Online Program

Statistical practices can be greatly enhanced through an effective use of software and advanced programming techniques. This course is designed to provide targeted instruction to help practitioners learn to become more productive in analyzing data and developing projects. The class will be taught in the R statistical programming language.

Through four modules, the course will teach students to analyze large data sets, adopt reproducible methods for research and reporting, implement the best practices of software design, and generate dynamic applications in a web-style interface. Merging concepts and applications, the curriculum will touch on statistical problems in a variety of industries and develop skills in statistical consulting.

The course is designed to help the participants to improve the accuracy, transparency, and efficiency of their work. These benefits accrue to both the practitioners and their organizations, empowering greater accountability and use of information in collaborations. By developing these skills in programming and analysis, you can become a more productive practitioner.

Outline & Objectives

The full-day course will consist of the following modules.

1. Analyzing Large (and Small) Data Sets with R's data.table Package: Introducing basic and advanced techniques for processing data using data.table’s efficience methods.

2. Reproducible Research: Summarizing the important concepts in generating reproducible reports, highlighting advanced uses of the Rmarkdown package, and demonstrating the improvements in productivity and accuracy associated with reproducible practices.

3. Software Design and Productivity: Showcasing the best practices from computer science in writing code and creating flexible designs to anticipate changes in the goals of a project.

4. Dynamic Reporting Engines with R's shiny Package: Developing user interfaces and reactive code to display customized content in a web-friendly design.


Each of these modules will focus on a number of core skills:

a) Writing effective code.

b) Anticipating the challenges of working with data in iterative projects.

c) Creating customized content for a wide range of audiences.

d) Utilizing these techniques in statistical practice and collaboration with others.

About the Instructor

David Shilane is a member of the full-time faculty of Columbia University’s Applied Analytics program. He teaches master’s level courses in machine learning, research methods, and data science consulting. As a practitioner, he has collaborated with numerous companies and organizations to design data-driven products and applications, build analytical infrastructures, and inform strategic decisions. He has worked in diverse fields including online dating, product recommendations, public health initiatives, and educational technology. David is excited to convey these experiences — from statistical methods and technical implementations to new discoveries derived from data — both inside and outside of the classroom. He has published research in a variety of fields, including medicine and public health, educational technology, applied statistics, optimization methods, and statistical software. He received a PhD in Biostatistics from the University of California Berkeley and prior degrees from Stanford University.

Relevance to Conference Goals

The conference is designed to facilitate new learning and career development for statistical practitioners. The proposed course is designed to develop the participants’ range and abilities in using statistical software. The courses are designed to build competencies and introduce advanced uses. Much of this material is based on a data science consulting curriculum developed by the instructor based upon the lessons learned as a practitioner. While the topics for the course focus on software design and statistical program, they will be introduced in a context that will enhance the participant’s knowledge of new domains, reinforce concepts from statistical analysis, and develop managerial skills that will be useful in statistical consulting.