This repository serves as a comprehensive, curated list of resources for individuals aspiring to become computational biologists or data scientists in the life sciences. It aggregates links to courses, books, tutorials, and tools covering Unix, R, Python, statistics, machine learning, and genomics-specific topics, aiming to provide a structured learning path for self-study and professional development.

How It Works

The repository is organized thematically, covering foundational skills like Linux commands, R programming, and Python, alongside specialized areas such as genomic data visualization, machine learning, and reproducible research workflows. It emphasizes practical application by linking to numerous tutorials, code examples, and best practices for data management, analysis, and workflow automation. The curated nature of the links aims to guide users through complex topics efficiently.

Quick Start & Requirements

This repository is a collection of links and does not require installation. Users will need internet access to follow the provided links to external resources, courses, and tools.

Highlighted Details

• Extensive coverage of R and its ecosystem for data wrangling, visualization, and statistical modeling.
• Detailed sections on command-line proficiency, including Bash scripting and essential Unix tools.
• Resources for understanding and implementing reproducible research workflows using tools like Snakemake and Nextflow.
• Links to numerous MOOCs and online courses for structured learning in bioinformatics and data science.

Maintenance & Community

The repository appears to be a personal collection of resources, with no explicit mention of a dedicated maintenance team or community channels.

Licensing & Compatibility

The repository itself, as a collection of links, does not have a specific license. The linked resources will have their own respective licenses.

Limitations & Caveats

As a curated list, the quality and availability of external resources are subject to change. The repository does not provide direct support or guarantee the accuracy of the linked content. The breadth of topics covered means that some areas may be less detailed than others.