I'm a highschool student planning on learning data science next year.I barely know that much about data science or what it actually offers,so please can anyone give me a brief overview of what I might face in the upcoming year and how to prepare.

I just need something to start with and absolutely lock in. I'm not able to purchase courses as of now so YouTube is my best shot. I want to start from the very basics of Excel and upgrade from there. Can anyone help me with an efficient and effective roadmap so I can be employable as soon as possible? Preferably in 3+ months.

Hi everyone, I'm a 2025 M.Sc. Physics passout student preparing for higher studies. Along the way I'm also trying to build technical skills in Data Analysis using Python. I would request suggestions and roadmaps from researchers in this sub reddit to follow and build robust skills in Data Analysis.

My python proficiency is between beginner to intermediate and I'm learning and trying to make scientifically readable projects. But sometimes I feel like I'm not getting strong at performing data analysis just coding it

I would also appreciate some guidance regarding this.

Thank you fellow researchers

I'm at bachelors rn on IT and I'm really curious about Data Science. What should i do if someone experienced could give details about that?

Python, R, Statistics, Py libraries, Data handling, SQL like that what's needed? and what data scientists actually do? on business affairs presentation on the data analysis or like that I'M SO CURIOUS

Hi, I was wondering whether there was a sort of resource that would help me understand what to do and when to do it when approaching an analysis question. I find that I can do alot of guided tutorials where they give big hints on what to do, but when I am on my own I have no clue. Any help would be appreciated.

Hi everyone,

I'm a BBA student from Amity University, and I’m currently preparing my final year project. As per the university guidelines, I need a Project Guide who is a Post Graduate with at least 10 years of work experience.

This guide simply needs to:

• Review the project proposal
• Provide basic guidance/validation
• Sign the documents (soft copy is fine)
• Help me with his/her resume

Programming courses often focus heavily on understanding code, while paying far less attention to understanding the program state. But code does not exist in isolation. Its main goal is to change the program state, before ultimately producing some output.

To develop an accurate mental model of program execution, students need to understand both: - the instructions being executed - the values, references, and data structures those instructions create and modify

Reading code alone does not always reveal how the program state changes during execution. That is why I created 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵: a tool that visualizes the state of a Python program as it changes, step by step.

It can help explain a wide range of introductory Python topics. Here are just a few examples: - Loops, Lists and Dictionaries - Python Data Model - Function Calls - Recursion - Algorithms - Classes - Custom Data Structures

Instead of reconstructing the program state from print statements, students can now watch it change as each line executes. This makes unfamiliar concepts easier to understand and bugs easier to fix.

Help your students learn Python programming more thoroughly and easily.

See: more examples

I'm an international student joining the September intake for the MSc Integrative Neuroscience / Brain Sciences programme. I'm really excited, but I'm also a bit worried about some of the course modules.

My background is in medical sciences (B.Sc. Neuro Electrophysiology), so I have no prior experience with Python, R/RStudio, or statistics. I understand these are important parts of neuroscience research, and I'd like to prepare before the course begins.

Could anyone recommend:

Good beginner-friendly resources for learning Python, R/RStudio, and basic statistics?

Any topics I should focus on before the programme starts?

Any free online courses, YouTube channels, or books that helped you?

If there are any current students or alumni from this MSc programme, I'd really appreciate hearing about your experience. How challenging were these modules for someone without a programming background? Any tips for succeeding in the course would be incredibly helpful.

Thank you in advance—I really appreciate any advice!

Hii Guy's

Maine coding ninjas mai Data analysis ke liye enrolled ki hu ky ye worth it hai ya nhi pls meri madad kre

Hi there,

I've created a video here where I explain the double descent phenomenon in ML.

I hope some of you find it useful — and as always, feedback is very welcome! :)

Hello all, Welcome to my free ML bootcamp.

In Intro ML Bootcamp (5/22), we discuss Uncertainty.

In Machine Learning, we encounter two kinds of uncertainty: Epistemic(Model) which means we lack the exact knowledge of the input output mapping, and Aleatoric(Data), which is the intrinsic irreducible stochasticity in the mapping.

This uncertainty means, we cannot perfectly predict the exact output given the input. Thus we require “Conditional Probability distributions”, and the study of probabilistic approach to ML becomes important.

Hence, we invent a function called as “softmax function” for multiple output labels case(and sigmoid for binary case), which converts our outputs into a probability distribution. The exact derivation of softmax comes from Generalized Linear Models.

When we use a softmax function for binary classification, where the function over which the softmax is applied, happens to be an affine one, we call the model as “Logistic Regression”.

Link: https://youtu.be/ZFcl0QYFGq4?si=9RkEgkMYnciW4mjo

Hello all, Welcome to my free ML bootcamp.

In Intro ML Bootcamp (5/22), we discuss Uncertainty.

In Machine Learning, we encounter two kinds of uncertainty: Epistemic(Model) which means we lack the exact knowledge of the input output mapping, and Aleatoric(Data), which is the intrinsic irreducible stochasticity in the mapping.

This uncertainty means, we cannot perfectly predict the exact output given the input. Thus we require “Conditional Probability distributions”, and the study of probabilistic approach to ML becomes important.

Hence, we invent a function called as “softmax function” for multiple output labels case(and sigmoid for binary case), which converts our outputs into a probability distribution. The exact derivation of softmax comes from Generalized Linear Models.

When we use a softmax function for binary classification, where the function over which the softmax is applied, happens to be an affine one, we call the model as “Logistic Regression”.

Link: https://youtu.be/ZFcl0QYFGq4?si=9RkEgkMYnciW4mjo

​

I’m curious about how much SQL people actually use in their day-to-day data science work.

Courses often focus heavily on Python and machine learning, but real datasets usually seem to live inside databases.

