Data Cleaning with pandas

Real-world data arrives messy. Missing values, inconsistent text, and outliers can quietly skew your results. In this hands-on course, discover how to turn that raw data into clean, analysis-ready datasets with pandas. Start with pandas fundamentals, then move into the techniques data professionals rely on to prepare data with confidence. Explore how to work with pandas Series and DataFrames, select rows and columns with iloc and loc, and filter data with Boolean conditions. Learn how to clean and match text using regular expressions, handle missing data by dropping or filling values with statistical techniques, and reason about what outliers mean before you remove them. By the end of this course, you'll be prepared to diagnose data-quality issues and clean a dataset for reliable analysis. This intermediate-level course is an ideal fit for data analysts, data scientists, Python developers, and anyone who works with data.

Learning objectives
Work with pandas series and DataFrames.
Select rows and columns using iloc and loc.
Clean and match text using regular expressions.
Handle missing data by dropping, filling, or imputing values.
Identify and remove outliers appropriately for the application.
Prepare a reliable dataset for downstream analysis.