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| behavioral data science masters: 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. |
| behavioral data science masters: Data Augmented Design Ying Long, Enjia Zhang, 2020-08-13 This book offers an essential introduction to a new urban planning and design methodology called Data Augmented Design (DAD) and its evolution and progresses, highlighting data driven methods, urban planning and design applications and related theories. The authors draw on many kinds of data, including big, open, and conventional data, and discuss cutting-edge technologies that illustrate DAD as a future oriented design framework in terms of its focus on multi-data, multi-method, multi-stage and multi-scale sustainable urban planning. In four sections and ten chapters, the book presents case studies to address the core concepts of DAD, the first type of applications of DAD that emerged in redevelopment-oriented planning and design, the second type committed to the planning and design for urban expansion, and the future-oriented applications of DAD to advance sustainable technologies and the future structural form of the built environment. The book is geared towards a broad readership, ranging from researchers and students of urban planning, urban design, urban geography, urban economics, and urban sociology, to practitioners in the areas of urban planning and design. |
| behavioral data science masters: Fundamental Statistics for the Behavioral Sciences David C. Howell, 2016-02-02 FUNDAMENTAL STATISTICS FOR THE BEHAVIORAL SCIENCES focuses on providing the context of statistics in behavioral research, while emphasizing the importance of looking at data before jumping into a test. This practical approach provides students with an understanding of the logic behind the statistics, so they understand why and how certain methods are used -- rather than simply carry out techniques by rote. Students move beyond number crunching to discover the meaning of statistical results and appreciate how the statistical test to be employed relates to the research questions posed by an experiment. Written in an informal style, the text provides an abundance of real data and research studies that provide a real-life perspective and help students learn and understand concepts. In alignment with current trends in statistics in the behavioral sciences, the text emphasizes effect sizes and meta-analysis, and integrates frequent demonstrations of computer analyses through SPSS and R. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| behavioral data science masters: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout. |
| behavioral data science masters: Modern Statistics for the Social and Behavioral Sciences Rand Wilcox, 2011-08-05 In addition to learning how to apply classic statistical methods, students need to understand when these methods perform well, and when and why they can be highly unsatisfactory. Modern Statistics for the Social and Behavioral Sciences illustrates how to use R to apply both standard and modern methods to correct known problems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practical problems with classic methods were missed for so many years, and why modern techniques have practical value. Designed for a two-semester, introductory course for graduate students in the social sciences, this text introduces three major advances in the field: Early studies seemed to suggest that normality can be assumed with relatively small sample sizes due to the central limit theorem. However, crucial issues were missed. Vastly improved methods are now available for dealing with non-normality. The impact of outliers and heavy-tailed distributions on power and our ability to obtain an accurate assessment of how groups differ and variables are related is a practical concern when using standard techniques, regardless of how large the sample size might be. Methods for dealing with this insight are described. The deleterious effects of heteroscedasticity on conventional ANOVA and regression methods are much more serious than once thought. Effective techniques for dealing heteroscedasticity are described and illustrated. Requiring no prior training in statistics, Modern Statistics for the Social and Behavioral Sciences provides a graduate-level introduction to basic, routinely used statistical techniques relevant to the social and behavioral sciences. It describes and illustrates methods developed during the last half century that deal with known problems associated with classic techniques. Espousing the view that no single method is always best, it imparts a general understanding of the relative merits of various techniques so that the choice of method can be made in an informed manner. |
| behavioral data science masters: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| behavioral data science masters: Behavioural and Experimental Economics Steven Durlauf, L. Blume, 2016-04-30 Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool. |
| behavioral data science masters: Information For Efficient Decision Making: Big Data, Blockchain And Relevance Kashi R Balachandran, 2020-11-19 Can there be reliable information that is also relevant to decision making? Information for Efficient Decision Making: Big Data, Blockchain and Relevance focuses on the consolidation of information to facilitate making decisions in firms, in order to make their operations efficient to reduce their costs and consequently, increase their profitability. The advent of blockchain has generated great interest as an alternative to centralized organizations, where the data is gathered through a centralized ledger keeping of activities of the firm. The decentralized ledger keeping is one of the main features of blockchain that has given rise to many issues of technology, development, implementation, privacy, acceptance, evaluation and so on. Blockchain concept is a follow-up to big data environment facilitated by enormous progress in computer hardware, storage capacities and technological prowess. This has resulted in the rapid acquiring of data not considered possible earlier. With shrewd modeling analytics and algorithms, the applications have grown to significant levels. This handbook discusses the progress in data collection, pros and cons of collecting information on decentralized publicly available ledgers and several applications. |
