| data science utd degree plan: It's All Analytics! Scott Burk, Gary D. Miner, 2020-05-25 It's All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690) Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, analytics, is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and terminology? This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series. |
| data science utd degree plan: Secure Data Science Bhavani Thuraisingham, Murat Kantarcioglu, Latifur Khan, 2022-04-27 Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science. |
| data science utd degree plan: Programming Challenges Steven S Skiena, Miguel A. Revilla, 2006-04-18 There are many distinct pleasures associated with computer programming. Craftsmanship has its quiet rewards, the satisfaction that comes from building a useful object and making it work. Excitement arrives with the flash of insight that cracks a previously intractable problem. The spiritual quest for elegance can turn the hacker into an artist. There are pleasures in parsimony, in squeezing the last drop of performance out of clever algorithms and tight coding. The games, puzzles, and challenges of problems from international programming competitions are a great way to experience these pleasures while improving your algorithmic and coding skills. This book contains over 100 problems that have appeared in previous programming contests, along with discussions of the theory and ideas necessary to attack them. Instant online grading for all of these problems is available from two WWW robot judging sites. Combining this book with a judge gives an exciting new way to challenge and improve your programming skills. This book can be used for self-study, for teaching innovative courses in algorithms and programming, and in training for international competition. The problems in this book have been selected from over 1,000 programming problems at the Universidad de Valladolid online judge. The judge has ruled on well over one million submissions from 27,000 registered users around the world to date. We have taken only the best of the best, the most fun, exciting, and interesting problems available. |
| data science utd degree plan: The Executive's Guide to AI and Analytics Scott Burk, Gary D. Miner, 2022-06-07 The Problem? Companies are failing to deliver on AI and analytics with over half stating they are not yet treating data as a business asset. Over half admit that they are not competing on data and analytics. Seven out of 10 companies in a 2020 MIT study reported minimal or no impact from AI so far. Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 (40%) report business gains from AI in the past three years. And only about 25% of organizations have actually forged this data-driven culture. Is investment lacking? No. Companies now are spending more than ever in data, analytics, and AI technologies. Is it a lack of technology? No. There are fascinating breakthroughs occurring on all fronts with image, voice, and streaming pattern recognition on the forefront. Is it a lack of technical talent? Not really. While some studies cite that we need to train more data scientists, developers, and related professionals, the curve of demand by supply is dampening. Is it a lack of creating an executable strategic plan? Yes. While there has been a lot of strategic wishing, organizations lack meaningful strategic plans. Specifically, the development of executable strategies and the leadership to see these strategies brought to fruition. This is the problem. Lack of execution and lack of incorporating key components that align and enable execution of the business strategy to delivery is killing AI and analytics programs. Scott Burk and Gary D. Miner have written this book for executives at all levels who are charged with executing on analytics that need to address this issue. The book provides unique insights into repairing the gaps that programs need to fill to provide value from analytics programs. It complements their three-part series, It’s All Analytics! by focusing on leadership decisions that augment data literacy, organizational architecture, and AI case studies. |
| data science utd degree plan: The Last Lecture Randy Pausch, Jeffrey Zaslow, 2010 The author, a computer science professor diagnosed with terminal cancer, explores his life, the lessons that he has learned, how he has worked to achieve his childhood dreams, and the effect of his diagnosis on him and his family. |
| data science utd degree plan: Financial Data Analytics with Machine Learning, Optimization and Statistics Sam Chen, Ka Chun Cheung, Phillip Yam, 2024-10-21 An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking. |
| data science utd degree plan: Cyber-Physical Systems Security Çetin Kaya Koç, 2018-12-06 The chapters in this book present the work of researchers, scientists, engineers, and teachers engaged with developing unified foundations, principles, and technologies for cyber-physical security. They adopt a multidisciplinary approach to solving related problems in next-generation systems, representing views from academia, government bodies, and industrial partners, and their contributions discuss current work on modeling, analyzing, and understanding cyber-physical systems. |
