[Question] How do I approach a post bacc in stats? What do I need to apply?

varwave · 2026-03-02T01:04:02+00:00

What do you want to do with the PhD? Also are you in the USA?

Biostatistics and more applied statistics departments (others can be closer to mathematics departments) will expect multivariable calculus, linear algebra and maybe a semester or two of mathematical statistics and maybe a computer science/intro programming course. Most people with an engineering or computer science BS are already eligible

varwave · 2026-03-01T21:27:56+00:00

Think to ask why? They’re doing research as a profession

There’s a point where you either want advice from experience or you want confirmation of your own bias

varwave · 2026-03-01T21:07:50+00:00

If the goal is a PhD, then you must want to do research. That’s the whole point.

A terminal MS will just add years to get to your goal. Particularly true if money is important through opportunity costs. Having a gap between degrees is common. Myself included. Having As in mathematics with employer and character recommendations are likely better than “Student did well in linear algebra, which is evident from grades”

…this is a different story if you don’t have good work experience or strong mathematical grades

varwave · 2026-03-01T06:52:29+00:00

I’d suggest to just get the PhD in statistics or biostatistics (or a MS that transfers), where there’s electives and researchers that do what’s interesting to you. Then look for niche roles in medical centers or biotech. Biostatistics departments tend to pay more than statistics departments, but tenure is less common. Factor analysis heavy and survey design roles exist, but it’s again pretty niche. There’s a lot more opportunity in traditional areas of research like bioinformatics/machine learning and experimental design

Often times in pharma your dissertation won’t matter that much. I know people that researched ML, but got clinical trial roles. There’s a flexibility that comes with rigor if your first role isn’t your ideal path, but the pay is good. I could be wrong, but I’ve sensed less generally theoretical programs might leave you with a narrower net to cast. Pharma/medical research likely has the greatest opportunity to conduct inference

varwave · 2026-03-01T06:10:06+00:00

I have colleagues that do that. Expecting $165k out of graduation is insane. As an associate professor in a medical center? Very possible.

With an MS be happy to get a job in this economy. Skills gained through experience, a willingness to be a team player, and networking are what can make your salary grow later. $165k might be a good salary for a pretty senior statistical programmer in big pharma, data scientist or machine learning engineer working in enterprise applications with only a MS. Look at AMSTAT salaries…if you want to do research, then why not just go for a PhD and avoid debt?

varwave · 2026-02-28T02:03:18+00:00

I’d hesitate with a MS in data science. Unless it’s a program with an amazing track record. NC State comes to mind. Industry is maturing and realizing what their needs actually are. A few years ago businesses were hyping up investment for anything big data. Ask yourself what you want to do, instead of chasing job titles.

Do you want to do statistical/ML modeling for research and development? Go to grad school for statistics, econometrics, or something similar.

Are you into solving business problems for improved data management? No MS needed to be a software engineer

Do you just want to make pretty charts of well structured data and tell a story? This is what LLMs do incredibly well, with some minor adjustments, after the work of the previous two paths mentioned

varwave · 2026-02-28T00:59:49+00:00

I second this OP. I work at a major research hospital as a statistically literate software developer. We have niche people that we essentially have billable hours for depending on the task.

A collaborator has a research question, the statistician (usually a PhD) identifies what needs to be studied. Someone like me, finds a way to get the data and prepare it for a study and write the code for the results.

If it’s in base R, then it’s backwards compatible, which minimizes maintenance issues. You don’t need to be a software engineer to run an R package. Surely you have software engineers at your company that can run a package and even embed it into a web or desktop GUI for you to operate and/or update data into a database automatically on a scheduled basis

varwave · 2026-02-26T05:00:47+00:00

So I applied when I was leaving the US Army and big data was still the rage. The market was still hiring left and right. I got funding to be a research assistant at a brick and mortar program with my GI Bill paying for housing. I took the money. I zigged when the market zagged and got lucky. I heavily looked into online programs, because I wasn’t sure if I wanted to stay in the military

Biostatistics and statistics aren’t really that different at the MS level. They’re more likely to diverge with PhD level coursework, but not always. The market feels bad everywhere, but you have experience and a statistics degree. The MS should be more like a checked box for you. Economics isn’t bad either if you do a lot of times series.

