| computational social science online course: Handbook of Computational Social Science, Volume 2 Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, Lars Lyberg, 2021-11-10 The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors. |
| computational social science online course: Computational Social Science R. Michael Alvarez, 2016-03-07 Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy. |
| computational social science online course: Handbook of Computational Social Science, Volume 1 Uwe Engel, Anabel Quan-Haase, Sunny Liu, Lars E Lyberg, 2021-11-10 The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors. |
| computational social science online course: Computational Social Science Wei Luo, Maria Ciurea, Santosh Kumar, 2021-02-18 Selected papers from the International Conference on New Computational Social Science, focusing on the following five aspects: Big data acquisition and analysis, Integration of qualitative research and quantitative research, Sociological Internet experiment research, Application of ABM simulation method in Sociology Research, Research and development of new social computing tools. With the rapid development of information technology, especially sweeping progress in the Internet of things, cloud computing, social networks, social media and big data, social computing, as a data-intensive science, is an emerging field that leverages the capacity to collect and analyze data with an unprecedented breadth, depth and scale. It represents a new computing paradigm and an interdisciplinary field of research and application. A broad comprehension of major topics involved in social computing is important for both scholars and practitioners. This proceedings presents and discusses key concepts and analyzes the state-of-the-art of the field. The conference not only gave insights on social computing, but also affords conduit for future research in the field. Social computing has two distinct trends: One is on the social science issues, such as computational social science, computational sociology, social network analysis, etc; The other is on the use of computational techniques. Finally some new challenges ahead are summarized, including interdisciplinary cooperation and training, big data sharing for scientific data mashups, and privacy protect. |
| computational social science online course: Text as Data Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart, 2022-03-29 A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry |
| computational social science online course: Computational Social Science and Complex Systems János Kertész, Rosario N. Mantegna, Salvatore Miccichè, 2019 For many years, the development of large-scale quantitative social science was hindered by a lack of data. Traditional methods of data collection like surveys were very useful, but were limited. The situation has of course changed with the development of computing and information communication technology, and we now live in a world of data deluge, where the question has become how to extract important information from the plethora of data that can be accessed. Big Data has made it possible to study societal questions which were once impossible to deal with, but new tools and new multidisciplinary approaches are required. Physicists, together with economists, sociologists, computer scientists, etc. have played an important role in their development.This book presents the 9 lectures delivered at the CCIII Summer Course Computational Social Science and Complex Systems, held as part of the International School of Physics Enrico Fermi in Varenna, Italy, from 16-21 July 2018. The course had the aim of presenting some of the recent developments in the interdisciplinary fields of computational social science and econophysics to PhD students and young researchers, with lectures focused on recent problems investigated in computational social science.Addressing some of the basic questions and many of the subtleties of the emerging field of computational social science, the book will be of interest to students, researchers and advanced research professionals alike. |
| computational social science online course: Bit by Bit Matthew J. Salganik, 2019-08-06 This essential guide to doing social research in this fast-evolving digital age explains how the digital revolution is transforming the way social scientists observe behavior, ask questions, run experiments, and engage in mass collaborations. |
| computational social science online course: Complex Adaptive Systems John H. Miller, Scott E. Page, 2009-11-28 This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents. John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended. |
| computational social science online course: Quantitative Social Science Kosuke Imai, Lori D. Bougher, 2021-03-16 Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a translation of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place-- |
