In the past few years, the research in the field of analytics/ big data has piled up and has led to an exponential increase in newer publications and research papers. And if your aim is to stay at par with the latest happenings and a step ahead of your peers, it becomes inevitable to go through these pieces. But how? Given academic research is not well publicized, it might still be tricky to find which are the ones essential for you? Which ones should you read to be aptly relevant for your area of interest? How to dig into the pool of those hundreds of thousands of resources? Well, many of you must have come across these doubts and we are here to offer 10 most essential academic journals one must definitely have access to if he/she is a data scientist.
Given academic research is not well publicized, it might still be tricky to find which are the ones essential for you? Which ones should you read to be aptly relevant for your area of interest? How to dig into the pool of those hundreds of thousands of resources? Well, many of you must have come across these doubts and we are here to offer 10 most essential academic journals one must definitely have access to if he/she is a data scientist.
PS: We have compiled this list based on a combination of factors including Impact factor of the journal, how relevant, active & up to date the journals are for a data scientist.
Artificial Intelligence, which commenced publication in 1970, is now the generally accepted premier international forum for the publication of results of current research in this field. The journal welcomes foundational and applied papers describing mature work involving computational accounts of aspects of intelligence.
Big Data, a highly innovative, peer-reviewed journal, provides a unique forum for world-class research exploring the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data, including data science, big data infrastructure and analytics, and pervasive computing.
The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions.
Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government.
The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic.
The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.
Computational Statistics & Data Analysis (CSDA), the official journal of the International Association of Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis.
Foundations and Trends® in Machine Learning publishes high-quality survey and tutorial monographs of the field using modern techniques to enable both instant linking to the primary research in its electronic form and affordable paper copies, finally delivering on the promise to authors of multiple channel publishing from a single source.
Each issue of Foundations and Trends ® in Machine Learning comprises a 50-100 page monograph written by research leaders in the field. Monographs that give tutorial coverage of subjects, research retrospectives as well as survey papers that offer state-of-the-art reviews fall within the scope of the journal.
IJBIDM provides a forum for state-of-the-art developments and research as well as current innovative activities in business intelligence, data analysis and mining. Intelligent data analysis provides powerful and effective tools for problem solving in a variety of business modelling tasks. IJBIDM highlights intelligent techniques used for business modelling, including all areas of data visualisation, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, data mining techniques, tools and applications, neurocomputing, evolutionary computing, fuzzy techniques, expert systems, knowledge filtering, and post-processing.
Data-driven scientific discovery is a key emerging paradigm driving research innovation and industrial development in domains such as business, social science, the Internet of Things, and cloud computing. The field encompasses the larger areas of data analytics, machine learning, and managing big data, while related new scientific challenges range from data capture, creation, storage, search, sharing, analysis, and visualization, to integration across heterogeneous, interdependent complex resources for real-time decision-making, collaboration, and value creation. The journal welcomes experimental and theoretical findings on data science and advanced analytics along with their applications to real-life situations.
Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. 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. Academic researchers and practitioners will find the Journal of Big Data to be the definitive, most comprehensive reference in the field.
Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems.
The journal features papers that describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems or methods provide solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. Research methodology papers improve how machine learning research is conducted.
All papers describe the supporting evidence in ways that can be verified or replicated by other researchers. The papers also detail the learning component clearly and discuss assumptions regarding knowledge representation and the performance task.
SIGKDD’s mission to provide the premier forum for advancement, education, and adoption of the “science” of knowledge discovery and data mining from all types of data stored in computers and networks of computers.