Conferences

2016 - Data Science Summit Europe - Jerusalem, Israel - June 6, 2016

Register here

ICC Jerusalem – International Convention Center  - June 6, 2016

Join us in making a better world utilizing big data analytics!

Our lives are undergoing dramatic changes. We are moving towards a digital society that expects real-time, 24/7 services; a society that is defined by knowledge sharing and real-time business exchange, accompanied and fueled by unstoppable innovation and big data creation that is being gathered in the clouds. Driving insights from big data will help industry make optimal business decisions and completely change the world, as we know it.

Data Science Summit Europe

This non-profit event is organized by Intel, IGTCloud, Dato and O’Reilly Media. The Summit brings together researchers and data scientists from academia as well as industry to discuss state of the art data science, applied machine learning and predictive applications. The conference agenda has been co-created with Dr. Ben Lorica, Chief Scientist of O’Reilly Media who serves as the content manager of the O’Reilly Strata Conferences.

Join us for the 2nd Data Science Summit Europe, an event focused around machine learning implementation, including data science tutorials and case studies. Full agenda TBA. Expected audience: approximately 1200 data scientists, applied machine learning researchers, CTOs, VPs of Engineering, VPs of data analytics, professors and students.

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2015 - The Data Science Conference

Register here.

Chicago, Nov. 12-13, 2015
Univ. of Chicago Gleacher Center

The first and only vendor-free, sponsor-free, and recruiter-free data science conference. This conference is for business analytics professionals working on data science, big data, data mining, machine learning, artificial intelligence, or predictive modeling who want to attend an event without being prospected by other attendees.

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2014 - Conference International Conference on Data Science and Advanced Analytics

Register @ http://bit.ly/1uksodj

Place: Shanghai, China - Shanghai East Asia Hotel
Dates: October 30 - November 1, 2014
Program: http://bit.ly/1uMolJi
Contact: dsaabesc@gmail.com

The 2014 International Conference on Data Science and Advanced Analytics (DSAA’2014) brings together researchers, practitioners, as well as potential users of big data and advanced analytics, to promote collaborations and exchange of ideas and practices, discuss new opportunities, and investigate the best actionable analytics framework for wide range of applications. The conference solicits experimental and theoretical works on datascience and advanced analytics along with their application to real life situations.

Topics of Interest

  • New mathematical, probabilistic and statistical models and theories
  • New learning theories, models and systems
  • Deep analytics and learning
  • Distributed and parallel computing (cloud, map-reduce, etc.)
  • Non-iidness (heterogeneity & coupling) learning
  • Invisible structure, relation and distribution learning
  • Intent and insight learning
  • Scalable analysis and learning
  • Mining multi-source and mixed-source information
  • Architecture, management and process
  • Data pre-processing, sampling and reduction
  • Feature selection and feature transformation
  • High performance/parallel/distributed computing
  • Analytics architectures and infrastructure
  • Heterogeneous data/information integration
  • Crowdsourcing
  • Human-machine interaction and interfaces
  • Web/social web/distributed search
  • Indexing and query processing
  • Information and knowledge retrieval
  • Personalized search and recommendation
  • Query languages and user interfaces
  • Mixed-type data
  • Mixed-structure data
  • Big data modeling and analytics
  • Multimedia/stream/text/visual analytics
  • Coupling, link and graph mining
  • Personalization analytics and learning
  • Web/online/network mining and learning
  • Structure/group/community/network mining
  • Big data visualization analytics
  • Large scale optimization
  • Security, trust and risk in big data
  • Data integrity, matching and sharing
  • Privacy and protection standards and policies
  • Privacy preserving big data access/analytics
  • Social impact
  • Data economy
  • Domain-specific applications
  • Quality assessment and interestingness metrics
  • Complexity, efficiency and scalability
  • Anomaly/fraud/exception/change/event/crisis analysis
  • Large-scale recommender and search systems
  • Big data representation and visualization
  • Post-processing and post-mining
  • Large Scale Application Case Studies
  • Online/business/government data analysis
  • Mobile analytics for handheld devices
  • Living analytics