Aim
Python for Biological Data Science: A Beginner’s Guide to Programming teaches Python from scratch for biological data. Learn core programming, data handling, analysis with NumPy/Pandas, visualization, and beginner bioinformatics workflows with hands-on practice.
Program Objectives
- Python Basics: syntax, variables, types, loops, functions.
- Data Handling: files (CSV/TSV/FASTA), cleaning, merging.
- Scientific Libraries: NumPy and Pandas for analysis.
- Visualization: Matplotlib/Seaborn basics.
- Biological Data: sequences and simple statistics.
- Reproducibility: notebooks, scripts, and project structure.
- Capstone: analyze a real biological dataset.
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.
Participant Eligibility
- Biology, Biotechnology, Bioinformatics students and professionals
- No programming background required
- Basic stats concepts helpful
Program Outcomes
- Write Python code to analyze biological datasets.
- Use NumPy and Pandas for data processing.
- Create clear plots and summaries.
- Build a portfolio-ready biological data project.
Program Deliverables
- e-LMS Access: lessons, exercises, datasets.
- Toolkit: notebooks, templates, cheat sheets.
- Assessment: certification after capstone submission.
- e-Certification and e-Marksheet: digital credentials.
Future Career Prospects
- Bioinformatics Trainee
- Biological Data Analyst (Entry-level)
- Research Assistant (Data)
- Computational Biology Intern
Job Opportunities
- Research Labs: data handling and analysis support.
- Biotech/CROs: data cleaning, reporting, and analytics teams.
- Universities: genomics and systems biology groups.
- Healthcare/Diagnostics: basic bioinformatics support roles.
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