| behavioral data science masters: Behavioral Operational Research Leroy White, Martin Kunc, Katharina Burger, Jonathan Malpass, 2019-10-24 This edited collection addresses the question of which capabilities and competencies enable Behavioral Operational Research to provide sustained improvement to decision processes. The aim is to show how a focus on capability and competency will not only meet short-term requirements for problem solving and decision support, but also build a solid foundation for the future. The contributors present recent advances in Behavioral OR, with a focus on the ways in which users of models deal with incomplete and imprecise information, subjective boundaries and uncertainty. These chapters are structured around three key dimensions of BOR: capabilities, cognition and aspects of practice. |
| behavioral data science masters: Fundamentals of Behavioral Research Pietro Badia, 1982 |
| behavioral data science masters: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| behavioral data science masters: Data Science For Cyber-security Nicholas A Heard, Niall M Adams, Patrick Rubin-delanchy, Mellisa Turcotte, 2018-09-26 Cyber-security is a matter of rapidly growing importance in industry and government. This book provides insight into a range of data science techniques for addressing these pressing concerns.The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences. Networks of connected devices, such as enterprise computer networks or the wider so-called Internet of Things, are all vulnerable to misuse and attack, and data science methods offer the promise to detect such behaviours from the vast collections of cyber traffic data sources that can be obtained. In many cases, this is achieved through anomaly detection of unusual behaviour against understood statistical models of normality.This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies. |
| behavioral data science masters: An Index to Undergraduate Science National Science Foundation (U.S.). Office of Experimental Projects and Programs, 1974 |
| behavioral data science masters: Getting Into Graduate School Gregory J. Privitera, 2014-07-15 In this exciting new book, experienced author, professor, and teacher Greg Privitera—2013 Advisor of the Year at St. Bonaventure University and recipient of the SBU Award for Professional Excellence in teaching in 2014—draws on his extensive expertise to give students a step-by-step plan for success in preparing for and applying to graduate school. Broad in scope and rich in detail, Getting Into Graduate School includes insights into how graduate school selection committees decide on candidates, a concrete freshman-to-senior-year plan, and samples of application materials, resumes, and cover letters. This empowering book provides everything students in psychology and the behavioral sciences need to map their course to academic and professional success. |
| behavioral data science masters: Grit Angela Duckworth, 2016-05-03 In this instant New York Times bestseller, Angela Duckworth shows anyone striving to succeed that the secret to outstanding achievement is not talent, but a special blend of passion and persistence she calls “grit.” “Inspiration for non-geniuses everywhere” (People). The daughter of a scientist who frequently noted her lack of “genius,” Angela Duckworth is now a celebrated researcher and professor. It was her early eye-opening stints in teaching, business consulting, and neuroscience that led to her hypothesis about what really drives success: not genius, but a unique combination of passion and long-term perseverance. In Grit, she takes us into the field to visit cadets struggling through their first days at West Point, teachers working in some of the toughest schools, and young finalists in the National Spelling Bee. She also mines fascinating insights from history and shows what can be gleaned from modern experiments in peak performance. Finally, she shares what she’s learned from interviewing dozens of high achievers—from JP Morgan CEO Jamie Dimon to New Yorker cartoon editor Bob Mankoff to Seattle Seahawks Coach Pete Carroll. “Duckworth’s ideas about the cultivation of tenacity have clearly changed some lives for the better” (The New York Times Book Review). Among Grit’s most valuable insights: any effort you make ultimately counts twice toward your goal; grit can be learned, regardless of IQ or circumstances; when it comes to child-rearing, neither a warm embrace nor high standards will work by themselves; how to trigger lifelong interest; the magic of the Hard Thing Rule; and so much more. Winningly personal, insightful, and even life-changing, Grit is a book about what goes through your head when you fall down, and how that—not talent or luck—makes all the difference. This is “a fascinating tour of the psychological research on success” (The Wall Street Journal). |