| computational social science online course: Computational Social Science Dan Dobrota, Roshan Chitrakar, Maria Ciurea, Liliana Mâţă, Wei Luo, 2022-07-01 The proceedings publish selected papers from the 2nd International Conference on New Computational Social Science, focusing on the following five aspects: Big data acquisition and analysis, Integration of qualitative research and quantitative research, Sociological Internet experiment research, Application of ABM simulation method in Sociology Research, Research and development of new social computing tools. With the rapid development of information technology, especially sweeping progress in the Internet of things, cloud computing, social networks, social media and big data, social computing, as a data-intensive science, is an emerging field that leverages the capacity to collect and analyze data with an unprecedented breadth, depth and scale. It represents a new computing paradigm and an interdisciplinary field of research and application. A broad comprehension of major topics involved in social computing is important for both scholars and practitioners. This proceedings presents and discusses key concepts and analyzes the state-of-the-art of the field. The conference not only gave insights on social computing, but also affords conduit for future research in the field. Social computing has two distinct trends: One is on the social science issues, such as computational social science, computational sociology, social network analysis, etc; the other is on the use of computational techniques. Finally, some new challenges ahead are summarized, including interdisciplinary cooperation and training, big data sharing for scientific data mashups, and privacy protect. |
| computational social science online course: Introduction to Computational Social Science Claudio Cioffi-Revilla, 2013-12-31 This reader-friendly textbook is the first work of its kind to provide a unified Introduction to Computational Social Science (CSS). Four distinct methodological approaches are examined in detail, namely automated social information extraction, social network analysis, social complexity theory and social simulation modeling. The coverage of these approaches is supported by a discussion of the historical context, as well as by a list of texts for further reading. Features: highlights the main theories of the CSS paradigm as causal explanatory frameworks that shed new light on the nature of human and social dynamics; explains how to distinguish and analyze the different levels of analysis of social complexity using computational approaches; discusses a number of methodological tools; presents the main classes of entities, objects and relations common to the computational analysis of social complexity; examines the interdisciplinary integration of knowledge in the context of social phenomena. |
| computational social science online course: Frontiers of Engineering National Academy of Engineering, 2007-03-08 This volume includes 15 papers from the National Academy of Engineering's 2006 U.S. Frontiers of Engineering (USFOE) Symposium held in September 2006. USFOE meetings bring together 100 outstanding engineers (ages 30 to 45) to exchange information about leading-edge technologies in a range of engineering fields. The 2006 symposium covered four topic areas: intelligent software systems and machines, the nano/bio interface, engineering personal mobility for the 21st century, and supply chain management. A paper by dinner speaker Dr. W. Dale Compton, Lillian M. Gilbreth Distinguished Professor of Industrial Engineering, Emeritus, is also included. The papers describe leading-edge research on commercializing auditory neuroscience, future developments in bionanotechnology, sustainable urban transportation, and managing disruptions to supply chains, among other topics. Appendixes include information about contributors, the symposium program, and a list of meeting participants. This is the twelfth volume in the USFOE series. |
| computational social science online course: Computational Analysis of Communication Wouter van Atteveldt, Damian Trilling, Carlos Arcila Calderon, 2022-03-10 Provides clear guidance on leveraging computational techniques to answer social science questions In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance. Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors—with expertise in both the social sciences and computer science—explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource: Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and others Integrates theory, methods, and tools to provide unified approach to the subject Includes sample code in Python and links to actual research questions and cases from social science and communication studies Discusses ethical and normative issues relevant to privacy, data ownership, and reproducible social science Developed in partnership with the International Communication Association and by the editors of Computational Communication Research Computational Analysis of Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work. |
| computational social science online course: Observation and Experiment Paul Rosenbaum, 2017-08-14 A daily glass of wine prolongs life—yet alcohol can cause life-threatening cancer. Some say raising the minimum wage will decrease inequality while others say it increases unemployment. Scientists once confidently claimed that hormone replacement therapy reduced the risk of heart disease but now they equally confidently claim it raises that risk. What should we make of this endless barrage of conflicting claims? Observation and Experiment is an introduction to causal inference by one of the field’s leading scholars. An award-winning professor at Wharton, Paul Rosenbaum explains key concepts and methods through lively examples that make abstract principles accessible. He draws his examples from clinical medicine, economics, public health, epidemiology, clinical psychology, and psychiatry to explain how randomized control trials are conceived and designed, how they differ from observational studies, and what techniques are available to mitigate their bias. “Carefully and precisely written...reflecting superb statistical understanding, all communicated with the skill of a master teacher.” —Stephen M. Stigler, author of The Seven Pillars of Statistical Wisdom “An excellent introduction...Well-written and thoughtful...from one of causal inference’s noted experts.” —Journal of the American Statistical Association “Rosenbaum is a gifted expositor...an outstanding introduction to the topic for anyone who is interested in understanding the basic ideas and approaches to causal inference.” —Psychometrika “A very valuable contribution...Highly recommended.” —International Statistical Review |
