Python for Genomic Data Science Course Syllabus

This course provides a practical introduction to Python programming in the context of genomic data science, designed for learners with basic programming knowledge. Over approximately 8 hours of content, you'll gain hands-on experience using Python to analyze real genomic datasets. The course is structured into four core modules followed by a final project, with each module building practical skills for processing, parsing, and automating workflows involving genomic data formats like FASTA and FASTQ. Learners will use Jupyter Notebooks throughout for interactive coding and data exploration, culminating in a portfolio-ready project that demonstrates proficiency in genomic data analysis with Python.

Module 1: Introduction to Python Programming

Estimated time: 2 hours

• Overview of Python’s relevance in genomic data science
• Setting up the programming environment with Jupyter Notebooks
• Writing and executing basic Python scripts
• Understanding variables, data types, and simple operations

Module 2: Data Structures and Control Flow

Estimated time: 1 hour

• Exploration of Python data structures: lists, dictionaries, tuples
• Implementing control flow using if statements and loops
• Practical exercises on manipulating genomic sequences

Module 3: Functions, Modules, and Packages

Estimated time: 1 hour

• Defining and invoking functions for code modularity
• Importing and utilizing Python modules and packages
• Applying functions to perform repetitive genomic data tasks

Module 4: Working with Genomic Data

Estimated time: 4 hours

• Reading and writing genomic data files (e.g., FASTA, FASTQ)
• Parsing and processing real genomic datasets
• Automating data analysis pipelines for genomic research

Module 5: Final Project

Estimated time: 2 hours

• Develop a Python script to process a provided FASTA file
• Analyze sequence data and generate summary statistics
• Document your workflow using Jupyter Notebook

Prerequisites

• Familiarity with basic programming concepts
• Basic understanding of biology or genomics
• No prior Python experience required, but comfort with computational thinking is helpful

What You'll Be Able to Do After

• Write Python scripts to manipulate and analyze genomic sequences
• Use Jupyter Notebooks for interactive data exploration
• Apply core programming constructs to biological data problems
• Automate common genomic data processing tasks
• Read, parse, and write standard genomic file formats like FASTA and FASTQ