| data science utd degree plan: Linear Algebra Done Right Sheldon Axler, 1997-07-18 This text for a second course in linear algebra, aimed at math majors and graduates, adopts a novel approach by banishing determinants to the end of the book and focusing on understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space has an eigenvalue. The book starts by discussing vector spaces, linear independence, span, basics, and dimension. Students are introduced to inner-product spaces in the first half of the book and shortly thereafter to the finite- dimensional spectral theorem. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition features new chapters on diagonal matrices, on linear functionals and adjoints, and on the spectral theorem; some sections, such as those on self-adjoint and normal operators, have been entirely rewritten; and hundreds of minor improvements have been made throughout the text. |
| data science utd degree plan: Redistricting and Representation Thomas Brunell, 2010-04-02 Pundits have observed that if so many incumbents are returned to Congress to each election by such wide margins, perhaps we should look for ways to increase competitiveness – a centerpiece to the American way of life – through redistricting. Do competitive elections increase voter satisfaction? How does voting for a losing candidate affect voters’ attitudes toward government? The not-so-surprising conclusion is that losing voters are less satisfied with Congress and their Representative, but the implications for the way in which we draw congressional and state legislative districts are less straightforward. Redistricting and Representation argues that competition in general elections is not the sine qua non of healthy democracy, and that it in fact contributes to the low levels of approval of Congress and its members. Brunell makes the case for a radical departure from traditional approaches to redistricting – arguing that we need to pack districts with as many like-minded partisans as possible, maximizing the number of winning voters, not losers. |
| data science utd degree plan: Secure Data Science Bhavani Thuraisingham, Murat Kantarcioglu, Latifur Khan, 2022-04-27 Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science. |
| data science utd degree plan: The Taiwan Voter Christopher Henry Achen, T. Y. Wang, 2017-07-26 The Taiwan Voter examines the critical role ethnic and national identities play in politics, utilizing the case of Taiwan. Although elections there often raise international tensions, and have led to military demonstrations by China, no scholarly books have examined how Taiwan’s voters make electoral choices in a dangerous environment. Critiquing the conventional interpretation of politics as an ideological battle between liberals and conservatives, The Taiwan Voter demonstrates in Taiwan the party system and voters’ responses are shaped by one powerful determinant of national identity—the China factor. Taiwan’s electoral politics draws international scholarly interest because of the prominent role of ethnic and national identification. While in most countries the many tangled strands of competing identities are daunting for scholarly analysis, in Taiwan the cleavages are powerful and limited in number, so the logic of interrelationships among issues, partisanship, and identity are particularly clear. The Taiwan Voter unites experts to investigate the ways in which social identities, policy views, and partisan preferences intersect and influence each other. These novel findings have wide applicability to other countries, and will be of interest to a broad range of social scientists interested in identity politics. |
| data science utd degree plan: It's All Analytics - Part II Scott Burk, David Sweenor, Gary Miner, 2021-09-28 Up to 70% and even more of corporate Analytics Efforts fail!!! Even after these corporations have made very large investments, in time, talent, and money, in developing what they thought were good data and analytics programs. Why? Because the executives and decision makers and the entire analytics team have not considered the most important aspect of making these analytics efforts successful. In this Book II of It’s All Analytics! series, we describe two primary things: 1) What this most important aspect consists of, and 2) How to get this most important aspect at the center of the analytics effort and thus make your analytics program successful. This Book II in the series is divided into three main parts: Part I, Organizational Design for Success, discusses ....... The need for a complete company / organizational Alignment of the entire company and its analytics team for making its analytics successful. This means attention to the culture – the company culture culture!!! To be successful, the CEO’s and Decision Makers of a company / organization must be fully cognizant of the cultural focus on ‘establishing a center of excellence in analytics’. Simply, culture – company culture is the most important aspect of a successful analytics program. The focus must be on innovation, as this is needed by the analytics team to develop successful algorithms that will lead to greater company efficiency and increased profits. Part II, Data Design for Success, discusses ..... Data is the cornerstone of success with analytics. You can have the best analytics algorithms and models available, but if you do not have good data, efforts will at best be mediocre if not a complete failure. This Part II also goes further into data with descriptions of things like Volatile Data Memory Storage and Non-Volatile Data Memory Storage, in addition to things like data structures and data formats, plus considering things like Cluster Computing, Data Swamps, Muddy Data, Data Marts, Enterprise Data Warehouse, Data Reservoirs, and Analytic Sandboxes, and additionally Data Virtualization, Curated Data, Purchased Data, Nascent & Future Data, Supplemental Data, Meaningful Data, GIS (Geographic Information Systems) & Geo Analytics Data, Graph Databases, and Time Series Databases. Part II also considers Data Governance including Data Integrity, Data Security, Data Consistency, Data Confidence, Data Leakage, Data Distribution, and Data Literacy. Part III, Analytics Technology Design for Success, discusses .... Analytics Maturity and aspects of this maturity, like Exploratory Data Analysis, Data Preparation, Feature Engineering, Building Models, Model Evaluation, Model Selection, and Model Deployment. Part III also goes into the nuts and bolts of modern predictive analytics, discussing such terms as AI = Artificial Intelligence, Machine Learning, Deep Learning, and the more traditional aspects of analytics that feed into modern analytics like Statistics, Forecasting, Optimization, and Simulation. Part III also goes into how to Communicate and Act upon Analytics, which includes building a successful Analytics Culture within your company / organization. All-in-all, if your company or organization needs to be successful using analytics, this book will give you the basics of what you need to know to make it happen. |
| data science utd degree plan: Innovation and Strategy Rajan Varadarajan, Satish Jayachandran, Naresh K. Malhotra, 2018-06-29 This volume focuses on substantive issues in innovation, marketing strategy, and the nexus of innovation and marketing strategy. |
| data science utd degree plan: Global Collective Action Todd Sandler, 2004-07-19 This book examines how nations and other key participants in the global community address problems requiring collective action. The global community has achieved some successes, such as eradicating smallpox, but other efforts to coordinate nations' actions, such as the reduction of drug trafficking, have not been sufficient. This book identifies the factors that promote or inhibit successful collective action at the regional and global level for an ever-growing set of challenges stemming from augmented cross-border flows associated with globalization. Modern principles of collective action are identified and applied to a host of global challenges, including promoting global health, providing foreign assistance, controlling rogue nations, limiting transnational terrorism, and intervening in civil wars. Because many of these concerns involve strategic interactions where choices and consequences are dependent on one's own and others' actions, the book relies, in places, on elementary game theory that is fully introduced for the uninitiated reader. |
| data science utd degree plan: Engineering Software as a Service Armando Fox, David A. Patterson, 2016 (NOTE: this Beta Edition may contain errors. See http://saasbook.info for details.) A one-semester college course in software engineering focusing on cloud computing, software as a service (SaaS), and Agile development using Extreme Programming (XP). This book is neither a step-by-step tutorial nor a reference book. Instead, our goal is to bring a diverse set of software engineering topics together into a single narrative, help readers understand the most important ideas through concrete examples and a learn-by-doing approach, and teach readers enough about each topic to get them started in the field. Courseware for doing the work in the book is available as a virtual machine image that can be downloaded or deployed in the cloud. A free MOOC (massively open online course) at saas-class.org follows the book's content and adds programming assignments and quizzes. See http://saasbook.info for details.(NOTE: this Beta Edition may contain errors. See http://saasbook.info for details.) A one-semester college course in software engineering focusing on cloud computing, software as a service (SaaS), and Agile development using Extreme Programming (XP). This book is neither a step-by-step tutorial nor a reference book. Instead, our goal is to bring a diverse set of software engineering topics together into a single narrative, help readers understand the most important ideas through concrete examples and a learn-by-doing approach, and teach readers enough about each topic to get them started in the field. Courseware for doing the work in the book is available as a virtual machine image that can be downloaded or deployed in the cloud. A free MOOC (massively open online course) at saas-class.org follows the book's content and adds programming assignments and quizzes. See http://saasbook.info for details. |
