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Transferring Data with ETL

Exercise: Data Cleaning

Learn how to clean and prepare raw data by removing missing credit scores, filling missing incomes for specific groups, and eliminating duplicate records. This lesson helps you apply data cleaning techniques essential for transforming datasets within ETL processes to ensure reliable analysis.

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In this exercise, we’re given a dataset about the company’s customers. Unfortunately, the data is not clean. There are a few missing and duplicate values. Our task is to clean the data and prepare it for further analysis. Let’s take a look at the data.

C++
+---------+----+---+------+------+------------+
|id_number|name|age|gender|income|credit_score|
+---------+----+---+------+------+------------+
|   135245|John| 25|     M| 50000|        null|
|   223452|Jane| 30|     F| 60000|         700|
|   341412| Joe| 35|     M| 70000|         800|
|   341412| Joe| 35|     M| 70000|         800|
|   574355|Jill| 40|     F| 80000|         900|
|   253774|Jack| 45|     M| 90000|        1000|
|   856585|Jack| 75|     M|  null|         300|
+---------+----+---+------+------+------------+

We can see that there are a few problems with the data:

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