DachshundDad

I'm aware of a few MS Stats programs in Southern California for which you meet the minimum admission requirements. Since your profile states you are located in New York, you may want to consider the following online program at California State University - Fullerton: Master of Science Degree in Statistics - Department of Mathematics | CSUF (fullerton.edu) Although you would need to pay out-of-state tuition, it may be cheaper than in-state tuition in NY. The expensive part of going to college in California is the cost of living. Tuition is actually very cheap. You would be able to avoid these living expenses by completing the program online. Of course, this only holds true if you don't live in NYC or on Long Island. Then it may be cheaper for you to complete the program in-person in beautiful Southern California. If you have time this summer, try to retake as many Math classes as possible at your school or local community college. Spend as much time possible studying and mastering the material in order to a earn an A in each class. This will benefit you in two ways: 1. It will impress the schools to which you apply. 2. You will be able to handle the courses you face in a MS Statistics program. In addition, take as many math and math-statistic courses as possible to complete your business degree. I went into my MS Stats program with only completing Calculus I through III, Linear Algebra, Mathematical Statistics I, and Mathematical Statistics II. It was not enough to get through the core classes for the program without a constant struggle to keep up with the material. I would highly recommend taking a proof-based course such as Discrete Mathematics. This will be very helpful for understanding all of the new mathematical notation and proof structures that will be throw at you during your first year. I went from a BA in Studio Arts to a MS in Stats. Your transition should be a little easier if you have taken upper division course in Finance and Economics. If you want to be fully prepared for a MS in Statistics program, make sure that you can complete all of the following problems: EXAM P SAMPLE QUESTIONS (soa.org) These are the practice problems for Exam P, which is the first exam towards becoming an Actuary. All of these problems are based on Calc I, Calc II, and at least one upper division Mathematical Statistics course. Being able to do well on these problems will indicate how much you will succeed in your graduate program. Good luck with your future academic endeavors. Don't ever feel like you are a failure. It is never too late to change the direction of your life.

