Key Dates

  • April 8 – May 12, 2015
    Short Course and Tutorial Abstract Submission
  • May 14 – June 25, 2015
    Concurrent Session Abstract Submission
  • July 1 – August 11, 2015
    Practical Computing Demo Submission
  • July 15 – August 27, 2015
    Poster Abstract Submission
  • October 1, 2015 – January 5, 2016
    Early Registration
  • November 4, 2015 – January 5, 2016
    Abstract Editing
  • January 6 – February 4, 2016
    Regular Registration
  • January 19, 2016
    Housing Deadline
  • February 18 – 20, 2016
    CSP in San Diego

Conference Information

The goal of the conference is to provide participants with opportunities to learn new statistical methodologies and best practices in statistical analysis, design, consulting, and statistical programming.

The Conference on Statistical Practice aims to bring together hundreds of statistical practitioners—including data analysts, researchers, and scientists-who engage in the application of statistics to solve real-world problems on a daily basis.

The goal of the conference is to provide participants with opportunities to learn new statistical methodologies and best practices in statistical analysis, design, consulting, and statistical programming. The conference also will provide opportunities for attendees to further their career development and strengthen relationships in the statistical community.

Theme 1: Communication, Impact, and Career Development

The objective for this theme is to help attendees develop skills and perspectives that will improve their personal effectiveness as statisticians in their roles as managers, leaders, strategists, consultants, and collaborators. Presentations will enable participants to return to their jobs with new ideas, techniques, and strategies to improve their ability to communicate effectively, have a greater effect on their organizations, and advance their careers. Potential topics include the following:

  • Career advancement and development
  • Organizational impact
  • Leadership and management skills
  • Presentation and oral communication skills
  • Presenting statistical results to nonstatisticians
  • Best practices in consulting and collaboration
  • Statistical ethics

Theme 2: Data Modeling and Analysis

The objective for this theme is to provide attendees with practical knowledge and techniques related to obtaining, creating, modeling, and analyzing data sets of various forms through the application of state-of-the-art statistical methods. Sources of data might be experimental, survey, or historical. Presentations will feature information relevant to a broad range of applied statisticians, regardless of industry or field of expertise. Potential topics include the following:

  • Experimental design
  • Collection or management of data
  • Modeling, inference, or prediction

Theme 3: Big Data Prediction and Analytics

The objective for this theme is to share current methods for solving inference, prediction, decision making, classification, and pattern recognition problems from extremely large, unconventional, or complex data. Presentations will involve large data applications, and topic surveys focused on tools and algorithms for large data applications are welcome. Potential topics include the following:

  • Data mining and business analytics applications
  • Capturing large amounts of raw data
  • Converting raw data into an analyzable structure
  • Exploring and modifying Big Data
  • Analysis techniques for high-throughput screening
  • Text analytics
  • Model validation and comparison approaches

Theme 4: Software, Programming, and Graphics

The objective for this theme is to help attendees integrate new or existing software, statistically oriented programming languages (R, SAS, etc.), or general programming languages (Python, Java, etc.) into their current processes. Presentations will focus on the practical application of such technology for the statistician or data analyst. Potential topics include the following:

  • Software and programming methods to obtain, clean, describe, or analyze data
  • Methods for interactively sharing analysis results with other analysts or end users
  • Visualization methods for displaying and exploring data
  • Statistical applications of general programming languages