This project will teach how to analyze the music playlist database. This analysis of the database with SQL will help the store to understand its business growth by answering some QUESTIONS.
This project focuses on the analysis of the dataset of an online music store by using SQL. In order to make better decisions about the store's growth, the project will answer a set of questions regarding its performance.
I learned to import data using python into my database. I used visual code IDE where I leveraged the power of pandas library and create_engine funtion from the SQLAlchemy library to import the .csv files into the database.
Tip
Click HERE to get help importing data to your database.
This project sharpened my SQL querying skills significantlly. I wrote various types of queries to retrieve specific information from the database based on the questions provided. This included simple SELECT statements, joins, aggregation functions, subqueries, and more complex operations.
Finally, working through a list of questions provided for the project, it required problem-solving and critical thinking skills. I was encountered with scenarios where I had to interpret the questions, identify the relevant data, and formulate SQL queries to extract the required information effectively.
Data was provided by Rishabh Mishra in his repository. The dataset included csv files which are 'album', 'artist', 'customer', 'employee', 'genre', 'invoice', 'invoice_line', 'media_type', 'playlist', 'playlist_track' and 'track'. I would also provide him credits for his guidance and efforts.
This Project was succesful in answering these QUESTIONS. You can check the solutions of the project HERE which would help the music store to make informed decisions about there marketing, business growth and performance.