| behavioral data science masters: Mastering Data Science Cybellium Ltd, Unleash the Power of Insights from Data Are you ready to embark on a transformative journey into the world of data science? Mastering Data Science is your comprehensive guide to unlocking the full potential of data for extracting valuable insights and driving informed decisions. Whether you're an aspiring data scientist looking to enhance your skills or a business leader seeking to leverage data-driven strategies, this book equips you with the knowledge and tools to master the art of data science. Key Features: 1. Dive into Data Science: Immerse yourself in the realm of data science, understanding its core principles, methodologies, and applications. Build a solid foundation that empowers you to extract meaningful insights from complex datasets. 2. Data Exploration and Visualization: Master the art of data exploration and visualization. Learn how to analyze datasets, uncover patterns, and create compelling visualizations that reveal hidden trends. 3. Statistical Analysis and Hypothesis Testing: Uncover the power of statistical analysis and hypothesis testing. Explore techniques for making data-driven inferences, validating assumptions, and drawing meaningful conclusions. 4. Machine Learning Fundamentals: Delve into machine learning concepts and techniques. Learn about supervised and unsupervised learning, feature engineering, model selection, and evaluation. 5. Predictive Analytics: Discover the realm of predictive analytics. Learn how to build predictive models that forecast future outcomes, enabling proactive decision-making. 6. Natural Language Processing (NLP) and Text Mining: Explore NLP and text mining techniques. Learn how to process and analyze textual data, extract sentiments, and uncover insights from unstructured content. 7. Time Series Analysis: Master time series analysis for modeling sequential data. Learn how to forecast trends, identify seasonality, and make predictions based on temporal patterns. 8. Big Data and Data Wrangling: Dive into big data analytics and data wrangling. Learn how to handle and preprocess large datasets, ensuring data quality and usability. 9. Deep Learning and Neural Networks: Uncover the world of deep learning and neural networks. Learn how to build and train deep learning models for tasks like image recognition and natural language understanding. 10. Real-World Applications: Gain insights into real-world applications of data science across industries. From healthcare to finance, explore how organizations harness data science for strategic decision-making. Who This Book Is For: Mastering Data Science is an indispensable resource for aspiring data scientists, analysts, and business professionals who want to excel in extracting insights from data. Whether you're new to data science or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of data for innovation. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| behavioral data science masters: OECD Public Governance Reviews Behavioural Insights for Public Integrity Harnessing the Human Factor to Counter Corruption OECD, 2018-05-11 - Foreword - Executive summary - Introduction - The dynamics of moral decision making - Integrity in the context of social interactions - Applying behavioural insights to integrity policies - References |
| behavioral data science masters: Mobility Patterns, Big Data and Transport Analytics Constantinos Antoniou, Loukas Dimitriou, Francisco Pereira, 2018-11-27 Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility 'structural' analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data's impact on mobility and an introduction to the tools necessary to apply new techniques. The book covers in detail, mobility 'structural' analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data's impact on mobility, and an introduction to the tools necessary to apply new techniques. - Guides readers through the paradigm-shifting opportunities and challenges of handling Big Data in transportation modeling and analytics - Covers current analytical innovations focused on capturing, predicting, visualizing, and controlling mobility patterns, while discussing future trends - Delivers an introduction to transportation-related information advances, providing a benchmark reference by world-leading experts in the field - Captures and manages mobility patterns, covering multiple purposes and alternative transport modes, in a multi-disciplinary approach - Companion website features videos showing the analyses performed, as well as test codes and data-sets, allowing readers to recreate the presented analyses and apply the highlighted techniques to their own data |
| behavioral data science masters: Advances in Behavioral Finance Richard H. Thaler, 1993-08-19 Modern financial markets offer the real world's best approximation to the idealized price auction market envisioned in economic theory. Nevertheless, as the increasingly exquisite and detailed financial data demonstrate, financial markets often fail to behave as they should if trading were truly dominated by the fully rational investors that populate financial theories. These markets anomalies have spawned a new approach to finance, one which as editor Richard Thaler puts it, entertains the possibility that some agents in the economy behave less than fully rationally some of the time. Advances in Behavioral Finance collects together twenty-one recent articles that illustrate the power of this approach. These papers demonstrate how specific departures from fully rational decision making by individual market agents can provide explanations of otherwise puzzling market phenomena. To take several examples, Werner De Bondt and Thaler find an explanation for superior price performance of firms with poor recent earnings histories in the tendencies of investors to overreact to recent information. Richard Roll traces the negative effects of corporate takeovers on the stock prices of the acquiring firms to the overconfidence of managers, who fail to recognize the contributions of chance to their past successes. Andrei Shleifer and Robert Vishny show how the difficulty of establishing a reliable reputation for correctly assessing the value of long term capital projects can lead investment analysis, and hence corporate managers, to focus myopically on short term returns. As a testing ground for assessing the empirical accuracy of behavioral theories, the successful studies in this landmark collection reach beyond the world of finance to suggest, very powerfully, the importance of pursuing behavioral approaches to other areas of economic life. Advances in Behavioral Finance is a solid beachhead for behavioral work in the financial arena and a clear promise of wider application for behavioral economics in the future. |
