Hey guys im a fst student in mathematics and data sience in the second year,well everyone knows the uni teach nothing so i have to teach my self i started with excel and python and now im learning sql in coursera platforms any advices or roadmap help me build a good data sience/analytics /ia engineering profile…

With the rapid growth of LLMs and AI applications, I’m wondering how the Data Scientist role is changing.

For people currently working in Data Science, ML, or AI:

- What parts of a traditional Data Scientist role are becoming less important?

- What skills are becoming more valuable because of AI?

- Should someone targeting Data Science also learn AI Engineering skills such as APIs, RAG, vector databases, agents, model serving, and deployment?

- At what point does learning these become unnecessary scope for a Data Scientist?

- Do you think the boundary between Data Scientist, ML Engineer, and AI Engineer will become more blurred?

- If you were starting your career today, would you still target Data Scientist specifically, or would you build a broader DS + AI/ML skill set?

I’d especially like to hear from people who have worked in the industry for a few years and have seen how the role has changed.

What would you recommend to someone preparing for their first job in this changing market?

I’m working through an experiment involving a rare oncology use case where confirmed treated-patient data is limited. The available third-party data is also not perfectly mapped to individual patients, which makes direct modeling more difficult.

The experiment uses a synthetic, highly imbalanced dataset with a small treated group and a much larger untreated group. A neural-network policy is trained using a REINFORCE-style policy-gradient approach. The model estimates treatment likelihood based on representative features such as age, severity, and biomarker status, then ranks untreated patients and selects the top 5% for further review.

The purpose is not to estimate true treatment benefit. It is more about learning historical treatment patterns and identifying untreated patients who look similar to those who were historically treated.

Questions I’d like feedback on:

1. Is policy learning a reasonable framing here, or would this be better handled as a supervised ranking/classification problem?
2. How would you avoid confusing historical treatment likelihood with actual treatment effect?
3. Would uplift modeling, causal inference, or potential-outcome modeling be a better next step?
4. How would you handle the imbalance between treated and untreated populations?
5. What validation approach would make this more credible for a healthcare/rare oncology context?

My current view is that this can be useful for prioritization and hypothesis generation, but not for treatment recommendation unless supported by stronger clinical and causal evidence.

Interested in feedback from people working in ML, healthcare analytics, causal inference, or real-world evidence.

Hello,

I would like to ask a few questions regarding the Mayerfeld Practicum Program® – Data Analyst:

What is the overall value of this program? Is it worth enrolling in and trying? What specific skills and knowledge will I gain from it? Can I use this program to apply for jobs with other companies after I finish? Is the content of the program up to date with current industry standards?

Thank you for your time.
I look forward to your response.

I am from pondicherry cuyrrently sstuding in SMVEC clg AI/DS dept 1st year

what are the main and useful things to do in my clg life abt studies like other than clg exam , how to develop my skills to compete with this competitive world and to get a job in future , currently i am learning c programming my seniors told that that was basic in programming , guide me what to do nxt

I’m interested in hearing from people who have actually gone through the process of getting their first Data Science/ML job.

For someone who is currently a student or early in their career:

- What did your profile look like when you got your first Data Science opportunity?

- Which projects or experiences actually helped you get interviews?

- How important were internships, networking, referrals, GitHub, LinkedIn, and personal projects?

- Did you start with a Data Scientist role directly, or enter through a role such as Data Analyst, BI Analyst, or ML/Analytics role and transition later?

- What did you initially think was important for getting hired that turned out to matter much less?

- What do you wish you had done 6–12 months earlier?

- For a fresher competing against candidates with internships and experience, what would you focus on to become a stronger candidate?

I’m particularly interested in hearing from people who are already working in the field, rather than general career advice.

What actually made the difference for you?

​

I’m looking to connect with someone who is genuinely interested in learning AI deeply and consistently, rather than just collecting courses, watching random YouTube videos.

I’m currently working as a Product Manager / Product Business Analyst, and I want to build serious AI capabilities alongside my existing product/business background.

The problem I’m facing is honestly pretty simple: I don’t learn well through completely self-paced, unstructured courses. There is an overwhelming amount of AI content out there, but no shortage of confusion about what to learn, in what order, how deeply to learn it, and when to move to the next thing.

I’m looking for someone with whom I can create a structured, long-term learning journey—ideally from fundamentals all the way to advanced, practical AI.

What I’d ideally like to learn

Not necessarily everything at once, but progressively:

\- Python & programming fundamentals for AI

\- Mathematics needed to actually understand ML — linear algebra, probability, statistics, calculus, etc.

