Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration two weeks or more before the course is scheduled to begin, you are entitled to receive your choice of either a credit for a future seminar (which can be applied toward any of our courses) or a refund of the registration fee (minus a processing fee of $50). 

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 4-Day Livestream Seminar Taught by Jeffrey Dotson, Ph.D.

Large language models have quietly become one of the most powerful tools available to academic researchers—not as a shortcut, but as a genuine force multiplier across every stage of the research lifecycle. Yet most researchers lack a systematic framework for deploying these tools effectively, reproducibly, and in ways that can withstand peer scrutiny.

This seminar provides that framework. Over four intensive units, you will learn how to use Claude (via claude.ai and Claude.ai Projects), Claude Cowork, Claude Code, and GitHub as an integrated research infrastructure, from the earliest stages of ideation through data collection, analysis, and final manuscript preparation.

The seminar is built around real research workflows. Rather than abstract demonstrations, you’ll work through the same tasks you face in your own projects: sharpening a research question, structuring a replication-ready codebase, scraping and wrangling data, running statistical models, and producing polished, submission-ready writing. Each module pairs conceptual grounding with hands-on exercises that you can immediately adapt to your own work.

A core theme throughout the seminar is replicability. Journal requirements for replication packages are now the norm rather than the exception, and the organizational habits and tooling introduced in this course make compliance a natural byproduct of good research practice rather than an afterthought.