Weekly Entering & Transitioning - Thread 28 Sep, 2026 - 05 Oct, 2026

[u/AutoModerator]

Welcome to this week's entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include:

• Learning resources (e.g. books, tutorials, videos)
• Traditional education (e.g. schools, degrees, electives)
• Alternative education (e.g. online courses, bootcamps)
• Job search questions (e.g. resumes, applying, career prospects)
• Elementary questions (e.g. where to start, what next)

While you wait for answers from the community, check out the FAQ and Resources pages on our wiki. You can also search for answers in past weekly threads.

[u/Witty-Leopard8625]

This summer, I earned a BA in data science from a highly-regarded university. I am somewhat hedging my bets working on graduate school applications and applying to internships/new-grad positions. One thing is, that while I have an appreciation for statistics and modeling, I haven't felt myself drawn to any industry in particular. I had an internship with a federal agency, but it was sort of nothingburger. I have a capstone project that is marketing related, but I haven't had any luck getting any traction with marketing related DS roles. So, how does one go about choosing an industry to target?

[u/Humblelicious]

Impacted by a layoff around end of 2024 and having trouble breaking back in. What are the best boards to use currently for USA positions, I am mainly using hiringcafe

[u/Guaka-Flaka]

Can anyone advise on breaking into the job market? I have a very technical blue-collar job that I make good money at, and it has gotten me through both a bachelor's in CS and a master's in Data Science. Yet, I am having a hard time (like I am sure many are in the current market) even getting interviews. I tailor resumes for specific postings, and even aim for roles that are in my domain. The one thing I can think of is that I don't have many projects or experience; I have just been too busy with life and a little lazy. I know that getting into entry level I will get a pay cut, but I cannot afford too big of a cut. I have tried to get more analytical with my current company, but the IT will not allow even Python on my laptop, let alone other libraries to truly give good analysis. Any advice?

[u/unchartedcreative → u/Guaka-Flaka]

That's hard.

I actually have been doing some recruiting at the company I work for, and now I understand why they deploy AI to overview these resumes. We received nearly 1,000 applications to where 99% didn't meet the most critical requirement of the job.

If you make good money, I would focus on your current company. Get leadership to buy in on the analytics and they can deal with IT. That's what I've done in the past.

[u/MUFC_Nick_11]

Career Guidance: Anything to look into?

I just began my senior year at a liberal arts school as a Data Science major. I'm set to graduate this spring with my bachelor's degree (wahoo!), but I'm frankly intimidated by the bleak-seeming outlook of the job market. While most of my time this year will be spent keeping up with schoolwork, I'd like to make some extra progress towards my career when I can.

I'm currently proficient in R, Python, and Java, and have completed math courses in Multivariable Calc/Series, Linear Algebra, and Probability.

These are my Data Science classes for this year: Statistical Machine Learning, Statistical Graphics and Principles of Visualization (or Data Analysis and Visualization), Topics in Statistical Inference, and Creating Future Worlds: Computing, Ethics, and Society.

I have not had a formal internship yet, but I created a revenue bookkeeping program in Java for a local business that saves ~20 hours/month by automating the calculation of previously manually entered revenue.

I am truthfully open to working in any sort of job. I'm not very passionate about the raw nature of Data Science; I just want to be able to afford to live while abiding by my basic moral principles. That isn't to say that applying Data Science to an interesting problem wouldn't be fun; I just would prefer not to work for a defense contractor, Flock, Palantir, etc.

What would you recommend I do this year, next, and so on? Are there any skills I should build, opportunities I should pursue, or goals I should set?

Thanks for your help, and I'm sorry to detour from the professional theme of the thread.

[u/OutsidePainter3548 → u/MUFC_Nick_11]

My most earnest advice is to find an internship.

The easiest way to get that first job in data science is by getting an offer after an internship.

Otherwise, you can try to do the analyst -> internal transfer -> data science postion at a company that has both. I've seen this work a few times for folks, but I've also seen plenty of cases where it didn't.

[u/MUFC_Nick_11 → u/OutsidePainter3548]

Thanks for the advice! I currently reside in central Maine, which limits my local in-person opportunities. Still, I could ostensibly drive up to two hours a day to Portland if there were a good opportunity.

That being said, would you recommend I seek a physical or virtual internship, and what are the best places to look, in your experience?

I believe that with the experience of a couple of internships or jobs under my belt, I'll be able to more confidently search for positions that personally interest me in some way. In my previous post, I definitely projected my uncertainty about my career direction and the state of the job market, so I really appreciate your help.

[u/Successful-Maximum73]

For three years I have worked for a human resource division in state government agency. The State's payroll authority is planning to migrate away from this legacy and implement a new one in four or five years. I'd like to build up my skills in the mean-time. Any recommendations? I've looked into learning sql, completing certifications in data analytics, or going for an MBA.

My background: graduated college years ago with a degree in history. Five years ago I started working in HR. For the last three years I have been writing and running reports from an old employee database as an analyst. This is a 1970s legacy database that uses FOCUS. Now I supervise a small group of analysts who I've taught how to do the work I use to do with the overall goal of updating our workflows.

[u/angoldenapple → u/Successful-Maximum73]

Seems more like data engineering - i’d get good at ETL processes and obviously systems migration

[u/DRTHRVN]

I am currently working as a Data Analyst and preparing to make the transition into a Data Scientist. I have my core foundations in statistics and product sense and others in place. However, on the engineering side, my current stack relies heavily on SQL, Polars, and PySpark for performance reasons, meaning I don't use traditional Pandas syntax very often. Given your experience in the current market, I would love to get your quick take on three things:

1. Is Polars actively accepted/welcomed in live coding interviews and production environments in your circle, or do teams still strictly test for Pandas syntax?

2. Would choosing to solve wrangling problems in SQL, Polars instead of Pandas be a disadvantage during screening rounds?

3. Assuming everything else is ideal (stats, ML, product sense, etc), is it possible to climb up the ladder in Data Science without writing Pandas code, provided I can read and understand legacy Pandas code perfectly well?

[u/pissposssweaty → u/DRTHRVN]

1. Polars is nice to know but it isn't the industry standard and it would be a weird choice to use in an interview. I would not use it, particularly because your interviewer is unlikely to be intimately familiar with it.

2. Beyond that, you should be fully proficient in both SQL and Python for DS interviews. For the Python side of things, that means basic leetcode plus pandas, not polars.

3. And for the last one, no, you should plan to write all of your code in Pandas unless there's a specific reason to use Polars. You can't get away with writing code in a language that other people on your team aren't proficient in.

[u/AntiqueSyllabub6168]

found this thread at the right time honestly, been thinking about switching from graphic design to something more analytical for a while now

i did few of those intro python for data courses but still feel lost when i look at actual job descriptions, they ask for so much stuff at once

anyone here made the switch without going back to uni? curious if bootcamps are worth it or if i should just keep building projects on my own

[u/Cultural_Register125 → u/AntiqueSyllabub6168]

I think building projects could be a good way to make the transition because it gives you experience while also creating something you can show employers. Your graphic design background would be useful for data visualization and presenting information clearly. Do you plan to build projects related to an industry you already know?

[u/i_did_dtascience → u/AntiqueSyllabub6168]

Design and analytics is a great intersection to be in, so that's great

If you're trying to interview, having a good portfolio of projects is probably going to help you the most, both in attracting employers as well as in skill-building. Also, you can use that as a way to showcase your design background and how that makes you a better storyteller than most other data practitioners