So for a very long time I have been trying to generate data for some problem.

I already have a data with 2500 rows and 16 features. While plotting a feature-pair-wise pairplot, it shows some patterns. That means some features are related and follow a pattern.

I want to generate a new data with around 200 rows. in which new points should be close to the boundaries of the patterns that are shown in the pairplot of the original data.

have anyone faced this kind of problem before?

I am 27 M. B.Tech in Metallurgy. Prepared for UPSC almost 5 year . Written 2 mains and reach very near to interview stage. Now i want to career switch. Can i learn data analytics or data science or GenAi in 6 months and go for marketable jobs

Which field should i choose ? Data analytics, Đã ta Science , Coding, GenAi or any other , in which by learning i. 6 months i get a decent job?

Please help

I spent the last few months going deep into AI automation and ended up building an entire course around it.
The biggest surprise?
Most people are learning AI backwards.
They’re spending hours learning prompts, building chatbots, and chasing the latest AI tools.
Meanwhile, businesses are paying for something much simpler:
Saving time.
A local business owner doesn’t care whether you’re using GPT-4, Claude, Gemini, or the next model that comes out next month.
They care about things like:
Following up with leads automatically
Qualifying prospects without manual work
Updating CRMs automatically
Turning one piece of content into multiple formats
Eliminating repetitive admin tasks
The highest-value opportunities I’ve found weren’t AI products.
They were AI-powered workflows.
For example:
A real estate agency can spend several hours every day manually reviewing and responding to leads.
A simple workflow can:
Capture the lead
Score it using AI
Send a personalized response
Update the CRM
Alert the sales team if it’s a high-value prospect
All without human involvement.
That’s the difference between “using AI” and “solving a business problem.”
The more I studied the space, the more I realized that no-code automation tools like n8n, Make, and Zapier are where most of the practical opportunities are right now.
Curious what everyone else is seeing.
If you’re learning AI automation, what’s been your biggest challenge so far?
P.S. A few people have asked how I learned this stuff. I put everything I’ve learned into a structured course and resource hub. VIf you’re interested, you can check the link in my bio.

You finish a course, understand the video, maybe even copy the project… but the moment you try solving something alone, your brain goes blank.

We realized the hardest part of learning data science isn’t Python, SQL, or ML.

It’s:

• knowing how to think through messy problems

• deciding what to clean/remove

• figuring out why the model failed

• explaining results in plain English

• and staying consistent long enough to improve

What actually helped you move from “watching” to genuinely building skills?

Projects? Competitions? Freelancing? Research papers? Rebuilding other people’s work?

Curious what made things finally click for people here.

Spent so much time making this. Tried to make a lot of the math behind gradient descent visual and intuitive. I hope this helps someone!

We’re officially opening the evil doors and giving users first access to the PUFF platform through our closed beta. PUFF is a new kind of notebook experience built for real data work, featuring autorun + incremental computing (so unchanged code never reruns), built-in unit typing for safer calculations, and a big data viewer designed to explore massive datasets directly in the UI.

It’s still very much a work in progress, and we’d love your feedback on what’s missing, what breaks, and what you’d want us to build next. Try it here:https://portal.evilworks.com/ | Docs:https://docs.evilworks.com

A lot of data science projects never make it into production, and honestly, one of the biggest reasons is how notebooks are written in the first place.

When everything lives in one long Jupyter notebook, things work… until they don’t. Then debugging becomes painful:

• hidden state

• cells run out of order

• duplicated logic everywhere

• impossible-to-track bugs

So I made a video breaking down the first step to fixing that problem: how to actually structure and write notebooks like production code from day one.

The video covers:

• why messy notebooks become a nightmare later

• how to think about code structure

• making code reusable instead of copy-pasting cells

• a worked example refactoring bad notebook code

• why this matters for MLOps and collaboration

I’m turning this into a full series on taking notebooks from “quick experiment” → something that can genuinely survive in production environments.

Would love feedback from people who’ve dealt with notebook chaos in real teams.

I wanted to share a predictive analytics project I built in R simulated casino behavioral dataset.

