Conference Information
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, collaboration, and statistical programming. The conference also provides opportunities for attendees to further their career development and strengthen relationships in the statistics community.
The objective of this theme is to help conference participants develop skills and perspectives that will improve their personal and professional effectiveness as statisticians and increase their organizational impact as managers, leaders, strategists, consultants, or collaborators.
Presentations will enable participants to return to their jobs with new ideas, techniques, and strategies for improving their ability to assume leadership roles, communicate effectively, forge productive professional relationships, and develop and advance their careers.
Potential topics include the following:
- Organizational impact and influence
- Leadership and management
- Mentoring
- Effective communication
- Presenting statistical results to nonstatisticians
- Best practices in consulting and collaboration
- Career development and advancement
- Statistical ethics
The objective of this theme is to provide attendees with practical knowledge about modeling and analyzing data of various forms through the application of state-of-the-art statistical methods. Presentation methods should use illustrative data analysis examples reproducible across several statistical packages and may focus on a variety of data types from varied applied settings.
Presentations will feature information relevant to a broad range of applied statisticians working in diverse settings. Potential topics include the following:
- Modeling
- Inference
- Prediction
Data science and big data are at the forefront of statistical practice and require a complex set of computing, statistical, and communication skills. This conference theme aims to help practitioners working in these fields to stay current with state-of-the-art methods for solving inference; prediction; decision-making; classification; and pattern-recognition problems from extremely large, unconventional, or complex data.
Presentations pertaining to this theme will involve large data applications; overviews/surveys of methodological tools; and algorithms for solving such applications and best practices for gathering, structuring, exploring, visualizing, and analyzing large amounts of data.
Potential topics include the following:
- Capturing, exploring, and visualizing large amounts of raw data
- Processing, tidying, and cleaning raw data to produce analyzable data structures
- Scaling up statistical models for big data and data science applications
- Analysis techniques for high-throughput screening
- Text analytics and sentiment analysis
- Applications of machine learning and deep learning
- Model validation and comparison approaches
The objective of this theme is to help attendees integrate new or existing software, statistically oriented programming languages (e.g., R, SAS), or general programming languages (e.g., Python, Java) 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 communication and data discovery
- Statistical applications of general programming languages