Public course syllabus

Data Science for Beginners

A self-paced Robotnix adaptation and modernization of Microsoft's complete 20-lesson Data Science for Beginners curriculum. Microsoft source lineage, contributor credit, license records, and useful original media remain visible, while every learner lesson is rewritten in the Mr. N / Robotnix voice with current systems examples, evidence-first reasoning, privacy and provenance boundaries, browser-local work, and explicit Data-to-AI connections. Each Unit ends with a Robotnix-original evidence check. Units 4-8 include a bounded browser data workbench with fictional data, interactive charting, and editable Pandas analysis. Five standalone Robotnix Data Labs add telemetry, sampling and bias, regression, data drift, and a Data-to-AI bridge. Microsoft Azure and cloud examples remain credited instructional references rather than required platform accounts. The core path does not require a paid cloud account.

Audience: 9-12
Structure: 10 units
Delivery: Self-paced

Course overview

This self-paced course combines the complete Microsoft Data Science for Beginners source sequence with Robotnix evidence checks. Learners move from data questions and ethics through preparation, visualization, analysis, lifecycle choices, cloud/low-code tradeoffs, and real-world workflows.

Audience and pace

Grades 9–12. Ten evidence-gated Units replace calendar deadlines; each Unit includes two source-faithful lessons and a Robotnix evidence check. The core path does not require a paid cloud account.

What you will learn

Frame defensible data questions and identify provenance, bias, limits, and ethical risk.
Prepare, analyze, and visualize data without overstating what a chart or model proves.
Compare local, cloud, low-code, and code-driven data workflows by privacy, cost, control, reproducibility, and accessibility.

Course structure

Units cover data ethics; statistics and probability; data models; Pandas and preparation; quantities, distributions, proportions, and relationships; communication; cloud tools; and SDK workflows. Units 4–8 include a bounded browser data workbench; five standalone Data Labs extend the core path with fictional data.

Learning evidence and assessment

Evidence checks ask learners to defend a question, chart, model, data-cleaning, analysis, or deployment decision. The final dossier connects question, provenance, preparation, visualization, limitations, tool choice, and a justified next question.

Expectations and boundaries

Use supplied or approved data. Do not upload sensitive records, present correlation as causation, or claim a model is fair or accurate without evidence. Source material remains attributed; Robotnix checks are original.

Capstone

The Data Science Evidence Dossier is a small, inspectable investigation defended from question through limitations and next action.

Access and support

The reader, browser workbench, fictional datasets, and evidence templates support the course. Learners may provide written, recorded, or diagrammed reasoning where appropriate.