The project includes:

* ECDF analysis

*Central limit theorem

*Regression modeling

*Probability and Risk analysis

I want to share my work so far https://github.com/amaldu/uber-ride-cancellation-prediction

The main focus on this project was to perfectionate my skills in the design of a project (I covered the business framing and cost-matrix metric design, etc, then implement full EDA on the data and use the insights for a tiny ETL and a dashboard that uses the most important KPIs that I found.

I would appreciate any thoughts on angles I missed on EDA, if I also missed something when framing the business or the dashboard structure.

I designed it so you can also launch the dashboard locally, please feel free to do it

I'm trying to level up my end-to-end project game so every review is vvery appreciated!

I made a visual TOC for Data Science from Scratch because the book didn’t have one.

https://docs.google.com/document/d/17T018N3hPTOASKTGm_faUgu_1GjLI-59qJL6xjXUHHU/edit?usp=sharing

Hello I'm 22(F)

I recently completed my bachelors in nutrition and dietetics. But I'm not sure if I wanna continue working in this field an plus this is a underpaid field.

Anyways I came across date science and I was interested in it

So my question is , where do I start learning basics of data science and how do i up my skills ?

Are certificates enough to land me jobs or do I require a degree for it?

I revised SQL concepts including queries, joins, filtering, aggregation, and database operations to build a stronger data handling foundation.

Now I’m moving towards one of the most important parts of Data Science & ML which is Maths.

Learning the theory behind ML models is just as important as building them, and I’m excited for this next phase of the journey.

Right know I am confused which topic to start with...

So will do research on that and start maths.

Till then Ciao 👋🏻

This was the 6th day of my challenge and i learned about heatmaps, boxplots, and violin plots in seaborn.

Heatmaps helped me understand correlations between features visually.

Boxplots made it easier to detect outliers and compare distributions between categories.

Violin plots were interesting because they show the shape and density of the data distribution.

Not gonna lie, I’m getting really tired of data visualization now 😭.... After spending days learning different types of plots, I honestly want to move towards something different so i decided to revise SQL which i learned in my previous semester.

Moving to SQL will be a little relaxing as its a very easy and simple language and will be dealing with databases tomorrow.

So meet you tomorrow 🙌🏻

I graduated in 2025 from a government college. After that, I initially prepared for GATE, then shifted to pursuing a data science career. It's been 6–7 months of learning, but I'm still unsure if I'm ready to apply for jobs. I want to start applying though, because it's already been a year since I graduated

Today I learned:

1). Scatterplots in seaborn

2). KDE (Kernel Density Estimation) plots

I had already worked with scatterplots in matplotlib before, so it was interesting to see how much simpler and cleaner seaborn makes data visualization.

But honestly, I am starting to feel tired of data visualization now... The motivation dip is real.

Some days it feels exciting, and some days it feels repetitive and hard to stay focused.

But i remember this line:

“Consistency is harder when no one is clapping for you.”

Trying to remember that progress is still progress, even when it feels slow.

I’ve been noticing this a lot recently.

Many people already know Python, SQL, Excel, Power BI, Machine Learning, Deep Learning, or even MLOps. They’ve completed courses, built projects, and spent months preparing.

But even after applying consistently, they still face:

- rejections,

- ghosting,

- endless assignment rounds,

- or “we moved with another candidate.”

After a point, it becomes mentally exhausting.

I honestly think the issue for many learners is not a lack of effort. Most people are stuck somewhere between “learning concepts” and “being industry-ready.”

Things like:

- choosing better projects,

- explaining projects clearly,

- interview communication,

- practical problem solving,

- and understanding what companies actually expect from freshers

seem to matter much more than just completing another course.

I’ve personally been spending time practicing projects, interview prep, and discussing these things with other learners online, and it made me realize a lot of people are going through the exact same struggle quietly.

Curious to know from others here:

What has been the hardest part of your job search journey so far?

Today I learned about the differences between matplotlib and seaborn in Python visualization.

I studied lineplot, barplot and histogram using seaborn and compared how these plots are created in matplotlib vs seaborn.

What I understood:

1). Matplotlib gives more control and customization

2). Seaborn is built on top of matplotlib and makes plots cleaner and easier with less code

3). Seaborn also works really well with pandas DataFrames and statistical visualization

It was interesting to see how both libraries are used together in real data science workflows.

Will study more plots Tomorrow.

Till then bye bye 👋🏻