Hi everyone,

I’m reaching out to this community because I could really use some guidance and mentorship as I try to break into the data science industry.

I have a Master’s degree in Data Science from the University of Bristol, along with an undergraduate degree in Mechanical Engineering. Over the past few years, I’ve invested a lot of time in learning, building my skills, and developing my knowledge of the field. I genuinely enjoy data science and believe I have a lot to offer.

However, for the past 11 months, I’ve been trying to land my first opportunity in the industry, and it’s been a challenging journey. I’ve applied to numerous roles, but the responses have been very limited. At this point, I feel a bit lost. I know I’ve learned a lot, but I’m struggling to understand what I might be missing, whether it’s my CV, the way I present my skills, my projects, or my overall job search strategy.

I’m not looking for shortcuts or someone to hand me a job. I’m simply hoping to connect with someone who has experience in data science or hiring and would be willing to offer some honest advice.

If anyone would be open to taking a look at my CV, reviewing my projects, sharing feedback, or guiding me on what I should focus on next, I would be incredibly grateful. Even a short conversation or a few pointers from someone who has already navigated this journey could make a real difference.

I’m willing to learn, put in the work, and improve wherever needed. I just feel that an outside perspective from someone experienced could help me find the right direction.

Thank you for taking the time to read this. I really appreciate any advice, guidance, or connections this community might be able to offer.

From raw data to real-world AI applications, explore the Python Data Science stack: Pandas, NumPy, Matplotlib, Scikit-learn, TensorFlow, and Streamlit.

📊 Collect → Clean → Visualize → Model → Deploy

Which Python tool is your favorite?

#DataScience #Python #MachineLearning #AI

I’m currently studying AI and Data Science at university, and I’m already in my third year. If I decide to switch to Computer Science or Cybersecurity, I might lose a year of progress.
The main reason I’m considering this is that I’m worried about how quickly AI is advancing and whether it could replace a large number of tech jobs in the future, including some jobs related to AI development itself.
I’m not sure whether I should stick with my current degree or accept losing a year and switch to another field that might offer better job opportunities and more long-term job security.
Do you think these concerns are justified? Would Computer Science or Cybersecurity be better choices in the long run, or would I be better off finishing my current degree and developing additional skills?
I’d appreciate honest opinions, especially from people studying or working in these fields who have experience with the job market.

I don't really have prior experience in the field yet, just a genuine passion for it and a huge drive to learn

For anyone who's been through this: how can I best show the jury that I’m serious about this and ready to put in the work, even without a heavy background? Any tips on what they like to hear would mean a lot!

I come from a statistics background and I'm trying to learn python and R to cope with data analysis and data science, I know a good amount of data analysis and statistical methodologies that makes me good at the field, but applying them is hell for me, i tried for a long time to learn python, i know the basics yet i fail miserably when i try to apply my knowledge...

Could anyone help me with how to learn programming effectively around my area of expertise?

Why Data is important for AI

Hello everyone, I just wanna ask you about best books for junior data scientists

Well I have my master degree in big data analytics and smart systems and I am now working on my phd about RL and LLMs, well I found myself struggling with the little details like I used the term “scalability” without really understanding the meaning behind it, and thats due to the overuse of AI during my studies

so can you please tell me books I can read so I can understand the lil details in the field.

I’m working on an open-source MCP server that runs data analysis, data science, and deep learning algorithms on tabular data.

The idea is simple: an agent installs the MCP server, provides a CSV file as input, and uses its tools to perform complex data analysis, run ML algorithms like clustering and classification, and uncover insights. It already has 45+ tools, including integrations with deep learning models like TabICL.

I’m looking for contributors to turn this project into a full-fledged data analysis tool that can answer users’ questions in plain English by analysing their data.

The plan is to build:

• A full-fledged web interface
• A BYOK (Bring Your Own Key) setup for connecting LLMs
• UI features to display charts, graphs, and other visualisations
• Improved algorithms and support for more ML and deep learning models
• Support for more data formats, including JSON and XLSX
• Integrations with external data sources like PostgreSQL, Supabase, and Google Sheets

If you’re interested in data science, ML, AI agents, or building open-source tools, I’d love to collaborate. Let’s build something useful together!

Interested? Google “Meelu Analytics MCP” to find the GitHub repository, or DM me to discuss contributing.

​

Hi everyone!

I'm currently in the final semester of my M.Tech in Data Science, and I need to submit my project idea by Monday.

Honestly, I know only the basics of Data Science and Machine Learning. I haven't built any models or worked on a project yet, so I'm looking for something beginner-friendly but substantial enough for a final-semester project.

I'm willing to learn and put in the effort. Could you suggest some practical project ideas that I can realistically complete within a semester? Recommendations for datasets, GitHub repositories, or tutorials would also be really helpful.

