The Spotify Data Analysis Project demonstrates the power of data analysis in fields like business, research, and meteorology. Using Python, I analyzed music datasets to extract insights, visualizing data and discovering patterns. This project enhanced my skills and understanding of how data connects to music, preparing me for future endeavors.

Python project to pull data from Spotify, YouTube, and other music streaming platforms for analytics purposes

A comprehensive data analytics project leveraging Power BI and a Spotify dataset to visualize and analyze music streaming patterns, artist performance, and audio characteristics.

This Power BI dashboard project dives deep into Spotify's music data to uncover trends, patterns, and insights across genres, artists, and listener behavior. Whether you're a data enthusiast, music lover, or aspiring analyst, this project showcases how data visualization can bring streaming analytics to life.

This project focuses on advanced SQL analysis using a real-world Spotify dataset. It covers end-to-end data exploration, schema design, and analytical querying to extract meaningful insights from music streaming data. The project includes exploratory data analysis, aggregation-based queries, subqueries, Common Table Expressions (CTEs), and window

This repository houses an implementation of a Spotify-esque streaming service and recommendation system utilizing Apache Spark and Kafka. It leverages a 100GB dataset of various mp3 files from the Free Music Archive. This was developed as part of a project for the course Fundamentals of Big Data Analytics (DS2004).

Data Analytics Project on Streaming data of Spotify

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Data analytics portfolio project analyzing Spotify streaming data for my band, Sakkaris

Data analytics project predicting top Spotify wrapped tracks and artists from personal streaming history.

This repository contains of my coding project about Data Analytics using Spotify Most Streamed Song

Final project for COSC301: End-to-end data analytics pipeline decoding Spotify's most-streamed songs.

Modern Data Stack project simulating Spotify streaming data with Kafka ingestion, Snowflake warehousing, dbt transformations, and Power BI analytics.

Data analytics project that processes and visualizes music streaming data to analyze Spotify listening patterns and algorithm-driven trends.

Advanced SQL project analyzing Spotify streaming data with exploratory and analytical queries using PostgreSQL.

Spotify analytics Data Lakehouse via Faker (python). Project is using Python, Kafka, Spark Structured Streaming, Trino, Iceberg, Airflow, Docker

Welcome to the **Spotify Streaming Analytics** project — a data-driven exploration of global music listening trends, artist performance, and platform engagement.

🚀 Spotify Data Analytics Project | Analyzing Spotify's streaming data using Power BI to uncover insights on user preferences, song popularity, and streaming trends. Features interactive dashboards, KPIs, and user segmentation to drive data-driven decisions. 🎵📊

"This project automates Spotify data processing using Apache Airflow. It includes tasks for extracting Spotify streaming data, cleaning it, and preparing it for future analytics workflows. The project is currently in progress and will be expanded with full data pipeline orchestration, scheduling, and monitoring."

TuneInsights is a streaming data pipeline that uses Apache Kafka to collect and process data from Spotify API. The project aims to provide real-time insights and analytics on Spotify Playback data using Opensearch Dashboard to help users analyze and understand trends, patterns, and user behavior on Spotify platform.

An end-to-end data analytics project exploring global music streaming trends using Spotify Charts data. This project combines Python for data wrangling and exploratory analysis with Power BI for interactive visualizations, offering insights into the most streamed songs, artists, and regional listening patterns.

This project analyzes Spotify music data using PostgreSQL to extract meaningful insights through SQL queries. It covers exploratory data analysis (EDA), data cleaning, and advanced analytics to understand track popularity, streaming patterns, and artist performance.

A data quality and analytics project that cleans, validates, and analyzes Spotify streaming data using SQL, Python, and Excel. Includes data quality checks, cleaned datasets, and visual analysis of how track features relate to popularity.

A data analytics project that explores a Spotify dataset using SQL to extract meaningful insights on tracks, artists, and user engagement. The project demonstrates data cleaning, aggregation, filtering, subqueries, and window functions to analyze streaming trends

End-to-end music industry analytics project — 25 years of Billboard, Grammy & Spotify data analyzed using SQL, Python, Tableau & PostgreSQL to uncover what drives artist longevity in the streaming era.

End-to-end data analytics project analyzing global Spotify streaming trends across tracks, artists, genres, and countries. The project leverages a structured data pipeline using Python (Pandas) for data ingestion, PostgreSQL for storage and transformation, and Power BI for interactive visualization.

A data analytics project that reconstructs a Spotify-style behavioral ecosystem to analyze streaming logs, model listening sessions, measure skip behavior, and derive mood-based product insights using enriched audio features.

POWER BI PROJECT—Spotify & YouTube Analysis (Integrated Music Analytics Dashboard) Analysed trends and customer response on Music Streaming & Video Viewing platform. Created a Power BI dashboard combining Spotify and YouTube data. Analysed user engagement, popular genres, and cross-platform trends.

This project implements a modern Azure Data Lakehouse architecture that enables scalable ingestion, transformation, and analytics of Spotify-like streaming data. It integrates Azure Data Factory, Databricks, and Delta Lake to handle incremental data loads, ensure high performance, and maintain strong data governance.

A project analyzing Spotify’s Most Streamed Songs of 2023 using R/Tableau. Includes visualizations for predictive analytics, ethical considerations in data visualization, and a detailed report with recommendations for Sony Music's CMO.