Making science more reproducible has the potential to advance scientific research and make researchers' work more effective and productive. This is particularly true for computational and data-intensive research. This hands-on workshop will teach a number of tools, resources, and practices that can be used today to make statistical analysis and data science more reproducible.

The workshop is organized in three parts: Introduction to Reproducible Research, Organizing your project to facilitate Reproducible Research, and Version Control. The course is aimed at researchers and professionals at all levels. The workshop will focus on R for illustrating the key concepts. Advanced knowledge of R is not needed, but some familiarity with R or another scripting language may make the workshop more accessible.

Outline

Outline

1. Introduction to Reproducible Research

- Recognize the problems that reproducible research helps address, featuring brief discussion of case studies case studies of (lack of) reproducibility gone wrong.
- Identify pain points in getting your analysis to be reproducible.
- The role of documentation, sharing, automation, and organization in making your research more reproducible.
Introducing some tools to solve these problems, specifically R/RStudio/RMarkdown.

2. Organizing your project to facilitate Reproducible Research

- Organize projects and folders to enable reproducibility and reusability
- Understand the structure of data files and the importance of documenting all changes made
- Using these practices, create a reproducible project workflow using R/RStudio/RMarkdown.

3. Version control

- Introduction to git/GitHub as a version control tool.
- Practice initiating a project directory, making / committing / pushing changes, and creating a pull request to someone else's remote repository.
- Discuss the role of version control in reproducibility of one's own project as well as in collaborative projects

Public speaking is the number one fear in America, yet being able to do so is absolutely critical for success in business settings. Statisticians must be able to effectively convey their ideas to clients, collaborators, and decision-makers. Presenting in the modern world is even more daunting when speakers have the opportunity to employ slideware, videos, and live demos. Unfortunately, university coursework and professional development programs are often not targeted towards sharpening these skills. This short course, developed and taught by statisticians, will provide an opportunity to learn how to employ different methods and tools.

Outline

The material covered in the course is geared toward scientific presentations and is based on the works of Garr Reynolds and Michael Alley, among others. The course will emphasize the importance of stepping away from the computer to Prepare an effective message aimed at your core point guided with a series of questions and tips. The Design phase emphasizes the importance of structure, streamlining, and good graphic design accompanied by a series of checklists. Of course, “Practice makes perfect” so we cannot skip this step. Finally, engaging the audience and effectively using the room and equipment is covered in the Deliver phase and is complimented with a handy list of dos and don’ts. No matter where you are in your journey for presentation success, improvement is always possible. We look forward to seeing you in this valuable class where you can hone your skills! Be prepared for an active class full of discussion and group exercises.

The Women in Statistics and Data Conference will provide unique opportunities to grow your influence, your community, and your knowledge. During the opening reception the speed mentoring session, like speed dating only better, will be a time dedicated to networking and making connections. Come prepared with your 30 Second Elevator Speech and join us for this fun and informal opportunity to build your network of colleagues who will be able to support, encourage, and challenge you as you pursue your goals.