Introduction to Python—IND216

Overview

This repository contains comprehensive educational materials for an introductory Python course (IND216) designed for analytics students. The course emphasizes practical data wrangling skills using Python's technical and scientific computing ecosystem, making it ideal for students with minimal or no programming background.

Course Philosophy

Rather than attempting an exhaustive coverage of Python's features, this course focuses on building a solid foundation in:

• Core Python programming concepts
• Data manipulation fundamentals
• Scientific computing with NumPy
• Data analysis with Pandas

Repository Contents

📚 Course Notes

• Location: /notes/
• Format: LaTeX source files and compiled PDF
• Content: Complete course notes covering six modules of Python programming for data analysis
• Building: Run pdflatex -shell-escape notes.tex from the notes directory

🌐 Interactive Website

The repository also hosts a Quarto-based website featuring a five-part introduction to Python for MSc students in analytics, providing interactive examples and exercises for hands-on learning.

Learning Objectives

Upon completion of this course, students will be able to:

• Write Python scripts for data processing tasks
• Manipulate and analyze data using NumPy arrays
• Perform data wrangling operations with Pandas DataFrames
• Develop reproducible data analysis workflows

Prerequisites

No prior programming experience is required. The course is designed to take students from zero coding knowledge to practical data analysis skills.

Getting Started

1. Clone the repository: bash git clone https://github.com/simoneSantoni/intro-to-Python-IND216.git

2. Set up Python environment (recommended): bash conda create -n ind216 python=3.9 conda activate ind216 conda install numpy pandas matplotlib jupyter

3. Access course materials:

   • PDF notes are available in /notes/notes.pdf after compilation
   • Interactive notebooks and examples are throughout the repository

Course Modules

1. Organization of the Notes and IND216 - Course overview and learning approach
2. Getting Started with Python - Installation, environments, and running programs
3. Python Objects - Data types, control flow, and basic programming constructs
4. Technical & Scientific Computation with NumPy - Array operations and numerical computing
5. Data Management with Pandas - DataFrames and data manipulation
6. Applied Examples and Best Practices - Real-world data analysis workflows

Contributing

This is an educational repository. If you find errors or have suggestions for improvements, please open an issue or submit a pull request.

License

This educational material is provided under the MIT License. See the LICENSE file for details.

Contact

For questions about the course content or materials, please open an issue in this repository.