Certified Professional in Digital Humanities Text Encoding
-- viewing nowCertified Professional in Digital Humanities Text Encoding (CP-DHTe) certification validates expertise in encoding texts for digital humanities research. It's for scholars, librarians, and archivists.
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Course Details
- TEI XML Fundamentals: Introduction to Text Encoding Initiative (TEI) Guidelines and XML for digital humanities projects.
- Schema Design and Implementation: Creating and using TEI schemas for structured text encoding.
- Metadata and Encoding Best Practices: Implementing Dublin Core and other metadata schemas, along with best practices for text encoding.
- XSLT Transformations and Data Visualization: Using XSLT to transform TEI XML data and create visualizations.
- Digital Text Encoding Projects: Case Studies and Examples: Examining real-world TEI projects and best practices.
- Advanced TEI Elements: Deep dive into complex TEI elements for representing nuanced textual features.
- Working with Digital Libraries and Repositories: Understanding the workflow for importing and exporting TEI XML data from digital libraries.
- Text Analysis Techniques for Encoded Texts: Using TEI-encoded data for computational text analysis.
Career Path
Career Role Description Digital Humanities Text Encoding Specialist Develops and implements text encoding strategies for digital humanities projects, focusing on XML and TEI .
High demand for expertise in metadata creation and schema design.
Digital Humanities Project Manager (Text Encoding Focus) Manages teams working on large-scale text encoding initiatives, ensuring adherence to best practices in text analysis and data curation .
Strong project management and leadership skills are essential.
Research Associate (Digital Text Encoding) Conducts research using digitally encoded texts, employing computational methods for analysis.
Requires strong analytical and programming skills (e.g., Python).
Data Scientist (Digital Humanities) Applies data science techniques to large text corpora, extracting insights and creating visualizations.
Requires advanced skills in statistical modeling and machine learning .
Entry Requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course Status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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