[Project Title]

[Institution], Sudan – [Year]

Study type: [e.g. Cross-sectional descriptive study]

Degree level: MBBS

Institution: [Faculty and University]

Sample size: N = [X] [population]

Data analyst: Abdulrahman Sirelkhatim

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Background

[2-3 sentences on the research context and why this study matters]

Objectives

• [Objective 1]
• [Objective 2]
• [Objective 3]

Study Design & Methods

Component	Detail
Design
Setting
Population
Sampling
Sample size
Data collection

Technical suite:

Tool	Purpose
Python (pandas)	Data cleaning
IBM SPSS Statistics v26	Full statistical analysis
Python (matplotlib, seaborn)	Figure generation
Jupyter Notebook	Exploratory data analysis

Statistical methods:

• Descriptive statistics: Frequencies, percentages, means, standard deviations
• Bivariate analysis: [tests used]
• Multivariate analysis: [methods used]

Dataset

File	Description
1_data/raw/	Raw survey responses — excluded from version control (privacy)
1_data/cleaned/	Cleaned and recoded dataset

Repository Structure

project-name/
│
├── README.md
├── .gitignore
├── .ls-lint.yml
├── .markdownlint.yml
├── .markdownlintignore
│
├── .github/
│   └── workflows/
│       └── ci-checks.yml
│
├── 1_data/
│   ├── raw/
│   └── cleaned/
│
├── 2_cleaning/
│   └── cleaning.py
│
├── 3_notebooks/
│   └── exploratory_analysis.ipynb
│
├── 4_analysis/
│   ├── analysis.sps
│   └── figures.py
│
├── 5_figures/
│
└── 6_docs/
    └── results_chapter.docx

Key Results

[Summary of main findings — 3 to 5 bullet points]

Selected Figures

[Figure title]

Limitations

• [Limitation 1]
• [Limitation 2]

Files

Script	Purpose
2_cleaning/cleaning.py	Data cleaning and recoding
3_analysis/figures.py	Figure generation
3_analysis/analysis.sps	Full SPSS syntax
4_notebooks/exploratory_analysis.ipynb	EDA

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Data analyst: Abdulrahman Sirelkhatim | Analysis conducted [Month Year]