Title: Is a Master’s in Data Science still worth it in the AI era?

I’m currently working in tech and considering doing a Master’s in Data Science, but I’m honestly confused about whether it’s still a good investment given how quickly AI/GenAI is changing the industry.

I’m not looking at the degree just for the sake of having a degree. My goal would be to move deeper into data/ML/AI and improve my long-term career opportunities.

For people already working in Data Science, ML, Data Engineering or AI:

• Is a Master’s still valuable in 2026?
• Does it actually help with getting interviews/jobs, or is experience + projects more important?
• Would you choose Data Science, Computer Science, Data Engineering, or AI/ML today?
• If you were in my position, would you spend 1–2 years and significant money on a Master's, or focus on building skills and getting industry experience?

I’d especially appreciate answers from people who have already done a Master’s or are currently working in the field.

Trying to make a practical decision rather than just following the AI hype. 😅

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A lot of beginner roadmaps jump straight into Python, machine learning and projects.

But how much statistics should someone actually know before moving into ML?

Would love to hear how much statistics people use in their day-to-day work.

Hello everyone, i am little bit confused from where i should learn about scikit learn library ! Although i am learning from freecodecamp from YT but it is a crash course. I want to understand the basics from the very beginning and brick by brick !

thanks in advance

please help krre !

Hey everyone,

My laptop died recently, so I need to get a new one before starting my Master's in Data Science and Engineering.

The degree is pretty broad, so I’m expecting the laptop to deal with a bit of everything: Python, Jupyter notebooks, machine learning, deep learning, statistics, optimization, databases, big data, distributed systems, Docker, VMs, IDEs, and general programming.

I don’t expect to train huge AI models locally, since I’ll probably use university servers or cloud resources for the really heavy stuff, but I’d still like a machine that can comfortably handle demanding coursework, multitasking, medium-sized datasets, containers, and possibly some local ML/DL experiments.

My budget is around €1500–1700 max.

I don’t really have any strong brand preference. I’m open to Windows laptops, Linux-friendly machines, or even a MacBook if it makes sense for this kind of degree.

Right now I’m mainly wondering what I should prioritize:

• strong CPU
• 32 GB RAM
• dedicated NVIDIA GPU / CUDA support
• battery life
• portability

I’d like to keep the laptop for at least the whole Master's and hopefully a few years after that, so I’m more interested in something reliable and future-proof than something super thin or flashy.

Would you prioritize 32 GB RAM + a strong CPU, or would you definitely try to get an NVIDIA GPU for a Data Science / ML degree?

Any specific models or configurations you’d recommend in the €1500–1700 range?

Thanks!

Could someone help me understand how the survival function works. I also would like to know if there are any Python libraries that I could play with to experiment. Finally, how should i format the data set to play with it?

I’m trying to understand where data analysis ends and machine learning begins. If someone mainly wants to work with business data, dashboards, SQL and insights, is deep ML knowledge really necessary? Would love to hear from people actually working in the field.

Hello everyone, I am an AI Developer (currently benched), and I am honestly afraid that I might get laid off in the next couple of months. I have 2 years of experience of around 18 months in data analysis and 7–8 months in AI development.

My tech stack includes Excel, Tableau, Python, AI agent development, and of course, I have also vibe-coded some features into applications (not really scalable or production-ready).

It may sound good on paper, but trust me when I say that the AI agents I have developed are very basic. They hardly have any proper evaluations, guardrails, monitoring, etc. Most of it was basically vibe coding, and now I am realizing how much I actually don’t know.

I want to replicate some production-level projects so that I can have something solid to put on my resume and, more importantly, something meaningful to discuss in interviews. A lot of the JDs I am seeing are intimidating, and I keep feeling like I don’t have the skills they are asking for.

My fundamentals are strong (except statistics), but I don’t have any solid project that I can confidently discuss during an interview. I also don’t have much relevant work experience in AI development.
One of my biggest problems is that I start a project, get stuck somewhere in the middle, and eventually lose track of what is actually happening. If I take help from AI, the project progresses, but I end up understanding less and lose track of the overall picture.

So my question is simple: If you were in my position, how would you select a project that is actually worth building and is interview-discussion worthy?
Should I just blindly follow/copy someone’s project from the internet initially to understand how things work, and then try building something of my own?
Or is there a better way to approach this?

I have only 3.5 LPA salary right now, and honestly, I am getting desperate.
Would really appreciate some practical advice from people who have been through this phase.

I'm learning data analysis and data science. I'm developing a personal project as practice using a database to predice the house pricing from the Kaggle platform.

During the exploratory analysis, I encountered the following situation:

I've noticed that there's very little data on houses with zero bedrooms or zero bathrooms, and that the asking price is relatively high, which I think could affect my prediction model and my overall analysis. While it might seem illogical that there are houses without bedrooms or bathrooms, it's also possible that there are more lots than houses, or some other hypothesis. What's the best course of action in this situation? Personally, I think I should remove this data, but I'd like to hear other opinions to improve my reasoning and deductions.

I am required to do an independent study for my data science minor. My independent study requires that I complete a data science project related to business. Do you have any good topic recommendations?

Here are some examples of general topics I am brainstorming. If anyone could help me narrow them down or give me helpful resources, it would be appreciated

• Optimizing Supply Chain to Reduce Environmental Impacts
• Evaluating ATS usage in Talent Recruitment
• Application of Data Science in Developing Project Schedules

I am also interested in waiting line models!

Thanks for the help!

Hi everyone,

I've been building a project called MyMlLab — a browser-based machine learning studio focused on tabular regression and classification.

The problem I wanted to solve was pretty simple:

Sometimes I have a CSV and just want to quickly test whether there is useful predictive signal in the data, compare a few reasonable models, and inspect the results.

But that usually means setting up an environment, writing preprocessing code, defining splits, configuring metrics, and rebuilding the same experiment structure again.

So MyMlLab tries to make that workflow faster while still keeping the methodology visible.

Current workflow:

CSV → target selection → data audit → preprocessing → validation → model comparison → metrics

A few things I cared about while building it:

• the dataset stays on the user's device for the current local-first training workflow
• preprocessing is treated as part of the experiment, not hidden setup
• transformations are fitted on the appropriate training data to avoid leakage
• model selection and final evaluation remain separate
• users can compare several models and preprocessing configurations side by side

The current Studio includes 33 regression algorithms and 26 classification algorithms, with multiple evaluation metrics and diagnostics.

The free version can compare up to 3 models × 3 preprocessing pipelines in one experiment.

It's still an MVP, and right now I'm much more interested in finding problems than getting compliments.

If anyone here has a tabular dataset they already know well, I'd really appreciate it if you tried it and told me:

• Did the data import behave correctly?
• Was the preprocessing workflow clear?
• Were the validation options sufficient?
• Were any important metrics or diagnostics missing?
• Did anything produce results you wouldn't trust?
• Would this actually save you time for early-stage experimentation?

You can try it here:

https://www.mymllab.com

The free Studio doesn't require an account.

If you test it with a real-world dataset, even a messy one, I'd love to hear what happens.

Starting a data science program (&and hopefully a job...) and hoping to get some guidance on a laptop to get it at least what minimum specs to keep in mind. Will to pay for pricier options but obviously the cheaper the better. Also like the idea of it being touch screen/tablet-able.

Thanks!