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| california state university data science: Data Science Careers, Training, and Hiring Renata Rawlings-Goss, 2019-08-02 This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build. |
| california state university data science: Analytics and Data Science Amit V. Deokar, Ashish Gupta, Lakshmi S. Iyer, Mary C. Jones, 2017-10-05 This book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data. The chapters examine the role of analytics and data science to create, spread, develop and utilize analytics applications for practice. Selected chapters provide a good balance between discussing research advances and pedagogical tools in key topic areas in analytics and data science in a systematic manner. This book also focuses on several business applications of these emerging technologies in decision making, i.e., business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading academics and practitioners that participated at the Business Analytics Congress 2015. Applications of analytics and data science technologies in various domains are still evolving. For instance, the explosive growth in big data and social media analytics requires examination of the impact of these technologies and applications on business and society. As organizations in various sectors formulate their IT strategies and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements in organizational information processing and decision making. Recent advances in computational capacities coupled by improvements in areas such as data warehousing, big data, analytics, semantics, predictive and descriptive analytics, visualization, and real-time analytics have particularly strong implications on the growth of analytics and data science. |
| california state university data science: Data Science Pallavi Vijay Chavan, Parikshit N Mahalle, Ramchandra Mangrulkar, Idongesit Williams, 2022-08-15 This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science. Key Features • Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science. • Presents predictive outcomes by applying data science techniques to real-life applications. • Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. • Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields. The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful. |
| california state university data science: Data Science Analytics and Applications Shriram R, Mak Sharma, 2018-02-23 This book constitutes the refereed proceedings of the First International Conference on Data Science Analytics and Applications, DaSAA 2017, held in Chennai, India, in January 2017. The 16 revised full papers and 4 revised short papers presented were carefully reviewed and selected from 77 submissions. The papers address issues such as data analytics, data mining, cloud computing, machine learning, text classification and analysis, information retrieval, DSS, security, image and video processing. |
| california state university data science: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| california state university data science: Data Science Thinking Longbing Cao, 2018-08-17 This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. |
| california state university data science: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| california state university data science: Intelligent Systems and Data Science Nguyen Thai-Nghe, |
| california state university data science: The Teaching Gap James W. Stigler, James Hiebert, 2009-06-16 A revised edition of a popular resource builds on the authors' findings that key problems in teaching methods are causing America to lag behind international academic standards, outlining a program for administrators, instructors, and parents that incorporates solutions based on current research. Reprint. |
| california state university data science: Advances in Data Science Ilke Demir, Yifei Lou, Xu Wang, Kathrin Welker, 2021-12-03 This volume highlights recent advances in data science, including image processing and enhancement on large data, shape analysis and geometry processing in 2D/3D, exploration and understanding of neural networks, and extensions to atypical data types such as social and biological signals. The contributions are based on discussions from two workshops under Association for Women in Mathematics (AWM), namely the second Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place between July 29 and August 2, 2019 at the Institute for Computational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, and the third Women in Shape (WiSh) Research Collaboration Workshop that took place between July 16 and 20, 2018 at Trier University in Robert-Schuman-Haus, Trier, Germany. These submissions, seeded by working groups at the conference, form a valuable source for readers who are interested in ideas and methods developed in interdisciplinary research fields. The book features ideas, methods, and tools developed through a broad range of domains, ranging from theoretical analysis on graph neural networks to applications in health science. It also presents original results tackling real-world problems that often involve complex data analysis on large multi-modal data sources. |
| california state university data science: Next Generation Data Science Henry Han, Erich Baker, 2024 Zusammenfassung: This book constitutes the refereed proceedings of the Sescond Southwest Data Science Conference, SDSC 2023, held in Waco, TX, USa, during March 24-25, 2023. The 16 full and 1 short paper included in this book were carefully reviewed and selected from 72 submissions. They were oragnized in topical sections named: Business social and foundation data science; and applied data science, artifiicial intelligence and data engineering. |
