anon333

My mistake! You are right, that book (and Intro to Stats Learning) is definitely geared more towards machine learning.

Honing in on your theoretical knowledge of linear algebra, real analysis (proofs of calculus concepts, heavily recommended), and even probabilistic/measure theory (not required) will be more than enough preparation for Statistics Graduate school. Also learning common statistical coding languages such as R and Python are useful, as well as some comp sci classes (objected-oriented programming, data structures, algorithms are what I recommend). Although called a "Statistics" PhD, you won't need too much Statistical knowledge (from undergrad) to succeed in it. Also order of importance when it comes time to apply: LoR's (Letter of Recs), GPA, GRE, GRE subject My biggest regrets are: -not having more, advanced theoretical knowledge in math -not taking GRE subject Actual answer to question: Elements to Statistical Learning, Hoff--First Course in Bayesian Statistical Methods, Introduction to Statistical Learning with Applications in R (easiest read out of them all)

Hello, Can people give me their opinions/ranking on any of the schools (Statistics PhD): UCLA, UF, Texas A&M I've been admitted into these schools, and I'm having hard time deciding... My opinion: UCLA -seems good for industry jobs (i.e. data science), since located in LA -course load seems heavy on machine learning (good) -expensive to live in though (apartment/food worries...) UF -course load seems to be more oriented toward traditional statistics (no machine learning courses) -I heard they're theoretical heavy (good) -located in my hometown (wouldn't have to worry about life stuff, i.e. cost of living, food, etc.) Texas A&M -I heard they're a very large department that does mostly applied stuff -I'm not that worried about the cult life thing Bio: -Undergraduate: UNC Chapel Hill, Graduating in May 2017 with Bachelors in Stats, Math -US Citizen -Hoping more for industry than academic job Thank you so much!