International Journal of Data Science
About International Journal of Data Science
Guidelines
Guidelines for data science practitioners are different and more lax than academics: Length between four (4) and eighteen (18) single spaced pages in Word format.
Paper Submission System
Follow the instructions for submitting papers / articles provided by Inderscience.
Need help? Email: submissions@inderscience.com, describing your problem in as much detail as possible. Don't forget to mention the International Journal of Data Science.
Essential Information
Publishing in the IJDS
Submitted articles should not have been previously published or are currently under consideration for publication elsewhere.
Conference papers may only be submitted if the paper has been completely re-written (taken to mean more than 50%) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
All our articles are refereed through a double-blind process.
All authors must declare they have read and agreed to the content of the submitted article.
About IJDS
The IJDS publishes both academic research and practitioner papers (descriptive or predictive, and/or prescriptive), innovative ideas, case studies, surveys/reports and book reviews. Special Issues devoted to important topics will be published.
With the Age of Big Data upon us, we risk drowning in a flood of digital data. Big data spans five dimensions (volume, variety, velocity, volatility and veracity), generally steered towards one critical destination - value. Big data has now become a critical part of the business world and daily life. Containing big information and big knowledge, big data does indeed have big value. IJDS confronts the challenges of extracting a fountain of knowledge from "mountains" of big data.
Objectives
IJDS employs an interdisciplinary approach and bridges the gap between different disciplines, including computer science, OR/MS, statistics, data mining, DSS, graphic design and human-computer interaction. The process of knowledge creation therefore can include multiple components and perspectives. By adopting such a diverse set of tools/techniques while employing the synergies involved, companies and organisations can make faster (real-time), frequent and fact-based decisions.
IJDS therefore aims to provide a professional forum for examining the processes and results associated with obtaining data, as well as munging, scrubbing, exploring, modelling, interpreting, communicating and visualising data. Data science takes data in cyberspace as a research object. The goal is an integrated and interconnected process designed to form a common ground from which a knowledge-based system can be built, shared and supported by professionals from different disciplines.
Topics Covered
Applied Data Science / investing
Applied Data Science / asset allocation
Applied Data Science / trading
Applied Data Science / artificial intelligence
Applied Data Science / healthcare
Applied Data Science / bio-technology
Applied Data Science / legal
Applied Data Science / decision-making
Applied Data Science / business
Applied Data Science / marketing
Applied Data Science / human resources
Applied Data Science / government
Applied Data Science / policy making
Applied Data Science / risk assessment & mitigation
Big data cloud, mining and management
Big data storage, processing, sharing and visualisation
Big data systems, tools, theory and applications
Business analytics, intelligence and mathematics
Computer science, hacking skills
Informatics and information systems and technology
Machine learning, web-based decision making
Management science, social sciences and statistics
Mathematical optimisation and mathematics of decision sciences
Multiple source data processing and integration
Network and social-graph analysis
Optimisation, performance measurement
Security and privacy
System analysis and theory
Volume, velocity and variety of big data on cloud
Readership
Applied data scientists
Research data scientists
Artificial intelligence engineers
Artificial intelligence process integrators
Investors
Traders
Healthcare management
Healthcare practitioners
Bio-technology innovators
Lawyers
Judges
Legal management
Organization executives
Managers
Strategic planners
Leaders / decision makers
Policy makers
Government regulators
Economists
Applied statisticians
Archivists
Consultants
Data analysts
Database administrators
Educators and graduate students
Engineers
Management scientists
Operation researchers
Programmers
Online Submissions System
Guidelines
Guidelines for data science practitioners are different and more lax than academics: Length between four (4) and eighteen (18) single spaced pages in Word format.
Paper Submission System
Follow the instructions for submitting papers / articles provided by Inderscience.
Need help? Email: submissions@inderscience.com, describing your problem in as much detail as possible. Don't forget to mention the International Journal of Data Science.
Essential Information
Publishing in the IJDS
Submitted articles should not have been previously published or are currently under consideration for publication elsewhere.
Conference papers may only be submitted if the paper has been completely re-written (taken to mean more than 50%) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
All our articles are refereed through a double-blind process.
All authors must declare they have read and agreed to the content of the submitted article.
About IJDS
The IJDS publishes both academic research and practitioner papers (descriptive or predictive, and/or prescriptive), innovative ideas, case studies, surveys/reports and book reviews. Special Issues devoted to important topics will be published.
With the Age of Big Data upon us, we risk drowning in a flood of digital data. Big data spans five dimensions (volume, variety, velocity, volatility and veracity), generally steered towards one critical destination - value. Big data has now become a critical part of the business world and daily life. Containing big information and big knowledge, big data does indeed have big value. IJDS confronts the challenges of extracting a fountain of knowledge from "mountains" of big data.
Objectives
IJDS employs an interdisciplinary approach and bridges the gap between different disciplines, including computer science, OR/MS, statistics, data mining, DSS, graphic design and human-computer interaction. The process of knowledge creation therefore can include multiple components and perspectives. By adopting such a diverse set of tools/techniques while employing the synergies involved, companies and organisations can make faster (real-time), frequent and fact-based decisions.
IJDS therefore aims to provide a professional forum for examining the processes and results associated with obtaining data, as well as munging, scrubbing, exploring, modelling, interpreting, communicating and visualising data. Data science takes data in cyberspace as a research object. The goal is an integrated and interconnected process designed to form a common ground from which a knowledge-based system can be built, shared and supported by professionals from different disciplines.
Topics Covered
Applied Data Science / investing
Applied Data Science / asset allocation
Applied Data Science / trading
Applied Data Science / artificial intelligence
Applied Data Science / healthcare
Applied Data Science / bio-technology
Applied Data Science / legal
Applied Data Science / decision-making
Applied Data Science / business
Applied Data Science / marketing
Applied Data Science / human resources
Applied Data Science / government
Applied Data Science / policy making
Applied Data Science / risk assessment & mitigation
Big data cloud, mining and management
Big data storage, processing, sharing and visualisation
Big data systems, tools, theory and applications
Business analytics, intelligence and mathematics
Computer science, hacking skills
Informatics and information systems and technology
Machine learning, web-based decision making
Management science, social sciences and statistics
Mathematical optimisation and mathematics of decision sciences
Multiple source data processing and integration
Network and social-graph analysis
Optimisation, performance measurement
Security and privacy
System analysis and theory
Volume, velocity and variety of big data on cloud
Readership
Applied data scientists
Research data scientists
Artificial intelligence engineers
Artificial intelligence process integrators
Investors
Traders
Healthcare management
Healthcare practitioners
Bio-technology innovators
Lawyers
Judges
Legal management
Organization executives
Managers
Strategic planners
Leaders / decision makers
Policy makers
Government regulators
Economists
Applied statisticians
Archivists
Consultants
Data analysts
Database administrators
Educators and graduate students
Engineers
Management scientists
Operation researchers
Programmers