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Guided learning journeys that build knowledge step by step.
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A beginner-friendly path introducing the core pillars of computational thinking—decomposition, pattern recognition, abstraction, algorithm design, and logical reasoning—with practice and application to scientific problems. Designed for high school students with no prior experience.
A beginner-friendly introduction to computational science as a discipline, covering its definition, core methods (modeling, simulation, data analysis), and the essential computational thinking skills needed. Learners will understand how computation is used to solve scientific problems and how the different components fit together.
A professional learning path covering the essential concepts and practices for managing bioinformatics data, including databases, data governance, sharing mechanisms, and standards. Designed for professionals seeking to build career skills in biotechnology data management.
This advanced learning path equips industry professionals with the knowledge and skills to apply bioinformatics in drug discovery, genomics, and data analysis within an industrial setting. It covers foundational concepts, practical tools, and strategic considerations for translating bioinformatics into products and processes.
This learning path is designed for graduate students in bioinformatics to review and update their career skills with a focus on recent advances and emerging technologies. It covers advanced topics in genomics, transcriptomics, single-cell analysis, computational methods, and professional skills necessary for a successful career in biotechnology.
This learning path provides a systematic introduction to the computer science concepts essential for bioinformatics, covering algorithms, data structures, and software engineering practices. It is designed for bioinformatics and computer science students seeking a rigorous foundation for computational biology research and development.
This advanced graduate-level learning path equips bioinformatics and computer science students with the knowledge and skills to apply artificial intelligence, particularly machine learning and deep learning, to solve biological problems. It covers essential biological and computational prerequisites, core machine learning algorithms, deep learning architectures, NLP for biological sequences, and culminates in practical applications such as genomic analysis and drug discovery.
This learning path provides a comprehensive, graduate-level curriculum for analyzing spatial transcriptomics data. It covers the biological background, experimental technologies, computational methods for data processing and analysis, and applications, with a strong emphasis on the prerequisites and dependencies between concepts.
This learning path guides graduate students in bioinformatics and genomics through the complete workflow of single-cell RNA-seq (scRNA-seq) data analysis, from raw data processing to biological interpretation. It covers essential computational and statistical concepts, quality control, normalization, dimensionality reduction, clustering, cell type annotation, trajectory inference, and downstream applications. The path emphasizes hands-on skills with popular tools and critical evaluation of results.
This advanced learning path equips bioinformatics students with the skills to visualize biological data effectively. It covers fundamental plotting techniques, genome browsers, and network visualization, providing both theoretical foundations and practical applications. The path emphasizes the interpretation and communication of complex biological information.