International Journal of Data Science and AnalyticsISSN: 2364-415X | eISSN: 2364-4168
The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations.
Computational Statistics & Data AnalysisISSN: 0167-9473
Computational Statistics & Data Analysis is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: Computational Statistics, Statistical Methodology for Data Analysis, Statistical methodology, pecial Applications, and Annals of Statistical Data Science.
International Journal of Data Analysis Techniques and StrategiesISSN: 1755-8050 | eISSN: 1755-8069
Many current data analysis techniques are beyond the reach of most managers and practitioners. Obscure maths and daunting algorithms have created an impassable chasm for problem solvers and decision makers. IJDATS bridges three gaps: firstly, a gap between academic ivory tower and the real world; secondly, a gap between quantitative data analysis techniques and qualitative data analysis techniques; and finally, a gap between a specific technique and an overall strategy.
Data Science and EngineeringISSN: 2364-1185 | eISSSN: 2364-1541
Data Science and Engineering (DSE) is an international, peer-reviewed, open access journal published on behalf of the China Computer Federation (CCF), and is affiliated with CCF Technical Committee on Database (CCF TCDB). Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area. It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering
Data Science JournaleISSN: 1683-1470
The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data.
IEEE Transactions on Big DataISSN: 2332-7790
The IEEE Transactions on Big Data publishes peer reviewed articles with big data as the main focus. The articles will provide cross disciplinary innovative research ideas and applications results for big data including novel theory, algorithms and applications.
Big Data Mining and AnalyticseISSN: 2096-0654
Big Data Mining and Analytics discovers hidden patterns, correlations, insights and knowledge through mining and analyzing large amounts of data obtained from various applications. It addresses the most innovative developments, research issues and solutions in big data research and their applications.
Journal of Applied AnalysisISSN: 1869-6082
Journal of Applied Analysis is an international journal devoted to applications of mathematical analysis. Among them there are applications to economics (in particular finance and insurance), mathematical physics, mechanics and computer sciences. The journal also welcomes works showing connections between mathematical analysis and other domains of mathematics such as geometry, topology, logic and set theory.
Journal of Big DataeISSN: 2196-1115
The Journal of Big Data publishes open-access original research on data science and data analytics. Deep learning algorithms and all applications of big data are welcomed. Survey papers and case studies are also considered.
The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems
Data Mining and Knowledge DiscoveryISSN: 1384-5810 | eISSN" 1573-756X
The premier technical publication in the field, this journal publishes original technical papers in both the research and practice of data mining and knowledge discovery, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications.
Advances in Data Analysis and ClassificationISSN: 1862-5347 | eISSN: 1862-5355
The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice.
IEEE Transactions on Knowledge and Data EngineeringISSN: 1041-4347 | eISSN: 1558-2191
The scope of the IEEE Transactions on Knowledge and Data Engineering includes the knowledge and data engineering aspects of computer science, artificial intelligence, electrical engineering, computer engineering, and other appropriate fields. This Transactions provides an international and interdisciplinary forum to communicate results of new developments in knowledge and data engineering and the feasibility studies of these ideas in hardware and software
Knowledge and Information SystemsISSN: 0219-1377 | eISSN: 0219-3116
Knowledge and Information Systems (KAIS) provides an international forum for researchers and professionals to share their knowledge and report new advances on all topics related to knowledge systems and advanced information systems. This monthly peer-reviewed archival journal publishes state-of-the-art research reports on emerging topics in KAIS, reviews of important techniques in related areas, and application papers of interest to a general readership.
Defense Technical Information Center (DTIC) Public SiteThe Defense Technical Information Center (DTIC®) serves the DoD community as the largest central resource for DoD and government-funded scientific, technical, engineering, and business related information available today.
Institute of Electrical and Electronics Engineers (IEEE) Xplore Digital LibraryIncludes access to the full text of IEEE content published since 1988 with select content published since 1893 from: IEEE journals, transactions, and magazines, including early access documents; IEEE conference proceedings; IET journals; IET conference proceedings; IEEE published standards; IEEE Standards Dictionary Online.
