The Computational Data Science $($CDS$)$ field, rooted in Western culture, needs to be de$-$Westernized for increased diversity, inclusion, and global perspectives. Key strategies for this transformation include:

Increase Diversity in Research and Practice:

Actively seek diversity in terms of gender, ethnicity, culture, language, religion, and geography in academic programs, conferences, and research projects.

Foster collaborations with researchers, institutions, and organizations from non$-$Western regions to broaden perspectives.

Adopt a Global Perspective:

Avoid assuming a Western context when developing algorithms, tools, and techniques.

Evaluate CDS algorithms in different cultural, linguistic, and geographical contexts through organized evaluation campaigns.

Promote Open$-$Source Software:

Emphasize the development and use of open$-$source software as a crucial resource for CDS researchers.

Develop Culturally Sensitive Methodologies:

Create CDS methodologies that are sensitive to multiple languages and cultures.

Ensure that natural language processing models can handle various languages, making technology globally accessible and relevant.

Educational Programs with Global Perspectives:

Develop educational programs that integrate global perspectives in CDS$,$ emphasizing the importance of cultural diversity in data science.

Provide case studies from diverse regions to enrich students' understanding.

Establish Global Standards for Data Collection and Usage:

Involve considerations of cultural norms and privacy concerns in data collection and usage.

Facilitate accessible sharing of knowledge across borders by establishing global standards.

By implementing these strategies, the CDS field can transition toward greater inclusivity and global representat