u/Usual-Recipe-5415

Hi everyone, I’m applying to Stat PhD programs this fall. I graduated with my master's in 2021 and have been working full-time for 5 years, but I never took Real Analysis in school.

I just enrolled in Fordham's Math 3003 (Real Analysis) this semester so it’ll be on my transcript for this application cycle (though will not have a final grade since apps are due before the semester ends). My concern is that it’s only a one-semester class. Does anyone know if admissions committees strongly prefer a two-semester sequence (Real Analysis 1 & 2) over a single semester? Will this be adequate?

MATH 3003. Real Analysis. (4 Credits)

This course focuses on analysis on Euclidean spaces. Topics include limits, continuity, uniform continuity, sequences of numbers and functions, modes of convergence, differentiability, Riemann integrability, and associated theorems. Students who have not taken MATH 2004 prior to taking Real Analysis may request permission from the instructor. Note: Four-credit courses that meet for 150 minutes per week require three additional hours of class preparation per week on the part of the student in lieu of an additional hour of formal instruction.

Prerequisites: MATH 2001 and (MATH 2004 or MATH 2008).

Hi all! I’m applying to Statistics PhD programs this fall and would appreciate some perspective and advice on my profile strength and how I should narrow down my school list.

Background:

• Domestic female applicant
• Undergraduate degrees in Statistics and Machine Learning, with a second major in Business
• Master’s degree in Applied Statistics
• Both degrees are from a university with a top-six U.S. statistics program and a highly ranked CS program
• Undergraduate GPA: 3.44
• Master’s GPA: 4.01
• Five years of experience working as a data scientist, including substantial applied causal inference work
• Intended research area: causal inference

Coursework:

I have not taken real analysis, which is probably the biggest weakness in my preparation. However, I received A’s in courses including:

• Statistical Inference
• Probability Theory
• Applied Linear Models
• Multivariate Analysis
• Matrix Algebra
• Modern Regression
• Concepts of Mathematics
• Differential and Integral Calculus
• Algorithms and Advanced Data Structures
• Time Series and Experimental Design
• Methods of Statistical Learning
• Imperative Computation

I also received two C’s in undergraduate weed-out courses.

Research and teaching:

• Approximately four to five months of research experience in contextual bandits and causal inference
• No publications
• TA for an undergraduate statistical inference course
• TA for a graduate-level experimental design course

Letters of recommendation:

I expect letters from:

1. An industry manager who supervised my causal inference work
2. A professor with whom I am currently doing research
3. My graduate capstone adviser

I previously received a 165 on GRE Quant and am retaking the exam. I plan to submit the score only to programs that require or strongly recommend it.

I understand that there is no way to guarantee a PhD admission, but I would like to maximize my chances of receiving at least one offer this cycle. I currently have 27 programs on my list and may need to narrow it down.

Does this list seem sufficiently balanced given my profile? Are there schools that appear especially unrealistic, and are there any lower-risk programs I should consider adding?

Current list:

• My home institution
• University of Michigan
• Yale University
• Cornell University
• UCLA
• North Carolina State University
• Pennsylvania State University
• University of Illinois Urbana-Champaign
• Purdue University
• UC Irvine
• Ohio State University
• Rice University
• Rutgers University
• UC Santa Barbara
• University of Connecticut
• University of Pittsburgh
• University of Virginia
• University of Wisconsin–Madison
• UC Santa Cruz
• UMass Amherst
• Boston University
• UT Austin
• Texas A&M University
• UC Riverside
• Iowa State University
• Washington University in St. Louis
• University of Florida

I would especially appreciate feedback on how many of these should be considered reaches, realistic possibilities, or relatively safer options. Thank you!

I’m applying to Statistics PhD programs and am deciding between two coworkers for one of my recommendation letters. My other letters would come from professors, so this would be my industry letter.

Neither coworker has been my direct supervisor.

The first person is someone I currently work with closely. We meet weekly, collaborate regularly, and he has more recent and direct knowledge of my work. However, he does not have a PhD.

The second person has a PhD and has known my work for somewhat longer. He has reviewed some of my projects and would be able to write positively about me, but we have not worked together as closely or as frequently.

Hello! How easy is it for a new assistant professor to hire a research assistant (that they collaborated with prior to joining) outside of their university? :