Data Analytics

Aims and Goals:

Building and promoting the field of data science and analytics in terms of publishing work on theoretical foundations, algorithms and models, evaluation and experiments, applications and systems, case studies, and applied analytics in specific domains or on specific issues.

Specific Topics:

This series encourages proposals on cutting-edge science, technology and best practices in the following topics (but not limited to):

·        Data analytics, data science, knowledge discovery, machine learning, deep learning, big data, statistical and mathematical methods, exploratory and applied analytics,

·        New scientific findings and progress ranging from data capture, creation, storage, search, computing, sharing, analysis, and visualization,

·        Integration methods, best practices and typical applications across heterogeneous, multi-sources, domains and modals for data-driven real-world decision-making, and value creation.

 Suggested Titles for Proposals:

• Introduction to data science
• Data science fundamentals
Applied analytics
• Advanced analytics: concepts and applications
• Banking data analytics
• Behavior analytics
• Big data analytics
• Biomedical data analytics
• Business analytics
Cloud analytics
• Computational intelligence methods for data science
• Data visualization
• Data optimization
• Data representation
• Distributed analytics and learning
• Educational data analytics
Environmental data analytics
• Ethics in data science
• Feature selection and mining
• Financial data analytics and FinTech
• Government data analytics
• Health and medical data analytics
• Heterogeneous data analytics
• High performance analytics

 

• In-memory analytics
• Insurance data analytics
• Large-scale inference
• Learning analytics
• Large-scale learning
• Mobile analytics
• Model optimization
• Multimedia analytics
Network analytics
• Non-IID learning
• Predictive analytics
• Prescriptive analytics
• Scientific data analytics
• Service analytics
• Smart cities, home and IoT
Statistics for data science
• Social analytics
• Social security data analytics
•  Smart city and analytics
• Spatial-temporal data analytics
• Telco data analytics
• Textual data analytics
• Time-series analysis
• Transport data analytics
•  Web analytics
Visual analytics