I was hoping I could get an update on my potential to be accepted into a Statistics PhD program. Due to financial aid issues that this year and that will continue through next school year, I was only able to take 6 credits per semester and will do the same next year. I took 3 math courses: Discrete Mathematics, Mathematical Programming, and Advanced Calculus I. Advanced Calculus I was an introductory course to Real Analysis. The course covered the first 6 chapters of Bartle’s “Introduction to Real Analysis”. In addition, I took a statistical consulting course that could only be take Pass/Fail. To complete my statistics master’s degree program next year, I need to complete 2 statistics courses (6 credits), a seminar (1 credit), pass two qualifying exams (2 credits), and complete 1 upper division mathematics course (3 Credits). Due to limited availability of statistic courses, I intend to take Advanced Biostatistics Methods I, the seminar, and qualifying exams in the Fall. In the spring, I will complete the degree program by taking either Advanced Biostatistics Methods II or another graduate level statistic course (if available) and either Advanced Calculus II, Advanced Linear Algebra, or Numerical Matrix Analysis. Fortunately, my university has very friendly professor and they are willing to let me be an observer (not auditor) in their classes this summer and next school due to my financial aid issues. I intend to observe an Advanced Linear Algebra course this summer and Abstract Algebra and Number Theory courses in the fall. I won’t get a grade or credit, but I believe that it will help me prepare for taking the Math GRE Subject test and widen my mathematical knowledge that I may need when I’m in a PhD program. I have included several variations of my GPA below. In addition, I have included the grades I have received in all Math and Stat courses. The Max GPAs I have included assume I earn As in my last three graded courses (9 credits). Please be brutally honest on what schools I should be considering and if I even have a chance of being accepted into any program. Any advice on what math course(s) I should take would also be appreciated. I intend to apply in the Fall of 2023. I’m open to take more classes in the Fall of 2023 to improve my GPA and mathematical background. My university offers graduate level course in Applied Math Analysis and Optimization. GRE: 168 Math, 155 Verbal, 3.0 Writing GPA: Current - 3.416 (209 Credits) Max – 3.440 GPA w\o First 60 Credits: Current - 3.620 (150 Credits) Max – 3.642 Post - Undergraduate GPA: Current - 3.705 (61 Credits) Max – 3.743 Graduate GPA: Current - 3.478 (27 Credits) Max – 3.608 Graduate Program GPA Current - 3.383 (18 Credits) Max – 3.589 Quantitative Course GPA: Current - 3.705 (69 Credits) Max – 3.729 Mathematic Courses GPA: Current 3.805 (27 Credits) Max – 3.824 Statistics Courses GPA: Current 3.464 (28 Credits) Max – 3.559 Math Course: Finite Mathematics – B+, Calculus I – A, Calculus II – A, Calculus III -A, Linear Algebra – A, Discrete Mathematics – B (I was one of 18 students of the 180 that could mathematically earn an A going into the final. This class is notoriously difficult at my school. However, it is taught by an exceptional professor that has prepared me very well for future mathematical studies. First B I was happy to earn in my life), Mathematical Programming – A (Upper Division), Advanced Calculus I – A (Upper Division) Stat Courses: Business Statistics – A, Prob and Math Statistics I – B (Upper Division), Prob and Math Statistics II – A- (Upper Division), Advanced Math Stats I – B+ (Graduate), Advanced Math Stats II – B+ (Graduate), Linear Regression Models – B (Graduate), Actuarial Modeling – A- (Upper Division), Time Series Analysis – A- (Graduate), Applied Multivariate Analysis – B+ (Upper Division) Advanced Math Stats I and II covered chapters 1 - 10 of Casella and Berger. I will have 3 years of TA experience by the time I apply to PhD programs. Will this justify not having any research experience? Or should I make it a priority to have some research experience on my CV? My school will not allow grad students to be a TA and RA at the same time. Since RAs do not receive any health insurance, I can't justify switching positions as my wife and I both need the health insurance that the TA position provides. In addition, the statistics department is really small at my university. There simply is not enough research opportunities for every graduate student. Would an independent research study be impressive to PhD admission officers? I would like to test some new teaching methods to see if there a significant improvement in my students grade percentage. I’m currently looking at a total of 40+ programs. I plan to narrow it down to 12 to 15 by the time I apply. Maybe even down to 8. I picked these schools based on ones that I believe would accept me based on rank, cost of living, climate, or if there is a PhD program nearby for my wife who wants to study Special Education/Applied Behavior Analysis. Clearly, I will need to do more analysis on what research opportunities are availble at these various schools. Iowa State University - Ames Ohio State University - Columbus Rice University Colorado State University - Fort Collins University of Pittsburgh University of Missouri - Columbia Virginia Tech University of Utah Boston University University of California - San Diego University of California - Riverside University of California - Santa Barbara University of California - Santa Cruz University of Colorado - Boulder University of Rochester University of Illinois - Chicago University of Massachusetts - Amherst Southern Methodist University Washington University in St Louis Oregon State University - Corvallis Northeastern University University of Notre Dame Baylor University University of Nebraska - Lincoln University of Colorado - Denver Lehigh University Worcester Polytechnic Institute University of New Mexico - Albuquerque Washington State University - Pullman Syracuse University Utah State University - Logan Montana State University - Bozeman New Jersey Institute of Technology University of Nevada - Reno Colorado School of Mines Stevens Institute of Technology Illinois Institute of Technology New Mexico State University University of Montana - Missoula Clarkson University University of Idaho - Moscow University of New Hampshire - Durham