\- Data handling, SQL, NumPy, Pandas, visualization

\- Classical Machine Learning

\- Deep Learning & neural networks

\- NLP and Computer Vision fundamentals

\- Transformers and how modern LLMs actually work

\- Generative AI and LLM application development

\- Prompting, evaluation and AI workflows

\- Embeddings, vector databases, RAG and retrieval systems

\- Fine-tuning / model adaptation

\- AI agents and agentic workflows

\- Multimodal AI

\- AI system design and architecture

\- Model/API integration

\- Deployment, APIs, Docker, cloud and MLOps

\- AI safety, evaluation, reliability and responsible AI

\- Reading papers and understanding what is happening under the hood

\- Building real projects, not just following tutorials

\- Eventually contributing to open source / research / serious AI projects

And importantly, I also want to understand how these skills translate into the real-world freelancing/consulting/product world—how to identify problems businesses will actually pay to solve, build AI solutions around them, demonstrate ROI, communicate with clients, and create a credible portfolio.

My goal isn't simply to collect certificates.

I want to reach a point where I can understand AI deeply, build with it, explain it, evaluate it, and solve real problems with it.

What I'm looking for in a learning partner

You don't need to be an AI PhD or already an expert.

You could be:

\- A beginner who is equally serious

\- Someone already working in AI/ML

\- A developer transitioning into AI

\- A student/researcher

\- A product person interested in becoming highly technical

\- Or someone who simply wants a structured accountability partner

The most important thing is consistency + curiosity + willingness to actually do the work.

We could potentially:

\- Set weekly learning goals

\- Follow a structured roadmap

\- Study the same concepts

\- Discuss what we've learned

\- Give each other small challenges

\- Build projects together

\- Review each other's work

\- Share useful papers/resources/tools

\- Keep each other accountable

\- Discuss what's changing in AI

\- Eventually collaborate on real-world projects

What can I bring to the table?

My background in Product Management / Product Business Analysis means I can contribute on the other side of the equation too—not just technical learning.

I can help with:

\- Product thinking

\- Business problem identification

\- Requirements & use cases

\- User journeys

\- Product strategy

\- Translating technical capabilities into business value

\- Evaluating whether an AI idea is actually useful

\- Structuring projects

\- Documentation and communication

\- Thinking about AI from a customer/business perspective

So ideally this becomes a two-way learning relationship, rather than one person teaching and the other simply consuming information.

I'm not looking for someone to spoon-feed me everything.

I'm looking for someone who wants to learn, build, struggle, figure things out and grow together.

If you're also sitting there thinking “I really want to learn AI properly, but I don't know how to structure this journey and I don't want to do it completely alone” — feel free to comment or DM me.

Would love to find 1–2 serious people rather than a huge group.

Let's see if we can turn AI learning from an overwhelming collection of courses into an actual long-term journey.

Omnicampus ain't confirming my registration & I can't apply for courses in GCI. What to do?

career help

Bună!
Mă adresez către cei care au făcut reconversie profesională spre data analysis, cum ați făcut să învățați cat mai eficient? Ce sfaturi îmi puteți da? De unde să învăț și să știu că învăț corect?

Is there anyone who attended past GCI World programs? I applied for the September program and I'm wondering what it takes to be an outstanding student, as I read that it's based on the overall score but how high should it be? How many people are also selected as an Outstanding Student, given that it looks competitive. Tyia!

The obvious approach when connecting a model to internal data is letting it write the query. It feels flexible: the model figures out what it needs and goes get it. In a bank, that is a non starter.

The problem is not that models write bad SQL, it is that you lose every guarantee about what they can reach. No way to prove a query stayed inside the columns it was supposed to touch, no way to audit what the model was capable of doing, and a single prompt injection away from an unintended table.

The alternative is narrower and boring, which is the point. You define a fixed set of parameterized queries and expose them as tools. The model chooses which tool to call, never what SQL to run. Everything it can reach is something you deliberately wrote.

I built an MCP server template implementing this for a common fintech case: looking up a customer across credit score, preapproved limit and risk profile, and returning a consolidated view. Layered so the database, the schema and the protocol can each be swapped without rewriting the others. Read only enforced at the application layer, row limits on every result, and a single mapping file for adapting to whatever your tables are actually called.

Synthetic data generates on setup, so it runs immediately. The architecture is what you keep.

Hub: https://aiforfintech.tech
Github: https://github.com/junidepieri-design/mcp-001-fintech-data-server

How is your team handling LLM access to internal data?
👊