This repository contains course summaries,notes, analysis reports, assignments, and practical projects completed during my R programming and statistical data analysis learning journey. It documents my progressive development in R programming, biostatistics, and data analysis for bioinformatics and molecular biology applications, carried out alongside my M.Sc. in Biochemistry and Molecular Biology.
| Course Title | Institution | Completion Date | Folder Link |
|---|---|---|---|
| R Programming | Johns Hopkins University, USA | January 2025 | Folder |
| Hands-on R: From Basics to Data Analysis | OmniGenics, Egypt | August 2025 | Folder |
R-Programming-and-Statistical-Analysis/
│
├── README.md # Main repository README (overview & index)
│
├── R-Programming/
│ ├── README.md # Course-specific README (Johns Hopkins)
│ ├── courseNotes.md
│ └── assignment/
│ ├── assignment_report.md
│ └── Assignment2_Programming_CacheMatrix.R
│
└── Hands_on_R_From_Basics_to_Data_Analysis/
├── README.md
├── courseNotes.md
├── assignment01/
│ ├── assignment1_report.md
│ ├── Assignment1_Fundamental_R_Programming_and_Gene_Data_Analysis.Rmd
│ └── Assignment1_Fundamental_R_Programming_and_Gene_Data_Analysis.pdf
└── BloodPressure_Statistical_Analysis_Project/
├── README.md
├── BloodPressure_Analysis_Report.md
├── ProjectA_BloodPressure_Analysis.Rmd
├── ProjectA_BloodPressure_Analysis.pdf
└── BloodPressure.RData
Each folder includes:
README.md— overview, detailed course information, and learning outcomescourseNotes.md— study notes, functions, and conceptual breakdownsassignments/andprojects/— reproducible scripts, analyses, and detailed reports
These courses were selected to:
- Strengthen my R programming foundation for use in biostatistics, bioinformatics, and molecular data analysis
- Learn practical coding workflows for data wrangling, visualization, and statistical inference
- Apply R programming to biomedical and gene expression datasets
- Bridge the gap between theoretical statistics and real-world biological data analysis
They provided a solid and reproducible framework for programming, critical thinking, and research-level data interpretation.
As an M.Sc. candidate in Biochemistry and Molecular Biology, specializing in Molecular Cancer Biology and Bioinformatics, these R programming and statistical analysis courses significantly enhanced my ability to:
- Handle large biological datasets efficiently
- Apply parametric and non-parametric statistical tests
- Perform regression analysis and data visualization
- Write reproducible R code for cancer and biomedical data analysis
- Integrate computational analysis with biological interpretation
- R programming fundamentals (variables, data types, lists, matrices, data frames)
- File handling, descriptive/inferential statistics, and visualization
- Statistical hypothesis testing and regression modeling
- Function-based programming (including caching and optimization)
- Debugging, profiling, and writing clean reproducible R code
- Applying R in genomics, transcriptomics, and biomedical datasets
These materials are shared as part of my personal educational documentation and open learning journey.
Citation:
Hussein, Mohamed H. (2025). R Programming and Statistical Analysis Repository — [Course Summaries, Assignments, and Projects in R Programming]. GitHub repository. https://github.com/Mohamed-H-Hussein/R-Programming-and-Statistical-Analysis
This repository is licensed under the MIT License. Full license details: https://opensource.org/licenses/MIT
© 2025 Mohamed H. Hussein — All work authored and documented independently for educational and research purposes.
The software and materials are provided “as is” without warranty of any kind.