| california state university data science: Information Services Today Sandra Hirsh, 2015-03-19 This essential overview of what it means to be a library and information professional today provides a broad overview of the transformation of libraries as information organizations, why these organizations are more important today than ever before, the technological influence on how we provide information resources and services in today’s digital and global environment, and the various career opportunities available for information professionals. The book begins with a historical overview of libraries and their transformation as information and technology hubs within their communities. It also covers the various specializations within the field emphasizing the exciting yet complex roles and opportunities for information professionals. With that foundation in place, it presents how libraries serve different kinds of communities, highlighting the unique needs of users across all ages and how libraries fulfill those needs through a variety of services, and addresses key issues facing information organizations as they meet user needs in the Digital Age. The book then concludes with career management strategies to guide library and information science professionals in building not only vibrant careers but vibrant information organizations for the future as well. |
| california state university data science: 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 |
| california state university data science: 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. |
| california state university data science: Going Alt-Ac Kevin Kelly, Kathryn E. Linder, Thomas J. Tobin, 2023-07-03 A growing number of people completing or holding graduate degrees now seek non-faculty positions—also called alternative academic, or “alt-ac” positions—at different stages in their careers. While an increasing number of people with doctoral degrees are hunting for a diminishing pool of tenure-track faculty jobs, most degree-granting institutions do not adequately prepare their graduate students to enter the new reality of the alt-ac job market. Yet the administrative ranks in higher education institutions are growing, as colleges and universities are creating a diverse range of positions that support teaching and learning efforts.Focusing on the range of potential alternative career choices, this highly practical book offers tools and prompts for readers who are:·Considering whether to choose an alt-ac career path·Seeking specific alt-ac positions·Advising graduate students or mentoring recent professional graduates·Encountering alt-ac career challengesThe authors offer case stories—their own and those of colleagues across North America in alt-ac roles—with concrete examples designed to help readers pursue, obtain, and excel in a wide variety of alt-ac positions. The book can equally be used as a resource for graduate courses on professional development and job-market preparation. |
| california state university data science: Recent Advances in Data Science Henry Han, Tie Wei, Wenbin Liu, Fei Han, 2020-09-28 This book constitutes selected papers of the Third International Conference on Data Science, Medicine and Bioinformatics, IDMB 2019, held in Nanning, China, in June 2019. The 19 full papers and 1 short paper were carefully reviewed and selected from 93 submissions. The papers are organized according to the following topical sections: business data science: fintech, management, and analytics.- health and biological data science.- novel data science theory and applications. |
| california state university data science: Mathematics for Social Justice Catherine A. Buell, Bonnie Shulman, 2021-11-18 Mathematics instructors are always looking for ways to engage students in meaningful and authentic tasks that utilize mathematics. At the same time, it is crucial for a democratic society to have a citizenry who can critically discriminate between “fake” and reliable news reports involving numeracy and apply numerical literacy to local and global issues. This book contains examples of topics linking math and social justice and addresses both goals. There is a broad range of mathematics used, including statistical methods, modeling, calculus, and basic algebra. The range of social issues is also diverse, including racial injustice, mass incarceration, income inequality, and environmental justice. There are lesson plans appropriate in many contexts: service-learning courses, quantitative literacy/reasoning courses, introductory courses, and classes for math majors. What makes this book unique and timely is that the most previous curricula linking math and social justice have been treated from a humanist perspective. This book is written by mathematicians, for mathematics students. Admittedly, it can be intimidating for instructors trained in quantitative methods to venture into the arena of social dilemmas. This volume provides encouragement, support, and a treasure trove of ideas to get you started. The chapters in this book were originally published as a special issue of the journal, PRIMUS: Problems, Resources, and Issues in Mathematics Undergraduate Studies. |
