Introduction
Raw data is often messy it may have missing entries,errors,or duplicates.Cleaning data is the crucial step that prepares your dataset for accurate analysis.It might take time,but clean data saves you from wrong conclusions.
Common Cleaning Tasks
(i)Removing duplicate rows.
(ii)Filling or removing missing values.
(iii)Correcting typos or inconsistent formatting.
(i)Filtering out irrelevant data.
How to Clean Efficiently
(i)Start by exploring your data to find issues.
(ii)Use tools like Excel filters or Python libraries (Pandas) to clean faster.
(iii)Keep track of what changes you make for transparency.
Try This Beginner Activity
Step 1:Open a spreadsheet with some data (can be your own or a downloaded set).
Step 2:Find and remove any duplicate entries.
Step 3:Identify any missing values and decide how to handle them.
Coming Next
In Part 8: How to Analyze Data Without Feeling Lost,we’ll explore simple analysis techniques you can start using today.
💬Over to You:
What’s the biggest data-cleaning challenge you’ve faced so far and how did you solve it?Share in the comments so other beginners can learn from your experience.
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