Speaker
Description
Many data scientists and other R users have no doubt been assigned to projects that quickly derailed—missed deadlines, scope creep, budget overruns—often because effective project management was assumed to be a "soft skill" anyone without deep technical expertise could handle. In reality, successful project delivery demands rigorous quantitative techniques: structured decomposition, dependency-aware scheduling, probabilistic risk assessment, and data-driven compression strategies. These are not just "management" tasks—they are applied statistics and operations research problems ideally suited to R.
At some point in their careers, many R users (maybe most) will be asked to lead a project—whether it’s delivering a predictive model pipeline, coordinating a research initiative, building a dashboard for stakeholders, or driving a cross-functional analytics effort. Dedicated project managers from a PMO may not always be available. Or individuals may choose to take ownership to expand their skill set, demonstrate leadership beyond coding, and show they can deliver end-to-end value rather than remain single-level contributors.
The good news is that any project can be planned and managed entirely within the R ecosystem. No need to learn Microsoft Project, Primavera, or some other project management software. With base R and packages like crtipath and ggplot2, users can build and manage projects reproducibly and quantitatively--right where they are otherwise ingesting, manipulating, and analyzing data.
My talk will demonstrate how to build a proper work breakdown structure (WBS), how to apply best project management practices to estimate task durations, how to identify the critical path that makes or breaks project success, how to calculate the precise probability of on-time completion, and how to evaluate where-and by how much-to crash critical activities to improve the probability of on-time completion-using many techniques attendees have no doubt applied to other domains.
Attendees will leave with ready-to-adapt code snippets that turn project management into a reproducible extension of their analytical work—empowering them to own project success without external tools or fragmented workflows.
If you used AI tools or services to support the preparation of this submission, please state the name and reason for using each of them.
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | Quant, statistics, PERT, CPM, project management |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |