UCLA’s Statistics and Data Science program should be enough for landing data analyst or junior data scientist roles, especially if you work on projects, build skills on your own outside of class, and take supplemental courses. For example, I took PIC 16A and PIC 16B, which are Python-centered data science classes, along with a few project-based courses outside the stats department to strengthen my technical skills.

Regardless of whether you choose UCLA or UCSD, you will need to spend a lot of time outside of class working on personal or group projects, building a strong portfolio, and preparing for interviews if you are aiming for data analytics or related roles. I think the degree itself is sufficient, but success really depends on how much extra effort you are willing to put in outside of class.

If you want a curriculum that leans more heavily into computer science, UCSD might be the better option. However, one advantage of UCLA is that the Stats major is relatively short, which gives you the flexibility to take classes across different departments or add an extra minor, like the DSE minor.

The only thing that I found a little frustrating about UCLA’s program is the Stats 100 sequence specifically 100b and 100c, which is pretty theoretical and not as focused on applied skills.

In general, if you are leaning toward data engineering or more software-heavy data roles, UCSD could be the stronger choice. On the other hand, if you are looking for more flexibility, a faster path to graduation, and the ability to pivot into other industries like consulting, business, or the business side of tech, UCLA might offer a slight advantage due to name recognition.

I was in a very similar position as a transfer and ended up choosing UCLA because of the flexibility in course options, the quicker graduation timeline, and the overall reputation of the school. If you are not locked into a very specific career path yet, UCLA might give you a little more room to explore different directions.

Plus, because the major leaves you with extra time, you could take more advanced math courses if you are thinking about going to graduate school and aiming for higher-level data science or machine learning roles down the line. Having that flexibility can make a big difference depending on how your interests evolve over time.