| data science utd degree plan: Sustainable Digital Communities Anneli Sundqvist, Gerd Berget, Jan Nolin, Kjell Ivar Skjerdingstad, 2020-03-19 This volume constitutes the proceedings of the 15th International Conference on Sustainable Digital Communities, iConference 2020, held in Boras, Sweden, in March 2020. The 27 full papers and the 48 short papers presented in this volume were carefully reviewed and selected from 178 submissions. They cover topics such as: sustainable communities; social media; information behavior; information literacy; user experience; inclusion; education; public libraries; archives and records; future of work; open data; scientometrics; AI and machine learning; methodological innovation. |
| data science utd degree plan: Sustaining University Program Research , 1969 |
| data science utd degree plan: Humanities Scholar in Residence National Endowment for the Humanities. Division of Education Programs, 1997 |
| data science utd degree plan: Cost Principles for Educational Institutions United States. Office of Management and Budget, 1979 |
| data science utd degree plan: Sustaining University Program Research United States. National Aeronautics and Space Administration, |
| data science utd degree plan: Statistical Machine Learning Richard Golden, 2020-06-24 The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models. |
| data science utd degree plan: Editorial Amendments (Us Federal Communications Commission Regulation) (Fcc) (2018 Edition) The Law The Law Library, 2018-10-07 Editorial Amendments (US Federal Communications Commission Regulation) (FCC) (2018 Edition) The Law Library presents the complete text of the Editorial Amendments (US Federal Communications Commission Regulation) (FCC) (2018 Edition). Updated as of May 29, 2018 In this document, the Federal Communications Commission (Commission) makes certain minor editorial amendments to its rules to correct errors or omissions of publication, eliminate duplicative language, or conform the rules with other rule sections in effort to provide clear and concise rules that are easy for the public to understand. This book contains: - The complete text of the Editorial Amendments (US Federal Communications Commission Regulation) (FCC) (2018 Edition) - A table of contents with the page number of each section |
| data science utd degree plan: Big Data Analytics with Applications in Insider Threat Detection Bhavani Thuraisingham, Pallabi Parveen, Mohammad Mehedy Masud, Latifur Khan, 2017-11-22 Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection. |
| data science utd degree plan: Big Data and Analytics for Infectious Disease Research, Operations, and Policy National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Global Health, Forum on Microbial Threats, 2016-12-30 With the amount of data in the world exploding, big data could generate significant value in the field of infectious disease. The increased use of social media provides an opportunity to improve public health surveillance systems and to develop predictive models. Advances in machine learning and crowdsourcing may also offer the possibility to gather information about disease dynamics, such as contact patterns and the impact of the social environment. New, rapid, point-of-care diagnostics may make it possible to capture not only diagnostic information but also other potentially epidemiologically relevant information in real time. With a wide range of data available for analysis, decision-making and policy-making processes could be improved. While there are many opportunities for big data to be used for infectious disease research, operations, and policy, many challenges remain before it is possible to capture the full potential of big data. In order to explore some of the opportunities and issues associated with the scientific, policy, and operational aspects of big data in relation to microbial threats and public health, the National Academies of Sciences, Engineering, and Medicine convened a workshop in May 2016. Participants discussed a range of topics including preventing, detecting, and responding to infectious disease threats using big data and related analytics; varieties of data (including demographic, geospatial, behavioral, syndromic, and laboratory) and their broader applications; means to improve their collection, processing, utility, and validation; and approaches that can be learned from other sectors to inform big data strategies for infectious disease research, operations, and policy. This publication summarizes the presentations and discussions from the workshop. |