| behavioral data science masters: Handbook of Research on Consumer Behavior Change and Data Analytics in the Socio-Digital Era Keikhosrokiani, Pantea, 2022-06-24 The emergence of new technologies within the industrial revolution has transformed businesses to a new socio-digital era. In this new era, businesses are concerned with collecting data on customer needs, behaviors, and preferences for driving effective customer engagement and product development, as well as for crucial decision making. However, the ever-shifting behaviors of consumers provide many challenges for businesses to pinpoint the wants and needs of their audience. The Handbook of Research on Consumer Behavior Change and Data Analytics in the Socio-Digital Era focuses on the concepts, theories, and analytical techniques to track consumer behavior change. It provides multidisciplinary research and practice focusing on social and behavioral analytics to track consumer behavior shifts and improve decision making among businesses. Covering topics such as consumer sentiment analysis, emotional intelligence, and online purchase decision making, this premier reference source is a timely resource for business executives, entrepreneurs, data analysts, marketers, advertisers, government officials, social media professionals, libraries, students and educators of higher education, researchers, and academicians. |
| behavioral data science masters: College Science Improvement Programs; COSIP A & B Report National Science Foundation (U.S.). Office of Experimental Programs, 1974 |
| behavioral data science masters: Behavioral Portfolio Management C. Thomas Howard, 2014-03-17 The investment industry is on the cusp of a major shift, from Modern Portfolio Theory (MPT) to Behavioral Finance, with Behavioral Portfolio Management (BMP) the next step in this transition. BPM focuses on how to harness the price distortions that are driven by emotional crowds and use this to create superior portfolios. Once markets and investing are viewed through the lens of behavior, and portfolios are constructed on this basis, investable opportunities become readily apparent. Mastering your emotions is critical to the process and the insights provided by Tom Howard put investors on the path to achieving this. Forty years of Behavioral Science research presents a clear picture of how individuals make decisions; there are few signs of rationality. Indeed, emotional investors sabotage their own efforts in building long-horizon wealth. When this is combined with the misconception that active management is unable to generate superior returns, the typical emotional investor leaves hundreds of thousands, if not millions, of dollars on the table during their investment lifetimes. Howard moves on to show how industry practice, with its use of the style grid, standard deviation, correlation, maximum drawdown and the Sharpe ratio, has entrenched emotion within investing. The result is that investors construct underperforming, bubble-wrapped portfolios. So if an investor masters their own emotions, they still must challenge the emotionally-based conventional wisdom pervasive throughout the industry. Tom Howard explains how to do this. Attention is then given to measureable and persistent behavioral factors. These provide investors with a new source of information that has the potential to transform how they think about portfolio management and dramatically improve performance. Behavioral factors can be used to select the best stocks, the best active managers, and the best markets in which to invest. Once the transition to behavioral finance is made, the emotional measures of MPT will quickly be forgotten and replaced with rational concepts that allow investors to successfully build long-horizon wealth. If you take portfolio construction seriously, it is essential that you make the next step forward towards Behavioral Portfolio Management. |
| behavioral data science masters: Machine Learning in Educational Sciences Myint Swe Khine, |
| behavioral data science masters: Health Behavior Karen Glanz, Barbara K. Rimer, K. Viswanath, 2024-09-11 The essential health behavior text, updated with the latest theories, research, and issues Health Behavior: Theory, Research and Practice provides a thorough introduction to understanding and changing health behavior—important facets of the public health role. Since the publication of the first edition, this comprehensive book has become the gold standard of health behavior texts. This new sixth edition has been updated to reflect the most recent changes in the public health field, including findings from real-world interventions based on the theories described in the book. Offering perspective applicable at the individual, interpersonal, group, and community levels, this essential guide gives public health students and practitioners an authoritative reference for both the theoretical and practical aspects of health behavior. Explore the link between culture, health, and the importance of community Get up to date on emerging theories of health behavior and their applications Examine the push toward evidence-based interventions and focus on diverse populations Learn how e-health and social media factor into health communication Written and edited by leading theorists and researchers in the field, Health Behavior builds a solid understanding of how to analyze and improve health behaviors and health. |