Worldwide ScienceA global science gateway comprised of national and international scientific databases and portals, WorldWideScience.org accelerates scientific discovery and progress by providing one-stop searching of databases from around the world. Multilingual WorldWideScience.org provides real-time searching and translation of globally-dispersed multilingual scientific literature.
Websites
Data Infrastructure at NIHSupporting a highly efficient and effective biomedical research data infrastructure is critical to achieving NIH’s mission of applying knowledge gained through research to improving health.
Discover Data Science: What's the Difference Between Data Science and Computer Science?"Data science, on the other hand, is a more focused field that centers in on one thing – big data. Occasionally, data science and CS are perceived to be the same thing, most likely because data scientists do some programming; but computer scientists and data scientists have different end games. Computer scientists generate software that data scientist’s use, while data scientists apply that software to identify trends and find significance through statistics."
Federal Data Strategy Data Ethics FrameworkFederal Data Ethics Tenets help federal data users make decisions ethically and promote accountability throughout the data lifecycle—as data are acquired, processed, disseminated, used, stored and disposed. Regardless of data type or use, those working with data in the public sector should have a foundational understanding of the Data Ethics Tenets. Federal leaders should also foster a data ethics-driven culture and lead by example.
Research Data Framework (RDaF) at NISTIn the past decade, research data have become widely recognized as a critical national and global resource, and the risks of losing or mismanaging research data can have severe economic and social consequences. The proliferation of artificial intelligence approaches in all fields has created a huge demand for trustworthy research data in both the natural (e.g., chemistry) and social (e.g., economics) sciences. To address these issues, NIST initiated a new, multi-stakeholder project in fall 2019 entitled the Research Data Framework (RDaF). The RDaF will provide the stakeholder community with a structured approach to develop a customizable strategy for various roles in the research data management ecosystem.
Harvard Online Learning Course - Data Science: VisualizationAs part of our Professional Certificate Program in Data Science, this course covers the basics of data visualization and exploratory data analysis. We will use three motivating examples and ggplot2, a data visualization package for the statistical programming language R. We will start with simple datasets and then graduate to case studies about world health, economics, and infectious disease trends in the United States.
O'Reilly Learning Online Video Course: R Programming for Statistics and Data ScienceIn this course, you will learn descriptive statistics and the fundamentals of inferential statistics; soar above the average data scientist and boost the productivity of your operations; learn to work with R's most comprehensive collection of tools and create meaning-heavy data visualizations and plots
Books
An introduction to statistical learning : with applications in Python by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan TaylorAn Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment.
Call Number: *Available By Reqest
ISBN: 9783031387470
Publication Date: 2023-07-01
Hands-On Exploratory Data Analysis with Python by Suresh Kumar Mukhiya; Usman AhmedExploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization.
You'll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You'll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you'll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you'll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence.
Call Number: *Digital copy available from O'Reilly Learning (formerly Safari)
ISBN: 9781789537253
Publication Date: 2020-03-27
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International Association for Statistical ComputingThe objectives of the Association are to foster world-wide interest in effective statistical computing and to exchange technical knowledge through international contacts and meetings between statisticians, computing professionals, organizations, institutions, governments and the general public.
IEEE International Conference on Data EngineeringThe annual IEEE International Conference on Data Engineering (ICDE) is the flagship IEEE conference addressing research issues in designing, building, managing, and evaluating advanced data-intensive systems and applications. For over three decades, IEEE ICDE has been a leading forum for researchers, practitioners, developers, and users to explore cutting-edge ideas and to exchange techniques, tools, and experiences. Due to the COVID-19 pandemic and imposed travel restrictions, the 38th edition of IEEE ICDE will be transformed into a fully virtual event that will take place May 9 – 12, 2022.
Association of Computing Machinery (ACM) Special Interest Group on Management of Data (SIGMOD)The ACM Special Interest Group on Management of Data is concerned with the principles, techniques and applications of database management systems and data management technology. Our members include software developers, academic and industrial researchers, practitioners, users, and students. SIGMOD sponsors the annual SIGMOD/PODS conference, one of the most important and selective in the field.
Note: Available to Research Commons users at Atlantic, Carderock, Corona, Crane, DTRA, Indian Head, Keyport, Newport, Panama City, US Naval Observatory, and Office of Naval Research.