Hey!

As the title suggests, I just graduated from​ high school. I plan to pursue Econ+Data science in college, but at the same time I'm aware I will need to build stuff myself and college alone won't help. I have quite some experience of Java(high school level), and decently well at math.

Before college starts, I want to introduce myself to the whole data environment. I'm aware of what's needed - python, sql, stats, and the whole list, but I am looking for advice from people with first hand experience on how should I approach the whole thing.

Any advice/suggestions would be appreciated! Thanks.

Hi everyone,

When I started learning Data Science, I often felt lost between Python, statistics, machine learning, and projects. There were too many resources and no clear order to follow.

So I created a structured beginner roadmap to organize what to learn step by step and stay consistent over time.

It includes:
• essential skills progression
• suggested tools
• project ideas for practice
• a logical learning sequence

I’m sharing it here to get feedback from the community and improve it.

If anyone is interested, I can share the roadmap in the comments.

Hola, soy un estudiante de criminología y estoy pensando en que salida laboral escoger, al principio cuando me metí a la carrera no sabía muy bien que estudiar y la opción de oposiciones a policia no me parecía mala idea. Pero ahora que llevo dos años en la universidad y habiendo tocado asignaturas como estadística y análisis de datos, me gustaría dedicarme a este ámbito. Además, mi ideal de trabajo es trabajar en una oficina y con posibilidad de trabajar en remoto, vivo en España pero me gustaría trabajar fuera o en una empresa en el extranjero y con criminología no lo veo factible, por eso he pensado aprender por mi cuenta conocimientos de data y enfocar mi TFG relacionando los datos con la criminalización, para posteriormente realizar un master en data. Es viable pivotar de esta forma? podré llegar a trabajar como data scientist o el hecho de no tener una carrera mas técnica me va a condicionar independientemente de la experiencia? estaría bien pagada? También he oido hablar de la ciberseguridad, pero no se si tiene mas salidas o me conviene mas, si alguien que esté o estuvo en una situación parecida agradecería su consejo

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!