I am putting together a beginner-friendly course that introduces absolute newcomers to both Python programming and core data-analysis skills in one coherent learning path. The material must guide someone with zero coding background from installing Python all the way to running their first exploratory analysis with pandas and visualising results in Matplotlib or Seaborn. Here is the scope I need covered: • A structured syllabus that balances theory and hands-on practice, broken into short modules that build logically from variables, loops, and functions to NumPy arrays, pandas DataFrames, and basic statistics. • Detailed, text-based lesson write-ups for every module, written in clear, jargon-free language. Each lesson should include runnable code snippets, real-world-style examples, and notes on common beginner pitfalls. • Self-graded interactive exercises (Jupyter Notebook or similar) at the end of every lesson so learners can immediately try what they have learned. These should include auto-checking where feasible and concise solution walkthroughs. • A capstone mini-project that ties everything together—loading a public dataset, cleaning it, performing descriptive analysis, and visualising insights. Acceptance criteria 1. Content stays accessible to absolute beginners while still introducing standard data-analysis tooling (Python 3.x, Jupyter, NumPy, pandas, Matplotlib/Seaborn). 2. All code runs without modification on a fresh Anaconda or plain-pip setup. 3. Exercises include expected outputs or unit tests so I can verify correctness quickly. 4. Final deliverable arrives as organized folders (syllabus PDF/Doc, lesson Markdown or notebooks, exercise notebooks, and datasets) and is ready for me to upload to an LMS. If anything in the outline above needs clarification, let me know early so we can keep the flow smooth. I’m excited to review your proposed lesson breakdown and sample materials.