Welcome to my repository showcasing the projects I've completed in the domain of Cultural Data Science. As the humanities revolutionize with vast amounts of data, digital humanities, computational linguistics, and humanistic data science are emerging as pivotal domains. The central questions addressed through this course range from utilizing vast data in humanities to employing computational methods for understanding text, geospatial interactions, and visual artifacts of culture.
Below is a brief overview of the four major courses that make up this comprehensive program:
Purpose: Equip students with fundamental data management and analysis skills pertaining to cultural data. This course delves into basic technical capabilities vital for quantitative analyses related to human culture, from command line tools to version control.
Key Takeaways:
- Knowledge on data stewardship and basic programming.
- Skills in automating tasks, programming in Python or R, and version control with Git.
- Competence in discovering digital tools and applying them to individual work.
Purpose: Understanding the conceptual and technical bedrock of spatial data science methods. Given the surge in geospatial data, this course imparts skills to analyze human spatial behaviors.
Key Takeaways:
- Knowledge on geospatial data representation and coordinate systems.
- Skills in handling spatial data, leveraging spatial software, and ensuring transparency in spatial analyses.
- Competence in independently executing projects with spatial data components.
Purpose: Introduction to the utilities of text mining and analytics in humanities research. This course illuminates the intersections between computational linguistics, information retrieval, and machine learning in the textual analytics domain.
Key Takeaways:
- Knowledge on strengths/weaknesses of automated text analysis and its significance in humanities.
- Skills in original research using computational text analysis.
- Competence in critically analyzing the role of text analytics in diverse fields.
Purpose: Systematic computational examination of visual objects ranging from artworks to digital products. This course emphasizes the blend of computational techniques with visual artifacts.
Key Takeaways:
- Knowledge on core theories of computational visual analysis.
- Skills in curating a visual materials database and executing large-scale visual analyses.
- Competence in combining quantitative and conceptual choices for an in-depth analysis.
Each of the above courses has its dedicated repository providing an in-depth look into the projects, methodologies, and learnings. Dive deeper to understand the multifaceted realm of Cultural Data Science and the myriad applications I've explored!