| behavioral data science masters: Decision Behaviour, Analysis and Support Simon French, John Maule, Nadia Papamichail, 2009-07-30 A multi-disciplinary exploration of how we can help decision makers to deliberate and make better decisions. |
| behavioral data science masters: Human Behavior and Environment Irwin Altman, Joachim F. Wohlwill, 2013-11-11 This is the first in a series of volumes concerned with research encompassed by the rather broad term environment and behavior. The goal of the series is to begin the process of integration of knowledge on environmental and behavioral topics so that researchers and professionals can have material from diverse sources accessible in a single publication. The field of environment and behavior is broad and interdiscipli nary, with researchers drawn from a variety of traditional disciplines such as psychology, sociology, anthropology, geography, and other social and behavioral sciences, and from the biological and life sciences of medicine, psychiatry, biology, and ethology. The interdis ciplinary quality of the field is also reflected in the extensive involve ment of environmental professionals from architecture, urban plan ning, landscape architecture, interior design, and other fields such as recreation and natural resources, to name just a few. At present, the field has a somewhat chaotic flavor, with research being carried out by a variety of scholars who publish in a multitude of outlets. Many researchers and practitioners are unaware of the state of knowledge regarding a specific topic because of the unavailability of integrated reference materials. There are only a handful of books dealing with environment and behavior, most of them unintegrated collections of readings, with only an occasional systematic analysis of some facet of the field. |
| behavioral data science masters: Norms in the Wild Cristina Bicchieri, 2017 Large scale behavioral interventions work in some social contexts, but fail in others. The book explains this phenomenon with diverse personal and social behavioral motives, guided by research in economics, psychology, and international consulting done with UNICEF. The book offers tested tools that mobilize mass media, community groups, and autonomous first movers (or trendsetters) to alter harmful collective behaviors. |
| behavioral data science masters: Fundamentals of Statistical Inference , 1977 |
| behavioral data science masters: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-10-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
| behavioral data science masters: The Signal and the Noise Nate Silver, 2015-02-03 One of the more momentous books of the decade. —The New York Times Book Review Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future. In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball to global pandemics, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise. With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read. |
| behavioral data science masters: Enhancing and Predicting Digital Consumer Behavior with AI Musiolik, Thomas Heinrich, Rodriguez, Raul Villamarin, Kannan, Hemachandran, 2024-05-13 Understanding consumer behavior in today's digital landscape is more challenging than ever. Businesses must navigate a sea of data to discern meaningful patterns and correlations that drive effective customer engagement and product development. However, the ever-changing nature of consumer behavior presents a daunting task, making it difficult for companies to gauge the wants and needs of their target audience accurately. Enhancing and Predicting Digital Consumer Behavior with AI offers a comprehensive solution to this pressing issue. A strong focus on concepts, theories, and analytical techniques for tracking consumer behavior changes provides the roadmap for businesses to navigate the complexities of the digital age. By covering topics such as digital consumers, emotional intelligence, and data analytics, this book serves as a timely and invaluable resource for academics and practitioners seeking to understand and adapt to the evolving landscape of consumer behavior. |
| behavioral data science masters: Algorithmic Learning Theory Michael M. Richter, 1998 This volume contains all the papers presented at the Ninth International Con- rence on Algorithmic Learning Theory (ALT’98), held at the European education centre Europ ̈aisches Bildungszentrum (ebz) Otzenhausen, Germany, October 8{ 10, 1998. The Conference was sponsored by the Japanese Society for Arti cial Intelligence (JSAI) and the University of Kaiserslautern. Thirty-four papers on all aspects of algorithmic learning theory and related areas were submitted, all electronically. Twenty-six papers were accepted by the program committee based on originality, quality, and relevance to the theory of machine learning. Additionally, three invited talks presented by Akira Maruoka of Tohoku University, Arun Sharma of the University of New South Wales, and Stefan Wrobel from GMD, respectively, were featured at the conference. We would like to express our sincere gratitude to our invited speakers for sharing with us their insights on new and exciting developments in their areas of research. This conference is the ninth in a series of annual meetings established in 1990. The ALT series focuses on all areas related to algorithmic learning theory including (but not limited to): the theory of machine learning, the design and analysis of learning algorithms, computational logic of/for machine discovery, inductive inference of recursive functions and recursively enumerable languages, learning via queries, learning by arti cial and biological neural networks, pattern recognition, learning by analogy, statistical learning, Bayesian/MDL estimation, inductive logic programming, robotics, application of learning to databases, and gene analyses. |
