📘 Python Foundations for Data Analysis

A structured, professional learning notebook series for mastering Python as a data analyst.

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🔍 About This Repository

This repository contains a curated collection of Jupyter notebooks, organized to mirror a complete learning path through Python Foundations for Data Analysis.
Each chapter includes:

• A clean Markdown-based lecture summary
• Fully annotated Python examples
• Tables for quick reference
• Mutability & iterability comparisons
• Type conversion and syntax rules
• End-of-chapter Key Takeaways optimized for revision

This repo is built as a master notebook library that will grow with future topics such as data structures, functions, debugging, OOP, Pandas, NumPy, data cleaning, visualization, and more.

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📂 Repository Structure

python_data_analysis/ ├── README.md ├── master_notebook/ │ └── python_data_analysis_master.ipynb ├── chapters/ │ ├── chapter01_data_types.ipynb │ ├── chapter02_variables.ipynb │ └── ... ├── datasets/ │ └── sample_data.csv └── exercises/ └──...

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Folders Explained

Folder	Purpose
notebook/	All polished chapter notebooks (Jupyter).
data/	CSV or JSON files used for practice, exercises, and projects.
scripts/	.py files to store reusable functions or automation (if needed later).
.gitkeep	Ensures folders sync to GitHub even when empty.

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🧠 Chapter 1: Python Data Types (Summary)

This chapter introduces foundational concepts including:

• Data type categories (numeric, text, sequence, mapping, set, boolean, NoneType)
• Single-value vs multi-value data types
• Type checking with type()
• Valid & invalid type conversions
• Mutability (what can change)
• Iterability (what can be looped through)
• Sets and automatic duplicate removal
• How curly braces behave in Python ({}, {1,2}, {"a":1}, etc.)

A fully annotated and structured notebook is located at:
📄 notebook/chapter01_data_types.ipynb

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🚀 Skills Gained So Far

• Understanding Python’s core data types
• Using type() to debug and inspect variables
• Differentiating between mutable and immutable objects
• Identifying which objects are iterable
• Converting between common data types
• Working with sets and handling duplicate values
• Building organized, clean, and professional notebooks

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🏗️ Roadmap: Upcoming Chapters

You can expand this repository with the following chapters:

1. Variables, Expressions & Operators
2. Collections (Lists, Tuples, Dictionaries, Sets)
3. Control Flow (if/elif/else, loops)
4. Functions (Parameters, Return Values, Scope)
5. Debugging Techniques
6. Object-Oriented Programming Basics
7. File Handling (CSV, JSON)
8. Pandas Foundations
9. NumPy Foundations
10. Data Cleaning & Wrangling
11. Exploratory Data Analysis (EDA)

Every chapter will follow your standardized format:

Markdown Overview → Annotated Python Code → Summary Tables → Key Takeaways

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💡 How to Use This Repository

✔️ For Learning

Study each chapter notebook line by line.
Run the code yourself.
Take notes directly in the Markdown cells.

✔️ For Portfolio Building

This notebook series will form the foundation of your future Python portfolio for data science roles.

✔️ For Reuse in Future Classes

Your MSBA coursework will require Python, Pandas, NumPy, statistics, and ML foundations.
This repo ensures you always have a solid reference.

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🔧 Setup Instructions

1. Clone the repo:

   git clone https://github.com/tripledbt1/python_data_analysis.git

2. Open in VS Code or Codespaces.

3. Install Jupyter + Python extensions if needed.

4. Run the notebooks

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⭐ Acknowledgments

This repository is built in collaboration with ChatGPT as a learning partner, following a structured study plan aligned with:

Maven Analytics: Python for Data Analysis Track

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📬 Questions or Improvements?

Feel free to open an issue or continue expanding this repository. This is your long-term Python foundation — build it once, build it right.