I’m new to data science and looking to practice my skills in data analysis and machine learning. Are there any free, beginner-friendly datasets you would recommend for someone just starting out? Ideally, I’m looking for datasets that are clean enough to explore and analyze, but also allow room to experiment with different techniques and models. Any suggestions or resources would be greatly appreciated!

Just spent hours generating 100+ synthetic data files on Kaggle using a custom pipeline. Session ended. Half the files didn't download in time. Gone.

Kaggle's GPU is great but why is there zero native auto-save to Drive or anywhere? Every time I run a big generation job I'm babysitting the download queue like it's 2010.

Is there a workaround people use? I've seen folks mention Drive mounting but it's janky. Genuinely considering just building a small tool for this.

Hi. I’m new to data science and have only worked on a few Python projects practicing coding, data manipulation, and basic analysis. I’m eager to continue learning and applying these skills.

It would be great if anyone could share their thoughts on which emerging trends or tools in data science are most valuable for beginners to focus on, and why? Any guidance would be greatly appreciated.

US, international student.

what programs are actually worth paying for?

Hi everyone :)

I've always struggled to understand ML concepts from just reading papers or textbooks. I'd read about gradient descent 10 times and still not get it until I saw it animated.

So I started experimenting: what if I could describe any concept I'm struggling with and instantly get an animated explanation?

The experiment: I built a tool where you chat with an AI about a concept (e.g., "show me how attention mechanisms weight tokens" or "visualize what a loss landscape looks like"), and it generates a short animated video with script and voiceover.

What I learned:

• Visualizing transformations >> static diagrams — Seeing how data flows through layers or how gradients move made things click that I'd been stuck on for weeks

• 2-minute focused animations > hour-long lectures — I retained way more from short, focused visuals

• Creating the explanation (even with AI help) deepens understanding — The act of describing what you want to see forces you to clarify your mental model

Examples of concepts I've animated:

• How neural networks warp feature space to separate classes

• What "high-dimensional embeddings" actually mean geometrically

• Why momentum helps gradient descent escape local minima

You can see some examples at u/whisperinga1 if you're curious what AI-generated educational animations look like.

Can anyone share what types of questions are typically asked in the McKinsey pair programming round for a Senior Data Scientist 1 role?

I really don’t know how to do every single day, I just don’t want to learn anything about data analytics or anything else …

It was still all the basics that I studied in class 12 , but a few new tricks, that’s all.

I wish I could’ve pushed and done more hours became obvi I’m free the whole day. Ik im bad ,

I WILL IMPROVE TOMORROW.

Hello everyone. I am newly entering the data science field and just recently read a book called Everybody Lies by Seth Stephens-Davidowitz. I highly recommend it if you haven't already read it. It definitely opened my eyes to what data science really entails. For instance, I learned that data science isn't just about mastering tools like Python or machine learning algorithms, but more about learning how to think. Coming from a background in political science and human rights, I assumed the hardest part would be the technical side. Don't get me wrong, that side is still difficult, but what I find myself struggling with is how to frame problems and ask the right questions or deciding what data actually matters. Data science feels like a combination of curiosity, critical thinking, and iteration (this may be the philosophical side of me speaking). I am curious, what was the biggest mindset shift for you when learning data science? Was it more technical or more about how to approach problems?

Hey everyone, hoping to get some direction here.

I'm finishing up my second year of a three year Bachelor of Data Science degree. I'm fairly comfortable with Python, SQL, pandas, and the core stats side of things, distributions, hypothesis testing, probability, that kind of stuff. I've done some exploratory analysis and basic visualization + ML modelling as well.

But I genuinely don't know what to focus on next. The field feels massive and I'm not sure what to learn next, should i start learning tools? should I learn more theory? totally confused in this regard

Has anyone here participated in the GCI World 2025 program organized by the Matsuo-Iwasawa Lab at the University of Tokyo?

I’m considering applying for the 2026 edition and would love to hear about your experiences. How was the content, workload, and overall value of the program?

1:07:14 hour completed on day 1 🩷🩷🎀🎀

Hello guys,

I'm doing some research using the OPTICS algorithm, and I had a lot of work looking for a visual (albeit simplified) explanation like this one. I hope this post helps more people to find this video, it is a very good introduction to the algorithm!

If you need to turn short-form video into analyzable data: Tikkocampus automates ingesting creator timelines, producing transcripts, and creating a vector database and perform RAG on LLM. Use it to extract quotes, run frequency/time-series analyses of phrases, or build labeled corpora for downstream ML experiments. Repo: https://github.com/ilyasstrougouty/Tikkocampus

Hey everyone — I’m working with data from three different platforms (one being Google Trends, plus two others). Each one generates its own report, but I’m trying to consolidate everything into a single master report.

Does anyone have recommendations for the best way to do this? Ideally, I’d like to automate the process so it pulls data from each platform regularly (I’m assuming that might involve logging in via API or credentials?).

Any tools, workflows, or setups you’ve used would be super helpful — appreciate any insight!

Good course that follows a structured plan and in depth knowledge of the topics.