| behavioral data science masters: Advances in Silk Science and Technology Arindam Basu, 2015-04-30 The remarkable properties of silk fibres have gained them a prominent place in the field of technical textiles. Advances in Silk Science and Technology explores recent developments in silk processing, properties and applications. Techniques for manufacturing spider silk are also discussed and the current and future applications of this fibre are reviewed. Part One focuses on the properties and processing of silk from both silkworms and spiders. It addresses recent advances in our understanding of the properties of silk and offers systematic coverage of the processing of silk from spinning through to finishing, as well as an analysis of quality testing for silk fibres, yarns and fabrics. Part Two then addresses important applications of silk from silkworms and spiders, and includes chapters on the use of silk in polymer matrix composites and in different kinds of biomaterial. The book concludes with a chapter on developments in the use of silk waste. - Reviews the properties of silk from both silkworms and spiders - Offers systematic coverage of the processing of silk from spinning through to finishing - Cover a range of applications, including on the use of silk in polymer matrix composites and in different kinds of biomaterial |
| behavioral data science masters: Social and Behavioral Science for Health Professionals Brian P. Hinote, Jason Adam Wasserman, 2019-12-26 Health professionals’ interest in social and behavioral science is rapidly increasing due to the growing recognition that social factors such as income, education, race, gender, and age all impact individuals’ health. These and other social conditions also shape patients’ illness experiences, the ways that they interact with health care providers, and the effectiveness of with which health professionals provide care. Understanding these social determinants and applying them to clinical practice is a major challenge for healthcare providers, which is why programs and accrediting bodies have been including more social and behavioral science content into the curricula for medical, nursing, and allied health programs. Social and Behavioral Science for Health Professionals provides in-depth coverage of the social determinants of health and how to directly apply these insights in clinical practice, thereby enhancing clinicians’ ability to engage their patients and more effectively render care. Broken into four parts, the book opens with the foundations of social science and health, including the shifting landscape of health and healthcare. The authors then cover the way in which social determinants of health shape large-scale features of health and illness in society, how they influence interactions between patients and providers in clinical settings, and how they shape health care systems and policies. Threshold concepts in each chapterfocus on conceptual and transformative learning while learning objectives, activities, and discussion questions provide instructors and students with robust sets of learning aids that intentionally focus on practical clinical, epidemiological, and policy issues. Ideal for students, educators, and professionals in health care, medical sociology, public health, and related fields, Social and Behavioral Science for Health Professionals is the only introduction available that clearly articulates why social and behavioral science matter in clinical care. New to This Edition: New Chapter 13 on Comparative Health Care Systems covers four models of health care systems and expands the global focus of the book Greater emphasis on the LGBTQ+ community provides coverage of how gender expression and sexual orientation influence health and quality of care received New coverage of current issues such as the opioid crisis and vaccine hesitancy that have been rendered especially important by the COVID-19 pandemic Added discussion questions at the end of every chapter strengthen students’ critical thinking skills and abilities to apply new insights to practical, real-world examples. |
| behavioral data science masters: Principles of Data Science Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau, 2020-07-08 This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice |
| behavioral data science masters: Advances in Artificial Intelligence: From Theory to Practice Salem Benferhat, Karim Tabia, Moonis Ali, 2017-06-10 The two-volume set LNCS 10350 and 10351 constitutes the thoroughly refereed proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, held in Arras, France, in June 2017. The 70 revised full papers presented together with 45 short papers and 3 invited talks were carefully reviewed and selected from 180 submissions. They are organized in topical sections: constraints, planning, and optimization; data mining and machine learning; sensors, signal processing, and data fusion; recommender systems; decision support systems; knowledge representation and reasoning; navigation, control, and autonome agents; sentiment analysis and social media; games, computer vision; and animation; uncertainty management; graphical models: from theory to applications; anomaly detection; agronomy and artificial intelligence; applications of argumentation; intelligent systems in healthcare and mhealth for health outcomes; and innovative applications of textual analysis based on AI. |
