Hi everyone,
I am a current junior at my undergraduate institution and have my sights set on pursuing a PhD in Statistics after my graduation. Unfortunately, I don't have much guidance on this process, and so I'm looking for some advice regarding what I can do in my last three semesters to prepare. Although I know it's ambitious (and unlikely), I'm excited by the programs at the high-ranked west coast schools (Stanford, Berkeley, UWashington).
First I'm including a short profile for context:
Undergrad Institution: Top 5 USNWR Majors: B.S. Statistics and B.A. Mathematics GPA: 4.0/4.0 (major), 3.95 (cumulative)
I'm anticipating that this will drop, but should stay > 3.85
Type of Student: Domestic Male
Programs Applying: Statistics
Research Experience:
Led a self-developed research project with a team of graduate students at the Department of Communication of a peer university. Project focused on the applications of statistical methods and machine learning to political science. Although the research wasn't directly related to statistical theory, it was a good experience gaining exposure to data mining and cleaning, ML methods, implementing GLMs, time series analyses, etc.
Awards/Honors/Recognitions:
Will hopefully graduate cum laude (perhaps magna cum laude)
Pertinent Activities or Jobs:
Oversaw data on 2020 campaign--worked for a year as the sole individual analyzing constituent demographic and support data for a local candidate in a sizable district; directed targeted outreach efforts; basic analysis of campaign statistics
Politics and data on campus-- analyzed past voting turnout statistics to discover discrepancies in student turnout across academic disciplines (presented to university Dean, administrators); scraped thousands of student records to create a targeted voter outreach push across campus
Undergraduate TA for Introductory Statistics course
Coding Skills:
Python, C, SQL,R
Relevant Classes, Grades:
Machine Learning and Data Mining, Probability Theory I & II, Multivariate Statistics, Computation for Data Science, Calculus sequence (II, III), Linear Algebra and Matrix Theory, Vector Calculus, Real Analysis I, Introduction to Computer Science, Data Structures (all proof-based except calculus II & III, all A's)
*many of these are cross-listed as graduate courses
Will take: Real Analysis II, Deep Learning, Abstract Algebra I & II, Discrete Math, Advanced Combinatorics, Stochastic Processes, Advanced Probability, Causal Inference I & II, etc.
And for my questions:
As for my courseload, I'm very much interested in more advanced courses in the computer science department. Would it be better to buckle down and focus on completing as many advanced math classes as possible, or doesn't this matter as much? I'm wondering if focusing on one area would be better than diving into CS as well.
I'm kinda conflicted with what to do this coming summer: many of my friends have internships lined up, and so I'm unsure whether I should go this route or strengthen my research background instead. This is what I'm struggling with most. Ideally, I'd like to gain more exposure to research in theoretical statistics, but I'm afraid that I won't have time with my heavy courseload during the academic year. I'm not too sure what the admission committees are looking for research-wise.
Would also appreciate any general suggestions related to what I should pack into my last year and a half!
Thank you for helping me out with this! I've enjoyed stalking this forum and reading about all the great things everyone is doing.
