Foundation of Data Science and Machine Learning

Dear Colleagues,

Data science has a long, rich history and is currently a very active topic with an extensive scope in machine learning, both in terms of theory and application. Recent advancement in data science has revolutionized the face of industrial development in the past decade. Data science applications in the real world environment provide important challenges that can often be addressed only with advanced machine learning methods. Research is therefore needed to understand and improve the potential and suitability of data analytics and machine learning in real-world applications. This will provide a deeper understanding and better decision making based on largely available data. This Special Issue focuses on the latest developments of novel machine learning methods in data science, as well as the synergy between data science and machine learning. We welcome new developments in data analytics regarding machine learning methods that are relevant for data science from a machine learning perspective.

This Special Issue will respond to research challenges by encouraging researchers in the computing world to present novel techniques, combinations of tools, and so forth to build effective ways to handle, retriev, and make use of data.

Topics of interest include, but are not limited to, the following:

• Emerging data-driven methods
• Deep learning
• Data analytics in healthcare
• Risk analysis
• Causality and learning casual models
• Green data science
• Privacy preserving, ethics, and transparency
• Multiple inputs and outputs: multi-instance, multi-label, and multi-target
• Semi-supervised and weakly supervised learning
• Data streaming and online learning
• Reinforcement learning
• AutoML
• IoT data analytics and big data
• Social data analytics
• Information retrieval

Dr. Imran Razzak
Dr. Md. Saiful Islam
Dr. Mohamed Reda Bouadjenek
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.