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Big Data Science with the BD2KLINCS Data Coordination and Integration Center

Course Summary

Learn various methods of analysis including: unsupervised clustering, gene-set enrichment analyses, Bayesian integration, network visualization, and supervised machine learning applications to LINCS data and other relevant Big Data.


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    Course Syllabus

    • Overview of the NIH Common Fund LINCS Program
    • Overview of the Data Signature Generation Centers (experiments and data)
    • Meta-Data and Ontologies
    • Data Normalization
    • Unsupervised Learning Methods: Data Clustering
    • Supervised Learning Methods
    • Enrichment Analyses
    • Bayesian Data Integration
    • Network Analysis and Network Visualization
    • Cheminformatics
    • Serving data through RESTful APIs and JSON
    • Interactive Data Visualization of LINCS Data

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    Recommended Background

    Basic courses in statistics and molecular biology are useful but not required. Ability to write short scripts in languages such as Python would be useful but not necessary.

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    Course Format

    The class will consist of lecture videos, which are between 8 and 12 minutes in length. The course will be divided into segments where each segment will have a quiz and a homework assignment. For evaluation, students will be graded through their participation in the assignments and quiz completion.

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    Suggested Reading

    Review articles and selected original research articles will be discussed in the lectures and can enhance understanding, but these are not required to complete the course. All materials will be from open access journals or will be provided as links to e-reprints, so there will be no cost to the student.


Course Fee:
Free

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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