Data Cleaning with Python for Finance

This course guides you through the process of transforming raw financial data into a clean, trustworthy dataset using Python and pandas. You’ll begin by exploring how to load data into a notebook environment and conduct quick inspections to identify structural issues, formatting inconsistencies, unusual numeric patterns, and missing values. Building on these observations, you’ll apply essential cleaning techniques used by analysts every day—fixing data types, standardizing text categories, resolving or documenting missingness, and removing duplicates. Through guided walkthroughs, hands-on practice, and interactive reflection, you’ll develop a repeatable workflow you can apply to budgeting, forecasting, reporting, or any analysis that relies on sound financial information. By the end of the course, you’ll confidently prepare analysis-ready datasets, make informed cleaning decisions, and communicate your process clearly to colleagues and stakeholders

• Load data into notebook environment
• Inspect structural and formatting issues
• Identify unusual numeric patterns and missing values
• Apply cleaning techniques: fix types and standardize categories
• Remove duplicate entries
• Use hands-on exercises for practice

• Basic topic familiarity and terminology
• Preparedness for practical exercises or case studies