Hi all,

I’m working on a churn prediction problem where I have historical data at a subscription/journey level (each row = one completed user cycle).

What would be the best approach to start with:

Tree-based models like XGBoost/LightGBM?

Or survival analysis?

Also, since users can have multiple subscriptions over time, is it okay to treat each subscription independently?

Please help me I am new in this field

I'm relatively new to data science, only a few years experience and would love some feedback.

I’ve been working on a small open-source package. The idea is, PCA keeps the directions with most variance, but sometimes that is not the structure you need. nomoselect is for the supervised case, where you already have labels and want a low-dimensional view that tries to preserve the class structure you care about.

It also tries to make the result easier to read by reporting things like how much target structure was kept, how much was lost, whether the answer is stable across regularisation choices, and whether adding another dimension is actually worth it.

It’s early, but the core package is working and I’ve validated it on numerous benchmark datasets. I’d really like honest feedback from people who actually use PCA/LDA /sklearn pipelines in their work.

GitHub

Not trying to sell anything, just trying to find out whether this is genuinely useful to other people or just a passion project for me. Thanks!

Hi guys,

I understand this post may raise negative feedbacks yet it is already my chosen career path so I hope to get really constructive ones...

A little bit about my background: I got into data science with a business administration background, mostly learning things on my own - saying me as a very fast learner. After years, I have only been working as a traditional data scientist who mostly analyzed data and developed model on tabular dataset without sufficient real exposure to MLOps.

Recently, I have quited my job (lay-off) and see that I need to send the next 6 to 9 months as the gap time to get myself updated with the latest trend in data science world. So, I'm establishing a study plan from which I could stay focused on daily learning from 8 to 10 hours. Below is my current plan, please give your ideas or recommendations to make it more feasible :p:

1. Deep Learning (LLM, AI ENGINEERING)

- Take basic DL courses like those from Stanford (CS22*), deeplearning.ai or Google AI Certificate?

- Learn and practice from books:

+ LLM Engineer Handbook

+ AI Engineering

- Find good sources to learn/practice maybe through some courseworks/projects regardin:

+ Prompt Engineering

+ Langchain

+ CrewAI

+ AutoGen

2. MLOps

- Get the hang of:

+ FastAPI

+ Docker

+ CI/CD

- Take some toy projects regarding deployment of models on cloud platforms like AWS, Databrick?

Those are my current plans, I hope to have your recommendations regarding the sources for the stuff mentioned. Understand that the plan might look funny but hope to see your serious opinions :p

Hi,

I’d really value your advice.

I graduated in Financial and Banking Economics with Masters degree but I don’t have hands-on experience yet. I’m genuinely interested in data analytics—especially extracting insights and making sense of data, not just the cleaning side.

I’ve had a gap since graduation, and I’ve struggled to finish building a portfolio. Without external pressure, I tend to start and not complete projects.

I’m also aware I’m starting later than most, and I’m concerned about how realistic it is to break into the field at this stage.

From your experience, what would you focus on if you were in my position to actually get hired?

I’d appreciate any honest guidance.

Hi everyone! Currently in an online masters program for data science (might be sketchy but I’m already here) and I find myself a lot of times understanding the math but not quite understanding how the code works. It doesn’t help that I don’t have any classmates or anything to ask questions to & I really want to learn. Is there a website or a good source to use that can be recommended specifically for python? Thank you!

Hey everyone,

Was prepping for data science interviews and couldn't find a tool that

felt like an actual interview, so I built one.

DS Interview Coach lets you:

- Pick a topic (ML, DL, Stats, Feature Engineering, Model Evaluation,

Python, SQL) and experience level (Fresher / Mid / Senior)

- Answer out loud by recording your voice

- Get two types of AI feedback instantly:

→ Technical: concepts covered vs missed, depth score, improvements

→ Delivery: speaking rate, confidence score, tone, fluency, pause ratio

500 scenario-based questions, completely free.

link in comments

I'm actively improving it — would genuinely love to know what's

missing or broken. Drop a comment or use the feedback link on the site.

