Guided Project: Profitable App Profiles for the App Store and Google Play Markets
In this guided project, you'll synthesize all the concepts you have learned in the Python Fundamentals for Data Science course and apply them to your efforts to add business value through practical data analysis. By this point, you have the Python skills to work on a Python-based data science project from beginning to end, and that’s exactly what you’ll do for this project.
In this guided project, you'll work as a data analyst for a company that builds Android and iOS mobile apps. The company you work at builds mobile apps and makes them available on Google Play and the App Store.
The only apps that are built are those that are free to download and install. This means the main source of revenue consists of in-app ads. It also means revenue for any given app is mostly influenced by the number of users who use the app — the more users who see and engage with the ads, the better. The goal of this project is to analyze data to help the developers understand what type of apps are likely to attract more users.
Showcasing how you can help make an organization profitable is a key skill for anyone in a Data Analyst or Data Scientist role, so this project will also make an excellent showcase of analytical skills and business acumen for your portfolio or GitHub.
As with all guided projects, we encourage you to experiment and extend your project, taking it in unique directions to make it a more compelling addition to your portfolio!
1. Analyzing Mobile App Data
2. Opening and Exploring the Data
3. Deleting Wrong Data
4. Removing Duplicate Entries: Part One
5. Removing Duplicate Entries: Part Two
6. Removing Non-English Apps: Part One
7. Removing Non-English Apps: Part Two
8. Isolating the Free Apps
9. Most Common Apps by Genre: Part One
10. Most Common Apps by Genre: Part Two
11. Most Common Apps by Genre: Part Three
12. Most Popular Apps by Genre on the App Store
13. Most Popular Apps by Genre on Google Play
14. Next Steps