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Hey everyone, I just graduated college, and due to COVID, my plans have changed. Originally, I was going to work in a sports analytics role for a professional baseball team this summer/winter with the chance of being hired full-time if I performed well. Then, after a year or two of work, I was going to apply to grad school. But, my job got canceled, and so now I've been home coaching pickleball (no lie, it's been a fun experience) and have decided to apply to grad school this winter. Thanks for any help you can offer!

Undergrad Institution: UC Berkeley

Major: Statistics 
GPA: 3.743 
Type of Student: Domestic White Male

GRE General Test: Taking it in a few weeks, obviously hoping for 165+ Quant
Programs Applying: Master's Statistics 
Research Experience: I had a negative research experience with a professor, and I ended up not passing the "research apprenticeship." This is one of the main dents in my transcript I feel, so I will probably address it in my applications. While I don't think the professor did his fair share in leading the apprenticeship and he admitted this, I definitely could've done things better too. Fortunately, I grew a lot from the experience, and he and I met a semester later and reconciled, leaving everything on good terms.
Work Experience: Does this matter? Just thought I'd mention--I was a data analyst intern for a baseball team after my sophomore year (used lots of R and SQL). After my junior year when I went abroad, I was a business analyst for a pro soccer team (mostly used Excel). This position was not a great experience, and I actually quit the role to go back to school early over the summer to prepare for senior year. I used this time to study machine learning on my own to be ready to apply to jobs, and I ended up getting the job I mentioned above with another pro baseball team. To this day, I'm really happy with my decision to leave the soccer job and do something that would be beneficial for my career.

Awards/Honors/Recognitions: I was selected as a TA for Berkeley's 1,000 student Intro to Data Science course, which involved me teaching weekly lab and discussion sections of 25 students and participating in weekly meetings about content and logistics. I taught topics like python, SQL, pandas, linear regression, and PCA. I did this my last semester.
Letters of Recommendation: Going to request a letter from my Game Theory professor who knew me personally and definitely liked me. Got an A in her class. Also will request a letter from one of the professors I TA'ed for (mentioned above). The third letter will probably be from an employer, though I'm a bit torn here. I can ask the person from the baseball team who hired me and who can speak to the models I built throughout the hiring process and my performance in interviews, or I can ask my current employer (the pickleball director) who I'm sure would have good things to say.
Math/Statistics Grades (freshman-senior year in order) Intro to Linear Algebra and Differential Equations (B), Calc III/Multivariable (A), Calc-Based Probability (B+), Computing with Data (basically an R class) (A+), Discrete Mathematics (intro to proof-writing as well) (A), Theoretical Linear Algebra I took one summer at UCLA (A), Intro to Real Analysis (B), Calc-Based Mathematical Statistics (B),  Fundamentals of Data Science (the class I TA'ed for) (A-), Intro to Time Series (A), Linear Models - Theory and Applications (A), Game Theory (A), Intro to Machine Learning (very theoretical) (A).
Regarding my grades, I definitely didn't do as well as I would've liked in some of the fundamental stats courses (probability, mathematical statistics). But, my senior year I got all As in my stats courses which I really worked hard to do, and I hope this shines through. There are some weak spots in my math grades too, like my Linear Algebra and Diff EQs class, but that was my first semester and I like to think my A in the summer Linear Algebra class makes up for it. Real Analysis was really tough and I took it during a difficult semester personally, and it basically showed me I don't want to do a PhD.

Schools: I'm very interested in applying to a few schools in Europe... please let me know if you know anything about this. And, regarding schools in general, I'm not quite sure which tier of schools I should attempt to target. From what I've read, Master's in Stats aren't too terribly competitive, so I'd like to think I can get into a top 20 program. Thanks for any guidance here.
- ETH Zurich
- EPFL (in Lausanne, Switzerland)
- Oxford/Cambridge? (They seem pretty expensive for European programs)
- Stanford
- Berkeley
- Harvard Data Science
- Washington
- NC State
- UNC Chapel Hill
- Minnesota
- Yale
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2 hours ago, Stat Assistant Professor said:

With a 3.7+ from UC Berkeley and grades of B or better in your math/stat classes, I imagine you will not have any difficulty getting into most Statistics Masters programs. 

Glad to hear this. How important are LoR for Stats Master’s? I’ve heard they’re not nearly as important as for PhD programs, but what do you think?

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18 minutes ago, mrstat said:

Glad to hear this. How important are LoR for Stats Master’s?

Not nearly as important as the letters for Stat PhD programs. Since most Masters programs are unfunded, they will admit most students who meet the minimum coursework, GPA, and general GRE requirements.

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