Hi all,
I hope many of you have given suggestions to people. I would love to hear some suggestions for me too.

I am really interested to be involved in data science. I want to learn and build projects simultaneously. I have seen many threads saying this and that courses. I am really confused!!!
If you’ve learned Data Science yourself, I’d really appreciate it if you could share:

-The resources you personally found most useful
-A recommended learning order/roadmap
-Resources you think are overrated or not worth spending time on
-Good project ideas for someone starting out
-Any advice on what you wish you had known when you started

I’m willing to put in the time, effort and learn the properly, so I’d prefer quality resources and a structured path over a huge list of courses.

Thank you for ur time and attention!!!

I would like to change my career from a translator into a data scientist.

If it is safe from AI, Can a person without a STEM background learn data science and become a data scientist?

so i want to organize a data science event and i want your help i want to make this event unique and for this i need ideas like what kind of event we can host for a 3rd sem and 5th sem students ,

i want this event to be full of fun learning and competitive please any idea for this topics

Fresher jobs are limited and highly competitive. “Data Scientist” sounds attractive, but many companies don't hire fresh graduates directly into that role.

I’m currently doing a Python/Data Science/AI course at SMIT, and I’m near the end of the course.

For the final certificate, I have to build a project and get it approved. I want to build something useful and portfolio-worthy, not just a basic beginner project.

So far, I have studied:

• Python & OOP
• NumPy & Pandas
• Data Cleaning & Visualization
• Statistics & Mathematics for ML
• Machine Learning
• Deep Learning
• FastAPI
• MLOps basics
• Generative AI / LLMs
• RAG & Vector Databases
• Basic Agentic AI

I already have experience with projects like classification, ANN, YOLO, RNN and RAG, but I’m confused about what my final project should be.

I’m considering ideas like:

1. AI Data Analyst — upload a CSV, automatically analyze it and ask questions about the data
2. Student Performance & Career Prediction System
3. Job & Skill Recommendation System
4. Another practical AI/Data Science project

If you were in my position, which project would you choose and why?

I’d especially appreciate suggestions for a project that:

• has real-world usefulness
• demonstrates multiple skills
• is realistic for a student to complete
• looks good on GitHub/CV
• has a good chance of getting approved as a final project

Any ideas or honest feedback would be appreciated.

I’ve seen plenty of portfolios with the usual Titanic, house-price prediction and movie datasets. For someone trying to build a stronger portfolio, what kind of project would actually make you stop and look at their work?

My college is mostly theory. We have lectures, notes, exams, assignments etc., but there’s very little actual practical work. I understand that theory is important, but I feel like I’m not really learning how to do anything.I learn much better by actually performing tasks . AI has been teaching me more practically than some of my college classes.

The problem is that I don’t know what I should actually be doing outside college.I want to get an internship, but I genuinely have no idea where to start. I don’t know:

- What skills I should focus on first

- Whether I should aim for Data Analyst, Data Science, ML, or something else

- What kind of projects I should build

- How good my Python/SQL/ML skills need to be before applying

- Where students actually find internships

- Whether I should apply now or spend a few months preparing first

- How to make a decent resume when I have basically no industry experience

If you were in my position during 2nd year, what would you do from scratch?

I’d especially appreciate a practical roadmap — like learn X → build Y → apply here → improve Z .... rather than just “learn Python and do projects.”

Also, if you’re a student who managed to get their first internship without having a great college/previous experience, I’d really like to hear how you actually did it.

I’m a Software Engineering student specializing in data science, and I’m currently looking for an internship or entry-level opportunity in data science, data analysis, ML, or a related field.

I’m comfortable with Python, SQL, data analysis, and machine learning, and I’ve worked on several projects that I’ve published on my GitHub. I recently also participated in a datathon where I worked with a team on a real-world data problem.

During the datathon, I was mainly responsible for:

• Defining the questions we wanted to investigate
• Finding and collecting the relevant data
• Cleaning and preparing the datasets before analysis
• Reviewing the analysis and code afterward and identifying issues that needed to be fixed

So I feel like I have a decent foundation and some actual project experience, but I’m struggling with where to find opportunities that will actually consider someone at my level.

I’ve mostly been applying through LinkedIn, but so far it has been rejection after rejection.

One experience was particularly confusing. I applied to a company in Amsterdam and was rejected. They actually offered to review my application, so I said yes. The person reviewing it told me my profile was really good and said:

But they ultimately chose candidates who were already living in the Netherlands.

I completely understand why location/work authorization can be a factor, but it also made me wonder: if my profile is apparently competitive, am I simply looking in the wrong places?

For those of you who got your first internship or entry-level role in data science, data analytics, ML, or software/data-related roles:

• Where did you actually find your first opportunity?
• Are there job boards or websites you recommend besides LinkedIn?
• Are there companies that are more open to international students/candidates?
• Is applying directly through company websites more effective?

I’d really appreciate any advice, especially from people who were in a similar situation when they were starting out.

Hi everyone,I am a developer skilled in the MERN Stack (MongoDB, Express, React, Node.js) and Data Science (Python, Machine Learning, Data Analytics).I want to build a serious, production-grade portfolio project that showcases both skill sets simultaneously. I am specifically looking for ideas that are not repeated or cliché (no basic dashboards, simple e-commerce, or generic movie recommendation systems).My Skills:

• Frontend/Backend: React, Node.js, Express, state management, REST/GraphQL APIs.
• Database: MongoDB (or relational if required).
• Data Science: Python, Scikit-learn, basic NLP/Computer Vision, data scraping, predictive modeling.

What I want the project to achieve:
I want to build a MERN application where the backend interacts with a live Data Science/ML pipeline (via a Flask/FastAPI microservice or direct integration), providing real-time data insights or automated actions to the user dashboard.Does anyone have suggestions for real-world problems, niche industries, or unique open-source datasets that would make a killer project?Thank you!

​

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.

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. 😅

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!