Lesson 5-2 explained: Data Cleaning and Transformation

Lesson summary

Raw data is rarely ready to use. Cleaning fixes missing values, outliers, and duplicates; transformation converts types, rescales, and reshapes data to fit the purpose. "Garbage in, garbage out" — the quality of any analysis depends on this preparation.

Takeaway: Clean first (missing values, outliers, duplicates), then transform (convert types, normalize or standardize, reshape by pivot, join, aggregation). Every fix is a judgment set by the purpose — garbage in, garbage out.

Key terms

• Data cleaning
• Data transformation
• Missing value
• Outlier
• Abnormal value
• Pivot
• Join
• Aggregation

Textbook page: Arabic book 11 · English book 12

Free questions on this lesson

From the textbook question bank. Think of the answer first, then open it.

1. 1. What do we call fixing data issues and arranging data into a format that fits the analysis?

   • Data cleaning and transformation
   • Presenting data in a final report
   • Deleting all of the old data
   • Data collection and sampling
   Answer

   Correct answer: Data cleaning and transformation

   Fixing missing values, outliers and duplicates and arranging data to fit the purpose is called data cleaning and transformation.

   1. The Data Cleaning and Transformation Stages · page 13

2. 2. Is there one response to missing values that is always correct?

   • Yes, imputing the mean is always right
   • Yes, always leaving the data exactly as it is
   • Yes, deletion is always the correct one
   • No, the choice needs judgment based on the purpose
   Answer

   Correct answer: No, the choice needs judgment based on the purpose

   There is no single correct response; judgment according to the purpose and the amount and pattern of missingness is required.

   2. Handling Missing Values · page 13

3. 3. What happens if date notations are not unified?

   • Data from the same day is treated as different days
   • The dates turn into a 0-to-1 range
   • The whole table is deleted automatically
   • Every date becomes a missing value
   Answer

   Correct answer: Data from the same day is treated as different days

   If date notations are not unified, data from the same day is treated as different days, so aggregation goes wrong.

   4. Data Type Conversion · page 14

Lessons in this chapter 5: Data Collection and Cleaning

1. 5-1 Methods of Data Collection
2. 5-2 Data Cleaning and Transformation
3. 5-3 Open Data and APIs