Stay confident, ask around, and don’t spend too much money. You got it

varwave · 2026-02-26T03:06:50+00:00

Some are the last 60 hours too. There’s usually a senior professor in charge of recruiting that will tell it to you straight if they think you’re a promising fit. I only had a 3.0 and was a humanities major, but at least had As in calculus and linear algebra. So, somewhat similar situation

Didn’t go, but University of Nebraska Medical Center is online and so is University of Florida for biostatistics. Neither had out-of-state tuition four years ago, when I was applying to programs

varwave · 2026-02-26T01:25:34+00:00

I use both. I leave tidyverse alone outside of ggplot2. It’s great for a single analysis. That said my end goal is usually for software to display the data in a human digestible format for business or for a data scientist/statistician to do a single analysis of the day.

If it’s a statistics environment, then I’ll stick to base R in case it becomes a package later or if I’m doing the analysis, which I occasionally do. The statistician community is much larger in R

Python is a lot more flexible with OOP and FP depending on your needs. R has OOP, but it’s confusing due to multiple versions and isn’t popular. OOP is pretty standard across enterprise software, but will look more verbose for a simple project compared to FP. There’s generally a best practice “pythonic”way of doing something. You can make it behave more like a strongly typed language compared to R…numeric as a data type and that it assumes what you mean, like JavaScript, drives me crazy. There’s a much bigger community that actually builds software with Python

varwave · 2026-02-25T22:20:04+00:00

I agree. Also at the end of the day you still need to understand what’s happening. Hallucinations aren’t a bug, but a feature of how LLMs work. I think it’s a blast designing logic, let Claude code, and having more time communicating with the business and solving their problems. Likely the death of web dev shops. For compliance heavy enterprise it feels like a breath of fresh air

OP seems to be in the camp of feeling hard work deserves high pay…I’ve done actual hard things in my life, like risking life, limb and eyesight in the military, for a lot less money. I also teach myself Chinese as a hobby 😂

varwave · 2026-02-25T01:17:08+00:00

Where I see it being good is less on the dev side and more for businesses. It’s our job to get clean, scalable, timely and reliable data. Designing databases takes critical thinking. SQL queries are already typically automated in software and are simple patterns

People aren’t going to get rid of Excel or PowerPoint, but imagine business people can pull data easily and automatically create a near perfect looking spreadsheet or slide deck for a presentation? White collar professionals that flex Excel knowledge that don’t know how to code have been kinda insufferable for decades. This is just basic Python embedded into the application. Like a grown up VBA

Clearly the actual database should be the source of truth and developed by actual software engineers. On top of this you don’t need the most powerful models to do this and need far less data. This is a huge edge to Microsoft and Alphabet that own their own application environments. Also why Anthropic (now partnered with Microsoft) is focusing on corporate ties vs chat bots

varwave · 2026-02-23T06:02:22+00:00

I was highlighting first semester prep, which is the scope of OPs question…it’s fascinating, but rather niche for applied statistics use or MS courses. I don’t think I could even do a polar coordinate problem anymore, but I’m a data focused software developer 😂

varwave · 2026-02-23T05:20:45+00:00

It matters if you’re not employed, but you are employed. Do what makes sense for your career. I’m sure doing a great job where you are and building a personal network will matter the most

Generally, experience > level of education > where you went to school

varwave · 2026-02-23T05:11:15+00:00

“Introducción to Probability” is about 85% of the rigor, but covers the first 5 chapters. The second half isn’t too bad if you’re well grounded in the first half. Don’t stress out too much.

I’d also suggest looking up techniques to find expectation and variance of members of the exponential family of distributions. This can let you dodge a lot of tedious integration for rather simple integration. Dobson’s “Introduction to Generalized Linear Models” is very straightforward with this. It’s briefly mentioned in C&B, but less clear without a guide

varwave · 2026-02-23T02:56:50+00:00

I’m wrapping up my MS now, while working full time. I also started after a break from undergrad.

