Program Structure

Module 1: Getting Started with Python

• Installing Python, Jupyter, and essential tools.
• Running code in notebooks vs scripts.
• Variables, data types, and basic operations.
• Writing clean code: naming and comments.

Module 2: Control Flow and Functions

• Conditionals and loops.
• Functions, parameters, and return values.
• Lists, tuples, dictionaries, sets.
• Common errors and debugging basics.

Module 3: Working with Biological Data Files

• Reading/writing CSV and TSV files.
• Parsing FASTA and basic sequence handling (intro).
• Data cleaning: missing values and formatting.
• Creating tidy datasets for analysis.

Module 4: NumPy for Scientific Computing

• Arrays and vectorized operations.
• Basic statistics and transformations.
• Filtering and indexing for biological datasets.
• Performance tips for large data.

Module 5: Pandas for Biological Data Analysis

• DataFrames: selecting, filtering, grouping.
• Merging datasets and reshaping tables.
• Basic exploratory analysis and summaries.
• Exporting results for reports.

Module 6: Visualization for Biology

• Line plots, scatter plots, bar plots, histograms.
• Boxplots and basic distributions.
• Heatmaps (intro) for expression-like tables.
• Making plots publication-ready (basics).

Module 7: Intro Bioinformatics Workflows

• Sequence statistics: GC%, length, motifs (intro).
• Simple variant table handling (overview).
• Basic metadata handling for experiments.
• Pipeline thinking: input → processing → output.

Module 8: Reproducible Projects

• Folder structure, environments, and requirements.
• Writing reusable functions and scripts.
• Basic documentation and reporting.
• Exporting notebooks and results.

Final Project

• Analyze a biological dataset (sequence or experiment table).
• Deliverables: cleaned dataset + analysis notebook + plots + short report.
• Submit: project notebook and report.