“The U”, as those from Minnesota call the UMN, has great housing options for graduate students. There is a student coop available within a mile of campus that has heavily subsidized rental rates (Under $1000 for a 2 bedroom apt). This would be a huge cost savings compared to living anywhere relatively close to UCLA. If you decide on “The U”, you should get on the waitlist asap. https://housing.umn.edu/como-student-housing I left MN almost two decades ago for the dream of living in Southern California. The weather has been amazing and there are tons of outdoor activities available all year. One can hike in the dessert and ski in the mountains on the same day. However, this has come with huge financial and standard of living sacrifices. Salaries are lower in Southern California (Sun Tax) and the cost of living is at least 50% higher than MN. If you want to live close to UCLA, expect to pay a minimum of $2000 a month for a studio apartment. You may be able to share a bedroom for a $1000. You also could live further away from campus, but traffic is going to cost you at least a few hours a day to save a small amount on rent and your housing will be in a questionable area. The people in Southern California are all in hurry and have no regard for anyone else. I actually have learned to appreciate this over the years, but Minnesota will be the complete opposite situation. People do everything slower in MN. Especially when driving or saying good bye to friends at a social function. They also are much more friendly, at least on the outside. Everyone will wave or say hi to each other, even if they are strangers, as they pass by each other in MN. Just note that this is more of a formality. Most people in MN are actually very passive aggressive and pretend to be nice. In LA, you could get the police called on you for saying hi to a stranger. It is a big no no to talk to strangers during most social settings in SoCal. However, if you get close to anyone they will be a true friend and loyal for life. My advice would be to attend “The U” if finances are going to be a major issue. You will not be able to enjoy the weather and comforts of Southern California without a large bank account. As another poster mentioned, there are tunnels connecting all of the building on campus. You will rarely need to be outside in the cold. There are also great public transportation systems to get you to school, around campus, and the entire Twin Cities Area. You will absolutely need a car to get anywhere in LA. With the money you save attending “The U”, you could afford to go on a trip during winter break to enjoy Southern California from the comfort of a nice hotel on the beach. If money is not an issue, I would advise you to attend UCLA. Southern California is awesome for those that can afford to live here. I would say that you need a minimum of 50K a year to just get by while attending UCLA (Eating instant ramen and hot dogs while living in a horrible neighborhood far from campus). 60 to 70K a year would allow you live in a better neighborhood and eat at a restaurant once a week. To have a one bedroom apartment within walking distance of campus and a car to visit all of the desirable places in SoCal, you will need a minimum of 100K a year. The U is as famous and respected as UCLA in academic and professional settings. The Twin Cities and Southern California both have ample opportunities for Biostatisticians. You will not lose a job opportunity because one of these schools is higher rated or is located in a better area for your field. Both universities will help get you in the door for an interview, but obtaining the job will depend on your personality, accomplishments, and experience. Congratulations on being accepted into two exceptional schools. Good luck on making your decision.

Thanks @bayessays! I will follow your advice and spend as much time as possible on the real classes. This summer will be spent taking a discrete math class and reading "How to Prove It: A Structured Approach" by Velleman. Hopefully this will prepare me sufficiently for the transition to real mathematics.

Thanks @Stat Assistant Professor I will finish up my MS program with as many upper division math classes that I can fit into my schedule. I addition to these classes, I'm thinking about taking some computer programming classes online through UCSD Extension. I have used SAS and R throughout my MS program, but I have never taken any formal programming classes. UCSD Extension offers classes in R, SAS, Python, Machine Learning, and Data Mining. Studying these subjects will help prepare me for the research period of a PhD program. Due to time and financial constraints, which of these subjects would be most helpful with my future studies? My guess is that R, Machine Learning, and Data Mining will be most helpful in an academic setting.

No apologies are necessary. You actually cleared everything up for me. Thank you.

Thanks for your reply DanielWarlock. Since my school does not have a Stats or Math PhD program, class selection is pretty limited at the graduate level. I have completed all available classes that were related to Probability, but none of them were theory courses.

Thank You Bayessays! That is exactly what I have been trying to figure out. Even the math professors at my school told me conflicting information. One said that Advanced Calc I and II are Real Analysis Courses (Advisor for Undergrad Math Students). Another said they are not Real Analysis courses and I would need to take the Lebesgue class (Advisor for Graduate Math Students). Then my Graduate Stats advisor told me none of them are necessary to take. I will find a way to take them next year so they are on my transcript when I apply to PhD program the following year. I definitely don't want to put the process off for an additional year.

