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Python Programming For Biologists :A Guide To Programming
USD $59.00 USD $249.00Price range: USD $59.00 through USD $249.00
Aim: To provide advanced-level training on python for biologists, focusing on specialized libraries and tools used in bioinformatics and computational biology.
Aim
Python Programming for Biologists: A Guide to Programming trains life-science learners to use Python for practical biological data handling, analysis, and automation. You’ll start from core programming concepts and progress to working with common bioinformatics data formats, basic statistics, visualization, and reproducible workflows—so you can write scripts that save time in the lab and accelerate research.
Program Objectives
- Learn Python from Zero: Variables, data types, conditions, loops, functions, and debugging.
- Work with Biological Data: Read/write FASTA/FASTQ, CSV/TSV, and basic metadata tables.
- Automate Routine Tasks: Batch file processing, renaming, parsing, and report generation.
- Analyze & Visualize Data: Use NumPy/Pandas for analysis and Matplotlib for plots (publication-friendly).
- Intro Bioinformatics Tools: Use Biopython basics for sequences, translations, and annotations (intro-level).
- Build Reproducible Workflows: Notebooks, scripts, environments, and project structure.
- Practice Research Communication: Present results with clear code, tables, and figures.
- Hands-on Application: Complete a capstone project using real biological datasets.
Program Structure
Module 1: Python Setup for Biologists
- Installing Python (Anaconda/Miniconda concepts), Jupyter, VS Code basics.
- Running Python: notebooks vs scripts; when to use each.
- Files, folders, paths: working safely with data directories.
- First biological scripts: simple calculators and file readers.
Module 2: Python Fundamentals (Core Programming)
- Variables, data types, strings, lists, tuples, dictionaries, sets.
- Conditions and loops: if/elif/else, for/while; common biological examples.
- Functions: writing reusable code blocks; parameters and return values.
- Errors and debugging: reading tracebacks, fixing common beginner mistakes.
Module 3: Working with Files & Biological Formats
- Reading/writing text files and tabular data (CSV/TSV).
- Parsing FASTA: sequence ID, header metadata, multi-line sequences.
- FASTQ concepts: reads, quality scores (intro-level parsing).
- Batch processing: process multiple files and write outputs reliably.
Module 4: Data Analysis with NumPy & Pandas
- NumPy arrays: fast computations, indexing, and basic statistics.
- Pandas dataframes: filtering, grouping, aggregation, joins/merges.
- Cleaning biological datasets: missing values, duplicates, and type conversions.
- Building analysis-ready tables from raw lab/omics metadata.
Module 5: Visualization for Biological Data
- Plotting fundamentals with Matplotlib: line, bar, histogram, scatter.
- Bio examples: growth curves, expression distributions, QC plots, read length histograms.
- Annotation and styling for publication clarity: labels, legends, scales.
- Exporting figures with appropriate formats and resolution.
Module 6: Sequence Analysis with Biopython (Practical Intro)
- Biopython essentials: Seq, SeqRecord, and SeqIO read/write.
- Basic sequence operations: GC%, reverse complement, translation, ORFs (conceptual + practice).
- Motif and pattern searching (intro-level).
- Simple annotation handling: parsing headers and organizing results tables.
Module 7: Bioinformatics Automation & Mini Pipelines
- Writing command-line scripts (argparse) for reusable tools.
- Logging and progress tracking for long runs.
- Running external tools safely (subprocess): concepts and best practices.
- Mini pipeline design: inputs → processing steps → outputs → summary report.
Module 8: Reproducible Research & Good Coding Practices
- Project structure: folders, README, requirements, data, outputs.
- Environments: pip/conda basics, version pinning, reproducibility.
- Testing basics: sanity checks and simple unit tests (intro).
- Documentation: docstrings, comments, and clean notebooks.
Final Project
- Choose a dataset (FASTA/FASTQ + metadata) or use a provided sample dataset.
- Build a Python workflow: import → parse → QC → summary → plots → output tables.
- Create a short report describing the dataset, methods, results, and limitations.
- Deliverables: Python scripts/notebook + results tables + figures + project README.
Participant Eligibility
- UG/PG students in Biotechnology, Microbiology, Genetics, Life Sciences, Bioinformatics
- PhD scholars and researchers needing programming for data analysis and automation
- Lab professionals handling sequencing, assay data, or large experimental datasets
- Beginners with no coding background who want to learn Python for biology
Program Outcomes
- Programming Confidence: Ability to write Python code independently for common research tasks.
- Data Handling Skill: Ability to read, clean, and analyze biological datasets and metadata tables.
- Sequence Literacy: Ability to perform basic sequence operations and summarization using Biopython.
- Automation Ability: Ability to build scripts that save time and reduce manual errors.
- Reproducible Workflow: Ability to package your analysis as a clean, repeatable project.
- Portfolio Deliverable: A completed capstone project you can showcase for internships/jobs/research roles.
Program Deliverables
- Access to e-LMS: Full access to course lessons, datasets, and code templates.
- Starter Code Pack: File parsing scripts, Pandas templates, and plotting templates.
- Practice Exercises: Weekly assignments with solutions and debugging walkthroughs.
- Capstone Support: Guided project planning, code review, and interpretation support.
- Final Assessment: Certification after assignments + capstone submission.
- e-Certification and e-Marksheet: Digital credentials provided upon successful completion.
Future Career Prospects
- Bioinformatics / Genomics Analyst (Entry-level)
- Biological Data Analyst / Research Data Associate
- Computational Biology Research Assistant
- Lab Automation & Data Processing Associate
- Junior Python Developer (Life Sciences)
Job Opportunities
- Academic & Research Labs: Omics data handling, scripting, and reproducible analysis support.
- Genomics & Diagnostics Companies: Data QC, reporting automation, targeted sequencing support.
- Biotech & Pharma R&D: Data processing, assay analytics, and computational support roles.
- Core Facilities & CROs: Pipeline support, dataset handling, and analysis documentation.
- Health/Agri-Bio Startups: Rapid prototyping for bio-data workflows and MVP analytics tools.
| Category | E-LMS, E-LMS+Videos, E-LMS+Videos+LiveLectures |
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