Title: Start Here
Course: Advanced Data Science: A Graduate Course

Advanced Data Science: A Graduate Course

Graduate · 3 credits · 15-week online course

Course overview

A complete graduate data-science course, its learning outcomes adapted from a published graduate course at the Colorado School of Mines. Fifteen weeks, three credits, outcomes first, and built entirely from vetted open educational resources. One real dataset of the student’s own choosing runs the length of the course, and every module moves from a recorded introduction and an interactive model, through a low-stakes exercise, to a professional simulation the student defends on their own data.

Course learning outcomes

Every module, reading, and assessment is designed back from seven observable outcomes. On completion a learner can:

- Acquire data from varied structured and unstructured sources (files, databases, APIs, and the web), documenting provenance and access method.
- Apply preprocessing strategies to complex, messy, and dynamic datasets: cleaning, missing-data handling, outlier treatment, and feature engineering.
- Differentiate among machine-learning approaches, regression, classification, and clustering, and select a method appropriate to a problem and its data.
- Evaluate the predictive power and validity of statistical and machine-learning methods using resampling, appropriate metrics, and the bias-variance trade-off.
- Communicate findings to a non-technical decision-maker through sound visualization and a defensible data narrative.
- Assess the ethical dimensions of a data problem using a recognized governance framework.
- Construct a comprehensive data-science project from inception to presentation, and defend its decisions.

How this course works

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 Regular and Substantive Interaction discussion. Plan about nine hours a week across the fifteen-week term; every task states its estimated time so you can plan.

Read this page, skim the seven outcomes and the week-by-week schedule, set up Python (Google Colab is the quickest path, with nothing to install), open the course AI tutor, and choose the one real dataset you will carry through the whole course. Then open Module 1 (Getting Started orients you first).

The graded simulations and oral defenses

The four graded simulations are the AI-resistant, authentic assessments at the heart of the course. Because each student is issued a scenario seeded from their own roster identifier, with no public answer key, and because several assessments end in a live oral defense, a generative model cannot complete them for the student. The four interactive simulations and the on-device oral-defense practice tool are gathered in the Web Links section of this cartridge:

- Simulation: Model-Evaluation Audit (Module 5, Ridgeline Renewables)
- Simulation: Clustering Decision (Module 6, How Many Segments, Really?)
- Simulation: AI-Governance Review Board (Module 7)
- Simulation: Capstone Readiness (Module 8, RiverCity)
- Oral-Defense Practice Tool (on-device rehearsal for the live defenses)

The interactive simulations 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.

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