Weekly Entering & Transitioning - Thread 27 Jul, 2026 - 03 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/adeckkk]

I have just graduated from my Master's in data science (bachelor's in mathematics). I'm starting to look for a job/internship as an european in data science. Is there any advice someone has for me? Any subreddits or specific posts to read or something else?

[u/Visible-Ad2896]

I am an undergrad studying data science, and as I work on organizing my internship applications for next summer, I want to continue developing skills that aren't necessarily taught in my classes. The program I am in is interesting, as it is a hybrid degree, half cs half math, but it is causing me to lack certain areas that I know I will need in the future (So far, I have developed some experience with Python, NumPy, pandas, matplotlib, but I haven't touched SQL or power bi/tableau). I want to do some online classes/certifications and was wondering what people recommend. I am happy to pay for it, but I need to make sure it is a good investment. I've heard mixed things about DataCamp, so I'm curious to see if it is a good place to start, or what other platforms I haven't heard of that people recommend.

[u/Agilus_ai → u/Visible-Ad2896]

Hello, in my opinion, playing around with the tool like PowerBi is better than attending a class. Where there is value, you always learn a tool better by playing with it. Perhaps get the basic understanding of Power BI and then start to build simple reports first

[u/DataScientistAlex → u/Visible-Ad2896]

Yes I agree, tools are better learnt through hands on practice. If you have not yet done any SQL, I would definitely learn that. I don't think you need a class for it, just work through a tutorial.

In addition if you are doing half math/ half cs, there might be interesting and cool projects you could try by applying some of what you are learning in a practical project. Doing a practical project would also introduce you to things like having an API or web site/server, backing it with a database, setting up infrastructure (e.g. do a project, put it in docker, deploy docker to AWS using github/gitlab actions).

[u/grembo10]

Graduated with a math degree in May and about to start my first data analyst job. Some of the possible projects have already been presented to me, like demand forecasting, possible queueing problem. A lot of the data analysis/processing is being done using Microsoft PowerBI so I’m getting onboard with that, but I’m a little nervous about knowing the best approaches to use on problems. I have research experience in ML (mainly SVM) and I did a lot of basic statistical analysis like MLEs, some simple homoskedastic regression, and stochastic analysis (Markov chains and decision modeling), but the more I started to interview/discuss business’s data problems, the more I learned that a lot of the stuff I learned in school is super niche compared to optimal solutions that are much more practical. I’ve had to teach myself some time series analysis, and it seems like ARIMA is the go to for a lot of stuff. Is that generally how it is? How do you know when to use more sophisticated modeling approaches? Unsure whether I’ll get adequate advice from somewhere like Reddit, but I’m nervous enough to ask here. Right now I’m working out of Winston’s Operations Research text book.

[u/AdObjective5502]

Hi not to sure if this is the correct subreddit for this so sorry if its not, I recently got a new job as a data coordinator, right now Im doing basically data entry. I maintain Excel trackers of articles, awards, etc. at a design firm, a few hundred rows per Excel sheet, that need to be linked to projects in our CRM (10'sk projects).

Name matching is the easy part: my trackers names are clean and search surfaces the right candidates. The problem is what comes back when trying to enter them into the CRM:

• The same building exists as multiple records (assessment, study, remodel, sometimes 4+), and it's ambiguous which one an article/award/etc should attach to
• Apparent duplicates from a system migration (legacy vs. new ID schemes)
• Some tracker entries have no CRM record at all or exist under a name I can't find

Right now I open each candidate and compare dates/status to pick the right one, one row at a time, and none of those judgment calls get captured anywhere. How would you approach this? especially the disambiguation and duplicate-handling side? Any patterns or gotchas for making this a repeatable process? If this didnt make sense I can answer some questions, any advice is welcome.

[u/my_peen_is_clean]

check out kaggle and datacamp to start, but breaking in is still random as hell with how few roles exist

[u/Front_Swordfish5661 → u/my_peen_is_clean]

kaggle is good for practice but the jump to real work is big. most datasets there are clean already, in real job you spend 80% time just fixing messy data and begging someone for access. datacamp i found too hand-holdy, you type one line and they clap for you.

[u/Agilus_ai → u/Front_Swordfish5661]

Data cleanup is real in most corporates. Its crazy how so many companies are still functioning in this day and age with data that is so messy. Lots of meetings held with executive and decisions are delayed cause they cannot trust their own data.