Data science is often described as the most in-demand job of the decade, yet I'm noticing a growing divide between what courses teach and what companies actually need. I've seen graduates ace machine learning theory but struggle to clean messy real-world data or communicate findings to non-technical stakeholders. Should universities focus more on practical problem-solving and business context rather than just algorithms and code? For me, the real value of data science lies in turning raw numbers into actionable decisions, not just optimizing models. Would we be better served if every data science program included apprenticeships or capstone projects with actual company data? I'm torn because theory gives you the foundation, but practice builds the instincts. What's your take on closing that gap?