Thanks for your response StatsG0d. That is what I was thinking as well. The majority of students in my program have an undergraduate degree in Mathematics. I think it is safe to assume they took Advanced Calculus and/or Real Analysis already. I agree that I will struggle without Real Analysis. The first year of my current program included a two course sequence using Casella-Berger. I had to spend a vast amount of time studying mathematical concepts that my peers already learned in undergrad. I managed to do well in this sequence of classes, but they easily required me to study 30 to 40 hours per week. I'm definitely not trying to avoid Real Analysis, but I do need to make the most time and cost efficient decisions about my schedule. The issue with taking the Applied Real Analysis course at my school is that it has prerequisites of taking Advanced Calc I and II. I also still need to complete 3 graduate level stat classes to graduate. In addition, I think it would be prudent to take Upper Division Linear Algebra and Numerical Mathematical Analysis. Since I'm paying for graduate school without loans and the salary of being a TA, I can only afford to take two classes a semester. Here is what I think would make most sense for my next two years based on when classes are offered: Fall 2021 - Database Management and Predictions, Statistical and Machine Learning Methods Spring 2022 - Upper Division Linear Algebra, Advanced Data Analytics Fall 2022 - Programming in Math, Advanced Calculus I Spring 2023 - Numerical Matrix Analysis, Advanced Calculus II Would these Advanced Calculus course be sufficient for learning Math Analysis? If an additional class would be necessary in your opinion, the University of Illinois offers an online Real Analysis course. I could take this course over the summer of 2023. My question then becomes if I should apply to PhD programs in the fall of 2022 or wait until the following year after I have completed all of my courses. What is your opinion? I must admit that I do not fully understand the difference between applied stats PhD vs stats PhD programs. My understanding is that a stats PHD would be to develop new methods. An applied stat PhD program would be using advanced stat techniques to solve problems in other fields. For example, I'm very interested in finding better ways to teach stats and math to students with learning disabilities. Or finding a way to improve math and stats scores for poor performers instead of taking away advanced math programs from high performers in the name of equity. Ultimately, I would like to teach and do research at a community college or state school. I'm not interested in working in industry or at an elite university. I have had corporate jobs and made good money in the past, but it made me fill like my life was pointless. Teaching has been a huge eye opener for me and I can see myself doing it until the day I die.

