Conferences related to Data Science

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ICC 2021 - IEEE International Conference on Communications

IEEE ICC is one of the two flagship IEEE conferences in the field of communications; Montreal is to host this conference in 2021. Each annual IEEE ICC conference typically attracts approximately 1,500-2,000 attendees, and will present over 1,000 research works over its duration. As well as being an opportunity to share pioneering research ideas and developments, the conference is also an excellent networking and publicity event, giving the opportunity for businesses and clients to link together, and presenting the scope for companies to publicize themselves and their products among the leaders of communications industries from all over the world.


2020 IEEE Applied Power Electronics Conference and Exposition (APEC)

APEC focuses on the practical and applied aspects of the power electronics business. Not just a power designer’s conference, APEC has something of interest for anyone involved in power electronics including:- Equipment OEMs that use power supplies and converters in their equipment- Designers of power supplies, dc-dc converters, motor drives, uninterruptable power supplies, inverters and any other power electronic circuits, equipments and systems- Manufacturers and suppliers of components and assemblies used in power electronics- Manufacturing, quality and test engineers involved with power electronics equipment- Marketing, sales and anyone involved in the business of power electronic- Compliance engineers testing and qualifying power electronics equipment or equipment that uses power electronics


2020 IEEE Frontiers in Education Conference (FIE)

The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.


2020 IEEE International Solid- State Circuits Conference - (ISSCC)

ISSCC is the foremost global forum for solid-state circuits and systems-on-a-chip. The Conference offers 5 days of technical papers and educational events related to integrated circuits, including analog, digital, data converters, memory, RF, communications, imagers, medical and MEMS ICs.


2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

All areas of ionizing radiation detection - detectors, signal processing, analysis of results, PET development, PET results, medical imaging using ionizing radiation


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Periodicals related to Data Science

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)


Communications Surveys & Tutorials, IEEE

Each tutorial reviews currents communications topics in network management and computer and wireless communications. Available tutorials, which are 2.5 to 5 hours in length contains the original visuals and voice-over by the presenter. IEEE Communications Surveys & Tutorials features two distinct types of articles: original articles and reprints. The original articles are exclusively written for IEEE Communications Surveys & Tutorials ...


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


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Xplore Articles related to Data Science

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EDISON Data Science Framework: A Foundation for Building Data Science Profession for Research and Industry

2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 2016

Data Science is an emerging field of science, which requires a multi- disciplinary approach and should be built with a strong link to emerging Big Data and data driven technologies, and consequently needs re-thinking and re- design of both traditional educational models and existing courses. The education and training of Data Scientists currently lacks a commonly accepted, harmonized instructional model ...


The ambiguity of data science team roles and the need for a data science workforce framework

2017 IEEE International Conference on Big Data (Big Data), 2017

This paper first reviews the benefits of well-defined roles and then discusses the current lack of standardized roles within the data science community, perhaps due to the newness of the field. Specifically, the paper reports on five case studies exploring five different attempts to define a standard set of roles. These case studies explore the usage of roles from an ...


Customisable Data Science Educational Environment: From Competences Management and Curriculum Design to Virtual Labs On-Demand

2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 2017

Data Science is an emerging field of science, which requires a multi- disciplinary approach and is based on the Big Data and data intensive technologies that both provide a basis for effective use of the data driven research and economy models. Modern data driven research and industry require new types of specialists that are capable to support all stages of ...


Data Science Challenges in Computational Psychiatry and Psychiatric Research

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 2018

The special session "Data Science is Computational Psychiatry and Psychiatric Research" at the 5th IEEE International Conference in Data Science and Advanced Analytics in Turin, Italy 2018 presents papers specifically addressing psychiatric research. In this overview, we describe the challenges of psychiatric research and demonstrates how the presented papers approach some of the problems.


The Michigan Data Science Team: A Data Science Education Program with Significant Social Impact

2018 IEEE Data Science Workshop (DSW), 2018

One role of universities is to provide students with the practical knowledge necessary to address broad societal needs. The growing field of data science has the potential to address many of these needs, primarily due to recent efforts to collect large amounts of data related to social, environmental, and political issues. A challenge is that there exists a disconnect between ...


