Removing Duplicates, Outliers and Inconsistencies in Data Analyst
Many beginners try to jump directly to tools, but strong understanding starts with the basic idea behind the technique.
Chapter Overview
Duplicates can make revenue look larger than reality, and outliers can hide the true pattern of normal behavior. Cleaning these issues is essential before reporting results.
How Duplicates Appear
They may come from system retries, repeated imports, or manual entry mistakes. Outliers may represent real events or data errors. An order worth 5 lakh may be a VIP customer, or it may be a decimal mistake.
Analyst Habit
Do not remove outliers blindly. First investigate them. In analytics, unusual values are sometimes the most informative rows in the whole dataset.
Quality Check
Use counts, distinct counts, boxplots, and summary statistics to identify suspicious records before deciding what to clean.
Key Takeaways
- Clean duplicate records and identify suspicious values before analysis.
- This chapter belongs to Data Cleaning & Data Wrangling and is written in a simple student-friendly style.
- Practice with messy dataset cleanup examples to build confidence faster.