| computational social science online course: The Usefulness of Useless Knowledge Abraham Flexner, Robbert Dijkgraaf, 2017-02-21 A short, provocative book about why useless science often leads to humanity's greatest technological breakthroughs A forty-year tightening of funding for scientific research has meant that resources are increasingly directed toward applied or practical outcomes, with the intent of creating products of immediate value. In such a scenario, it makes sense to focus on the most identifiable and urgent problems, right? Actually, it doesn't. In his classic essay The Usefulness of Useless Knowledge, Abraham Flexner, the founding director of the Institute for Advanced Study in Princeton and the man who helped bring Albert Einstein to the United States, describes a great paradox of scientific research. The search for answers to deep questions, motivated solely by curiosity and without concern for applications, often leads not only to the greatest scientific discoveries but also to the most revolutionary technological breakthroughs. In short, no quantum mechanics, no computer chips. This brief book includes Flexner's timeless 1939 essay alongside a new companion essay by Robbert Dijkgraaf, the Institute's current director, in which he shows that Flexner's defense of the value of the unobstructed pursuit of useless knowledge may be even more relevant today than it was in the early twentieth century. Dijkgraaf describes how basic research has led to major transformations in the past century and explains why it is an essential precondition of innovation and the first step in social and cultural change. He makes the case that society can achieve deeper understanding and practical progress today and tomorrow only by truly valuing and substantially funding the curiosity-driven pursuit of useless knowledge in both the sciences and the humanities. |
| computational social science online course: Computational Social Science R. Michael Alvarez, 2016-03-10 This book provides an overview of cutting-edge approaches to computational social science. |
| computational social science online course: The SAGE Handbook of Research Methods in Political Science and International Relations Luigi Curini, Robert Franzese, 2020-04-09 The SAGE Handbook of Research Methods in Political Science and International Relations offers a comprehensive overview of research processes in social science — from the ideation and design of research projects, through the construction of theoretical arguments, to conceptualization, measurement, & data collection, and quantitative & qualitative empirical analysis — exposited through 65 major new contributions from leading international methodologists. Each chapter surveys, builds upon, and extends the modern state of the art in its area. Following through its six-part organization, undergraduate and graduate students, researchers and practicing academics will be guided through the design, methods, and analysis of issues in Political Science and International Relations: Part One: Formulating Good Research Questions & Designing Good Research Projects Part Two: Methods of Theoretical Argumentation Part Three: Conceptualization & Measurement Part Four: Large-Scale Data Collection & Representation Methods Part Five: Quantitative-Empirical Methods Part Six: Qualitative & Mixed Methods |
| computational social science online course: Computer Age Statistical Inference, Student Edition Bradley Efron, Trevor Hastie, 2021-06-17 The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science. |
| computational social science online course: Computational Social Psychology Robin R. Vallacher, Stephen J. Read, Andrzej Nowak, 2017-05-25 Computational Social Psychology showcases a new approach to social psychology that enables theorists and researchers to specify social psychological processes in terms of formal rules that can be implemented and tested using the power of high speed computing technology and sophisticated software. This approach allows for previously infeasible investigations of the multi-dimensional nature of human experience as it unfolds in accordance with different temporal patterns on different timescales. In effect, the computational approach represents a rediscovery of the themes and ambitions that launched the field over a century ago. The book brings together social psychologists with varying topical interests who are taking the lead in this redirection of the field. Many present formal models that are implemented in computer simulations to test basic assumptions and investigate the emergence of higher-order properties; others develop models to fit the real-time evolution of people’s inner states, overt behavior, and social interactions. Collectively, the contributions illustrate how the methods and tools of the computational approach can investigate, and transform, the diverse landscape of social psychology. |
