The Stats MA at Berkeley is a two or three semester program, with about 1/3rd of students electing to do a third semester. In order to complete the program, you must complete a capstone project and the comprehensive exams. There is a “thesis option” but only a couple students per year will choose this option. The program has an industry focus, with no option to transition into the PhD program upon completion. As such, there is no overlap between the masters students and the PhD students in the coursework. If you are able to make connections separately to find a suitable RA position at Berkeley, you will be able to do research, but it is not a part of the Stats MA program. My cohort had ~50 students, with around 40% being from mainland China, a smaller share of students from India, and international students from several other countries. Of the students holding American passports, a few were from the east coast, and the rest were California natives.
During my time in the program, the head of the department assured us that anyone who is hoping to be a GSI in the second and third semesters will be assigned to a position, usually for undergraduate lower division courses. I also was a GSI in the first semester, but you need to obtain special permission to do that, and I worked for the math department, and applied separately through their application process instead of a guaranteed assignment. Then in my second semester I was a GSI for an upper division statistics course.
In the Summer preceeding the program, the department offered a “Summer bridge” two week preparation for the program, with bootcamps in R, Python, Stats, Probability, and some review of calculus and linear algebra. The R and Python sections were very useful, and the others were not as much, but YMMV.
Although in the years before I arrived, the Stats MA coordinator had a strong positive influence on the students’ preparation for industry, she left just before my program began. Our class suffered the lack of career counseling in conjunction with the market crash of 2020, so I cannot speak to the counterfactual career preparation offered by the program. However, if you sift carefully through the alumni list, you can use LinkedIn to find some reasonable trends in career placements, as well as the relative start dates compared to graduation month.
In terms of expected salary upon graduation, you can check the Data Scientist levels.fyi page for industry standards, and benchmark with your current years of experience. From the modest sample I have collected from colleagues as they shared about negotiating offers and talking numbers, 100k is very attainable if you’re staying in a HCOL (high cost of living) area like the bay or another major city. Of course, if you are able to land a Data Scientist role at a well funded fast growing startup or at a large company, you may be able to achieve well over 200k total compensation. This is highly idiosyncratic at the student level and will depend heavily on your ability to network, communicate your skill set, and negotiate. Total compensation is also highly idiosyncratic on the firm and even team within firm level, where some hiring managers will have a headcount to fill and a specific need for their project that you might be more or less compatible with. Remember also that you’re competing with top students from top programs from the entire world to land a top job in the bay, not just your fellow classmates at Cal.
Update: The UC Berkeley Statistics department has since updated their website to include much more information than before
The stats MA program combines probability, statistics, linear algebra, computer science, and some elements of pure mathematics all together in one. Unless you have a double major in Stats and CS with a minor in math and a full upper division course in linear algebra, you’ll have to learn some things for the first time or brush up on other forgotten material.
Coding / Computer Science:
Probability and Statistics:
Coming into the program, I had several semesters of abstract algebra and linear algebra under my belt from undergrad as a math major, and it was a huge help. As time has gone on in the program, it has become more and more valuable. Students who are not strong in linear algebra hated the end of Stats 243, where it was very heavy on linear algebra, and are completely lost in Stats 230 - linear models. If you have the bandwidth, consider auditing a lower division class, check out David Lay’s undergraduate level book (Math 54 at Cal), or Stephen Friedberg’s upper division level linear algebra text.
Stats 243 - Statistical Computing
Stats 201A - Probability
Stats 201B - Statistics
Stats 222 - Capstone project course
Stats 230 - Linear Models
Qualifying elective of your choice
Many assignments will require you to learn something new and apply it immediately. For instance, an assignment in Stats 243 early on was to do the following:
The assignment was due 10 days after it was announced. The description of the task itself was two pages long. For some students, it meant learning up to four or five languages, two or three new programs/applications, and several packages or libraries across those languages. This is part of the learning process. It begins as something that seems entirely unreasonable, and then at the end of the ten days, it all seems obvious and you can’t imagine calling library() before you call install.packages(), and you make fun of each other when you don’t have quotes around string objects or your for loop has no conditional statement causing a runtime error.
Much of the material will be learned on your own, on stack overflow, and through others in the program with more expertise. Immediately several distinct names of my classmates come to mind if I have questions of different categories: stats questions (Fitch), ggplot2 and data visualization questions (Mirella) , LaTeX questions (Kyle), and Linear Algebra (myself!). Practice being a resource and asking for help: that’s what industry is like!
You will receive many emails from the department:
Pro-tip: if you know an email chain will continue, but don’t want to get updates and notifications, gmail supports “mute”. Many times a person in our department will be awarded a grant or have something named after them and a dozen or so people will reply all, which is a wonderful way to celebrate an important accomplishment! But it’s also distracting if you’re trying to work or waiting for more important / relevant emails for group projects or interviews.
When a new admit emails me with questions, I’ll answer them and add them here.
Question: Did you enjoy your programme?
Question: What were elements you enjoyed the most?
Question: what did you not like?
Question: How much work experience do students have (I have worked in quant finance for three years)? What percentage of students have work experience?
Question: From the module pages and your websites the modules seem to provide a very deep understanding of fundamental statistical models. Is that true? How much does the degree cover more modern approaches of statistical learning?
Question: How much application is included in the degree? From what I understand from the module page and your website, the lectures are theoretical but the coursework will make you apply the learned models on real world data. How did you experience it?
Question: Could you tell me a bit more about the capstone project? Should I think of it as a master thesis? Could you tell me about 1-2 example projects?
Question: How do do you see the job prospects of graduate? What are roles that students tend to go for? In my case, given my previous work experience, I would not want to join a corporate graduate programme but join as an experienced hire.