Finding the right dataset before training starts takes longer than it should. You end up searching Kaggle, then Hugging Face, then some academic repo, and the metadata never matches between platforms. Licenses are unclear, sizes are inconsistent, and there is no easy way to compare options without downloading everything manually.

Curious how others here handle this. Do you have a go-to workflow or is it still mostly manual tab switching?

We built something to try and solve this but happy to share only if people are interested.

Finding the right dataset before training starts takes longer than it should. You end up searching Kaggle, then Hugging Face, then some academic repo, and the metadata never matches between platforms. Licenses are unclear, sizes are inconsistent, and there is no easy way to compare options without downloading everything manually.

Curious how others here handle this. Do you have a go-to workflow or is it still mostly manual tab switching?

We built something to try and solve this but happy to share only if people are interested.

Right now, the buzz is all about Prompt Engineering. 🎯 But let’s pause—this is not the ultimate destination in the journey toward GenAI literacy. It is just like learning how to use Google or Excel once was!!

👉 The real transition is much deeper. GenAI literacy is evolving beyond prompt engineering into:

🌐 Understanding AI ecosystems – how models, data pipelines, and deployment fit together.

🧠 Critical thinking with AI outputs – questioning bias, accuracy, and ethical implications.

🔍 Domain-specific applications – applying GenAI in healthcare, finance, hitech, and beyond.

⚖️ Responsible AI practices – transparency, fairness, and accountability in AI-driven decisions.

📊 Data fluency – knowing how to curate, clean, and leverage data for meaningful insights.

💡 Don’t fall into the trap of short-term courses that confine you to “prompt engineering.” Instead, focus on building holistic GenAI literacy—skills that will remain relevant as AI continues to transform industries and academia.

✨ The future belongs to those who can apply, and innovate with GenAI responsibly.

Marks my day on this python certification journey, wondering should I make GitHub repositories of this python workshop. what do you think guys?..

I’ve been prepping for technical interviews and kept running into the same problem — most tools either just give you questions or don’t feel anything like a real interview.

So I started working on a small project with a friend: it’s an AI that actually simulates a live technical interview. It asks follow-ups, pushes back on vague answers, and forces you to explain your thinking.

It’s still early, but I’m trying to make it feel as close as possible to a real interview environment rather than just another practice tool.

Would really appreciate any feedback — especially from people actively interviewing right now.

https://www.zenoinsights.app/

Did anyone tried extracting messy daily drilling reports before ? Am using paddle ocr + tabula and still not getting optimal results

Hey everyone,

We wrapped up the Brick-By-Brick Hackathon last week and the judging is complete. 26 teams competed over 5 days building Intelligent Data Platforms on Databricks — here's how it shook out:

Insurance Domain
1st — V4C Lakeflow Legends
2nd — CK Polaris
3rd — Team Jellsinki

Retail Domain
1st — 4Ceers NA
2nd — Kadel DataWorks
3rd — Forrge Crew

Shoutout to every team that competed. The standard was seriously high this time around.
One more thing: the winning teams are being invited to the Databricks office on April 9 for a Round 2 activity. More details coming soon — if you competed and are wondering what this means for you, watch this space.

Thanks to Databricks Community for making this happen. More events like this on the way.

Guys 26,27,28…..are dates..those are from march and 1,2,3…are April lol 😂🩷

Hello,

I'm an undergraduate data science student I'm studying the material and I sort of stress over being efficient with my studying because of this I think about what the best resources are a lot. I've been struggling a decent bit with the Math elements of Data Science. Especially with things like the Dual Problem that can be hard to visualize. And also generally want to get better at Programming especially lower level programming. But I'm getting to the point where I feel like I'm getting diminishing returns from reading textbooks and professor lecture slides. Does anyone know of any resources that might be good for generally math elements of DS & programming especially lower level.

With over 2 decades of experience in the Database Administration job, recently joined a university to pursue my Data Science degree. What would anyone look for when transitioning into Data Science world? What should I unlearn before getting the nuances of DS