Should Data Scientists Explore Beyond Python? | Experts Weigh In on Language Skills
The discussion surrounding whether data scientists should learn programming languages beyond Python is intensifying. Numerous professionals across forums have raised important points, emphasizing the need for skill diversification in the tech industry as of 2025.
Current Opinions and Industry Insights
While Python remains a cornerstone for data science, some professionals advocate exploring other languages to enhance skill sets. Recent forum conversations highlight varied opinions, bringing new perspectives into the debate.
Key Themes from Recent Discussions
Value of Complementary Languages
R and SQL: A user reinforced the utility of R, claiming, "R has a pretty good track record, though not so useful for production products." Others insist that a strong grasp of SQL continues to enhance a data scientist’s capabilities.
SAS Usage Insights: A contributor noted that while SAS can still be relevant in specific sectors, particularly in clinical testing, many experts advise against it for broader use. "SAS is dying as opposed to growing," they argued. A similar point was made regarding its declining presence in academic programs.
The Importance of Adaptability
Learning Beyond Python: There's a consensus that branching out can be beneficial. "Learning data engineering would make you a better data scientist since you can build pipelines and communicate more effectively with engineers in the business," asserted one contributor.
Older Languages Still Hold Value: While some participants underscored Python and SQL as foundational, one user pointed out that languages like Fortran still have niches where they're necessary. This speaks to the need for flexibility in learning.
"If you’re good at Python, you can easily pick up other languages," one user noted, reflecting a general sentiment about cross-language adaptability.
Employment Landscape and Expectations
Practical Skills for Job Readiness: A scientist noted, "I use Python but also write SQL queries when necessary," highlighting the practical blend of languages needed in today’s job market.
Core Language Foundation: Some professionals argue that while Python (+SQL) form a strong basis, familiarity with additional languages showcases adaptability. "There are jobs where another language will be useful but not to the degree that I would recommend them just for resume reasons,” one commented, hinting at the selective nature of language utility based on job roles.
Concluding Thoughts
As discussions evolve, professionals in the data science realm are encouraged to actively evaluate and expand their skill sets to meet the demands of diverse projects. This shift signifies an openness to learning and adapting, ensuring relevance in an ever-changing industry.
Key Takeaways
△ R is recognized for its statistical capabilities, though not widely used in production.
▽ SAS may still be relevant in niche areas like clinical trials but is generally advised against unless necessary.
※ "Learning data engineering would make you a better data scientist" - Important perspective from the community.