| california state university data science: Intelligent Data Analytics for Power and Energy Systems Hasmat Malik, Md. Waseem Ahmad, D.P. Kothari, 2022-02-17 This book brings together state-of-the-art advances in intelligent data analytics as driver of the future evolution of PaE systems. In the modern power and energy (PaE) domain, the increasing penetration of renewable energy sources (RES) and the consequent empowerment of consumers as a central and active solution to deal with the generation and development variability are driving the PaE system towards a historic paradigm shift. The small-scale, diversity, and especially the number of new players involved in the PaE system potentiate a significant growth of generated data. Moreover, advances in communication (between IoT devices and M2M: machine to machine, man to machine, etc.) and digitalization hugely increased the volume of data that results from PaE components, installations, and systems operation. This data is becoming more and more important for PaE systems operation, maintenance, planning, and scheduling with relevant impact on all involved entities, from producers, consumer,s and aggregators to market and system operators. However, although the PaE community is fully aware of the intrinsic value of those data, the methods to deal with it still necessitate substantial enhancements, development and research. Intelligent data analytics is thereby playing a fundamental role in this domain, by enabling stakeholders to expand their decision-making method and achieve the awareness on the PaE environment. The editors also included demonstrated codes for presented problems for better understanding for beginners. |
| california state university data science: Data and Decision Sciences - Recent Advances and Applications , 2023-10-25 This book provides an overview of Data and Decision Sciences (DDS) and recent advances and applications in space-based systems and business, medical, and agriculture processes, decision optimization modeling, and cognitive decision-making. Written by experts, this volume is organized into four sections and seven chapters. It is a valuable resource for educators, engineers, scientists, and researchers in the field of DDS. |
| california state university data science: Dose Finding and Beyond in Biopharmaceutical Development Jingjing Ye, |
| california state university data science: Equity and Justice in Developmental Science: Theoretical and Methodological Issues , 2016-02-27 The first of two volumes in the Advances in Child Development and Behavior series, Equity and Justice in Developmental Science: Theoretical and Methodological Issues focuses on conceptual issues, definitions, and critical concepts relevant to equity and justice for the developmental sciences. This volume covers critical methodological issues that serve to either challenge or advance our understanding of, and ability to promote, equity and justice in the developmental sciences. Both volumes bring together a growing body of developmental scholarship that addresses how issues relevant to equity and justice (or their opposites) affect development and developmental outcomes, as well as scholarship focused on mitigating the developmental consequences of inequity, inequality, and injustice for young people, families, and communities and ensuring that all young people have opportunities to develop and thrive. - Contains contributions from leading authorities in the field of child development and behavior - Presents a coherent picture of the importance of the development of children's participation in ongoing activity - Provides a major step forward in highlighting patterns and variability in the normative development of the everyday lives of children, expanding beyond the usual research populations that have extensive Western schooling in common - Focuses on conceptual issues, definitions, and critical concepts relevant to equity and justice for the developmental sciences |
| california state university data science: Data Literacy in Academic Libraries Julia Bauder, 2021-07-21 We live in a data-driven world, much of it processed and served up by increasingly complex algorithms, and evaluating its quality requires its own skillset. As a component of information literacy, it's crucial that students learn how to think critically about statistics, data, and related visualizations. Here, Bauder and her fellow contributors show how librarians are helping students to access, interpret, critically assess, manage, handle, and ethically use data. Offering readers a roadmap for effectively teaching data literacy at the undergraduate level, this volume explores such topics as the potential for large-scale library/faculty partnerships to incorporate data literacy instruction across the undergraduate curriculum; how the principles of the ACRL Framework for Information Literacy for Higher Education can help to situate data literacy within a broader information literacy context; a report on the expectations of classroom faculty concerning their students’ data literacy skills; various ways that librarians can partner with faculty; case studies of two initiatives spearheaded by Purdue University Libraries and University of Houston Libraries that support faculty as they integrate more work with data into their courses; Barnard College’s Empirical Reasoning Center, which provides workshops and walk-in consultations to more than a thousand students annually; how a one-shot session using the PolicyMap data mapping tool can be used to teach students from many different disciplines; diving into quantitative data to determine the truth or falsity of potential “fake news” claims; and a for-credit, librarian-taught course on information dissemination and the ethical use of information. |