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Educational Resources on Data Science

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IEEE.tv Videos

Better Science Together - John Wilbanks - IEEE EMBS at NIH, 2019
Data for Good: Data Science at Columbia - Jeannette Wing - IEEE Sarnoff Symposium, 2019
Francisco Herrera: Evolutionary fuzzy systems for data science & big data: Why & What For?
Data Science for Revenue Forecasting System | DSBC 2020
Fiery Data: Safeguarding Future Cities Against Fire Hazards | DSBC 2020
Yasuhiko Arakawa, Pallab Bhattacharya, Dieter H. Bimberg - IEEE Jun-Ichi Nishizawa Medal, 2019 IEEE Honors Ceremony
Daniel Engels: Security in IoT Begins With Data - Industry Forum Panel: WF IoT 2016
Honors 2020: Michael I. Jordan Wins the IEEE John von Neumann Medal
Stephen Brodsky: Big Data at the Speed of Business
Consequences of Big Data on the Individual
Vladimir Cherkassky - Predictive Learning, Knowledge Discovery and Philosophy of Science
Q&A with Dr. Ling Liu: IEEE Big Data Podcast, Episode 8
Deep Graph Learning: Techniques and Applications - Haifeng Chen - IEEE Sarnoff Symposium, 2019
Netflix's Chris Pouliot: How to Build a Data Science Team from Scratch
Analytics for Anomaly detection & Classification | DSBC 2020
Time-series Workloads and Implications for Time-series Databases - Michael Freedman - IEEE Sarnoff Symposium, 2019
The Power of seeing and understanding your DATA & Closing Ceremony | DSBC 2020
Panel: Precision Health & Big Data Analytics - IEEE EMBS at NIH, 2019
Collection, Modeling & Interpretation of Mobile Sensor Big Data - Santosh Kumar - IEEE EMBS at NIH, 2019
Solving Real World Problems with Computing: Exploring Careers in Engineering and Technology

IEEE-USA E-Books

  • EDISON Data Science Framework: A Foundation for Building Data Science Profession for Research and Industry

    Data Science is an emerging field of science, which requires a multi- disciplinary approach and should be built with a strong link to emerging Big Data and data driven technologies, and consequently needs re-thinking and re- design of both traditional educational models and existing courses. The education and training of Data Scientists currently lacks a commonly accepted, harmonized instructional model that reflects by design the whole lifecycle of data handling in modern, data driven research and the digital economy. This paper presents the EDISON Data Science Framework (EDSF) that is intended to create a foundation for the Data Science profession definition. The EDSF includes the following core components: Data Science Competence Framework (CF- DS), Data Science Body of Knowledge (DS-BoK), Data Science Model Curriculum (MC-DS), and Data Science Professional profiles (DSP profiles). The MC-DS is built based on CF-DS and DS-BoK, where Learning Outcomes are defined based on CF-DS competences and Learning Units are mapped to Knowledge Units in DS-BoK. In its own turn, Learning Units are defined based on the ACM Classification of Computer Science (CCS2012) and reflect typical courses naming used by universities in their current programmes. The paper provides example how the proposed EDSF can be used for designing effective Data Science curricula and reports the experience of implementing EDSF by the Champion Universities that cooperate with the EDISON project.

  • The ambiguity of data science team roles and the need for a data science workforce framework

    This paper first reviews the benefits of well-defined roles and then discusses the current lack of standardized roles within the data science community, perhaps due to the newness of the field. Specifically, the paper reports on five case studies exploring five different attempts to define a standard set of roles. These case studies explore the usage of roles from an industry perspective as well as from national standard big data committee efforts. The paper then leverages the results of these case studies to explore the use of data science roles within online job postings. While some roles appeared frequently, such as data scientist and data engineer, no role was consistently used across all five case studies. Hence, the paper concludes by noting the need to create a data science workforce framework that could be used by students, employers, and academic institutions. This framework would enable organizations to staff their data science teams more accurately with the desired skillsets.

  • Customisable Data Science Educational Environment: From Competences Management and Curriculum Design to Virtual Labs On-Demand

    Data Science is an emerging field of science, which requires a multi- disciplinary approach and is based on the Big Data and data intensive technologies that both provide a basis for effective use of the data driven research and economy models. Modern data driven research and industry require new types of specialists that are capable to support all stages of the data lifecycle from data production and input to data processing and actionable results delivery, visualisation and reporting, which can be jointly defined as the Data Science professions family. The education and training of Data Scientists currently lacks a commonly accepted, harmonized instructional model that reflects all multi-disciplinary knowledge and competences that are required from the Data Science practitioners in modern, data driven research and the digital economy. The educational model and approach should also solve different aspects of the future professionals that includes both theoretical knowledge and practical skills that must be supported by corresponding education infrastructure and educational labs environment. In modern conditions with the fast technology change and strong skills demand, the Data Science education and training should be customizable and delivered in multiple form, also providing sufficient data labs facilities for practical training. This paper discussed both aspects: building customizable Data Science curriculum for different types of learners and proposing a hybrid model for virtual labs that can combine local university facility and use cloud based Big Data and Data analytics facilities and services on demand. The proposed approach is based on using the EDISON Data Science Framework (EDSF) developed in the EU funded Project EDISON and CYCLONE cloud automation systems being developed in another EU funded project CYCLONE.