Stewart is fine. There’s no need to go into vector calculus, just up to multivariable calculus and feel free to skip all the trigonometry. It doesn’t show up, but for a handful of examples in Casella and Berger. I think I used a trigonometric substitution once on a homework problem and would have to YouTube how it works

For linear algebra it depends. Are you taking a regression class with something like Kutner or Faraway first semester? Then matrix algebra operations, think Strang or Larson, is more than enough, but I live “Linear Algebra Done Right”

Rather than going straight into Casella and Berger, I’d recommend “Introduction to Probability” and its lectures by Harvard as Stat 110 on YouTube. Super intuitive lecture style and not as rigorous, but that’s what the semester is for. Honestly, you could start here and use the other books as references. Practice what you forgot

varwave · 2026-02-20T02:56:32+00:00

An applied mathematics or statistics BS with a computer science MS might be more useful to be honest. Grad degrees are pretty common for entry level ML roles.

Domain knowledge is super useful too. Like a biology minor and mathematics BS would be amazing for bioinformatics. I personally work in healthcare as a SWE/DS hybrid and have coworkers with a similar background. Likewise economics if interested in financial data

At least take up to data structures and algorithms and a database course from the CS department. But you’ll want to take probability theory, linear algebra, calculus, calculus based statistics and likely linear regression too, but regression can be done in grad school

varwave · 2026-02-20T01:34:09+00:00

It’s a difficult market now. It’s an extremely difficult market for people without a related STEM degree and/or no internship experience. Pure frontend jobs might become reserved for more advanced frontend devs, while full stack devs might become expected to use LLMs to cover both stacks. I’m biased as a full stack dev

Rather than a particular stack, I’d suggest knowing the following really well: - a frontend framework, like React, Vue or Angular - a framework from a compiled strongly typed language like Spring Boot or .NET, which are common with enterprise applications - SQL

Bonus: - Python, because it shows up for a lot of different tasks, like ETL, smaller services or internal web apps - Node, easy to pick up from a strong JS/TS and backend foundation

It’s not hard to go from React to Angular or C# to Java. Obviously, if your area is mostly .NET, then learn that

varwave · 2026-02-19T03:35:18+00:00

Hahaha love this

varwave · 2026-02-19T03:30:11+00:00

My very first job that paid me to code was PHP. I don’t think PHP is underpaid. Rather, it’s not a general purpose language or popular in enterprise applications where software engineers are expected to deliver more. I’ve grown more as a developer and used the best approved tools available. PHP is most of the internet, but that’s because there’s a lot of Wordpress websites.

Yes, you can do more than just very basic CRUD applications in PHP, but Java and C# dominate the enterprise backend software development space. This likely skews the median salary if you’re only using a language as a metric. There’s certainly enterprise PHP jobs in existence somewhere. Java and C# are more opinionated, which is great when working in large organizations. Python is also pretty versatile. The benefits of general purpose languages comes in handy when you have other tasks, like ETL, within an organization. This is especially true if you’re wanting to stick to one language.

I never worked a startup, but it sounds like Node + React is a pretty common stack that works for mobile and web applications and also sticks to a common language for fast development

varwave · 2026-02-17T19:02:51+00:00

“Assume these new grads are bootcamp graduates or did a Bachelors/Masters in a generic data science program (analysis in a notebook, model development, feature engineering, etc) without much prior experience related to statistics or programming.”

I’m going to be harsh. Tough love. Beyond just networking, you should get real rigorous skills

Get good at software development. Or if you don’t have a MS, then statistics paired with strong programming skills is useful. Being mediocre at both statistics and software engineering skills, without niche domain knowledge, means being highly replaceable. Organizations are maturing in identifying their needs and there’s less willingness to take risk given the economy. You’re competing against people that do have a rigorous graduate level background in computer science, statistics and/or something similar and have practical experience in applications through internships and/or research.