| computational social science online course: Social and Economic Networks Matthew O. Jackson, 2010-11-01 Networks of relationships help determine the careers that people choose, the jobs they obtain, the products they buy, and how they vote. The many aspects of our lives that are governed by social networks make it critical to understand how they impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do. In Social and Economic Networks, Matthew Jackson offers a comprehensive introduction to social and economic networks, drawing on the latest findings in economics, sociology, computer science, physics, and mathematics. He provides empirical background on networks and the regularities that they exhibit, and discusses random graph-based models and strategic models of network formation. He helps readers to understand behavior in networked societies, with a detailed analysis of learning and diffusion in networks, decision making by individuals who are influenced by their social neighbors, game theory and markets on networks, and a host of related subjects. Jackson also describes the varied statistical and modeling techniques used to analyze social networks. Each chapter includes exercises to aid students in their analysis of how networks function. This book is an indispensable resource for students and researchers in economics, mathematics, physics, sociology, and business. |
| computational social science online course: Handbook of Research on Integrating Computer Science and Computational Thinking in K-12 Education Keengwe, Jared, Wachira, Patrick, 2019-12-13 As technology continues to develop and prove its importance in modern society, certain professions are acclimating. Aspects such as computer science and computational thinking are becoming essential areas of study. Implementing these subject areas into teaching practices is necessary for younger generations to adapt to the developing world. There is a critical need to examine the pedagogical implications of these technological skills and implement them into the global curriculum. The Handbook of Research on Integrating Computer Science and Computational Thinking in K-12 Education is a collection of innovative research on the methods and applications of computer science curriculum development within primary and secondary education. While highlighting topics including pedagogical implications, comprehensive techniques, and teacher preparation models, this book is ideally designed for teachers, IT consultants, curriculum developers, instructional designers, educational software developers, higher education faculty, administrators, policymakers, researchers, and graduate students. |
| computational social science online course: Data Science and Social Research N. Carlo Lauro, Enrica Amaturo, Maria Gabriella Grassia, Biagio Aragona, Marina Marino, 2017-11-17 This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices. |
| computational social science online course: Using R for Data Analysis in Social Sciences Quan Li, 2018-05-09 Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research. |
| computational social science online course: Big Data and Social Science Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, 2016-08-10 Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website. |
| computational social science online course: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| computational social science online course: Introduction to Computation and Programming Using Python, second edition John V. Guttag, 2016-08-12 The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics. |
| computational social science online course: Decoding the Social World Sandra Gonzalez-Bailon, 2017-12-22 How data science and the analysis of networks help us solve the puzzle of unintended consequences. Social life is full of paradoxes. Our intentional actions often trigger outcomes that we did not intend or even envision. How do we explain those unintended effects and what can we do to regulate them? In Decoding the Social World, Sandra González-Bailón explains how data science and digital traces help us solve the puzzle of unintended consequences—offering the solution to a social paradox that has intrigued thinkers for centuries. Communication has always been the force that makes a collection of people more than the sum of individuals, but only now can we explain why: digital technologies have made it possible to parse the information we generate by being social in new, imaginative ways. And yet we must look at that data, González-Bailón argues, through the lens of theories that capture the nature of social life. The technologies we use, in the end, are also a manifestation of the social world we inhabit. González-Bailón discusses how the unpredictability of social life relates to communication networks, social influence, and the unintended effects that derive from individual decisions. She describes how communication generates social dynamics in aggregate (leading to episodes of “collective effervescence”) and discusses the mechanisms that underlie large-scale diffusion, when information and behavior spread “like wildfire.” She applies the theory of networks to illuminate why collective outcomes can differ drastically even when they arise from the same individual actions. By opening the black box of unintended effects, González-Bailón identifies strategies for social intervention and discusses the policy implications—and how data science and evidence-based research embolden critical thinking in a world that is constantly changing. |
| computational social science online course: Computational Social Science Xiaogang Wu, Yongjun Zhang, Tianji Cai, 2024-06-07 This edited collection provides an overview of the recent developments in computational social science related to China studies and presents interdisciplinary empirical work from diverse scholars on culture, public opinion, and education using advanced computational methods and big data. The topics covered in this book include the surge of anti-China sentiment amid the COVID-19 pandemic, the nuances of E-governance, public opinion, authoritarian reactions, artistic innovation, and educational inequality. The chapters in this book provide important insights into how computational social science can be applied generally, but also underscore the importance of combining conventional sociological research with contemporary computational methods in the context of China studies. This cutting-edge volume will be valuable resource for researchers, scholars and practitioners of Sociology, China Studies and for those interested in computational approaches to the social sciences. The chapters in this book were originally published in Chinese Sociological Review. |