Question: When you say that the program is very interdisciplinary, do you mean in terms of background of students taking it? I am asking because from my understanding, the degree sounds fairly focused on statistics, compared to other Stats/DS programs that have more elements of computer science in it for example.
Question: As far as my third question on “modern” statistical methods is concerned, I tried to ask if the degree is focusing more on fundamental statistics only or will also teach you commonly applied statistical methods in the industry such as dimensionality reduction techniques, ML, NLP, computer vision, etc. I have seen some other graduate programmes (e.g. NYU’s MS in DS programme) that seems a bit more focused on ML applications. Berkeley’s programme is of course a statistics programme and to that extend focuses more on fundamental statistical methods. However, I am trying to understand how much of the application of these in ML is covered. I see that the module Statistical Learning Theory seems to cover these methods but am wondering how much else they are covered (you mentioned 201B and 230A also cover some of these methods). Would you think it is a fair assumption that this programme provides a rigorous understanding of statistics and includes some elements of ML but leaves the application of these for the capstone project and coursework?
Question: Speaking about electives, which elective did you take and which one would you recommend? For example, would you recommend taking the ML focused module from the stats department (241A) or would you take the module offered by the computer science department?
Lastly, I am also holding offers from NYU, Imperial College London and some other European programs. From your insights into the field, how would you say Berkeley ranks up against NYU and Imperial? From my understanding Berkeley is really top notch in the field of statistics. However, I wanted to ask what you view is and how you see NYU and Imperial in the field of statistics/ml/ds.
Question: On the point on modern / ML techniques, do you feel that these are sufficiently covered as part of the programme (lectures+coursework+captstone project) so that you would be able to apply them? My objective of this degree is to a) deepen my understanding of statistic sand b) learn new tools that I have not learned in my econometrics classes / professional experience, such as Neural Networks, NLP, support vector machines, etc. From how you describe the coursework (web scraping + analysis) and the capstone project (airbnb NLP project, Alzheimer computer vision project), it sounds like you do learn these tools as part of the degree. Do you see it the same way? Do you feel there are some things you did not learn (compared to your expectations or compared to a DS degree?)
Question: Lastly, you mentioned studying for 3 semesters. Is this common? It does of course sound nice to spend 3 semesters instead of 2 semesters studying, however this would mean a 50% increase in (already high) cost of the degree…
How did you like the UCB Stats MA? I enjoyed it, but the last few months being remote were rough; do not recommend doing a remote program, especially because your connections are a huge part of getting a masters.
I have been going back and forth between whether I should do a 2-year program or this one. I currently work as a Data Analyst and want to move into Data Scientist positions. Could you speak a little to how this MA helps prepare you for a Data Scientist position and how it is perceived by the industry (do employers see this as equivalent to a traditional Stats MS in your experience)? How the program is viewed is based on ranking and degree; I didn’t have any trouble because it was a 1-year program. Also, I didn’t need to specify, so it’s possible some employers assumed it was a 2 year program. There’s the option to do a third semester so for some folks its 1.5 years. If I were you, I would choose a 2 year program over UCB only if the school is a name brand like Berkeley.
Any advice if you were in my position (UCI Math BS, worked as a Data Analyst for a year now, plan on doing a Masters this Fall, and getting a job as a Data Scientist)? You’ll be seen as not having enough CS experience, so I would try to do as much CS as possible in your current work. Try to build your data engineering skills and understand the infrastructure and software because the industry is currently a bit more partial to CS folks than stats folks if you want to be a Data Scientist as opposed to an analyst. Would you be able to get promoted to DS at your current company before you started a program? That would be one of the best things for your resume.
Any technical advice (skills to focus on to move from Analyst to Scientist, whether it’s statistical concepts or coding languages or beyond)? Try to find a project that works well at several scales, and build it out over the course of several months using cloud computing resources. If it costs money per month to maintain those cloud resources, just count that as tuition. I would recommend the GCP courses (which are very cheap) or AWS certs if you want to get a feel for what it’s actually like in industry with enterprise tools. I would advise you to take Andrew Ng’s ML course (I think it’s offered for free online) and make sure you brush up on your Linear Algebra, in case your 121A and B were taken earlier on in your UCI math career (I took mine winter & spring quarter of freshman year, so it was rusty, lol) I also recommend John Rice’s book for brushing up on stats and probability.
How tight-knit/ collaborative is the MA cohort? Did you interact frequently with the other MA students in classes/studying/etc.? The cohort is as tightly knit as you want it to be. In my cohort there were several subgroups that as far as I can tell were extremely close. In the subgroup that I spent time with, we basically got to school around the same time, studied between classes together, then stayed in the lounge at night until we finished the assignments.
I understand that graduate degrees are meant to be focused on studying, but was there time to do other activities such as joining clubs/events/exploring San Fransisco? Or is the program rigorous enough that I should expect to focus all my time on my studies? It depends. If your background is strong, you will probably be able to do well in the program and also do several activities outside. In my case, I lifted weights at a local gym, worked as a GSI and was phasing out of my pre-masters job, and found time here and there to explore. I knew early on I wasn’t going to be the top of my class, because there are students who have no desire to do anything outside of class and have stronger more recent coursework from top institutions. But nobody really asks about your GPA if you’re going into industry after you graduate, so I would focus more on skill building and getting familiar with technologies anyway. To answer your first question directly, anyone in my cohort who wanted to find time to join clubs/explore the city found that time. To your second question, the program is rigorous enough that the top students focused much of their time on their studies and did not explore or have as much “fun”, but I don’t know if they would have if the program were not rigorous.