Looking for Advice - Statistics MS for Fall 2022 - Lower GPA

[bruinlookdown]

Hey everyone, I Just got out of college and am now working full time as a Data Scientist. I'm planning to apply for an MS in Stats for the Fall 2021 cycle. I'm very worried about my GPA and am unsure of which schools I should even aim for. I know my grades don't look the best. This is mainly because I started off very rough (hated my major, had trouble adjusting to college) and did progressively better as I went through school. While I floundered for my first couple years, I really got my stuff together towards the end and am hoping to continue my passion through these mess up. Im not sure if this is a good assumption, but I'm hoping since I actually did better in my more difficult stats classes that I may look a bit better than my GPA implies.

If anyone can offer any advice for where I should apply please let me know! Here is my profile:

 

Undergrad Institution:  UCLA
Major(s): Cognitive Science (Computing Spec)

Minor(s): Stats 
GPA: 3.225 (Overall), 3.365 (Major)

Type of Student: 
Domestic Caucasian Male

GRE General Test: 

Will take in 1-2 months, aiming for 165Q+

Relevant Courses: 

• Psych 100A (Psych Stats) - C
• Econ 41 (Econ Stats) - C-
• Math 32B (CALC OF SEVRL VAR) - C-
• Math 33A ( LINEAR ALGBRA&APPLS) - B-
• Math 32A (CALC OF SEVRL VAR) - A-
• Stats 20 (PROGRAMMING WITH R)- A
• Stats 100A (INTRODCTN-PROBABLTY) - B
• Stats 102A (INTRO-COMP STATS-R) - B+ 
• Stats 100B (INTRO-MATH STATS) - A-
• Stats 101A (INTRO-COMP STATS-R) - A+
• Comm 188 (Intro to Data Science) - A+
• Stats 100C (LINEAR MODELS) - A-
• Stats 101B  (DSGN&ANLY-EXPERIMNT) - A- 
• Stats 102B (INTRO CMPTTN&OPTZTN) - A
• Math 115A (LINEAR ALGEBRA) - C-

Programs Applying: 

MS in Statistics (Maybe Applied Statistics?), interested in possibly going for a PhD sometime in the future but until I finish a Master's I don't know yet. If anyone wants to know I would like to work in Data Science or possibly as BioStatistician to leverage my Health Sciences background and a Master's degree in Statistics seems like a great way to achieve this. After working for a while I would prefer to get a PhD and go into Academia.

Extra Academic Experience: 

Currently getting the Data Science certificate from UCLA Extension (4 Classes - Intro to Data Science, Machine Learning, Databasing, and Data Visualization) . Likely getting/ Planning to get straight A's in each course.

Work Experience:  

Data Scientist for a small Tech Company

Letters of Recommendation:  Have 3 professors from the statistics department who have agreed to write me letters, but I don't know them all too well. Will ask a manager at my company who is an Adjunct Data Analytics Professor and the CEO, who I have worked with often.

Applying to Where (Current List): 

UCLA, Texas A&M_______UCSD, UCD, UCSC, UCSB, UCI, SDSU, Ohio State, Colorado State, U Conn, Penn State, U Colorado- Denver, U Virginia__________ San Jose State, Long Beach State, SF State

 

Questions:

1) What could I do to improve my profile between now and when applications are due (Dec 2021)?

2) What schools do you think I could realistically get into? Should I change around my list of schools I'm applying to? I would like to stay within California due to cost if possible.

3) Should I try to go for just pure Stats or maybe Applied Stats? Is there a benefit acceptance wise for applying to Applied Stats programs? Will it hurt me a lot if I get into an applied stats program but decide to try for a PhD down the line?

4) Any target schools I'm missing that I should really check out?

 

Thank you for taking the time to read my post, I'd appreciate any advice at all! 

 

Edited November 4, 2020 by bruinlookdown

[kingduck]

I had an almost similar profile to you. My major GPA (econ) and gen eds are what significantly lowered my GPA. I'm in the application process right now so take my advice with a grain of salt.

1) From what I've noticed, for the masters, adcoms care a lot about linear algebra, multivariate calc, and some sort of calculus based probability. Since Dec 2021 is still a year away, so you might want to retake a more advanced version of those courses, maybe linear alg and probability at the graduate level - and really any course that you might think will cause adcoms to question your mathematical ability. Retaking the courses and doing well will compensate a bit for your lower lin alg, econ stat, and psych stat grades. Also the GRE Q will be important. Try to get as high as you can; 165+ is a good goal to have.

2) As for schools in CA, I would also add CSU East Bay. I work with several brilliant data scientists and statisticians who got their MS in Stats from there. As I'm in the application process, I am not confident of my ability to suggest some more appropriate schools.

3) I believe quite a few reputable members on this forum have mentioned in other threads, that going for a stat masters would be better for a PhD down the line, if the master's coursework is rigorous. As for applied stats, you might want to consider certain online programs or business schools that offer the applied stats MS, and I believe Santa Clara University is one of those schools (not 100% sure). It's my impression that applied stats usually isn't as rigorous as traditional statistics degrees, but in the industry, I don't think it makes much of a difference. However, I do think the math grades are what will hurt you the most in getting into a PhD program in the future, and the lack of Real Analysis.

4) Maybe also add CSU East Bay and Santa Clara. Since you already have SJSU in the list, might as well add a few schools in the area as well.

Again, my advice is just from what I've observed from lurking around here, and from talking to professors about my background. I hope I'm not spreading fake news here. YMMV, good luck.