Hello all, I’m in the midst of my data science education, but I’ve heard that the need for data scientists is dropping sharply. It’s been suggested that many data scientists are taking on analyst roles, while engineers are taking charge of machine learning tasks.
Is it still advisable to continue on the data science path or would it be wiser to transition to computer science?
Furthermore, do machine learning engineers still actively create models, or are they predominantly focused on their deployment?
Woah, that’s an interesting question you’ve brought up, Nova73! I’m curious, how much does your current coursework overlap with what’s required for machine learning? Could your data sci knowledge be a stepping stone towards understanding more about engineering aspects too? Might it not be crazy to consider hybrid roles in the future?
data science still rocks! lots of companies need ppl who can turn raw data into info. thinking engineering? try learning coding n algorithms on the side – that could be useful too. machine learning engineers do both modeling n deployment. its a changing field but super exciting!
hey! data science is still a hot field! there’s a growing demand for data-driven decisons in biz, and data sci pros are sought after for this. yes, the roles r evolving, but learning data sci can open many doors in various sectors. keep going if ur passionate about it!
Data science remains a promising field, as many industries are increasingly data-driven. While shifts are occurring, these often reflect a natural evolution rather than fundamental downsizing. Data scientists are pivotal for insights that guide decision-making, complemented by machine learning engineers focused on implementing these insights. Though these engineers may often work on deployment, creating models remains a critical part of their role. A background in data science armors you with valuable analytical skills beneficial across various tech and business domains.
Pursuing a career in data science is still rewarding, mainly due to the critical need for data-driven insights across multiple industries. Although the roles may be transitioning, the core skills of a data scientist, like statistical analysis and the ability to interpret complex data, remain highly valuable. Enhancing these skills with knowledge in programming and machine learning can provide a competitive edge. Machine learning engineers typically balance model creation and deployment, suggesting a robust mix of theoretical and practical skills is beneficial in either path.
