How do I handle inconsistent or missing data in large datasets?

Handling inconsistent or missing data in large datasets is crucial for accurate analysis. Common techniques include removing duplicates, using mean or median imputation for missing values, and applying normalization or transformation to fix inconsistencies. Taking an online course data analytics helps you master tools like Python, R, and Excel for efficient data cleaning. Additionally, earning a Data Analytics certification equips you with industry-recognized methods to manage real-world data issues confidently. These courses often include hands-on projects where you work with messy datasets, preparing you for practical challenges in business intelligence, machine learning, and decision-making roles.