Title: Getting Started
Course: Advanced Data Science: A Graduate Course

Getting Started

Before you begin · about 1 hour

How this course works

This course is designed backward from seven learning outcomes: everything you watch, read, practice, and are assessed on traces to one of them (the full list and the week-by-week schedule are just above). Each of the eight modules follows the same rhythm, so the structure never becomes the obstacle: a short recorded introduction (Watch), a vetted open reading or lab (Read), a low-stakes interactive practice (Practice), and a summative simulation you defend on your own work (Assessment), closing with a discussion. Plan about nine hours a week across the fifteen-week term; every task states its estimated time so you can plan.

Tools you will use

- Canvas (or your institution’s LMS): where you submit work, join the weekly discussion, and receive feedback.
- Python, in Jupyter or Google Colab: the language for every analysis. Colab runs in the browser with nothing to install, which is the fastest way to start.
- The course AI tutor, a public Gemini Notebook grounded only in this course and its open resources, answering with citations. It is study support, not your instructor, and it does not replace the weekly instructor interaction.
- Interactive simulations: playable, self-scoring assessments for Modules 5 through 8. The Module 1 to 4 simulations are described in full inside those modules.
- The on-device defense-practice tool, which rehearses the oral defenses by speaking randomized questions one at a time. Your recording stays on your own device; nothing is uploaded.

Where to start

Read this module, skim the seven outcomes and the schedule above, set up Python (Colab is the quickest path), open the course tutor, and choose the one real dataset you will carry through the whole course. Then open Module 1.

Simulations and oral defenses: what is graded

The interactive simulations here are self-scoring practice you can run as often as you like. Launched with a roster identifier, each student is issued their own version, fixed for that person and reproducible by the instructor, and additional written and code artifacts are submitted for the remaining points. Three assessments (Modules 3, 7, and 8) include a live or recorded oral defense, where a role-played stakeholder asks unscripted questions; that defense is the graded event, and the defense-practice tool lets you rehearse it first. The summative simulations and defenses carry the majority of the grade. Formative practice earns completion points: do the hands-on work, then submit a short reflection or evidence that you ran it, credited for completing it rather than scored for correctness. Suggested weighting: summative simulations and defenses 60%, formative practice 15%, discussions 25%.

AI use and academic integrity

AI tools are permitted and disclosed. Mark where a model helped and reflect on the judgment you exercised. The secured parts of the grade, the seeded-data recovery, the private-test prediction, the planted-defect audit, the drift response, and the live oral defense, are what establish your own achievement, so using AI honestly helps you rather than stands in for you. Never present another person’s or a model’s work as your own, and follow your institution’s academic-integrity policy.

Getting help and accessibility

Ask questions in the course Q&A space and in office hours; the instructor initiates contact every week. The course is built to WCAG 2.1 AA: videos are captioned with a slide or transcript alternative, every interactive is paired with a non-interactive path to the same concept, and most deliverables accept a written or a recorded form. If you need an accommodation, contact your disability-services office and your instructor early.

“Advanced Data Science by Michelle Blomberg is licensed under CC BY-NC-SA 4.0.”