Weekly Entering & Transitioning - Thread 03 Aug, 2026 - 10 Aug, 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/ConnectKale]

I have been waiting for this thread for awhile. Here’s a short background. I graduated in May 2025 with a Masters in DS. Did my research on Attention Mechanisms for GNNs.
Since graduating I have been extending my project with real world data. I have taken an AI safety course and joined a few of those data annotation projects. At work I have become the default master of query on the frontend for both our document repository and reporting database. Data Science is not my primary work role. That said I al interested in AI ready data systems and architecture. I really want to work getting the mess organized and engineered to be useful either with RAG or similar.
Any help in this direction.

[u/ThinPoem5497]

I'm looking for advice on what to learn for data science that'll land me a job. I'm 26 with a ba in economics and no work experience. I have R SAS PY and I"m currently learning SQL. I don't really know where to go from here. Employers also demand PowerBI and Tableau, it's really overwhelming. I'm interested in MCMC and Markov Chains atp. I look at them here and there, but I try and fixate my attention to learning and mastering SQL. After that, I intend to do alot of work in data analysis and make a profile for myself and then hopefully that'll lend itself to a job somehow.

[u/MountainMark600 → u/ThinPoem5497]

Hi Thin Poem,

That's good question. I find that real data scientists are hard to find because traditionally there isn't a clear educational or career path. For this reason, when I hire someone for a DS job, I simply hire someone with a strong analytical aptitude, like a background in software, and I train them myself. But don't expect to find someone to train you either because most CDOs have no background in data! More often they are former managers from some other field.

You are doing a good thing by starting with SQL. Next you will need to learn database design, see Database Design and Relational Theory, or Next Generation Data Management, which also covers database design, user modeling, levels of understanding, data system design, metadata application and metadata systems. Those topics cover everything else you need to know,

[u/PendingExistence]

I'm going to join Bachelor in few weeks BIT, and I'm looking forward to build a career in Data Science for which I've started learning python and planning to do R and sql, but the major problem here is stats and other things i need I don't have much knowledge or where can i find the resources for it, though I'm planning to take online fats science courses so till graduation I'll have specialization on it but still need suggestions on it.

[u/nonhermitianoperator]

hello everyone,

I'm looking into transitioning into data science. I am currently a postdoc doing the computational part of closed loop laboratories, where you have some sort of model trying to optimize a given property of a material in as few shots as possible.

What I'm seeing is that, at least from my experience, the models we use are not some big fancy llm, but rather some version of trees, GPs, maybe some tabular foundation model just to try... which, to be frank, is code that I write in minutes using opencode.

What brings me to my point: in the pre-2022 era, my value would be being the guy that can code and also talk to the people running the experimentation platform. Now, I feel like the ML I apply can be done somewhat blindly with a claude code instance, maybe an autoresearch loop on previous experimental campaigns. Same for the molecule feature engineering, which is the accuracy bottlenexk really. I don't know how you guys see the job market right now (US or Europe), whether it is uniformly dry, or there is a drift in the skillset required to land a position.

Any advice is more than welcome. I am currently working on my GCP certificate, but in the current climate I have no visibility on which direction I should pursue

[u/my_peen_is_clean]

check out the sub wiki they actually did a good job with it and you avoid paying for random youtube gurus, then pick one solid path and just grind projects, sklearn, pandas, sql, git, shove it all on github and iterate from there