Foundations of Biological Data Analysis

Week 1: Bioinformatics Foundations & Setup

1. Day 1: Introduction to bioinformatics and industry applications
2. Day 2: Central Dogma and FASTA/FASTQ file formats
3. Day 3: Biological databases — NCBI, UniProt, and PDB
4. Day 4: Linux command line, grep, awk, and sed for biological data
5. Day 5: Sequence alignment, BLAST basics, and retrieval of gene/protein sequences linked to a human disease

Week 2: Python for Biology & Statistics

1. Day 1: Python fundamentals — variables, loops, functions, and file handling
2. Day 2: NumPy arrays and statistical analysis of sequence data
3. Day 3: Pandas for metadata, filtering, and biological datasets
4. Day 4: Biopython — FASTA parsing, GC content, and sequence translation
5. Day 5: Matplotlib, Seaborn, basic statistics, and FASTQ quality/GC distribution analysis

Week 3: Sequence Analysis & Phylogeny

1. Day 1: Pairwise alignment — Needleman–Wunsch and Smith–Waterman
2. Day 2: Multiple sequence alignment using Clustal Omega and MUSCLE
3. Day 3: Phylogenetic trees — Neighbor-Joining, Maximum Likelihood, and interpretation
4. Day 4: Motifs and domains using MEME, PROSITE, and Pfam
5. Day 5: Protein structure fundamentals, PDB, PyMOL, and a practical sequence/structure analysis project

Week 4: Remote Sensing & AI Applications

1. Day 1: Remote sensing fundamentals — NDVI, satellite bands, and Google Earth Engine
2. Day 2: Object detection using YOLO for fruit and pest identification
3. Day 3: NLP applications in agriculture and crop-advisory chatbot concepts
4. Day 4: Reinforcement learning for irrigation and machine learning for crop-price forecasting
5. Day 5: AI ethics, data privacy, bias, and agricultural application case studies

Week 5: Applied Projects & Development

1. Day 1: Building a disease-detection web application using Streamlit
2. Day 2: Preparing and processing data for crop disease detection
3. Day 3: Developing a crop-yield prediction dashboard
4. Day 4: Integrating data visualization and model outputs into applications
5. Day 5: Testing, evaluating, and improving project performance

Week 6: Capstone & Career Readiness

1. Day 1: Finalize the project problem, dataset, and objectives
2. Day 2: Data preprocessing, analysis, and model development
3. Day 3: Application development and result visualization
4. Day 4: Project documentation, testing, and presentation preparation
5. Day 5: Final project demonstration, revision, and future learning roadmap