I’m a software engineer who is still early in my Python journey, and I want to sharpen my skills specifically around data-analysis workflows. My immediate goal is to take raw financial datasets—think CSV exports from trading platforms or quarterly reports—and turn them into clean, analysis-ready tables and insightful visuals. Here’s what I need: • Practical guidance on cleaning and preparing financial data with pandas and NumPy, including handling missing values, date indices, currency conversions, and basic feature engineering. • Step-by-step examples (preferably Jupyter notebooks) that demonstrate how to build clear visualizations of key metrics using Matplotlib, Seaborn, or Plotly. These examples should be well commented so I can adapt them to future datasets. Acceptance criteria: – At least one notebook that loads a sample raw file, cleans it, and produces two or more annotated charts. – Code runs end-to-end on Python 3.11 with only standard data-science libraries. – Clear explanations sprinkled throughout so I understand the “why,” not just the “how.” If you enjoy breaking down concepts and you’re comfortable working with financial data quirks, I’d love to collaborate and level up my Python analytics skills together.