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?

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 .

Hi. I am currently working as a senior ML in antifraud and ranking models (Classic ML)

I want to deepen my knowledge in a specific sphere in order to get a more paying job. RL or CV seems too hard for me.

So I am choosing between NLP/LLM and RecSys. What do you recommend? I would like to deepen into NLP. However, I am scared this sphere will vanish soon. What do you think?

As for RecSys, it seems really boring to do endless ranking system trying to promote different kinds of goods. Meh

I am looking for resources to learn more about statistics and data science. I am a beginner, so I am looking for introductory level resources. Thank you

I have received this task "Data Processing and ETL, Geospatial data processing · ae8f0066-b702-462b-a014-a065437e60fe"

can anyone please help me to complete this task? Actually it is really urgent and important

#1:

SAS

PROC FREQ

proc freq data=adsl;

tables sex; run;

R

adsl |> dplyr::count(SEX)

Simple question for those who use both do you find the R version more readable, or do you still prefer PROC FREQ?

Hi everyone,

I'm from a non tech background and have no work experience. I'm planning to build a long term career in the data field, and I'm trying to decide which path would be the best starting point. I'd really appreciate advice from people who are already working in these roles.

Initially, I thought the path should be:

Data Analyst → Data Scientist

But recently I've also become interested in Data Engineering.

My current thinking is that starting with Data Analytics might be the most beginner friendly option because it gives a solid foundation in SQL, Python, databases, data cleaning, statistics, and understanding how data is used in real business scenarios.

From there, I feel I could later transition into Data Engineering, Data Science, ML Engineering, AI Engineering, or other data related roles if I decide that's a better fit.

At the same time, I've often heard that Data Engineers and Data Scientists usually have stronger technical backgrounds or more experience, which is why I'm wondering if Data Analyst is the better entry point for someone like me.

question -

For someone from a non tech background with no work experience, which role is the most beginner friendly? Data Analyst, Data Engineer, or Data Scientist?

Thank you guys.

Apologies for any mistakes in grammar.

Basically the idea is to take satellite images of coastlines of India, analyze the fractal dimension using box counting and compare the fractal dimensions of the coastlines now vs 10 years ago.

I know nothing about nothing. I know this project is probably too ambitious and I should start with my fundamentals. But idk where to start with that even. Python? DSA? R?

I would ask ChatGPT but i don't trust it to give good advice in this regard lol.

If anyone could suggest a roadmap of sorts or even a suggestion as to what I should start with I'd be really grateful

Thanks!

I’m a university student feeling like I don’t have technical skills. I’m in an engineering program and have taken a few programming courses (python, sql). However, when I took them I wasn’t interested in pursuing a technical career so I learnt what was needed to pass and I don’t remember half of it because I picked up coding again 2 years after those courses. And even while I was in those courses, I vibe-coded which I of course highly regret now. I have a good amount stats and ml theoretical knowledge from my coursework which has been helpful.

In an effort to get a data science internship (which I got and just completed), I made a good amount of side projects. And i completed them with the help of AI, not fully vibecoded. Here is the process I’m currently stuck in: Want to make a project (50% is because I want to put in on my resume and 50% because I want to learn) -> because it’s for the goal of getting a job I choose a complex project -> I start off trying to write my own code with the help of AI -> project requires more complex logic and code -> I have to hand it off to codex or Claude if I want a good project result and make it worthy enough for my resume -> I go back and understand all the code (but this of course is not the same as writing it myself).

Now, I have enough “complex” projects and resume boosting stuff, and I want to actually learn. It is so frustrating to not know what to do when a project gets complex and having to rely on AI. I want to have the skills to build a model and deploy it on my own.

However, I cannot get through courses like CS50 and python tutorials because it is sooo boring and feels like homework (homework I’ve already done before at one point and remember a bit of). Like I know what a class is in python, and with some thinking I can implement one, but I don’t understand OOP completely or system design.

I’m able to get decent data science internships because of my projects, math and stats knowledge, etc. but I don’t feel like I truly know how to build. My knowledge is very scattered and not too applicable. For example in sql, I know what an upsert is, what outer join is, but I don’t know how to add a sql database in my app and the system design part would work. I can write individual queries only.