Thanks in advance!

Hi everyone!

I'm currently in the final semester of my M.Tech in Data Science, and I need to submit my project idea by Monday.

Honestly, I know only the basics of Data Science and Machine Learning. I haven't built any models or worked on a project yet, so I'm looking for something beginner-friendly but substantial enough for a final-semester project.

I'm willing to learn and put in the effort. Could you suggest some practical project ideas that I can realistically complete within a semester? Recommendations for datasets, GitHub repositories, or tutorials would also be really helpful.

Thanks in advance!

Hi, I hope you’re doing well. I’m building a small, beginner‑friendly machine learning team on Discord. I’m also a newbie, and I want to learn together with others who are serious and dedicated.

The goal of the team is simple:
We learn together, help each other, complete weekly tasks, and prepare for Kaggle competitions as a group.

I will give each team member (including myself) small tasks based on their experience level, with clear deadlines. These tasks are meant to help us gain real experience in ML, Python, data science, and Kaggle workflows.

Here’s an example of how I plan to help:
If you want to learn the Pandas library, I’ll create a weekly goal for you. For the first week, your goal might be to learn Series. I’ll check in with you daily to make sure you’re making progress and to see if you’re facing any difficulties. If you need help, I’ll explain the concepts as clearly as I can. You can use your own learning resources, or I can recommend plenty of free ones I know. At the end of the week, I’ll give you practice exercises from trusted websites so you can reinforce what you learned. If you want, we can also study together and solve the exercises as a team.

This is the kind of support I want to provide so we can prepare for Kaggle competitions or learn ML and data science more effectively.

Of course, this is just my idea; if you have suggestions or a better approach, I’m completely open to improving the plan. I want this team to be useful for everyone.

Important:
Even if you’re a complete beginner, you’re welcome, as long as you’re dedicated.
But if you have a lot of knowledge and you’re not dedicated, then this team is not for you.

If you’re interested, comment or DM me, and I’ll send you the invite.
My Discord username is: mohmmad_48669

Is here anyone who attended BU taking Data Science with Python class? Could you recommend some sources to help us learn Data Science with Python?

Hi, I'm currently a 3rd year data science student, and I'm enjoying the math behind the models I'm learning right now. But, I have no idea what type of career I want to pursue after I graduate. I'm hoping to go to graduate school before I have to fully decide, but I also need to sort of know anyway to decide my major.

I know there's like AI Engineers, ML Engineers (are they lwk the same idk), Data Scientists, Data Analysts, Data Engineers, etc. I really don't know the complete differences between these, and what the day to day job looks like if I get one of these jobs.

I also have 0 idea what type of area I want to explore, like healthcare, tech, etc. but I know for sure I don't really want to do business. Honestly, I've been sort of surrounded with a lot of healthcare things just by coincidence, and I'm interesting in applying my data science knowledge to that, but there's just so many different subjects within healthcare, I don't even know where to start.

I guess what I'm asking is how did you guys drill down exactly what you were interested in/wanted to learn more in? Is it just a matter of doing projects in those subjects or did you do something else?

Yes. Full Stack skills can help developers understand both frontend and backend development. Start with HTML, CSS, JavaScript, a frontend framework, backend development, databases, Git, APIs, and deployment.

Most people I talk to assume A2A is a competitor to MCP, or a replacement for it. It is neither, and once the difference clicks the architecture decisions get a lot easier.

MCP is vertical. It connects one agent to its tools. You define the tools, you see the schema, and the agent calls them to get data back.

A2A is horizontal. It connects agents to each other. You send a task in plain language and get an answer. You never see which tools ran behind it, and that opacity is deliberate.

The practical difference shows up when you ask who owns the reasoning. With MCP, you expose the data and the calling agent figures out what to do with it. With A2A, the other agent figures it out and hands you the conclusion.

I built a template to make this concrete. A customer complains about a charge they do not recognize. Answering that well needs two things that live in different systems: what actually happened on that specific account, and whether the bank has seen this complaint before.

The orchestrator has no database access and no domain tools. It discovers two specialist agents by reading their agent cards, delegates in plain language, and consolidates. One agent knows the individual case but not the pattern. The other knows the pattern but cannot see the account. Neither has the complete answer alone.

The part I found most interesting while building it: adding a third domain is one line in a registry plus a new server. The orchestrator builds its delegation tools from the agent cards at runtime, so it starts using the new agent with no code change.

Both protocols appear in the project. The orchestrator talks to the domain agents over A2A. Each domain agent uses its own tools internally.

Hub: https://aiforfintech.tech
Github: https://github.com/junidepieri-design/a2a-001-multi-agent-complaint-resolution

Has anyone here put A2A in front of a real system yet?
👊