I sent an email to my academic advisor last week about my intended plan of study for the next two years. They replied by suggesting we meet on zoom to discuss this further. After meeting today, I was provided with information that was a bit different from the responses I received to this post earlier this year. It was suggested that I do not need to take Math Analysis or Advanced Calculus for a PhD program in Stats. In fact, I was told that none of the prior students from my program that were accepted into PhD programs took Advanced Calculus or Real Analysis. A few of these students are attending top 20 schools. Since originally posting, I have narrowed down my list of prospective schools that are much more realistic. After reviewing the prerequisites for these programs, none of them require Real Analysis and only 1 requires a semester of Advanced Calculus. I'm assuming this is because most of the programs are applied based versus theoretical. My advisor suggested that I take more courses related to Data Analysis/Computer Programming to be more competitive for these programs. Here is what I'm planning to take over the next year or two: Statistics Courses: Database Management and Predictions, Statistical and Machine Learning Methods, Advanced Data Analytics Mathematics Courses: Programming in Math, Upper Division Linear Algebra, Numerical Matrix Analysis I'm also taking Discrete Mathematics this summer at a community college. However, my advisor suggested I just learn this material on my own. I'm a little hesitant to do so as all of the math classes I listed above have Discrete as a prerequisite. Here are the programs I'm now considering: Dreaming? - Rice, Pittsburgh Reach? - Southern Methodist, UC - Riverside, New Hampshire, South Carolina Safe? - Utah State, Montana State, UT - San Antonio Only Pitt has a prerequisite of 1 semester of Advanced Calculus. I have prerequisites covered for all other programs. South Carolina does not require real analysis, but their website states that admitted students often took real analysis. Based on these schools, do you think I have a good plan of study? I probably can fit two more classes into my schedule over the next 3 to 4 semesters. Clearly taking a semester of Advanced Calculus would be good for Pitt, but would taking a two semester sequence of Advanced Calculus be worth it for any of these programs? How about Real Analysis? I definitely don't want to take the bare minimum of courses, but I also want to be as efficient as possible with my time. Feel free to suggest any other classes I should take during my MS program. Here are the course descriptions for my school: Advanced Calculus I Completeness of the real numbers and its implications, sequences of real numbers, and continuity and differentiability of functions of one real variable. This course is a proofs-based, rigorous examination of many of the results used in a first semester Calculus class. The purpose of this course is to provide students with the opportunity to develop mastery of rigorous thinking and the techniques needed to develop rigorous approximations in Calculus. The techniques and problem solving skills developed in this course are crucial to future success in courses in mathematics or in the teaching of mathematics since they form the core of how one thinks in a rigorously mathematical way. Advanced Calculus II Formal definitions and analysis within the framework of single variable functions. Advanced concepts in analysis. The purpose of this course is to extend the notions seen in the Advanced Calculus I and complements your knowledge of the standard formalism used in analysis. Such knowledge is of importance for future (pure/applied) mathematicians who plan to have a career in the industry or in research and who need to learn the origin, as well as, to gain a clear understanding of the assumptions' validity. This course will give students the mathematical foundations needed for more advanced courses in mathematics and more specifically in analysis. The first half lectures will address the construction of Riemann integrals, convergence of series of numbers, introduce sequences and series of functions (in particular, we will explain the difference between pointwise and uniform convergence). The second half of lectures will be dedicated to the introduction of some basic topological concepts like metric spaces, open and closed sets, compact sets. The notion of continuity will be extended in the framework of metric spaces. Applied Real Analysis - (Highly unlikely I will take this course. I assume that Advanced Calc 1 or 2 is sufficient for Real Analysis) Lebesgue measure and integration, metric spaces, Banach spaces, Hilbert spaces. The purpose of this course is to develop a strong core knowledge in analysis by generalizing concepts seen in advanced calculus in the case of single variable. Such knowledge is of importance for pure or applied mathematicians who plan to have a career in the industry or in research (the covered concepts will be useful to more advanced courses in mathematics like analysis, partial differential equations,...). This course will generalize some concepts learned during the advanced calculus sequences. All notions will be precisely defined and all results will be proven. First, basics about measure theory and Lebesgue integration theory will be presented. Next, the concepts of metric spaces, normed spaces, Banach spaces and Hilbert spaces will be developed. Finally, definitions and properties of bounded operators between spaces will be studied. Linear Algebra Vector spaces, linear transformations, orthogonality, eigenvalues and eigenvectors, normal forms for complex matrices, positive definite matrices and congruence. The goal of this course is to provide a rigorous exposition of the fundamental components of linear algebra: Linear Transformation on Vector- and Inner Product Spaces; and show some of the foundational results — including Diagonalization (or Non-Diagonalization) using eigen-bases, the Gram-Schmidt Method, the Riesz Representation Theorem, the Real and Complex Spectral Theorems, the Cayley-Hamilton Theorem — and finally discuss the Singular Value Decomposition, as well as similarity transformation of a matrix into Jordan Canonical Form. Numerical Matrix Analysis Singular value decomposition. Projections, QR-factorization, orthogonalization, conditioning and stability, Gaussian Elimination, LU-Factorization, pivoting strategies, Cholesky Factorization. Iterative methods for diagonalization and eigensystem computation. Tridiagonal, Hessenberg, and Household matrices. The QR algorithm. Programming in Mathematics Introduction to programming in mathematics. Modeling, problem solving, visualization. (Prerequisite for Numerical Matrix Analysis)

Thank You for the advice. It does appear that Real Analysis is part of the Advanced Calculus II course at my school. Here is the class description: “Formal definitions and analysis within the framework of single variable functions. Advanced concepts in analysis.” I will also take the Advanced Linear Algebra and Optimization courses you recommended. I will be taking my first Computer Science courses during the next few semesters. I plan to take programming courses in Python, Java, and C++ and courses covering SQL and Linux. I’m proficient with R and can get by with SAS, but I have no formal training with coding/programming. I have been reviewing my grades and it seems I have about a 3.35 GPA across my entire college career. If the first 2 years are removed, I jump up to about 3.6. My GPA for Quantitative courses is over 3.7. Hypothetically, if I were to ace my remaining classes, my cumulative GPA would be 3.5 and Quantitative would be over 3.85. Hopefully that would help me be more competitive. I also plan to study the entire summer for the GRE. I think I can reasonably get a 167 to 170 on Quant and a 158 to 160 on Verbal. Hopefully I can get into one of my reach schools, by pulling off those results and writing a great SOP. LOR will be a challenge though. It has been hard to build relationships with professors over Zoom. Some of my graduate level classes next semester will be recorded videos only. Thanks again for your advice!