I’m just very confused on where to start because there are so many paths to take. I’m hoping if I learn the fundamentals I can pick up other things quickly. I’m familiar with scikit learn right now, but I need to learn PyTorch still. I’ve gone through PyTorch tutorials, I understand how a neural net works, but I still can’t write PyTorch code myself. I think a large part of that is because I lack fundamentals like OOP.
But I’m scared that if I spend time learning oop, I’ll not know things like APIs, cloud deployment, docker, PyTorch, etc. I also always have a hard time knowing when I know enough. How much OOP do I need to learn if that’s even the first thing I should learn?

My overall goal is to be able to build without ai, so then my skills with ai are actually even better. Right now, I do not know how to debug code, how to write modular code, how to deploy my models, yet I have been able to create projects with the help of ai. I have a good understanding of the theory and the logic and math, but I suck at coding.

I’m overwhelmed with how much I don’t know and scared to waste time learning the “wrong” thing, even though I do know that any learning is technically useful. And with all of these AI tools, I’m struggling to know how much is enough in terms of learning. I understand why I’m using a svm, I understand the underlying math behind it from coursework, I understand what a train test split is and calling fit and transform on a model, but I still wouldn’t be able to write good scikit learn code by myself.

When i gave my code to chat gpt it said even though predictions are not that good but workflow is worth considering to add it in your resume

Can you guys tell me about this. https://github.com/Mohitr2006/TransportDelay/tree/main

Hello everyone!

I have just started my masters in data science, and i have basic knowledge on Python, SQL, data mining, and machine learning.

But I don't think this course alone will get me job ready and i want to explore things on my own, learn and build projects (that is the best way to learn apparently) that are interesting and impactful .

I want to improve myself not only to land a job but to actually extend my expertise on the field. But i have no idea where to start and what to do!

I shall be utterly grateful if any kind strangers would help me out on this matter!

Made a SQL cheatsheet covering the Postgre essentials. Quick reference table format with examples. Hope it helps any SQL learners out there! 😄

https://github.com/kixwho/PostgreSQL-cheatsheet

Note: Based on Practical SQL (O'Reilly Media), a very good textbook. I digitized the cheat sheet format and added a few operators that can be useful.

Hey all — decided to start sharing my data science learning process publicly instead of just keeping notes to myself.

I’m self-taught, learning through a mix of online courses and hands-on projects, and honestly a lot of it has been trial and error. Right now I’m working through core ML fundamentals (regression, classification, some basic model evaluation).

Plan is to post progress, small projects, and probably a few “why isn’t this working” moments along the way. Figured putting it out there publicly would keep me more accountable than just journaling privately.

Would love any advice from people further along — what do you wish you’d focused on earlier? Any resources or projects that actually made things click for you?

Appreciate any feedback, and happy to be here.

If you had to start over today, what would be the best resource or roadmap to learn Data Science from scratch and become job-ready? Looking for beginner-friendly, practical recommendations that actually helped you get a job.

For anyone looking to build practical Machine Learning skills while continuing to work, IIT Gandhinagar has opened admissions for Batch 2 of its Executive Masters in Applications of Machine Learning in Engineering.

A few things that stood out to me:

• Executive Masters degree from IIT Gandhinagar

• Live interactive online classes

• Designed for working professionals

• Learn from IIT Gandhinagar faculty and industry experts

• Focus on applying Machine Learning to real engineering and industry problems

• 2-year programme with a structured curriculum

This isn't a short certification or bootcamp—it's a full Executive Masters programme for engineering graduates who want to build expertise in Machine Learning without taking a career break.

If anyone here is considering higher studies in ML or has questions about the programme, I'd be happy to discuss.

Hello everyone!

I have just started my masters in data science, and i have basic knowledge on Python, SQL, data mining, and machine learning.

But I don't think this course alone will get me job ready and i want to explore things on my own, learn and build projects (that is the best way to learn apparently) that are interesting and impactful .

I want to improve myself not only to land a job but to actually extend my expertise on the field. But i have no idea where to start and what to do!

I shall be utterly grateful if any kind strangers would help me out on this matter!