| california state university data science: Granular, Fuzzy, and Soft Computing Tsau-Young Lin, Churn-Jung Liau, Janusz Kacprzyk, 2023-03-29 The first edition of the Encyclopedia of Complexity and Systems Science (ECSS, 2009) presented a comprehensive overview of granular computing (GrC) broadly divided into several categories: Granular computing from rough set theory, Granular Computing in Database Theory, Granular Computing in Social Networks, Granular Computing and Fuzzy Set Theory, Grid/Cloud Computing, as well as general issues in granular computing. In 2011, the formal theory of GrC was established, providing an adequate infrastructure to support revolutionary new approaches to computer/data science, including the challenges presented by so-called big data. For this volume of ECSS, Second Edition, many entries have been updated to capture these new developments, together with new chapters on such topics as data clustering, outliers in data mining, qualitative fuzzy sets, and information flow analysis for security applications. Granulations can be seen as a natural and ancient methodology deeply rooted in the human mind. Many daily things are routinely granulated into sub things: The topography of earth is granulated into hills, plateaus, etc., space and time are granulated into infinitesimal granules, and a circle is granulated into polygons of infinitesimal sides. Such granules led to the invention of calculus, topology and non-standard analysis. Formalization of general granulation was difficult but, as shown in this volume, great progress has been made in combing discrete and continuous mathematics under one roof for a broad range of applications in data science. |
| california state university data science: Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications; Volume 1 Amit Kumar, |
| california state university data science: Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Applied and Theoretical Statistics, Committee on Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions, 2017-02-06 The Office of the Under Secretary of Defense (Personnel & Readiness), referred to throughout this report as P&R, is responsible for the total force management of all Department of Defense (DoD) components including the recruitment, readiness, and retention of personnel. Its work and policies are supported by a number of organizations both within DoD, including the Defense Manpower Data Center (DMDC), and externally, including the federally funded research and development centers (FFRDCs) that work for DoD. P&R must be able to answer questions for the Secretary of Defense such as how to recruit people with an aptitude for and interest in various specialties and along particular career tracks and how to assess on an ongoing basis service members' career satisfaction and their ability to meet new challenges. P&R must also address larger-scale questions, such as how the current realignment of forces to the Asia-Pacific area and other regions will affect recruitment, readiness, and retention. While DoD makes use of large-scale data and mathematical analysis in intelligence, surveillance, reconnaissance, and elsewhereâ€exploiting techniques such as complex network analysis, machine learning, streaming social media analysis, and anomaly detectionâ€these skills and capabilities have not been applied as well to the personnel and readiness enterprise. Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions offers and roadmap and implementation plan for the integration of data analysis in support of decisions within the purview of P&R. |
| california state university data science: Research in Data Science Ellen Gasparovic, Carlotta Domeniconi, 2019-03-25 This edited volume on data science features a variety of research ranging from theoretical to applied and computational topics. Aiming to establish the important connection between mathematics and data science, this book addresses cutting edge problems in predictive modeling, multi-scale representation and feature selection, statistical and topological learning, and related areas. Contributions study topics such as the hubness phenomenon in high-dimensional spaces, the use of a heuristic framework for testing the multi-manifold hypothesis for high-dimensional data, the investigation of interdisciplinary approaches to multi-dimensional obstructive sleep apnea patient data, and the inference of a dyadic measure and its simplicial geometry from binary feature data. Based on the first Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place in 2017 at the Institute for Compuational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, this volume features submissions from several of the working groups as well as contributions from the wider community. The volume is suitable for researchers in data science in industry and academia. |
| california state university data science: Progresses in Artificial Intelligence and Neural Systems Anna Esposito, Marcos Faundez-Zanuy, Francesco Carlo Morabito, Eros Pasero, 2020-07-09 This book provides an overview of the current advances in artificial intelligence and neural nets. Artificial intelligence (AI) methods have shown great capabilities in modelling, prediction and recognition tasks supporting human–machine interaction. At the same time, the issue of emotion has gained increasing attention due to its relevance in achieving human-like interaction with machines. The real challenge is taking advantage of the emotional characterization of humans’ interactions to make computers interfacing with them emotionally and socially credible. The book assesses how and to what extent current sophisticated computational intelligence tools might support the multidisciplinary research on the characterization of appropriate system reactions to human emotions and expressions in interactive scenarios. Discussing the latest recent research trends, innovative approaches and future challenges in AI from interdisciplinary perspectives, it is a valuable resource for researchers and practitioners in academia and industry. |
| california state university data science: 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. |
