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21. Ontology Development

Learn to design and create ontologies with an ontology editor.

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22. Provenance

Provenance is very important not only to describe how data is collected, but also for regenerating and reproducing results. Provenance records how data is created, providing very useful context for understanding data.

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23. Data formats and standards

When data is represented with standard formats, it is easier to use and integrate with other data.  How are standards designed?

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25. Data Stewardship: Publishing Data Science Results

Reporting the results of any data analysis must be done in a systematic way according to best practices for publishing, preserving, and disseminating data and software.

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26. Advanced topics (I)

Privacy and ethics in data science Any data analysis should handle appropriately any sensitive data, and respect people’s right to privacy. Learn recent techniques for protecting sensitive data, and understand their limitations and any potential for breaches of privacy in your work.

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27. Advanced topics (II)

Introduction to databases Databases facilitate the storage and retrieval of data. Learn basic ideas about databases, how data is organized, and how to find the data you need.

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