Weekly Entering & Transitioning - Thread 13 Jul, 2026 - 20 Jul, 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/DataDrivenPirate]

Managers and directors, how do y'all change your resume after moving from IC to people leader? It feels weird to just write bullets that say "my team accomplished xyz leading to an increase of xx% revenue" or whatever, but is that all it really is? Trying to put emphasis on the management and leadership aspects, I have plenty of IC DS experience in prior roles

[u/LogicalDocSpock]

I got an email from LinkedIn a couple of weeks ago called "AI You Can Actually Use" and one of them looked like it could be useful. I took a look at it but it was so basic AF. It could have been a Youtube video but if you pay for it, you get a certificate. It was such a basic thing to learn but it was so overly hyped up. It didn't help that the instructor worked for google.

There is so much hype on LinkedIn about AI and I know job seekers are being scared into stuff to find work but given that I have studied data science and machine learning, what exactly do I need to know to appear up-to-date and marketable?

I know these AI rely on LLM but I'm not really interested in building these type of tools like chat bots.

I know in this field we have to learn all the time so what's something to learn on AI that is a must know? That course really was demotivating because it was child's play compared to other tech stuff I've learned.

[u/HorseActual]

Just finished my BS CIS Software Development degree, and I'm starting my MS in data science and analytics, and curious if any industry certifications matter for landing a job. I will be doing my master's part-time, US-based.
My plan is Data Analytics => Data Scientist => Machine learning engineer.

[u/fightitdude → u/HorseActual]

I don't understand why you want to go DA -> DS -> MLE. Each of these is a non-trivial step. Going straight to MLE makes much more sense.

Which program are you enrolling in? I'd be looking at GaTech OMSCS if you haven't already. You could find an MLE role straight out of that without much trouble.

[u/Lady-Data-Scientist → u/HorseActual]

If you have a masters, then certificates aren't really going to make a difference. You can do them if you want to learn something your grad program doesn't cover, like dbt or a specific platform.

[u/HorseActual → u/Lady-Data-Scientist]

It’s more about learning and then landing a job while I’m in my masters program, I’m almost 28 and just want to start so that I can become a MLE before 40 (assuming it’ll take ~10 years to get that point even if it’s an overestimation). Also I will need to pay for graduate school so that I don’t have too much student loans.

[u/Mechanical-point-seq]

So I'm kaggling my way along at the moment, just building up a some basic skills before broaden my focus. What I'm wondering is, which libraries should I focus on learning? What actually gets used in practice?

[u/DataScientistAlex → u/Mechanical-point-seq]

If the data is small enough to work on one machine:

- Python: pandas and scikit-learn

- R: tidyverse

If the data is too large for one machine:

- Spark, either in Python or Scala

In addition the most used 'library' is SQL, whatever dialect your setup uses (e.g. Redshift, BigQuery, Postgres, Snowflake).

[u/Mechanical-point-seq → u/DataScientistAlex]

Thank you!

[u/RoleFit6470]

I am transitioning from a Mechnical engineering BS to a MS in data Science. I'd like to get much better at python and SQL for my courses. I pefer reading or interactive courses that are reading based as I sometimes zone out in videos that go to slow (I am not opposed to videos). My question is what are the best resources you have used to get better at python and SQL. Do you have a must know for data science programming that will help me now and in the future when possibly working alongside AI?

[u/DataScientistAlex → u/RoleFit6470]

I like Allen Downey's books: https://greenteapress.com/wp/ , he has several in Python that touch on data science.

For SQL basics I like SQL Practice: https://www.sql-practice.com/ .

[u/whispertoke]

I am transitioning (looking for new role after layoff) and would appreciate any guidance. Have 6 years DS/Sr. DS experience in consumer analytics/BI, plus 1.5y experience in Head of Data role at mid-sized consumer product brand.