| data science utd degree plan: Software Visualization Kang Zhang, 2012-12-06 Software Visualization: From Theory to Practice was initially selected as a special volume for The Annals of Software Engineering (ANSE) Journal, which has been discontinued. This special edited volume, is the first to discuss software visualization in the perspective of software engineering. It is a collection of 14 chapters on software visualization, covering the topics from theory to practical systems. The chapters are divided into four Parts: Visual Formalisms, Human Factors, Architectural Visualization, and Visualization in Practice. They cover a comprehensive range of software visualization topics, including *Visual programming theory and techniques for rapid software prototyping and graph visualization, including distributed programming; *Visual formalisms such as Flowchart, Event Graph, and Process Communication Graph; *Graph-oriented distributed programming; *Program visualization for software understanding, testing/debugging and maintenance; *Object-oriented re-design based on legacy procedural software; *Cognitive models for designing software exploration tools; *Human comprehensibility of visual modeling diagrams in UML; *UML extended with pattern compositions for software reuse; *Visualization of software architecture and Web architecture for better understanding; *Visual programming and program visualization for music synthesizers; *Drawing diagrams nicely using clustering techniques for software engineering. |
| data science utd degree plan: Strategic Information Systems and Technologies in Modern Organizations Howard, Caroline, Hargiss, Kathleen, 2017-01-25 The role of technology in business environments has become increasingly pivotal in recent years. These innovations allow for improved process management, productivity, and competitive advantage. Strategic Information Systems and Technologies in Modern Organizations is an authoritative reference source for the latest academic research on the implementation of various technological tools for increased organizational productivity and management. Highlighting relevant case studies, empirical analyses, and critical business strategies, this book is ideally designed for professionals, researchers, academics, upper-level students, and managers interested in recent developments of technology in business settings. |
| data science utd degree plan: Provenance and Annotation of Data and Processes Bertram Ludäscher, Beth Plale, 2015-03-20 This book constitutes the revised selected papers of the 5th International Provenance and Annotation Workshop, IPAW 2014, held in Cologne, Germany in June 2014. The 14 long papers, 20 short papers and 4 extended abstracts presented were carefully reviewed and selected from 53 submissions. The papers include tools that enable provenance capture from software compilers, from web publications and from scripts, using existing audit logs and employing both static and dynamic instrumentation. |
| data science utd degree plan: Convex Optimization Theory Dimitri Bertsekas, 2009-06-01 An insightful, concise, and rigorous treatment of the basic theory of convex sets and functions in finite dimensions, and the analytical/geometrical foundations of convex optimization and duality theory. Convexity theory is first developed in a simple accessible manner, using easily visualized proofs. Then the focus shifts to a transparent geometrical line of analysis to develop the fundamental duality between descriptions of convex functions in terms of points, and in terms of hyperplanes. Finally, convexity theory and abstract duality are applied to problems of constrained optimization, Fenchel and conic duality, and game theory to develop the sharpest possible duality results within a highly visual geometric framework. This on-line version of the book, includes an extensive set of theoretical problems with detailed high-quality solutions, which significantly extend the range and value of the book. The book may be used as a text for a theoretical convex optimization course; the author has taught several variants of such a course at MIT and elsewhere over the last ten years. It may also be used as a supplementary source for nonlinear programming classes, and as a theoretical foundation for classes focused on convex optimization models (rather than theory). It is an excellent supplement to several of our books: Convex Optimization Algorithms (Athena Scientific, 2015), Nonlinear Programming (Athena Scientific, 2017), Network Optimization(Athena Scientific, 1998), Introduction to Linear Optimization (Athena Scientific, 1997), and Network Flows and Monotropic Optimization (Athena Scientific, 1998). |
| data science utd degree plan: Intelligent Information Access Giuliano Armano, Marco de Gemmis, Giovanni Semeraro, 2010-06-23 Written from a multidisciplinary perspective, Intelligent Information Access investigates new insights into methods, techniques and technologies for intelligent information access. The chapters are written by participants in the Intelligent Information Access meeting, held in Cagliari, Italy, in December 2008. |
