Duration: 3 Weeks (January 19 – February 6)
Timing: Afternoon 2:00 PM – 5:00 PM
Covers: Python, R Programming, Linux, Applied Statistics & HPC
Total Duration: 3 Weeks – 37.5 Hours
Python Programming – 5 Days (12.5 Hrs)
Goal: Learn Python for data analysis & bioinformatics.
- Python Fundamentals, Functions & Data Structures
- File Handling
- Pandas, NumPy, Matplotlib
- BioPython
Hands-on: Parse biological files, analyze data & visualize results.
Outcome: Work with biological datasets & build reproducible workflows.
R Programming – 5 Days (12.5 Hrs)
Goal: Use R for statistics, visualization & genomics analysis.
- Data Types & R Basics
- Data Manipulation & Visualization
- Statistical Analysis
- Genomic Data using ggplot2 & Bioconductor
Hands-on: Run statistical tests & visualize genomic data.
Outcome: Analyze genomics dataset using R effectively.
Linux – 2 Days (5 Hrs)
Goal: Work efficiently in Linux environment.
- Linux Architecture, Terminal & File Hierarchy
- Commands (Basic → Advanced)
- Bash Scripting
Hands-on: Automate tasks via shell scripts.
Applied Statistics – 2 Days (5 Hrs)
Goal: Build a foundation in statistical methods for biological data analysis.
- Descriptive & Inferential Statistics
- Hypothesis Testing
- Correlation & Regression
- Probability & Distributions
- Dimensionality Reduction
- Clustering
Hands-on: Apply statistical methods on gene-expression dataset.
Outcome: Understand hypothesis testing, modeling & unsupervised learning.
HPC – 1 Day (2.5 Hrs)
Goal: Use HPC for large-scale biological data analysis.
- IBDC-HPC Infrastructure
- Job Submission using PBS Scripts
Hands-on: Execute jobs & monitor on HPC cluster.
