters of Applied Data Science Schedule Fall 2022 - Spring/Summer 2023
Data Science Program Guide - College of LSA | U-M LSA
Students majoring in Data Science will learn computer programming, data analysis and database systems, and will learn to think critically about the process of understanding data.
University of Michigan - Masters of Applied Data Science …
University of Michigan - Masters of Applied Data Science Schedule Fall 2021 - Spring/Summer 2022
Master of Data Science Strategy and Leadership
Gain a hands-on blend of technical and communication skills infused with RMIT’s excellence in technology, design and innovation. Know how to engage and influence executive stakeholders …
Data Science Graduate Program Student Handbook
Welcome to the Data Science Graduate Program at Michigan Technological University. The Data Science program is an interdisciplinary program, meaning the faculty and courses for the …
MASTER OF APPLIED D ATA SCIENCE EMPLOYMENT …
The University of Michigan School of Information (UMSI) Career Development Office surveys our graduates to identify their post-graduation plans and outcomes. This report summarizes …
Master of Science Program in Health Data Science (HDS)
Health Data Science in U-M Biostatistics •A pilot HDS sub-plan in U-M Biostat MS was launched in Fall 2019. •Focused on enhanced training of computational and data analytic skills for …
Handbook for Graduate Students - Michigan State University
Aug 12, 2021 · MSU students have a wide variety of needs with regards to computational and data sci-ence, which may range from taking a single course on computational modeling or …
Data Science Program Guide (Effective for declarations WN …
Students majoring in Data Science will learn computer programming, data analysis, and database systems, and will learn to think critically about the process of understanding data.
SUGS Program in Computer Science and Engineering
Electrical Engineering, Data Science, and those with a minor in Computer Science are eligible for application to a SUGS program in Computer Science and Engineering. MS/MSE Computer …
Data Science Accelerated Masters Policy - www-cf.mtu.edu
The accelerated Masters in Data Science program is open to all high achieving undergraduate students at Michigan Tech. It allows students to double count up to six courses toward both …
Quantitative Finance and Risk Management Program Overview
Feb 3, 2024 · Data): introduces the basic statistical tools for financial data, including regression and time series models, as well as various inference techniques. For each sequence, the …
Data Science Graduate Program Handbook.Aug
Welcome to the Data Science Graduate Program at Michigan Technological University. The Data Science program is an interdisciplinary program, meaning the faculty and courses for the …
University of Michigan - Masters of Applied Data Science …
*Please note that the add/drop deadline for Month 1 in Fall 2024 has been moved back one day, as the University is closed in observence of Labor Day.
MASTER OF BUSINESS ANALYTICS - Ross School of …
The Michigan Ross Master of Business Analytics (MBAn) Program readies you to be the impactful, data-driven leader today’s businesses demand. Our action-based curriculum …
Michigan Institutes for Data Science: Successes and …
The Michigan Institute for Data Science (MIDAS) is a virtual institute that is “the gathering place” for the University of Michigan (U-M) data science community, with ~350 affiliate faculty …
Master of Science in Data Science and Analytics (MSc DSA)
Consider applying for Strathmore Univer-sity’s MSc in Data Science and Analytics today! The course work will cover core areas, such as: Data Mining, Storage and Retrieval, Time Series …
MASTER OF APPLIED D ATA SCIENCE EMPLOYMENT …
The University of Michigan School of Information (UMSI) Career Development Office surveys our graduates to identify their post-graduation plans and outcomes. This report summarizes …
MASTER OF BUSINESS ANALYTICS
The Michigan Ross Master of Business Analytics (MBAn) Program readies you to be the impactful, data-driven leader today’s businesses demand. Our action-based curriculum …
University of Michigan - Masters of Applied Data Science …
*Please note that the add/drop deadline for Month 1 has been moved back one day, as the University is closed in observence of Labor Day.
University of Michigan - Masters of Applied Data Science …
University of Michigan - Masters of Applied Data Science Schedule Fall 2022 - Spring/Summer 2023
Data Science Program Guide - College of LSA | U-M LSA
Students majoring in Data Science will learn computer programming, data analysis and database systems, and will learn to think critically about the process of understanding data.
University of Michigan - Masters of Applied Data Science …
University of Michigan - Masters of Applied Data Science Schedule Fall 2021 - Spring/Summer 2022
Master of Data Science Strategy and Leadership
Gain a hands-on blend of technical and communication skills infused with RMIT’s excellence in technology, design and innovation. Know how to engage and influence executive stakeholders …
