I already know the very basics of coding and now want to move confidently into Python, focusing on data-science style work rather than general scripting or web development. My immediate goal is to get comfortable with the full workflow—importing data, cleaning it with Pandas, running exploratory analysis in Jupyter, building simple models with scikit-learn, and presenting insights through Matplotlib or Seaborn visualisations. The help I am after is practical, hands-on guidance that moves beyond theory. I learn fastest when I can share my screen, try something myself, get real-time feedback, then follow up with short exercises that cement the concept. A structured path that starts with core language features I might have missed and quickly progresses to mini-projects—like analysing a public CSV dataset, creating a small recommendation engine, or automating a data-cleaning pipeline—would be perfect. I value clear explanations, annotated code, and small challenges between sessions. If you can supplement live meetings with brief written notes or recorded walkthroughs, even better. Deliverables I’d like to see: • Regular interactive sessions (voice or video) with live coding • Short practice tasks after each session, plus review of my solutions • At least one end-to-end data-science project we build together, ready for my portfolio If this teaching style resonates with you, please tell me how you would structure the first few lessons and any relevant experience you have guiding learners at my “some knowledge, not yet confident” level.