| data science utd degree plan: Geographic Information Systems and Crime Analysis Fahui Wang, 2005-01-01 Computerized crime mapping or GIS in law enforcement agencies has experienced rapid growth, particularly since the mid 1990s. There has also been increasing interests in GIS analysis of crime from various academic fields including criminology, geography, urban planning, information science and others. This book features a diverse array of GIS applications in crime analysis, from general issues such as GIS as a communication process and inter-jurisdictional data sharing to specific applications in tracking serial killers and predicting juvenile violence. Geographic Information Systems and Crime Analysis showcases a broad range of methods and techniques from typical GIS tasks such as geocoding and hotspot analysis to advanced technologies such as geographic profiling, agent-based modeling and web GIS. Contributors range from university professors, criminologists in research institutes to police chiefs, GIS analysts in police departments and consultants in criminal justice. |
| data science utd degree plan: Trumponomics Stephen Moore, Arthur B. Laffer, 2018-10-30 Conservative economists offer a well-informed defense of Trump’s approach to trade, taxes, employment, infrastructure, and other economic policies. Donald Trump promised the American people a transformative change in economic policy after eight years of stagnation under Obama. But he didn’t adopt a conventional left or right economic agenda. His is a new economic populism that combines some conventional Republican ideas—tax cuts, deregulation, more power to the states—with more traditional Democratic issues such as trade protectionism and infrastructure spending. It also mixes in important populist issues such as immigration reform, pressuring the Europeans to pay for more of their own defense, and keeping America first. Coauthors Stephen Moore and Arthur B. Laffer worked as senior economic advisors to Donald Trump in 2016. They traveled with him, frequently met with his political and economic teams, worked on his speeches, and represented him as surrogates. They are currently members of the Trump Advisory Council and still meet with him regularly. In Trumponomics, they offer an insider’s view on how Trump operates in public and behind closed doors, his priorities and passions, and his greatest attributes and liabilities. |
| data science utd degree plan: Graph Theoretic Approaches for Analyzing Large-Scale Social Networks Meghanathan, Natarajan, 2017-07-13 Social network analysis has created novel opportunities within the field of data science. The complexity of these networks requires new techniques to optimize the extraction of useful information. Graph Theoretic Approaches for Analyzing Large-Scale Social Networks is a pivotal reference source for the latest academic research on emerging algorithms and methods for the analysis of social networks. Highlighting a range of pertinent topics such as influence maximization, probabilistic exploration, and distributed memory, this book is ideally designed for academics, graduate students, professionals, and practitioners actively involved in the field of data science. |
| data science utd degree plan: An Introduction to Crime and Criminology Hennessey Hayes, Tim Prenzler, 2014-10-01 An Introduction to Crime & Criminology 4e, continues to bring together some of Australia’s most widely respected authorities on criminology. The text explores popular knowledge and understanding about crime, contrasting it with what we know about crime from official sources as well as from crime victims. The authors present and analyse the various ways that crime is defined and measured, the many and varied dimensions of crime, the broad range of theories offered to explain crime as well as some of the main ways governments and other agencies respond to and attempt to prevent crime. |
| data science utd degree plan: Commerce, Justice, Science, and Related Agencies Appropriations for 2009 United States. Congress. House. Committee on Appropriations. Subcommittee on Commerce, Justice, Science, and Related Agencies, 2008 |
| data science utd degree plan: Topics in Applied Statistics Mingxiu Hu, Yi Liu, Jianchang Lin, 2013-09-14 This volume presents 27 selected papers in topics that range from statistical applications in business and finance to applications in clinical trials and biomarker analysis. All papers feature original, peer-reviewed content. The editors intentionally selected papers that cover many topics so that the volume will serve the whole statistical community and a variety of research interests. The papers represent select contributions to the 21st ICSA Applied Statistics Symposium. The International Chinese Statistical Association (ICSA) Symposium took place between the 23rd and 26th of June, 2012 in Boston, Massachusetts. It was co-sponsored by the International Society for Biopharmaceutical Statistics (ISBS) and American Statistical Association (ASA). This is the inaugural proceedings volume to share research from the ICSA Applied Statistics Symposium. |