| computational social science online course: Doing Computational Social Science John McLevey, 2021-12-15 Computational approaches offer exciting opportunities for us to do social science differently. This beginner’s guide discusses a range of computational methods and how to use them to study the problems and questions you want to research. It assumes no knowledge of programming, offering step-by-step guidance for coding in Python and drawing on examples of real data analysis to demonstrate how you can apply each approach in any discipline. The book also: Considers important principles of social scientific computing, including transparency, accountability and reproducibility. Understands the realities of completing research projects and offers advice for dealing with issues such as messy or incomplete data and systematic biases. Empowers you to learn at your own pace, with online resources including screencast tutorials and datasets that enable you to practice your skills and get up to speed. For anyone who wants to use computational methods to conduct a social science research project, this book equips you with the skills, good habits and best working practices to do rigorous, high quality work. |
| computational social science online course: Introduction to Data Science for Social and Policy Research Jose Manuel Magallanes Reyes, 2017-09-21 This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R. |
| computational social science online course: Trends in Computational Social Choice Ulle Endriss, 2017 Computational social choice is concerned with the design and analysis of methods for collective decision making. It is a research area that is located at the interface of computer science and economics. The central question studied in computational social choice is that of how best to aggregate the individual points of view of several agents, so as to arrive at a reasonable compromise. Examples include tallying the votes cast in an election, aggregating the professional opinions of several experts, and finding a fair manner of dividing a set of resources amongst the members of a group -- Back cover. |
| computational social science online course: Handbook of Computational Social Science for Policy Eleonora Bertoni, Matteo Fontana, Lorenzo Gabrielli, Serena Signorelli, Michele Vespe, 2023-01-23 This open access handbook describes foundational issues, methodological approaches and examples on how to analyse and model data using Computational Social Science (CSS) for policy support. Up to now, CSS studies have mostly developed on a small, proof-of concept, scale that prevented from unleashing its potential to provide systematic impact to the policy cycle, as well as from improving the understanding of societal problems to the definition, assessment, evaluation, and monitoring of policies. The aim of this handbook is to fill this gap by exploring ways to analyse and model data for policy support, and to advocate the adoption of CSS solutions for policy by raising awareness of existing implementations of CSS in policy-relevant fields. To this end, the book explores applications of computational methods and approaches like big data, machine learning, statistical learning, sentiment analysis, text mining, systems modelling, and network analysis to different problems in the social sciences. The book is structured into three Parts: the first chapters on foundational issues open with an exposition and description of key policymaking areas where CSS can provide insights and information. In detail, the chapters cover public policy, governance, data justice and other ethical issues. Part two consists of chapters on methodological aspects dealing with issues such as the modelling of complexity, natural language processing, validity and lack of data, and innovation in official statistics. Finally, Part three describes the application of computational methods, challenges and opportunities in various social science areas, including economics, sociology, demography, migration, climate change, epidemiology, geography, and disaster management. The target audience of the book spans from the scientific community engaged in CSS research to policymakers interested in evidence-informed policy interventions, but also includes private companies holding data that can be used to study social sciences and are interested in achieving a policy impact. |
| computational social science online course: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| computational social science online course: An Introductory Course in Computational Neuroscience Paul Miller, 2018-10-09 A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits. |
| computational social science online course: 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. |