| california state university data science: Python for DevOps Noah Gift, Kennedy Behrman, Alfredo Deza, Grig Gheorghiu, 2019-12-12 Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform. Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to get stuff done in Python? This is your guide. Python foundations, including a brief introduction to the language How to automate text, write command-line tools, and automate the filesystem Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing Cloud computing, infrastructure as code, Kubernetes, and serverless Machine learning operations and data engineering from a DevOps perspective Building, deploying, and operationalizing a machine learning project |
| california state university data science: 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. |
| california state university data science: Advances in Data Science and Information Engineering Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, Cheng-Ying Yang, Hamid R. Arabnia, Leonidas Deligiannidis, 2021-10-29 The book presents the proceedings of two conferences: the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020), which took place in Las Vegas, NV, USA, July 27-30, 2020. The conferences are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Papers cover all aspects of Data Science, Data Mining, Machine Learning, Artificial and Computational Intelligence (ICDATA) and Information Retrieval Systems, Information & Knowledge Engineering, Management and Cyber-Learning (IKE). Authors include academics, researchers, professionals, and students. Presents the proceedings of the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020); Includes papers on topics from data mining to machine learning to informational retrieval systems; Authors include academics, researchers, professionals and students. |
| california state university data science: Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23) Kevin Daimi, Abeer Al Sadoon, 2023-06-16 The Second International Conference on Innovations in Computing Research (ICR’23) brings together a diverse group of researchers from all over the world with the intent of fostering collaboration and dissemination of the innovations in computing technologies. The conference is aptly segmented into six tracks: Data Science, Computer and Network Security, Health Informatics and Medical Imaging, Computer Science and Computer Engineering Education, Internet of Things, and Smart Cities/Smart Energy. These tracks aim to promote a birds-of-the-same-feather congregation and maximize participation. The Data Science track covers a wide range of topics including complexity score for missing data, deep learning and fake news, cyberbullying and hate speech, surface area estimation, analysis of gambling data, car accidents predication model, augmenting character designers’ creativity, deep learning for road safety, effect of sleep disturbances on the quality of sleep, deep learning-based path-planning, vehicle data collection and analysis, predicting future stocks prices, and trading robot for foreign exchange. Computer and Network Security track is dedicated to various areas of cybersecurity. Among these are decentralized solution for secure management of IoT access rights, multi-factor authentication as a service (MFAaaS) for federated cloud environments, user attitude toward personal data privacy and data privacy economy, host IP obfuscation and performance analysis, and vehicle OBD-II port countermeasures. The Computer Science and Engineering Education track enfolds various educational areas, such as data management in industry–academia joint research: a perspective of conflicts and coordination in Japan, security culture and security education, training and awareness (SETA), influencing information security management, engaging undergraduate students in developing graphical user interfaces for NSF funded research project, and emotional intelligence of computer science teachers in higher education. On the Internet of Things (IoT) track, the focus is on industrial air quality sensor visual analytics, social spider optimization meta-heuristic for node localization optimization in wireless sensor networks, and privacy aware IoT-based fall detection with infrared sensors and deep learning. The Smart Cities and Smart Energy track spans various areas, which include, among others, research topics on heterogeneous transfer learning in structural health monitoring for high-rise structures and energy routing in energy Internet using the firefly algorithm. |
| california state university data science: Learning Gap Harold Stevenson, James W. Stigler, 1994-01-26 Compares United States elementary education practices with those in Asia and comes to some surprising conclusions. |
| california state university data science: Introduction to Biomedical Data Science Robert Hoyt, Robert Muenchen, 2019-11-24 Overview of biomedical data science -- Spreadsheet tools and tips -- Biostatistics primer -- Data visualization -- Introduction to databases -- Big data -- Bioinformatics and precision medicine -- Programming languages for data analysis -- Machine learning -- Artificial intelligence -- Biomedical data science resources -- Appendix A: Glossary -- Appendix B: Using data.world -- Appendix C: Chapter exercises. |