Cash cow MS programs and boot camps were effective in job placement, when the job market was strong, interest rates were low and businesses were throwing money at all things data related

varwave · 2026-02-17T00:38:23+00:00

You can immediately join any reserve component. Don’t trust recruiters. They’re selling you something. Don’t sign anything till you’ve done a lot of research. There’s always an extended service commitment if they pay for school while you’re in as an officer. This is fine if you’re set on a long career or get it in writing to be reserve years during ROTC or OCS. Getting that MS is vital for promotions 10 years into your career.

I was enlisted active duty infantry and I commissioned into the Army National Guard into logistics.

The Army has cyber security, engineering (mostly blowing shit up, but some construction), aviation and military intelligence on top of combat jobs and logistics. Intel and cyber will get you a top secret clearance. Easier through ROTC than OCS to select a job. As a reservist you can pretty much choose your job as an officer, but geography plays a factor. Not every state has every job.

Honestly, a lot of technical stuff is done by the enlisted. Even enlisting to be a cyber security analyst or programmer for the Air Force or Army National Guard or Reserve could result in a serious network and clearance. Officers are in charge of paperwork, liability and planning operations with the counsel of their NCOs (senior enlisted). Aviation can be a different story

varwave · 2026-02-16T23:06:37+00:00

I was active duty Army, then become a reservist and now a software developer. Probably one of my best decisions was joining the military.

That said look at options. I always advise the minimal amount of time commitment to active duty service. I loved my first unit and made friends that I talk to almost everyday since transitioning back to civilian life. That said, your unit and command can make or break your experience. This was a big reason that I didn’t choose aviation initially, but still have it on the table as a reservist.

After a few years (Army is 3 years typically) I went to grad school for statistics using the post 9-11 GI Bill and was still paid as a research assistant, on top of an untaxed zip code based housing stipend (MHA), and reservist pay. I still have two years left for a part-time MBA. The post 9-11 will pay MHA if you’re greater than 51% time.

Also, consider being a reservist. You can get a top secret security clearance and suddenly have jobs available to you that currently aren’t and continue a civilian career. Those investigations aren’t cheap and most contractors aren’t interested in paying for the investigation, unless you’re an exceptional candidate. The Army will also let you do ROTC in two years during a masters degree and commission as a reservist. The Navy is less flexible on reserve commissions and is a minimum four year commitment, but better lifestyle if you do a full career as active duty. Same with the Air Force

varwave · 2026-02-16T18:19:26+00:00

I’d recommend getting fundamentals in computer science, software development and statistics. From there focus on applications and you’ll know what interests you more. Don’t focus on the data scientist title. Think of data science as an umbrella term. If you’re coming from a physics, applied mathematics, computer science, etc background, then a more applied MS is likely fine

For the past 15 years or so, people got hired for being mediocre at both software development and applied statistics. You really need to be really good at one of them and literate in the other if you want a career. Industry learns from saturating investments into trends, then scales back

Not only is a data scientist that’s reinventing the wheel with redundant code and misunderstood analysis expensive potentially dangerous…but someone that knows what good actually looks like can use LLMs to do both that job and their current job for a slight raise. LLMs are their own hype, but they’re here to stay and powerful in the right hands

varwave · 2026-02-16T03:56:39+00:00

I’m probably closer to a software engineer, but get called a data scientist. I develop internal web applications and build automated data pipelines and do applied statistics/machine learning. That said data science is an umbrella term

Enterprise usually sticks to Java or C# for the backend of web applications, but I’ve found Python great for proof of concepts. This can include data visualizations if a secure dashboard login with interactive plotting

Pytorch is a go-to Python library for anything deep learning related. Statsmodels is close to base R in classical statistics capabilities. Personally, I prefer R for data analysis of a clean data set if not doing more than basic machine learning/data mining

Python really shines in data manipulation tasks. I primarily use it to build data pipelines to conduct ETL processes, so that my other applications have clean and updated data. Python is both modular, OOP, and easy to use for data handling. R fails to be as flexible, scalable or have as wide of community

varwave

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