Is Carnegie Mellon or Penn better for data science?

For data science specifically, Carnegie Mellon is generally considered the stronger choice. CMU has deeper institutional strength in computer science, machine learning, statistics, and AI, and those areas drive a lot of modern data science. Its School of Computer Science and related programs are among the most highly regarded in the country, and employers in technical fields know that.

Penn is also excellent, but its edge is broader interdisciplinary flexibility rather than being the outright stronger technical data science school. At Penn, you can combine strong quantitative work with business, economics, healthcare, or social science especially well, which can be a big advantage if you want applied analytics, fintech, or business-focused data work.

If your priority is the most rigorous technical preparation for data science, especially in machine learning, algorithms, computing, and research-heavy work, CMU usually comes out ahead. If you want a path that blends data science with Wharton, policy, biotech, or other cross-school opportunities, Penn can be the better fit.

Career-wise, both place very well, but CMU tends to have a slightly stronger reputation for pure tech and AI-oriented roles. Penn has excellent placement too, especially in finance, consulting, health, and business analytics, alongside strong tech recruiting.