| california state university data science: Improving Equity in Data Science Colby Tofel-Grehl, Emmanuel Schanzer, 2024-06-03 Improving Equity in Data Science offers a comprehensive look at the ways in which data science can be conceptualized and engaged more equitably within the K-16 classroom setting, moving beyond merely broadening participation in educational opportunities. This book makes the case for field wide definitions, literacies and practices for data science teaching and learning that can be commonly discussed and used, and provides examples from research of these practices and literacies in action. Authors share stories and examples of research wherein data science advances equity and empowerment through the critical examination of social, educational, and political topics. In the first half of the book, readers will learn how data science can deliberately be embedded within K-12 spaces to empower students to use it to identify and address inequity. The latter half will focus on equity of access to data science learning opportunities in higher education, with a final synthesis of lessons learned and presentation of a 360-degree framework that links access, curriculum, and pedagogy as multiple facets collectively essential to comprehensive data science equity work. Practitioners and teacher educators will be able to answer the question, “how can data science serve to move equity efforts in computing beyond basic inclusion to empowerment?” whether the goal is to simply improve definitions and approaches to research on data science or support teachers of data science in creating more equitable and inclusive environments within their classrooms. |
| california state university data science: Recent Advances in Next-Generation Data Science Henry Han (Computer scientist), 2024 This book constitutes the refereed proceedings of the Third Southwest Data Science Conference, on Recent advances in next-generation data science, SDSC 2024, held in Waco, TX, USA, in March 22, 2024. The 15 full papers presented were carefully reviewed and selected from 59 submissions. These papers focus on AI security in next-generation data science and address a range of challenges, from protecting sensitive data to mitigating adversarial threats. |
| california state university data science: Biometrics in a Data Driven World Sinjini Mitra, Mikhail Gofman, 2016-12-01 Biometrics in a Data Driven World: Trends, Technologies, and Challenges aims to inform readers about the modern applications of biometrics in the context of a data-driven society, to familiarize them with the rich history of biometrics, and to provide them with a glimpse into the future of biometrics. The first section of the book discusses the fundamentals of biometrics and provides an overview of common biometric modalities, namely face, fingerprints, iris, and voice. It also discusses the history of the field, and provides an overview of emerging trends and opportunities. The second section of the book introduces readers to a wide range of biometric applications. The next part of the book is dedicated to the discussion of case studies of biometric modalities currently used on mobile applications. As smartphones and tablet computers are rapidly becoming the dominant consumer computer platforms, biometrics-based authentication is emerging as an integral part of protecting mobile devices against unauthorized access, while enabling new and highly popular applications, such as secure online payment authorization. The book concludes with a discussion of future trends and opportunities in the field of biometrics, which will pave the way for advancing research in the area of biometrics, and for the deployment of biometric technologies in real-world applications. The book is designed for individuals interested in exploring the contemporary applications of biometrics, from students to researchers and practitioners working in this field. Both undergraduate and graduate students enrolled in college-level security courses will also find this book to be an especially useful companion. |
| california state university data science: Methodological and Technological Advances in Death Investigations Ann H Ross, Jason H Byrd, 2023-12-08 Methodological and Technological Advances in Death Investigations: Application and Case Studies focuses on advancements in both methods and technology in death investigations. Specifically, in the areas of latent fingerprints, facial recognition, wildlife forensics, using aerial vehicles and 3D-ID. The combination of national and international authors and a discussion of the state of forensic science over a decade after the National Academies 2009 Report, Strengthening Forensic Science in the United States: A Path Forward, further highlights the boundaries, limitations and context in which these newer technologies and applications act synergistically to enhance forensic science. - Synthesizes new and emerging technologies to put them in perspective for researchers and practitioners, such as facial recognition, using aerial vehicles and 3D-ID - Includes case studies throughout that explain how certain advanced technologies impact investigations - Fills a gap in literature with more cross-disciplinary topics that pertain to death investigations |
| california state university data science: The Recent Advances in Transdisciplinary Data Science Henry Han, Erich Baker, 2023-01-28 This book constitutes the refereed proceedings of the First Southwest Data Science Conference, on The Recent Advances in Transdisciplinary Data Science, SDSC 2022, held in Waco, TX, USA, during March 25–26, 2022. The 14 full papers and 2 short papers included in this book were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Business and social data science; Health and biological data science; Applied data science, artificial intelligence, and data engineering. |
| california state university data science: ACM ... Administrative Directory of College and University Computer Science/data Processing Programs and Computer Facilities , 1988 |
California - Wikipedia
California (/ ˌ k æ l ɪ ˈ f ɔːr n j ə /) is a state in the Western United States that lies on the Pacific Coast. It borders Oregon to the north, Nevada and Arizona to the east, and shares an …
California State Portal | CA.gov
CA.gov is the official website for the State of California. You can find and access California services, resources, and more.
