MaphallaKabelo1/NDTA631-Group-Assignment

Data Analysis and Visualisation - South African World Bank Data # NDTA631 Group Assignment ## South Africa: GDP Growth vs Employment by Economic Sector Data Analysis and Visualisation project using two South African datasets from World Bank Open Data (Data360): annual GDP growth and employment by economic sector (1961–2017). ## Group Members - Jodeane Kyle Bee – 202408335 - Kamogelo Angela Macena – 202424481 - Kabelo Solomon Maphalla – 202451685 - Kabelo George Sethunya – 202424816 - Raymond Thabang Mothoa – 202409842 ``` NDTA631-Group-Assignment/ ├── data/ │ ├── raw/ # Original World Bank CSV files │ └── final_clean/ # Cleaned, merged dataset used by all notebooks ├── deliverables/ │ ├── 01_data_preparation/ # Data loading, cleaning, validation │ ├── 02_numpy_analysis/ # NumPy statistical analysis │ ├── 03_visualisation/ # Matplotlib charts (bar, line, pie, scatter, histogram, box plot) │ ├── 04_database_integration/ # SQLite database build and queries │ └── 05_excel_python_analysis/ # Conditional formatting and further analysis ├── GROUP_ASSIGNMENT_combined.ipynb # All five deliverables combined into one notebook └── README.md ``` ## Environment Configuration This project requires: - Python 3.10 or later - Jupyter Notebook Required Python libraries: pandas numpy matplotlib (`sqlite3` and `csv` are used in Section 4 and are part of Python's standard library — no separate installation needed.) ### Installing dependencies ```bash pip install pandas numpy matplotlib jupyter ``` ## Execution Instructions 1. Clone this repository: ```bash git clone github.com cd NDTA631-Group-Assignment ``` 2. Launch Jupyter Notebook **from the repository root folder**: ```bash jupyter notebook ``` 3. To see the full project in one place, open `GROUP_ASSIGNMENT_combined.ipynb` at the root and use "Restart Kernel and Run All Cells". 4. To view an individual deliverable, run the notebooks in this order, using "Restart Kernel and R …