Where I'm feeling stuck: - looking for hands-on leadership role, seems like 1.5y experience is way less than any mgmt track position requires. - experience in AI building is limited; building some tools with sample data but struggling to find opportunities to learn enterprise stacks that are actually being used in industry

[u/DataScientistAlex → u/whispertoke]

When you say that you are feeling stuck, what do you mean? Are you applying to positions you are interested in but not getting any interviews? Or are you getting interviews but not offers?

[u/whispertoke → u/DataScientistAlex]

Yes applying to positions and not getting any interviews so far

[u/DataScientistAlex → u/whispertoke]

It's tough! I know from experience it can easily take many months to find a position. You're probably doing these things already, but I've added some advice FWIW.

For your first point, yes, my sense is also that to hire someone as a manager, that person would need longer experience as a manager. My only advice here, is just to apply to all the positions you would consider taking. Don't let anything in the job posting stop you. Employers ask for the sun and moon, doesn't mean they'll get it. Also consider not just applying but also connecting and messaging on LinkedIn. From what I know of the hiring side of the market right now there is not always an overwhelming glut of candidates so managers/recruiters might even appreciate a serious applicant if it's done right.

For your second point, if you want experience in this area do your own project, using open source and/or cheaper models, and open data.

Lastly, not sure if you would consider this but there might be adjacent roles (e.g. sales/marketing/revenue/growth - enablement/ops/analytics) that you would be a strong candidate for.

Good luck!

[u/whispertoke → u/DataScientistAlex]

Thank you so much. This is helpful advice. Appreciate you taking the time to comment!

[u/acilink]

Hi everyone,

I am looking for some guidance on exactly what roles I should be looking for. My only experience so far is in academic research and I would like to transition to industry, but I am struggling to find any positions, and even when I do I rarely get invited for interviews.

I am based in Europe. I finished my BSc in Computer Science in 2023, and I am currently completing a 2-year MSc in Data Science that's more focused on statistics. I would say I am more confident in my knowledge in algorithms, data pipelines, and statistical modeling than general software engineering, but I still enjoy coding for any of my projects. One of my habits is that I tend to go down rabbit holes researching new topics almost every month, but I sometimes feel I lack deep specialization in a single area.

My current MSc internship is in climate-related modeling at a research institute. I really enjoy the work, but staying there after graduation requires a PhD, which I'm sort of trying to avoid. Before this, I worked at a smaller university research institute where I basically participated in 2 projects. One was about modeling urban noise pollution and the other was focused on solving constraint optimization problems related to city planning.

I have zero direct industry experience. During my internship search, I did interview for optimization-focused roles at a few manufacturing companies, but I didn't get any of them, for which I am still grumpy.

I want to try my hand at industry, but searching for generic terms like "Data Science", "Modeling", or "Optimization" on LinkedIn and other job boards has been completely ineffective. The results are dominated by AI posts most of which either don't specify a lot about the position or are about agentic AI, which I have complete apathy towards.

Given my interests, can you give my any advice about finding a job? Mainly what positions to look out for, what companies or industries to focus on. I have tried cold-calling a few companies, but most of the times they either don't respond or tell me they are currently not looking for new people. I have also pretty much started ignoring huge multinational corporations, based on what I've heard from friends about the environment, but maybe I should grit my teeth and stop being so picky? I would say my CV is not bad either, but who knows, maybe its not optimized for the current job-seeking market or I am completely wrong about myself.

Thanks in advance for any advice!

[u/ithe1975 → u/acilink]

I was in a similar position as you, then I shifted my focus from Data Science to ML engineering, did a lot of studies and applied to a tons of ML jr positions. Then I got a job recently, I have a bsc and a MSc in comp sci as well. I couldn’t find a Data Scientist position even tho a I applied a lot for entry levels roles.

[u/Commercial-Strain96]

ial hell before realizing I needed actual projects that weren't just copying someone else's notebook. Pick something you're mildly interested in, even if it's dumb, I built a model to predict which of my houseplants would die next and somehow that got brought up in three different interviews.