California | Flag, Facts, Maps, Capital, Cities, & Destinations ...
2 days ago · California, constituent state of the United States of America. It was admitted as the 31st state of the union on September 9, 1850, and by the early 1960s it was the most populous …
Visit California - Official Travel & Tourism Website
Find things to do, places to visit, and experiences to explore at Visit California, the Golden State’s official tourism site. Learn about national parks, hotels, restaurants, beaches, mountains, cities, …
California Maps & Facts - World Atlas
May 16, 2024 · California, nicknamed the Golden State, sits on the United States Western coast. It borders the states of Arizona , Nevada , and Oregon . Additionally, it extends southward to …
Protests live updates: Marines make 1st temporary detention in LA
Jun 8, 2025 · Tensions are escalating between President Donald Trump and California Gov. Gavin Newsom as protests against Immigration and Customs Enforcement continue to grip Los Angeles …
Best Places to Visit in California for 2025 - U.S. News Travel
Apr 22, 2025 · Embark on an adventure with our guide to California's best places to visit. Experience stunning national parks, vibrant cities and serene beaches.
California | State Facts & History - Infoplease
Nov 30, 2023 · California, often referred to as the "Golden State," is known for a variety of attractions and landmarks. This includes its stunning Pacific coastline, the glamorous Hollywood …
California State Information - Symbols, Capital, Constitution, Flags ...
Blank Outline Maps: Find printable blank map of the State of California, without names, so you can quiz yourself on important locations, abbreviations, or state capital. City Guide: Visit California …
California - Simple English Wikipedia, the free encyclopedia
California, officially the State of California, is a state in the western part of the United States, along the Pacific Ocean. California is known for its Mexican food , Mexican culture, Cholo subculture …
California - Wikipedia
California (/ ˌ k æ l ɪ ˈ f ɔːr n j ə /) is a state in the Western United States that lies on the Pacific Coast. It borders Oregon to the north, Nevada and Arizona to the east, and shares an …
California State Portal | CA.gov
CA.gov is the official website for the State of California. You can find and access California services, resources, and more.
California | Flag, Facts, Maps, Capital, Cities, & Destinations ...
2 days ago · California, constituent state of the United States of America. It was admitted as the 31st state of the union on September 9, 1850, and by the early 1960s it was the most populous …
Visit California - Official Travel & Tourism Website
Find things to do, places to visit, and experiences to explore at Visit California, the Golden State’s official tourism site. Learn about national parks, hotels, restaurants, beaches, mountains, cities, …
California Maps & Facts - World Atlas
May 16, 2024 · California, nicknamed the Golden State, sits on the United States Western coast. It borders the states of Arizona , Nevada , and Oregon . Additionally, it extends southward to …
Protests live updates: Marines make 1st temporary detention in LA
Jun 8, 2025 · Tensions are escalating between President Donald Trump and California Gov. Gavin Newsom as protests against Immigration and Customs Enforcement continue to grip Los Angeles …
Best Places to Visit in California for 2025 - U.S. News Travel
Apr 22, 2025 · Embark on an adventure with our guide to California's best places to visit. Experience stunning national parks, vibrant cities and serene beaches.
California | State Facts & History - Infoplease
Nov 30, 2023 · California, often referred to as the "Golden State," is known for a variety of attractions and landmarks. This includes its stunning Pacific coastline, the glamorous Hollywood …
California State Information - Symbols, Capital, Constitution, Flags ...
Blank Outline Maps: Find printable blank map of the State of California, without names, so you can quiz yourself on important locations, abbreviations, or state capital. City Guide: Visit California …
California - Simple English Wikipedia, the free encyclopedia
California, officially the State of California, is a state in the western part of the United States, along the Pacific Ocean. California is known for its Mexican food , Mexican culture, Cholo subculture …