| data science utd degree plan: Developing and Securing the Cloud Bhavani Thuraisingham, 2013-10-28 Although the use of cloud computing platforms and applications has expanded rapidly, most books on the subject focus on high-level concepts. There has long been a need for a book that provides detailed guidance on how to develop secure clouds. Filling this void, Developing and Securing the Cloud provides a comprehensive overview of cloud computing technology. Supplying step-by-step instruction on how to develop and secure cloud computing platforms and web services, it includes an easy-to-understand, basic-level overview of cloud computing and its supporting technologies. Presenting a framework for secure cloud computing development, the book describes supporting technologies for the cloud such as web services and security. It details the various layers of the cloud computing framework, including the virtual machine monitor and hypervisor, cloud data storage, cloud data management, and virtual network monitor. It also provides several examples of cloud products and prototypes, including private, public, and U.S. government clouds. Reviewing recent developments in cloud computing, the book illustrates the essential concepts, issues, and challenges in developing and securing today’s cloud computing platforms and applications. It also examines prototypes built on experimental cloud computing systems that the author and her team have developed at the University of Texas at Dallas. This diverse reference is suitable for those in industry, government, and academia. Technologists will develop the understanding required to select the appropriate tools for particular cloud applications. Developers will discover alternative designs for cloud development, and managers will understand if it’s best to build their own clouds or contract them out. |
| data science utd degree plan: Data Science at the Command Line Jeroen Janssens, 2021-08-17 This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 100 Unix power tools--useful whether you work with Windows, macOS, or Linux. You'll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you're comfortable processing data with Python or R, you'll learn how to greatly improve your data science workflow by leveraging the command line's power. This book is ideal for data scientists, analysts, engineers, system administrators, and researchers. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTML, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create your own tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, regression, and classification algorithms Leverage the command line from Python, Jupyter, R, RStudio, and Apache Spark |
| data science utd degree plan: Techniques and Applications for Advanced Information Privacy and Security: Emerging Organizational, Ethical, and Human Issues Nemati, Hamid, 2009-03-31 This book provides a thorough understanding of issues and concerns in information technology security--Provided by publisher. |
| data science utd degree plan: Human Resources Information Systems Nicolas A. Valcik, Meghna Sabharwal, Teodoro J. Benavides, 2023-07-19 This volume provides an introduction to Human Resource Information Systems (HRIS) for those in the public administration field. At the intersection between human resource management and information technology, HRIS is often the key to having and maintaining the personnel data that is essential for hiring and recruitment, strategic planning and analysis, and legal requirements in most public organizations. Revised and updated for the second edition, this book describes what an HRIS system is, what the functionality of such a system should be, and outlines the practical aspects of an HRIS. It also compares the different aspects of human resources in public organizations, non-profit organizations, and private corporations, and how differences across organizations may influence the functionality requirements of the HRIS. Finally, the volume contains both an organizational theory component, which frames how an HRIS interacts with an organization both from a functional standpoint and a reporting standpoint. The book includes a practical component, which includes real-world case studies that illustrate the advantages and pitfalls to implementing an HRIS enterprise system. Providing a thorough introduction to HRIS for both academics and practitioners, this volume is appropriate for researchers, graduate students, and practitioners in the fields of public administration, higher education administration, information systems, computer science, and human resources. |
| data science utd degree plan: Journal of the American Statistical Association , 1973 |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
