Biological Languages

Week 1: Linux, Python & R Foundations

1. Day 1: Linux commands, file navigation, permissions, and file management
2. Day 2: Bash scripting — grep, awk, sed, pipes, and automation
3. Day 3: Python fundamentals — data types, loops, functions, and OOP
4. Day 4: NumPy, Pandas, data cleaning, and biological data handling
5. Day 5: R programming, dplyr, ggplot2, and practical FASTA/CSV processing

Week 2: Biological Sequence Analysis

1. Day 1: Biological sequence formats, Biopython, ORF identification, and GC content
2. Day 2: NCBI database searches and sequence retrieval
3. Day 3: BLAST, sequence alignment, and similarity analysis
4. Day 4: Gene dataset exploration and biological data visualization
5. Day 5: Mini-project — gene sequence analysis and visualization report

Week 3: Statistics & SQL for Biology

1. Day 1: Statistical foundations — t-tests, ANOVA, p-values, and FDR
2. Day 2: PCA, clustering, correlation, and omics data analysis
3. Day 3: SQL — SELECT, JOIN, GROUP BY, and database design
4. Day 4: Accessing NCBI, Ensembl, and UniProt data using APIs and Python
5. Day 5: Practical project — build a gene annotation database

Week 4: Scientific Visualization & Reporting

1. Day 1: Data visualization using Matplotlib and Seaborn
2. Day 2: ggplot2 — volcano plots, MA plots, and heatmaps
3. Day 3: Interactive biological dashboards using Plotly
4. Day 4: Publication-quality figures and scientific reporting
5. Day 5: Project — differential gene expression visualization report

Week 5: Machine Learning & NGS Fundamentals

1. Day 1: ML foundations, train/test splitting, evaluation metrics, and Random Forest
2. Day 2: Feature selection and classification for biological datasets
3. Day 3: NGS quality control using FastQC, MultiQC, and trimming tools
4. Day 4: DNA/RNA sequence alignment using BWA and STAR
5. Day 5: Practical — DNA-seq alignment and BAM file processing

Week 6: Variant Calling & RNA-seq Analysis

1. Day 1: Variant calling concepts, GATK, VCF files, and variant annotation
2. Day 2: RNA-seq quantification using featureCounts and Salmon
3. Day 3: Differential expression analysis using DESeq2 and edgeR
4. Day 4: Functional enrichment — GO, KEGG, and GSEA
5. Day 5: Project — end-to-end RNA-seq analysis and pathway interpretation

Week 7: Advanced ML & Workflow Engineering

1. Day 1: SVM and XGBoost for genomic classification and biomarker discovery
2. Day 2: Model interpretation using SHAP for omics data
3. Day 3: Workflow automation with Snakemake
4. Day 4: Nextflow — processes, channels, and containers
5. Day 5: Practical — build a reproducible RNA-seq workflow

Week 8: Advanced Omics & Systems Biology

1. Day 1: ChIP-seq and ATAC-seq analysis fundamentals
2. Day 2: Single-cell RNA-seq — Cell Ranger, UMAP, and clustering
3. Day 3: Marker gene identification and cell-type annotation
4. Day 4: Multi-omics integration and biological network analysis
5. Day 5: Project — single-cell clustering and marker gene analysis

Week 9: Cloud Computing & HPC

1. Day 1: AWS/GCP fundamentals, EC2, and S3
2. Day 2: Docker for bioinformatics tools and reproducible environments
3. Day 3: HPC fundamentals — SLURM, job arrays, and parallel computing
4. Day 4: Running NGS workflows using cloud and HPC resources
5. Day 5: Practical — execute and evaluate an RNA-seq workflow on available compute resources

Week 10: Reproducibility & Pipeline Deployment

1. Day 1: Running STAR and GATK workflows in computational environments
2. Day 2: Database hosting, cloud storage, and cost optimization
3. Day 3: Git, GitHub, Conda, and environment management
4. Day 4: FAIR principles, workflow documentation, and reproducible notebooks
5. Day 5: Project — publish a documented bioinformatics pipeline on GitHub

Week 11: Specialized Computational Biology

1. Day 1: Biological APIs — Entrez and Ensembl REST
2. Day 2: Metagenomics and long-read sequencing fundamentals
3. Day 3: Epigenomics and structural bioinformatics
4. Day 4: AI in biology — AlphaFold and large language models
5. Day 5: Practical — retrieve and analyze a GEO dataset

Week 12: Capstone Project & Portfolio

1. Day 1: Select a research problem and download the required dataset
2. Day 2: Data preprocessing, quality control, and pipeline setup
3. Day 3: Statistical analysis, machine learning, or functional interpretation
4. Day 4: Visualization, report writing, and reproducibility checks
5. Day 5: Final presentation, viva, GitHub portfolio review, and project feedback