| behavioral data science masters: Transforming the Workforce for Children Birth Through Age 8 National Research Council, Institute of Medicine, Board on Children, Youth, and Families, Committee on the Science of Children Birth to Age 8: Deepening and Broadening the Foundation for Success, 2015-07-23 Children are already learning at birth, and they develop and learn at a rapid pace in their early years. This provides a critical foundation for lifelong progress, and the adults who provide for the care and the education of young children bear a great responsibility for their health, development, and learning. Despite the fact that they share the same objective - to nurture young children and secure their future success - the various practitioners who contribute to the care and the education of children from birth through age 8 are not acknowledged as a workforce unified by the common knowledge and competencies needed to do their jobs well. Transforming the Workforce for Children Birth Through Age 8 explores the science of child development, particularly looking at implications for the professionals who work with children. This report examines the current capacities and practices of the workforce, the settings in which they work, the policies and infrastructure that set qualifications and provide professional learning, and the government agencies and other funders who support and oversee these systems. This book then makes recommendations to improve the quality of professional practice and the practice environment for care and education professionals. These detailed recommendations create a blueprint for action that builds on a unifying foundation of child development and early learning, shared knowledge and competencies for care and education professionals, and principles for effective professional learning. Young children thrive and learn best when they have secure, positive relationships with adults who are knowledgeable about how to support their development and learning and are responsive to their individual progress. Transforming the Workforce for Children Birth Through Age 8 offers guidance on system changes to improve the quality of professional practice, specific actions to improve professional learning systems and workforce development, and research to continue to build the knowledge base in ways that will directly advance and inform future actions. The recommendations of this book provide an opportunity to improve the quality of the care and the education that children receive, and ultimately improve outcomes for children. |
| behavioral data science masters: Research in Biological and Medical Sciences Walter Reed Army Institute of Research, 1973 |
| behavioral data science masters: Applied Behavior Science in Organizations Ramona A. Houmanfar, Mitch Fryling, Mark P. Alavosius, 2021-09-30 Applied Behavior Science in Organizations provides a compelling overview of the history of Organizational Behavior Management (OBM) and the opportunity it presents for designing and managing positive work environments that can in turn have a positive impact on society. The book brings together leading experts from industry and research settings to provide an overview of the historical approaches in Organizational Behavior Management. It begins with an introduction to recognized practices in OBM and the applications of fundamental principles of behavior analysis to a variety of performance problems in organizational settings. The book then highlights how organizational practices and consumers’ behavior combine in a complex confluence to meet an organization’s goals and satisfy consumer appetites, whilst often unintentionally affecting the wellbeing of organizational members. It argues that the science of behavior has a responsibility to contribute to the safety, health and wellbeing of organizational members, consumers of organizational products, and beyond. Finally, the book recognizes the essential role of organizations in initiating, shaping, and sustaining the development of more nurturing and reinforcing work environments, through discussion of the need for innovation while adapting and responding to growing social upheaval, technological advances, and environmental concerns, alongside crises in the global economy, health, education, and environment. Showcasing emerging work by internationally recognized scholars on the application of behavior science in organizations, the book will be an essential read for all students and professionals of Organizational Behavior Management, as well as those interested in using organizational applications to create new models of management. |
| behavioral data science masters: Happiness by Design Paul Dolan, 2014-08-28 This is not just another happiness book. In Happiness by Design, happiness and behavior expert Paul Dolan combines the latest insights from economics and psychology to illustrate that in order to be happy we must behave happy Our happiness is experiences of both pleasure and purpose over time and it depends on what we actually pay attention to. Using what Dolan calls deciding, designing, and doing, we can overcome the biases that make us miserable and redesign our environments to make it easier to experience happiness, fulfilment, and even health. With uncanny wit and keen perception, Dolan reveals what we can do to find our unique optimal balance of pleasure and purpose, offering practical advice on how to organize our lives in happiness-promoting ways and fresh insights into how we feel, including why: • Having kids reduces pleasure but gives us a massive dose of purpose • Gaining weight won’t necessarily make us unhappier, but being too ambitious might • A quiet neighborhood is more important than a big house Vividly rendering intriguing research and lively anecdotal evidence, Happiness by Design offers an absorbing, thought-provoking, new paradigm for readers of Stumbling on Happiness and The How of Happiness. |
Master Psychology: Behavioural Data Science (track)
During this track, you will learn to analyse data about human behaviour, which is an important and valuable skill in today's society. The police use data to predict the risk of burglary by area and …
What Is Behavioral Data Science and How to Get into It?