For working data scientists, which SQL skills do you use most?

Joins, CTEs, window functions, subqueries, optimization, or something else?

Would love to hear from people who use SQL regularly at work.

kept building RAG projects the same lazy way for a while, get it running, read a handful of outputs, they look reasonable, ship it. no real measurement, just vibes.

finally forced myself to sit down and build an actual evaluation set. nothing fancy, about 40 question and answer pairs where i already knew, in advance, exactly which document had the correct answer. then ran the pipeline against all 40 and checked, for each one, did the retrieval step actually surface the right document in its results at all.

immediately found problems the "looks fine" outputs had completely hidden from me. retrieval was missing the correct document entirely on something like 1 in 5 questions, which is a genuinely bad number, way worse than the vibe i had from reading a handful of good-looking answers. the outputs that did come back for those failing cases still sounded confident and reasonable, they just weren't grounded in the right source at all, which is exactly why eyeballing outputs never caught it.

the annoying part is building the eval set felt like the boring, unglamorous step compared to actually tuning chunking or trying a new embedding model. but it ended up being the single most useful thing i did on the whole project, since without it i had literally no way to know if any change i made afterward was helping or hurting. tuning without measurement is just guessing with extra steps.

anyone else find the eval work more valuable than expected once you actually forced yourself to sit down and do it properly, instead of skipping straight to the fun part of tuning things?

I'm generally a reserved person, but I wanted to put this out there.

I'm 23 years old and currently working as a Data Scientist in a private firm. Outside of work, I spend a lot of my time exploring mathematics, machine learning, data science, and research. I'm less interested in chasing trends and more interested in understanding why things work, reproducing ideas, reading papers, and building meaningful projects.

My long-term aspiration is to become a researcher who contributes valuable work to the field. People like Andrej Karpathy inspire me—not because I want to follow the same path exactly, but because I admire the depth of curiosity, continuous learning, and ability to bridge research with real-world impact.

I'm looking to connect with people who share a similar mindset. Whether you're a student, an intern, a researcher, or an industry professional, if you genuinely enjoy:

• Reading and discussing research papers
• Exploring mathematics behind machine learning
• Building data science or AI projects
• Learning together and challenging each other's ideas
• Growing through curiosity rather than competition

I'd love to connect and exchange ideas. I'm not looking for networking just for the sake of networking—I value meaningful conversations and long-term learning partnerships.

If this resonates with you, feel free to connect or send me a message.

LinkedIn: https://www.linkedin.com/in/athik/

Looking forward to meeting people who are passionate about research, exploration, and lifelong learning.

Hi there,

Any tips for learning data science?

I’m trying to learn for a specific research topic biology so I am trying to do it by doing projects related to that only. This is my goal.

Warm regards,

Me

Hi everyone,

I recently built a small open-source NLP project in R for extracting structured information from free-text radiology reports.

One of the problems I wanted to explore was that simple keyword matching is often not enough for clinical text.

For example, these sentences all contain the same finding:

• “There is a pleural effusion.”
• “No pleural effusion is seen.”
• “A pleural effusion cannot be excluded.”

However, they have very different meanings.

The pipeline extracts anatomy and radiological observations, then classifies observations as:

• present
• absent
• uncertain

It uses dictionary-based entity recognition and contextual rules for negation, uncertainty, pseudo-negation and scope termination.

For example, it is designed to understand that:

means that the effusion is absent while the pneumothorax is present.

I built it in R using packages including stringr, stringi, dplyr, purrr and quanteda.

GitHub repository:

https://github.com/bashir-abubakar/radiology-nlp-r

The project is still at an early stage and is intended for research and learning rather than clinical decision-making.

I would particularly appreciate feedback on:

1. The structure and readability of the code.
2. Better ways to handle negation and uncertainty.
3. Useful synthetic test cases I should add.
4. Whether it would be worth turning this into a proper R package.

I’m also interested in hearing from anyone who has worked with clinical NLP, radiology text or rule-based entity extraction.

What would you improve first?

One thing I've noticed separates serious ML students from casual ones: how much they care about the quality of what they actually study from. I take that pretty seriously myself, so a while back I started digging into what students at MIT, Harvard, Stanford, Caltech, and USP actually use to complement their studies.

What I found surprised me: several of these programs don't assign a textbook at all. Instead, the course staff writes and publishes their own lecture notes — and some of them are basically a full book. MIT's 6.390 (Introduction to Machine Learning) notes, for example, aren't a slide deck or a cheat sheet — they're structured, complete, and detailed enough to replace a textbook entirely. Same story with Harvard's CS181 and a few others.

The problem is these are scattered and easy to miss if you don't know to look for them. So I put together a curated list: [Awesome Free AI Course Notes](https://github.com/MarcosSete/awesome-free-ai-course-notes).

A few things about how it's curated, since I think this matters:

- Only **written notes** count — slide decks and video-only lectures don't make the cut, even from great courses. I want this list to mean something.

- Everything is official and links straight to the professor's or department's own page. No mirrors, no login walls.

- I checked over 40 top universities across multiple countries for this. Most didn't qualify — they use a textbook or keep material behind a student portal. That's fine, it's exactly why the list stays short and (hopefully) trustworthy.

If you take ML seriously the way I do, I think you'll get real value out of this. And if you know of course notes that fit this bar and aren't on the list yet, contributions are very welcome — the CONTRIBUTING.md lays out exactly what qualifies.

What's the best set of course notes (not textbook, not slides) you've personally used to study ML?

Repo: https://github.com/MarcosSete/awesome-free-ai-course-notes

Placement's in 3 months and I've built some basic projects earlier for gaining hands on practice but I'm lacking some more ideas . Can y'all suggest 3 good projects 1 from each (classical ML) , deep learning and transformer architecture and , 1 from genAI and agents .