Data Cleaning Project in SQL

Steps followed:

1. Create schema

2. Load the data into a table

3. Create a staging table to be used for analysis

4. Remove duplicates

5. Standardize the data

6. Remove any unnecessary rows/columns (decide which are the best to kep for further analysis)

   

   Snippet of finding duplicates in a more advanced manner in MYSQL Workbench 8.0

   
   
   
   

   

   Data standardization

   
   

   

   Removing unnecessary data

7. Takeaways:

   1.Duplicate rows are sneaky, but patterns reveal them. (Even without a primary key, you can catch duplicates by thinking about what makes a record “unique.)

   2. Consistency matters more than completeness. A simple trim or standardization (Crypto vs. Cryptocurrency, United States vs. United States.) can dramatically improve the quality of your analysis.

   3. Missing values aren’t always a dead end. Instead of dropping rows with blanks, you can sometimes teach your dataset to fix itself.

#I used this Alex the Analyst's guide for this project: https://www.youtube.com/watch?v=4UltKCnnnTA