A comprehensive coursework repository containing Jupyter notebooks and practical exercises for learning data science and analytics at Curtin University. This repository serves as a structured learning path covering Python programming fundamentals, data manipulation with pandas, and analytical thinking concepts.
This repository contains educational materials organized into modules that progressively build data science and programming skills. Each module focuses on specific learning objectives, from basic Python syntax to advanced data manipulation techniques using pandas. The content is designed for university-level coursework and includes hands-on activities, mini-projects, and practical applications in data analysis.
ISYS2001/
├── Module 01/ # Python Fundamentals
│ ├── 01_Hello_Python!_Your_First_Steps_in_Coding.ipynb
│ ├── 02_Fixing_Mistakes_in_Python.ipynb
│ ├── 03_Making_Your_Python_Code_Clear_and_Readable.ipynb
│ └── Mini_Project_01_AI_Budget_Tracker.ipynb
├── Module 02/ # User Input and Data Collection
│ ├── Activity_1_Personalised_Greeting_&_User_Preferences.ipynb
│ ├── Activity_2_Ad_Hoc_User_Preferences_Survey.ipynb
│ ├── Activity_3_User_Preferences_with_Simplified_Methodology.ipynb
│ └── Mini_Project_02_Finance_Tracker_Profile_Setup.ipynb
├── Module 03 - Making Computers Think/ # Conditional Logic
├── Module 04 - Going Loopy/ # Loops and Iteration
├── Module 05 - Function Junction/ # Functions and Code Organization
├── Module 06 - Organising Your Thoughts/ # Data Structures
└── Module 07 - Directing Pandas/ # Data Analysis with Pandas
- Python 3.7 or higher
- Jupyter Notebook or JupyterLab
- Clone the repository:
git clone https://github.com/michael-borck/ISYS2001.git
cd ISYS2001- Create a virtual environment (recommended):
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Install required packages:
pip install jupyter pandas numpy matplotlib seaborn- Launch Jupyter Notebook:
jupyter notebookBegin with Module 01 to establish Python programming fundamentals:
- Start with
01_Hello_Python!_Your_First_Steps_in_Coding.ipynbfor basic syntax - Progress through
02_Fixing_Mistakes_in_Python.ipynbto learn debugging - Review
03_Making_Your_Python_Code_Clear_and_Readable.ipynbfor best practices - Complete the mini-project to apply learned concepts
Each module builds upon previous knowledge:
- Modules 01-02: Python basics and user interaction
- Modules 03-04: Control structures and loops
- Modules 05-06: Functions and data organization
- Module 07: Data analysis with pandas
Open any notebook file (.ipynb) in Jupyter and execute cells sequentially:
# Example from Module 01
print("Hello, Data Science World!")Each module includes practical mini-projects:
- AI Budget Tracker: Apply Python basics to financial tracking
- Finance Tracker Profile Setup: User input and data validation
- Additional projects in subsequent modules
By completing this coursework, students will:
- Master Python programming fundamentals
- Understand data structures and algorithms
- Learn data manipulation using pandas
- Develop analytical thinking skills
- Apply programming concepts to real-world scenarios
- Create functional data science applications
This repository is primarily for educational purposes. For suggestions or improvements:
- Fork the repository
- Create a feature branch
- Submit a pull request with detailed descriptions
This repository is part of the ISYS2001 course curriculum at Curtin University, focusing on:
- Data science foundations
- Python programming for analytics
- Practical application of computational thinking
- Preparation for advanced data science concepts
For course-related questions or technical issues:
- Consult course materials and documentation within notebooks
- Review comments and markdown explanations in each notebook
- Refer to Python and pandas official documentation for additional help
This repository contains educational materials for academic use. Please respect academic integrity guidelines when using these materials for coursework.