Hello Everyone! This is my first post on this website, but I have read many posts regarding PhD Statistic programs. I earned a BA in a unrelated field almost 20 years ago, but recently started a MS in Statistics program at a Public State School in my area. I'm doing fairly well considering I do not have a large background in math or statistics. I even was able to secure a Teaching Assistant position this year. It turns out that I really like teaching college level students. I have now decided that I would like to pursue acceptance into a PhD program. My understanding from posts on this website and extensive research of PhD stat program online, it seems an extensive background in math is more important than one in statistics to be accepted into a program. I will post my profile below for your evaluation, but I was wondering if you could make suggestions as to what math classes would be most helpful for gaining acceptance into a PhD program. Here is my current and planned math course completion: I currently have completed the following math courses: Calculus I, Calculus II, Calculus III, and Linear Algebra. I plan to take the following math courses: Discrete Mathematics, Differential Equations, Advanced Calculus I and II, Applied Regression Analysis. Math courses I'm considering to take if time permits: Abstract Algebra, Programming in Mathematics, Advanced Linear Algebra, Advanced Abstract Algebra, Intro to Mathematical Modeling, Number Theory, Numerical Matrix Analysis, "Group, Rings, and Fields", Matrix Theory with Linear Algebra, Cryptography, Applied Fourier Analysis. Since programs all have different prerequisites for math backgrounds, here are the programs I'm considering: Extra Long Reach - Carnegie Mellon Long Reach - Cornell, Iowa State, Texas A&M, Minnesota, Yale, California - San Diego Reach - Iowa, Rice, Northwestern, Pittsburgh, Rochester, Maryland - College Park, Stony Brook, Washington University in St. Louis Chance - SMU, California - Riverside, Utah Back Up - Montana State, Utah State, Texas - San Antonio I will appreciate any advice provided. Here is my profile: Type of Student: Domestic Undergrad Institution: Top 30 Public National University Major: BA Unrelated Field GPA: 3.25 Related Courses (undergrad): Calculus I (A) Undergrad Institution: Various Community Colleges Major: Math GPA: 4.0 Related Courses (undergrad): Calculus II, Calculus III, Linear Algebra, Statistics Planned Future Courses (undergrad): Discrete Mathematics, Differential Equations (Taking at a community college to save money and save time by taking classes during the summer). GRE: 153 Verbal, 163 Quantitative (Took without any studying and many years after being out of school.) Graduate Institution: Top 100 Ranked Statistics Program Major: MS in Statistics (finishing Spring of 22/23) GPA: 3.5 Related Courses (undergrad): Mathematical Statistics I, Mathematical Statistics II, Actuarial Modeling Related Courses (graduate): Theoretical Statistics I, Theoretical Statistics II, Linear Regression, Time Series Analysis Planned Future Courses (undergrad): Applied Multivariate Analysis, Python Programming, Advanced Calculus I, Advanced Calculus II, (Maybe Abstract Algebra, Programming in Mathematics, Advanced Linear Algebra, Advanced Abstract Algebra, Intro to Mathematical Modeling, Number Theory, Numerical Matrix Analysis) Planned Future Courses (graduate): Survival Analysis, Data Analysis Methods, Data Mining Statistical Methods, Applied Real Analysis, (Maybe Nonparametric Statistics, “Group, Rings, and Fields”, Matrix Theory with Linear Algebra, Cryptography, Applied Fourier Analysis) Experience: 3 years of teaching statistics at the university level by the time I graduate. Have over 25 years working in sales and service jobs.