  • Data Science Challenges in Computational Psychiatry and Psychiatric Research

    The special session "Data Science is Computational Psychiatry and Psychiatric Research" at the 5th IEEE International Conference in Data Science and Advanced Analytics in Turin, Italy 2018 presents papers specifically addressing psychiatric research. In this overview, we describe the challenges of psychiatric research and demonstrates how the presented papers approach some of the problems.

  • The Michigan Data Science Team: A Data Science Education Program with Significant Social Impact

    One role of universities is to provide students with the practical knowledge necessary to address broad societal needs. The growing field of data science has the potential to address many of these needs, primarily due to recent efforts to collect large amounts of data related to social, environmental, and political issues. A challenge is that there exists a disconnect between these real-world issues and the course materials taught to university students. This gap is due in part to a lack of engagement between universities and their local communities, leaving students with few opportunities to work with datasets relevant to real-world problems. To address this disconnect, the authors have implemented a novel data science education and outreach program in which students acquire new knowledge and skills while creating positive social impact through community service. In this work, we outline this outreach program, the Michigan Data Science Team, and provide empirical evidence of positive educational and social impact.

  • Model Curricula for Data Science EDISON Data Science Framework

    This paper presents the Data Science Model Curriculum (MC-DS) that is based on the Data Science Competence Framework and Data Science Body of Knowledge defined in EDISON Data Science Framework (EDSF). MC-DS follows a competence- based curriculum design approach grounded in the Data Science competences (CD- DS) defined in EDSF and correspondingly defined Learning Outcomes (LO). The DSBoK provides a basis for structuring the proposed MC-DS by Knowledge Area Groups (KAG) defined in correspondence with the CF-DS competence groups. ECTS point allocation to specific areas is recommended for Master's and Bachelor's program covering professional profile groups.

  • Quality assurance for data science: Making data science more scientific through engaging scientific method

    Credibility of science is fundamentally due to the strenuous efforts made to verify the general consistency among relevant facts, theories, applications, research methodologies, etc. and scientific method which emphasizes the significance of continuously building and testing hypotheses has withstood the test of time as a successful methodology of acquiring a body of knowledge, we can rely on, at least within a certain context. A paradigm based on composition of data rich services to gather data to replicate real world scenarios through complexity science based simulators, where quality of data as well as theories explaining them is primarily assured via building and testing of hypotheses, can improve our understanding of what we try to comprehend by engaging data science. While simulators would at least partially automate the implementation of scientific method, a credibility ranking mechanism, would not only help determining and disseminating rankings pertaining to the quality of data as well as theories explaining them but also receive & publish feedback regarding rankings. Including methods used in complex system analysis as part of simulators would enhance the scientific rigor of establishing the credibility of knowledge we have. Providing simulation as a service and making graphical hypothesis builders & testers available for external parties (sometimes even members of general public) would democratice the process of ascertaining the believability of data & associated theoretical models thereby further enhancing the Quality of Knowledge.

  • Smart Blockchain Badges for Data Science Education

    Blockchain technology has the potential to revolutionise education in a number of ways. In this paper, we explore the applications of Smart Blockchain Badges on data science education. In particular, we investigate how Smart Blockchain Badges can support learners that want to advance their careers in data science, by offering them personalised recommendations based on their learning achievements. This work aims at enhancing data science accreditation by introducing a robust system based on the Blockchain technology. Learners will benefit from a sophisticated, open and transparent accreditation system, as well as from receiving job recommendations that match their skills and can potentially progress their careers. As a result, this work contributes towards closing the data science skills gap by linking data science education to the industry.

  • DSAA 2018 Special Session: Data Science for Social Good

    We provide an overview of the DSAA 2018 Data Science for Social Good special session, its aims and contributions.

  • A New In-Car Navigation System Based on V2C2V and Data Science

    As a solution to the limitations of traditional in-car navigation systems, this study proposes a new architecture that integrates cloud technology and data science technology. DSV2C2V (Data Science Vehicle-to-Cloud-to-Vehicle) is able to perform multi-dimensional data analysis, including information inside and outside the vehicle. The proposed architecture is expected to help reduce automotive energy consumption and traffic congestion problems.



Standards related to Data Science

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Jobs related to Data Science

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