Jul 12, 2020 · Behavioral Data Science courses at the Master’s Level are currently available from the University of Warwick (a dedicated MSc program) and the University of Amsterdam (a …
Behavioural and Data Science (MSc) (2025 Entry) - The University …
Explore our Behavioural and Data Science taught Master's degree at Warwick. Understand the underlying factors driving human behaviour on Behavioural and Data Science MSc. Warwick's …
Psychology: Data Science in Human Behavior MS | UW-Madison …
Leverage the power of data to understand human behavior in the Psychology: Data Science in Human Behavior MS program. You’ll gain hands-on experience designing psychological …
Master of Behavioral and Decision Sciences | Penn LPS
Penn’s Master of Behavioral and Decision Sciences (MBDS) is informed by contemporary theories and research methods of behavioral economics, decision sciences, network analysis, and …
Data Science in Human Behavior – Master of Science in …
Develop a broad understanding of concepts and methods in data science and machine learning as they pertain to research in human behavior. Develop a proficiency in statistical analysis and …
Psychology: Data Science in Human Behavior, MS < University of ...
This program is designed to train students who have an undergraduate degree in a core behavioral science (e.g., Psychology, Economics, Sociology) to use modern data-science …
Master’s in Applied Psychology Data Analytics - Marquette University
Use data to drive meaningful change in the world. The applied psychology data analytics master’s program housed in the Department of Psychology at Marquette University combines analytical …
Psychology - Data Science in Human Behavior M.Sc. at University …
You’ll learn to design and execute data analyses relevant to questions about human psychology and behavior. As part of this, you will receive rigorous training in statistics and learn how to …
Behavioural Data Science – Official Website of the Behavioural Data ...
What is Behavioural Data Science (BDS)? BDS is a new upcoming interdisciplinary field which combines knowledge from Psychology, Statistics and Data science techniques needed to …
Master Psychology: Behavioural Data Science (track)
During this track, you will learn to analyse data about human behaviour, which is an important and valuable skill in today's society. The police use data to predict the risk of burglary by area and …
What Is Behavioral Data Science and How to Get into It?
Jul 12, 2020 · Behavioral Data Science courses at the Master’s Level are currently available from the University of Warwick (a dedicated MSc program) and the University of Amsterdam (a …
Behavioural and Data Science (MSc) (2025 Entry) - The University …
Explore our Behavioural and Data Science taught Master's degree at Warwick. Understand the underlying factors driving human behaviour on Behavioural and Data Science MSc. Warwick's …
Psychology: Data Science in Human Behavior MS | UW-Madison …
Leverage the power of data to understand human behavior in the Psychology: Data Science in Human Behavior MS program. You’ll gain hands-on experience designing psychological …
Master of Behavioral and Decision Sciences | Penn LPS
Penn’s Master of Behavioral and Decision Sciences (MBDS) is informed by contemporary theories and research methods of behavioral economics, decision sciences, network analysis, and …
Data Science in Human Behavior – Master of Science in …
Develop a broad understanding of concepts and methods in data science and machine learning as they pertain to research in human behavior. Develop a proficiency in statistical analysis and …
Psychology: Data Science in Human Behavior, MS < University of ...
This program is designed to train students who have an undergraduate degree in a core behavioral science (e.g., Psychology, Economics, Sociology) to use modern data-science …
Master’s in Applied Psychology Data Analytics - Marquette University
Use data to drive meaningful change in the world. The applied psychology data analytics master’s program housed in the Department of Psychology at Marquette University combines analytical …
Psychology - Data Science in Human Behavior M.Sc. at University …
You’ll learn to design and execute data analyses relevant to questions about human psychology and behavior. As part of this, you will receive rigorous training in statistics and learn how to …
Behavioural Data Science – Official Website of the Behavioural Data ...
What is Behavioural Data Science (BDS)? BDS is a new upcoming interdisciplinary field which combines knowledge from Psychology, Statistics and Data science techniques needed to …