| computational social science online course: Opportunities and Challenges for Computational Social Science Methods Abanoz, Enes, 2022-03-18 We are living in a digital era in which most of our daily activities take place online. This has created a big data phenomenon that has been subject to scientific research with increasingly available tools and processing power. As a result, a growing number of social science scholars are using computational methods for analyzing social behavior. To further the area, these evolving methods must be made known to sociological research scholars. Opportunities and Challenges for Computational Social Science Methods focuses on the implementation of social science methods and the opportunities and challenges of these methods. This book sheds light on the infrastructure that should be built to gain required skillsets, the tools used in computational social sciences, and the methods developed and applied into computational social sciences. Covering topics like computational communication, ecological cognition, and natural language processing, this book is an essential resource for researchers, data scientists, scholars, students, professors, sociologists, and academicians. |
| computational social science online course: Comparative Textual Media N. Katherine Hayles, Jessica Pressman, 2013-12-01 For the past few hundred years, Western cultures have relied on print. When writing was accomplished by a quill pen, inkpot, and paper, it was easy to imagine that writing was nothing more than a means by which writers could transfer their thoughts to readers. The proliferation of technical media in the latter half of the twentieth century has revealed that the relationship between writer and reader is not so simple. From telegraphs and typewriters to wire recorders and a sweeping array of digital computing devices, the complexities of communications technology have made mediality a central concern of the twenty-first century. Despite the attention given to the development of the media landscape, relatively little is being done in our academic institutions to adjust. In Comparative Textual Media, editors N. Katherine Hayles and Jessica Pressman bring together an impressive range of essays from leading scholars to address the issue, among them Matthew Kirschenbaum on archiving in the digital era, Patricia Crain on the connection between a child’s formation of self and the possession of a book, and Mark Marino exploring how to read a digital text not for content but for traces of its underlying code. Primarily arguing for seeing print as a medium along with the scroll, electronic literature, and computer games, this volume examines the potential transformations if academic departments embraced a media framework. Ultimately, Comparative Textual Media offers new insights that allow us to understand more deeply the implications of the choices we, and our institutions, are making. Contributors: Stephanie Boluk, Vassar College; Jessica Brantley, Yale U; Patricia Crain, NYU; Adriana de Souza e Silva, North Carolina State U; Johanna Drucker, UCLA; Thomas Fulton, Rutgers U; Lisa Gitelman, New York U; William A. Johnson, Duke U; Matthew G. Kirschenbaum, U of Maryland; Patrick LeMieux; Mark C. Marino, U of Southern California; Rita Raley, U of California, Santa Barbara; John David Zuern, U of Hawai‘i at Mānoa. |
| computational social science online course: The Behavioral and Social Sciences National Research Council, Division of Behavioral and Social Sciences and Education, Commission on Behavioral and Social Sciences and Education, Committee on Basic Research in the Behavioral and Social Sciences, 1988-02-01 This volume explores the scientific frontiers and leading edges of research across the fields of anthropology, economics, political science, psychology, sociology, history, business, education, geography, law, and psychiatry, as well as the newer, more specialized areas of artificial intelligence, child development, cognitive science, communications, demography, linguistics, and management and decision science. It includes recommendations concerning new resources, facilities, and programs that may be needed over the next several years to ensure rapid progress and provide a high level of returns to basic research. |
| computational social science online course: Computational Social Science and Complex Systems J. Kertész, R.N. Mantegna, S. Miccichè, 2019-11-20 For many years, the development of large-scale quantitative social science was hindered by a lack of data. Traditional methods of data collection like surveys were very useful, but were limited. The situation has of course changed with the development of computing and information communication technology, and we now live in a world of data deluge, where the question has become how to extract important information from the plethora of data that can be accessed. Big Data has made it possible to study societal questions which were once impossible to deal with, but new tools and new multidisciplinary approaches are required. Physicists, together with economists, sociologists, computer scientists, etc. have played an important role in their development. This book presents the 9 lectures delivered at the CCIII Summer Course Computational Social Science and Complex Systems, held as part of the International School of Physics Enrico Fermi in Varenna, Italy, from 16-21 July 2018. The course had the aim of presenting some of the recent developments in the interdisciplinary fields of computational social science and econophysics to PhD students and young researchers, with lectures focused on recent problems investigated in computational social science. Addressing some of the basic questions and many of the subtleties of the emerging field of computational social science, the book will be of interest to students, researchers and advanced research professionals alike. |
| computational social science online course: Handbook of Computational Social Science, Volume 1 Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, Lars E Lyberg, 2021-11-10 The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors. |
COMPUTATIONAL definition | Cambridge English Dictionary
COMPUTATIONAL meaning: 1. involving the calculation of answers, amounts, results, etc.: 2. using computers to study…. Learn more.
