Book Series on Data Analytics, Springer

1. Title: Springer book series on Data Analytics,

2. Series Editors:

  • Longbing Cao, University of Technology Sydney, Australia
  • Philip S Yu, University of Illinois at Chicago, USA

3. Editorial Board:

  • Ming-Syan Chen, National Taiwan University, Taiwan
  • João Gama, University of Porto, Portugal
  • Eric Gaussier, Laboratoire d’Informatique de Grenoble, France
  • Dimitrios Gunopulos, University of Athens, Greece
  • Ravi Kumar, Google, USA
  • Gabriella Pasi, Università Degli Studi di Milano-Bicocca, Italy
  • Kyuseok Shim, Seoul National University, South Korea
  • Wei Wang, University of California, Los Angeles, USA
  • Takashi Washio, Osaka University, Japan
  • Xin Yao, University of Birmingham, UK
  • Wenwu Zhu, Tsinghua University, China

4. 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.

5. 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, big data, statistical and mathematical methods for data and applied analytics,
  • New scientific findings and progress ranging from data capture, creation, storage, search, sharing, analysis, and visualization,
  • Integration methods, best practices and typical examples across heterogeneous, interdependent complex resources and modals for real-time decision-making, collaboration, and value creation.

6. Suggested Titles for Proposals:

  • Introduction to data science
  • Data science foundation/fundamentals
  • Applied analytics
  • Advanced analytics: concepts and applications
  • Banking data analytics
  • Behavior analytics
  • Big data analytics
  • Biomedical data analytics
  • Business analytics
  • Computational intelligence methods for data science
  • Data visualization
  • Data optimization
  • Data representation
  • Educational data analytics
  • Environmental data analytics
  • Feature selection and mining
  • Financial data analytics
  • Government data analytics
  • Health data analytics
  • Heterogeneous data analytics
  • High performance analytics
  • In-memory analytics
  • Insurance data analytics
  • Learning analytics
  • Mobile analytics
  • Model optimization
  • Multimedia analytics
  • Network analytics
  • Non-iidness learning
  • Predictive analytics
  • Prescriptive analytics
  • Scientific data analytics
  • Service analytics
  • Smart cities
  • Statistics for data science
  • Social analytics
  • Social security data analytics
  • Smart city and analytics
  • Spatial-temporal data analytics
  • Telco data analytics
  • Textual data analytics
  • Transport data analytics
  • Visual analytics

7. Contacts:

Click to find the call for book proposals, and submit your proposal to

Longbing Cao:


Philip S Yu:


Melissa Fearon, Springer