Description

• Module 1. Why Cleaning Matters

  • Errors and inconsistencies in raw data.

• Module 2. Tools: Excel & OpenRefine

  • Cleaning without coding.

• Module 3. Python & Pandas

  • Removing duplicates, filling missing values.

• Module 4. Data Normalization

  • Standardizing dates, numbers, and text.

• Module 5. Practical Project

  • Preparing a dataset for BI or ML.

  • Final project: cleaning a real-world dataset.

Format:

• 📘 Illustrated PDF (40+ pages).

• 💻 Jupyter Notebook with working code.

• 📝 Excel templates for exercises.

• 🎥 Video walkthroughs of case studies.