Installation/setup of Python tools and execution of basic commands for understanding the Python programming workflow.

Writing first Python code using print(), strings, comments, and biology-themed text examples.

Hands-on practice with print statements, strings, comments, and simple message-based programs.

Understanding variables and basic data types for storing biological and experimental information.

Create variables for genes, sequences, sample IDs, expression values, and experiment labels.

Basic arithmetic, comparison operators, and biological calculations using Python.

Perform GC percentage, concentration, dilution, and simple lab data calculations.

Using ordered collections to store genes, samples, nucleotide bases, and biological observations.

Create and manipulate lists/tuples for DNA bases, sample names, and experimental values.

Using key–value pairs and unique collections for biological annotation and metadata management.

Build dictionaries for gene annotations, species data, and experimental sample information.

Using conditions and loops to automate repetitive biological data tasks.

Apply loops and conditions for DNA sequence counting, validation, and simple analysis.

Assessment of Python basics, variables, collections, control flow, biological calculations, and mini coding task submission.

Writing reusable functions for biological computations and sequence-based analysis.

Develop Python functions for GC content, sequence length, and nucleotide composition.

Reading and writing TXT, CSV, and simple biological data files using Python.

Hands-on practice importing, processing, and exporting biological datasets.

Using NumPy arrays for numerical computing and matrix-based biological data analysis.

Analyze gene expression-style matrices and numerical biological datasets using NumPy.

DataFrame-based biological data cleaning, filtering, sorting, and summarization.

Analyze biological sample datasets using Pandas for filtering, grouping, and interpretation.

Create basic scientific plots such as line plots, bar plots, and scatter plots.

Generate and interpret plots for biological and experimental datasets using Matplotlib.

Introduction to Biopython for DNA/protein sequence handling and FASTA file analysis.

Import, analyze, and interpret FASTA sequence datasets using Biopython tools.

Combine Python, Pandas, visualization, and Biopython for small biological analysis workflows.

Scientific review writing involving literature survey, biological data interpretation, visualization integration, and Python-based analysis summary.

Final evaluation