COMPUTATIONAL Definition & Meaning - Merriam-Webster
The meaning of COMPUTATION is the act or action of computing : calculation. How to use computation in a sentence.
Computation - Wikipedia
Mechanical or electronic devices (or, historically, people) that perform computations are known as computers. Computer science is an academic field that involves the study of computation.
Computational science - Wikipedia
Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science, and more specifically the Computer Sciences, …
Computational - Definition, Meaning & Synonyms
Computational is an adjective referring to a system of calculating or "computing," or, more commonly today, work involving computers. Tasks with a lot of computational steps are best …
COMPUTATIONAL definition in American English - Collins Online …
Computational means using computers. Students may pursue research in any aspect of computational linguistics. Collins COBUILD Advanced Learner’s Dictionary. Copyright © …
Computational - definition of computational by ... - The Free …
Define computational. computational synonyms, computational pronunciation, computational translation, English dictionary definition of computational. n. 1. a. The act or process of …
COMPUTATIONAL - Definition & Translations | Collins English …
Discover everything about the word "COMPUTATIONAL" in English: meanings, translations, synonyms, pronunciations, examples, and grammar insights - all in one comprehensive guide.
What is computational thinking? - Introduction to computational …
Learn about the four cornerstones of computational thinking including decomposition, pattern recognition, abstraction and algorithms.
Computational Definition & Meaning - YourDictionary
Computational definition: Of or relating to
computation.
COMPUTATIONAL definition | Cambridge English Dictionary
COMPUTATIONAL meaning: 1. involving the calculation of answers, amounts, results, etc.: 2. using computers to study…. Learn more.
COMPUTATIONAL Definition & Meaning - Merriam-Webster
The meaning of COMPUTATION is the act or action of computing : calculation. How to use computation in a sentence.
Computation - Wikipedia
Mechanical or electronic devices (or, historically, people) that perform computations are known as computers. Computer science is an academic field that involves the study of computation.
Computational science - Wikipedia
Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science, and more specifically the Computer Sciences, …
Computational - Definition, Meaning & Synonyms
Computational is an adjective referring to a system of calculating or "computing," or, more commonly today, work involving computers. Tasks with a lot of computational steps are best …
COMPUTATIONAL definition in American English - Collins Online …
Computational means using computers. Students may pursue research in any aspect of computational linguistics. Collins COBUILD Advanced Learner’s Dictionary. Copyright © …
Computational - definition of computational by ... - The Free …
Define computational. computational synonyms, computational pronunciation, computational translation, English dictionary definition of computational. n. 1. a. The act or process of …
COMPUTATIONAL - Definition & Translations | Collins English …
Discover everything about the word "COMPUTATIONAL" in English: meanings, translations, synonyms, pronunciations, examples, and grammar insights - all in one comprehensive guide.
What is computational thinking? - Introduction to computational …
Learn about the four cornerstones of computational thinking including decomposition, pattern recognition, abstraction and algorithms.
Computational Definition & Meaning - YourDictionary
Computational definition: Of or relating to
computation.
