Chapter 1
Foundations
1: Introduction to Python
Chapter 2
Data Structures
2: Variables and Data Types
Chapter 3
Modularity
3: Functions
Chapter 4
Logic & Flow
4: Control Structures & Loops
Chapter 5
Genomic I/O
5: File Input and Output
Chapter 6
Linked Page
6: Object-Oriented Programming
Chapter 7
Linked Page
7: Modules and Packages
Chapter 8
Linked Page
8: Data Analysis & Datasets
Chapter 9
Linked Page
9: Data Analysis & Visualization
Chapter 10
Linked Page
10: Introduction to Bioinformatics
Chapter 11
Linked Page
11: Analyzing Biological Data
Chapter 12
Linked Page
12: Machine Learning for Biology
Peer-Reviewed Foundations
Seminal peer-reviewed benchmarks and methodology literature
📚 Foundational Literature & Methodology Benchmarks
Biopython & Sequence Computing
- Cock PJ, Antao T, Chang JT, et al. (2009). Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25(11):1422–1423. doi:10.1093/bioinformatics/btp163
- Harris CR, Millman KJ, van der Walt SJ, et al. (2020). Array programming with NumPy. Nature 585:357–362. doi:10.1038/s41586-020-2649-2
- Virtanen P, Gommers R, Oliphant TE, et al. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17(3):261–272. doi:10.1038/s41592-019-0686-2
Data Wrangling & Visualization
- McKinney W. (2010). Data structures for statistical computing in Python (pandas). Proc 9th Python Sci Conf :56–61. doi:10.25080/Majora-92bf1924-00a
- Hunter JD. (2007). Matplotlib: A 2D graphics environment. Comput Sci Eng 9(3):90–95. doi:10.1109/MCSE.2007.55
- Waskom ML. (2021). Seaborn: statistical data visualization. J Open Source Softw 6(60):3021. doi:10.21105/joss.03021
Machine Learning & Biological AI
- Pedregosa F, Varoquaux G, Gramfort A, et al. (2011). Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825–2830. doi:10.5555/1953048.2078195
- Lundberg SM, Lee SI. (2017). A unified approach to interpreting model predictions (SHAP). NeurIPS 30:4765–4774. doi:10.48550/arXiv.1705.07874
- Chen T, Guestrin C. (2016). XGBoost: A scalable tree boosting system. ACM KDD :785–794. doi:10.1145/2939672.2939785