Project Overview

The iStore Insights project illustrates my journey as a data analyst, utilizing SQLite to explore and extract insights from the rich dataset of the Apple AppStore. The project encompasses two primary phases: Exploratory Data Analysis (EDA) and Data Analysis.

Exploratory Data Analysis

In the EDA phase, I merged diverse datasets to create a comprehensive view of the Apple AppStore, forming the basis for in-depth exploration.

Unique App Identification

Ensuring data integrity, I identified the number of unique apps and checked for missing values in critical fields.

Genre Insights

Analyzing the number of apps per genre, I uncovered the AppStore’s genre landscape, highlighting popular and niche categories.

Ratings Overview

An overview of app ratings revealed minimum, maximum, and average user ratings, providing insights into user sentiment.

Data Analysis

In the Data Analysis phase, I delved deeper into app characteristics and user behavior, yielding valuable insights.

Paid vs. Free Apps

I investigated whether paid apps tend to have higher ratings compared to free apps, offering insights into monetization strategies.

Multilingual Apps

By categorizing apps based on the number of supported languages, I explored potential correlations between language diversity and user ratings.

Low-Rated Genres

Identifying genres with low ratings shed light on areas for potential improvement and innovation.

App Description Length

An exploration of the relationship between app description length and user ratings unveiled intriguing insights, offering guidance on content presentation.

This project showcases my SQL proficiency and my ability to derive actionable insights from complex data. It reflects my evolution as a data analyst, from data exploration to hypothesis testing, providing a strong foundation for data-driven decision-making.