Project Overview
Data Cleaning, Data Exploration
SQL
Repository Contents
Netflix EDA.sql
Exploratory Data Analysis of Netflix content dataset
Data Explorationnetflix data cleaning.sql
Data cleaning and standardization processes for Netflix dataset
Data Cleaningvideo_game_sales_EDA.sql
Comprehensive analysis of video game sales data
Data Explorationnetflix_titles.csv
Raw Netflix dataset for analysis
DatasetThe Goal: Showcasing My SQL Skills
I put together this collection of SQL scripts to show my hands-on skills in data cleaning and exploratory data analysis (EDA). Each script is a practical example of how I use SQL to take raw, messy data and turn it into something clean and useful for finding insights.
My Process: Cleaning and Exploring Data
In these projects, I worked with a couple of different datasets, like the Netflix content library and a video game sales database. My process was always the same:
- Data Cleaning: First, I use SQL to clean everything up. This means finding and removing duplicates, handling null values, and making sure all the data formats are standardized and consistent.
- Data Exploration: Once the data is clean, I use SQL to start exploring. I write more complex queries using JOINs, subqueries, and CTEs to analyze the data, identify trends, and recognize patterns that can answer business questions.
Featured Scripts
Netflix Data Analysis
A project focused on data cleaning and EDA for the Netflix content dataset.
Video Game Sales Analysis
An in-depth analysis of video game sales, exploring market trends and platform performance.
