I need an intermediate-level data-analysis learning path I can follow online, focusing on sharpening practical skills rather than re-teaching beginner concepts. The content should deepen my grasp of data cleaning, exploratory analysis, visualization, and introductory modeling with tools such as Python (pandas, matplotlib, seaborn) or R and tidyverse packages—whichever you feel suits the flow best. The task is to curate, structure, and document a self-paced course that lives entirely on existing web resources (MOOC videos, interactive notebooks, articles, or short challenges). Everything must be accessible through a standard browser and either free or low-cost. Deliverables • A sequenced syllabus of 10–12 lessons, each with a clear objective and estimated study time • Direct links to every video, article, dataset, or interactive lab used in those lessons • At least one hands-on exercise per lesson, with expected outputs or answer keys where relevant • A capstone mini-project brief that pulls the lessons together (including dataset sources and evaluation criteria) • A short note outlining why the chosen resources match an intermediate learner’s needs and how they build progressively Acceptance criteria: All links work globally, no mandatory paywalls beyond a modest course fee, and captions are available on every video. The entire plan should be deliverable as a single, well-formatted document (Google Doc or PDF) ready for me to follow step by step.