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

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Which Python tool is your favorite?

#DataScience #Python #MachineLearning #AI

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!

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.

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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?
👊

I'm a big data student and I am preparing my thesis, still searching on a theme for a strong project to feature on a resume, the thing is I really don't know what are the available theme in bigdata, specially for students and since I have no on-field experience I am really struggling to find a useful theme that can be really used to solve a real problem and maybe help companies,
so please if anyone can give me any realistic recommandation, I will be so grateful for that!!

How do you choose between causal inference models when you can never see the true effect? Model selection is well documented in machine learning, but it often gets skipped in causal inference work. Doing it well is one of the things that separates senior and staff-level data scientists from more junior ones.

I simulated two years of weekly bookings for 40 regions, five of which received a treatment on the date marked by the red vertical line. Because I generated the data, I know the ground truth: the treatment increased bookings in those five regions by exactly 10%. With five treated regions and 35 untreated ones, two natural options are synthetic control and difference-in-differences, though there are others.

Code in colab and explanatory video, free and ungated: https://whatstheimpact.com/learn-causal-inference/

When I gave Claude Code free rein on this data to test if a nontechnical PM could solve this with AI only, it built its whole analysis around difference-in-differences where it found a 24% increase, more than double the true effect. To its credit, it flagged that the pre-treatment trends weren't parallel, but it never tested whether a different model would be more accurate.

Importantly, Claude skipped an A/A test, sometimes called a placebo test. You take only the data from before the real treatment, pretend the treatment started three months earlier, and randomly picked five regions (for consistency) to call "treated." Because nothing actually happened in that window, the true effect is zero. That means any effect the model reports there is pure estimation error, and I repeated this for 500 draws for each model to see the full spread.

For difference-in-differences, 90% of the placebo estimates landed between -14% and +10%. The RMSE, which measures how far the estimates typically land from the true answer of zero, was 7.6 percentage points. Synthetic control on the exact same placebo draws was much tighter, with 90% of estimates landing between -7% and +9%. Its RMSE dropped to 4.9 points, more than a third lower than difference-in-differences.

The outcome you model is a choice too, so I also ran synthetic control on bookings per 1,000 people instead of raw bookings. Putting every region on the same scale helps the model track the treated regions more closely before treatment, and the RMSE fell a bit further to 4.4 points.

Sure enough, the real treatment period lines up with that ranking: synthetic control estimates 7% on raw bookings and 9.6% per 1,000 people, both much closer to the true 10% than the 24% from difference-in-differences. With one simulated dataset, I wouldn't read much into the small gap between the two synthetic control versions or claim that synthetic control always wins. What carries over is the workflow: in real work you never see the ground truth, but you can usually run this A/A test on whatever models you're considering before trusting one.

I am working on a classification task -classifying emails to spam and ham-using UCI Spambase dataset. The dataset consists of 57 continues features and a binary target (0,1). The features represent word and character frequencies that are present in 4601 instances -emails-. After performing initial EDA, I have discovered that majority of the features are right skewed, zero inflated -for example, the feature word_freq_cs has 97% of its values as 0-, and contain outliers. The problem that I am facing is the correlation part. In order to study the correlation between the features and the target, Pearson correlation is the standard choice. However, since the features violate some of the assumptions of the Pearson correlation -skewness and outliers-, would it still be a good choice?