Data Science Conference Planning Committee

The Data Science Conference Planning Committee is seeking members to help plan and organize a large data science conference for Spring of 2015. Committee members shall recieve significant conference perks and fee waivers.

If you would like to join email Committee Chair Michael Walker ( or contact us here.

Conference presenters will have the opportunity to be published in the  International Journal of Data Science (IJDS). The IJDS publishes both academic research and practitioner papers (descriptive or predictive, and/or prescriptive), innovative ideas, case studies, surveys/reports and book reviews. Special Issues devoted to important topics will be published.

With the Age of Big Data upon us, we risk drowning in a flood of digital data. Big data spans five dimensions (volume, variety, velocity, volatility and veracity), generally steered towards one critical destination - value. Big data has now become a critical part of the business world and daily life. Containing big information and big knowledge, big data does indeed have big value.

The solution to getting value from big data is data science. The Data Science Association defines data science as the scientific study of the creation, validation and transformation of data to create meaning.

A "Data Scientist" is a professional who uses scientific methods to liberate and create meaning from raw data - somebody who can play with data, spot trends and learn truths few others know. Data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes.

Conference topics include:

  • Algorithm design and execution
  • Artificial intelligence
  • Big data cloud, mining and management
  • Big data storage, processing, sharing and visualisation
  • Big data systems, tools, theory and applications
  • Business analytics, intelligence and mathematics
  • Computer science, hacking skills
  • Data mining
  • Deep learning
  • Informatics and information systems and technology
  • Machine learning, web-based decision making
  • Management science, social sciences and statistics
  • Mathematical optimisation and mathematics of decision sciences
  • Multiple source data processing and integration
  • Natural language processing
  • Network and social-graph analysis
  • Neural networks
  • Optimisation, performance measurement
  • Security and privacy
  • System analysis and theory
  • Volume, velocity and variety of big data on cloud