Python Foundations Projects

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Project Overview

This repository contains two foundational Python projects completed during the early stages of the TripleTen Data Analytics program.

The projects focus on core Python programming concepts, including data structures, loops, conditional statements, functions, error handling, string manipulation, and introductory data analysis techniques.

These exercises provided the programming foundation required for more advanced data analytics and machine learning projects.

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Projects Included

Sprint 1 — Store 1 Customer Data

A data quality and customer information management project.

Main topics:

• Data cleaning
• String manipulation
• Data type conversion
• Error handling with try/except
• Lists and nested lists
• Loops and conditional statements
• Basic business calculations

Key objectives:

• Standardize customer information
• Validate user data
• Analyze customer spending behavior
• Calculate business metrics

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Sprint 2 — Music Streaming Analysis

An introductory exploratory analysis project using music streaming data.

Main topics:

• Data inspection
• Data preprocessing
• Handling missing values
• Data filtering
• Grouping and aggregation
• Comparative analysis

Key objectives:

• Compare music listening habits between cities
• Identify behavioral differences among users
• Generate data-driven insights

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Technologies Used

• Python
• Pandas
• Jupyter Notebook
• Git
• GitHub

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Skills Demonstrated

• Variables and data types
• Lists and dictionaries
• Loops (for and while)
• Conditional statements (if, elif, else)
• Functions
• Exception handling (try/except)
• String methods
• Data cleaning techniques
• Basic analytical thinking
• Problem solving

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Repository Structure

python-foundations-projects/
│
├── sprint1_store1_customer_data/
│   └── store1_customer_data.ipynb
│
├── sprint2_music_streaming_analysis/
│   └── music_streaming_analysis.ipynb
│
├── README.md
├── LICENSE
└── .gitignore

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How to Run

1. Clone the repository:

git clone https://github.com/etienne-94/python-foundations-projects.git

2. Open either notebook:

sprint1_store1_customer_data/store1_customer_data.ipynb

or

sprint2_music_streaming_analysis/music_streaming_analysis.ipynb

3. Run the notebook cells sequentially.

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Conclusion

These projects established the programming foundation necessary for data analytics work.

Through practical exercises involving data cleaning, validation, manipulation, and analysis, essential Python skills were developed and later applied in more advanced projects involving statistical analysis, exploratory data analysis, machine learning preparation, and business intelligence.

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Author

Etienne Viegas dos Santos

Data Analytics Student | TripleTen

GitHub: https://github.com/etienne-94