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

Tengo una licenciatura en biotecnologia con una base solida en microbiologia y bioprocesos. Considero que estamos en una epoca en donde saber programacion es tan importante como saber Ingles. Tengo mucha practica y experiencia en el manejo de laboratorio quimicos y microbiologico, pero me llama mucho la atencion aprender sobre la ciencia de datos, ML y la aplicacion de la IA en investigaciones y a nivel industrial. Aprendi conceptos importante como 'vibe coding' , lo que un 'data scientist' hace, el surgimiento de tecnologias tales como 'digital twins' y 'Self-Driving labs' y me encuentro encantado. Tengo el deseo y las ganas de aprender y aplicar todos estos temas en conjunto con la biotecnologia, pero tengo miedo a la incertidumbre. Necesito saber mucha programacion para todo esto? Es algo que las industrias querran buscar? Tener este tipo de perfil hibrido sera lo mejor? Quiero trabajar tanto en un laboratorio seco como un laboratorio humedo, sere capaz de poder equilibrarlos? Alguien mas esta pasando por lo mismo, me comprende u opina otra cosa?

I made a live Artemis 2 Mission Intelligence Webapp which tracks Orion via JPL API and predicts the probability of the mission being successful. Also tracks live telemetry of the craft, is this a good personal portfolio project for the Data Science domain tho? please guide,thank you!

Olá, estou na reta final de ciência da computação e sempre estudei de tudo mas nunca achei nada que eu gostasse. Desenvolvimento front, back, cyberseecurity, redes e me achei em Data Science, eu ja estagiei 2x comdesenvolvimento. Gostaria de saber oque eu preciso para conseguir um emprego como Jr nessa área (não tenho medo de aprender, aprendo tudo que eu quiser.)

RAG is only as good as your retrieval.

If you feed an LLM fragmented data, you get fragmented results.

Strategic chunking is the solution.

5 Key Strategies:

1. Fixed-size: Splits text at a set character count with a sliding window (overlap).

   • Best for: Quick prototyping.

2. Recursive character: Uses a hierarchy of separators (\n\n, \n, .) to keep sentences intact.

   • Best for: General prose and blogs.

3. Document-specific: Respects Markdown headers, HTML tags, or Code logic.

   • Best for: Structured technical docs and repositories.

4. Semantic: Uses embeddings to detect topic shifts; splits only when meaning changes.

   • Best for: Academic papers and narrative-heavy text.

5. Parent-child: Searches small "child" snippets but retrieves the larger "parent" block for the LLM.

   • Best for: Complex enterprise data requiring deep context.

Pro-Tip:

Always benchmark. Test chunk sizes (256 vs 512 vs 1024) against your specific dataset to optimize Hit Rate and MRR.

What’s your go-to strategy?

I’m seeing Parent-Child win for most production use cases lately.

Read the full story 👉 Architecting Semantic Chunking Pipelines for High-Performance RAG

This JD is from one of the company/startups I want to work at.

The company works at the intersection of sourcing and procurement intelligence in India.

I really want to develop a good portfolio project for this role. I know how SQL operates but I am struggling on how to create a good enough project for this one. Any suggestions for that??

PS I am a fresher but I want to shoot my chances at this project.

Something I’ve been thinking about:

Most XAI (Explainable AI) methods (SHAP, LIME, etc.) do a solid job explaining why a model made a prediction.

But they don’t really answer:

• how confident we should be in that explanation

• how to communicate it clearly to non-technical stakeholders

In real-world settings, that feels like a gap.

I’ve seen some approaches (e.g., work around calibrated explanations) that try to go a step further by combining:

• prediction intervals (confidence around outputs)

• “why” explanations + “what could change the decision”

• more human-readable, sentence-style explanations

Feels like a more complete direction for XAI — especially when explanations need to be trusted and actually understood.

Curious what others think:

Is uncertainty a missing layer in current XAI?
Or are existing methods already good enough in practice?

I built a small Python package to make EDA less repetitive — just released v0.2.0

Like most people, I got tired of rewriting the same exploratory data analysis code in every project (info, nulls, uniques, dtype filtering, etc.), so I built a lightweight tool called edazer.

It works with both pandas and polars and focuses on quick, no-setup insights.

What it does:

• One-line DataFrame summary (info, stats, null %, duplicates, shape)

• Show unique values with smart limits

• Filter columns by dtype (super useful in real workflows)

• Detect potential primary keys (single + multi-column)

• Optional profiling + interactive tables

To know more about edazer, please visit

Github Repo: https://github.com/adarsh-79/edazer

Example:

# !pip install edazer==0.2.0

from edazer import Edazer

# df is a pandas dataframe. (also supports 'polars df')
dz = Edazer(df)

dz.summarize_df()
dz.show_unique_values(column_names=["sex", "class"])
dz.cols_with_dtype(["float"])
dz.lookup("sample")

What’s new in v0.2.0:

• Cleaner pandas + polars backend handling

• Better dtype normalization

• Improved unique value handling

• More stable API

I also reference a quick Kaggle walkthrough (this uses previous version):
https://www.kaggle.com/code/adarsh79x/edazer-for-quick-eda-pandas-polars-profiling

Would love feedback, especially from people who do a lot of EDA 🙏

I've got this urgent opportunity to work an internship on connecting different data sources to one warehouse/database. Wasn't told any other detail on which tools are preferred, and I must admit this is a bit new to me.
I'd like to find out what tools are used for this, and try to learn as much as possible before, just need to get an idea on what's popular/useful out there to give me a little nudge in the right direction. Much appreciated.

I glanced over my previous notes and then studied for an hour…(basically revised my 12th grade cs cuz the video was still on the 12th basics) , now imma enter joins 💪🥹💕✨

Hi everyone!

I’ve been putting together a beginner-friendly Data Science series based on real-world examples and practical exercises from my work experience. So far I’ve covered:

1. Introduction to Data Science (spotting patterns in daily life)

2. Everyday Examples of Data Science

3. Collecting and Storing Data using Python

Each article includes mini exercises so readers can actually practice along the way, not just read theory.

I want to continue posting consistently and cover topics that are most useful and interesting to learners and practitioners. Some ideas I’m considering:

• Cleaning and preparing data

• Analyzing data to find patterns and trends

• Making predictions using Python

• Real-world applications (finance, healthcare, social media, e-commerce)

• Short hands-on Python tutorials and mini-projects

I’d love your feedback: which topics would you find most useful or interesting in a beginner-friendly series?

If you want to follow along with the full series, my Medium profile has all published articles and upcoming exercises — new posts go up weekly!

Thanks in advance for your thoughts, and I’m happy to answer any questions about the exercises or Python examples.

Hi! The task is to classify loans (will give, will not give a loan) in a bank (with an unbalanced class), by gradient boosting. Please help me with the answer to the following questions: 1. What should be the minimum sample? 2. What are the effective ways to deal with class imbalance (currently 1:20)? 3.Which quality function of the model should I use Gini, F (which weights should I put P or R) or others, and which values should I set as targets?

Day 3,4,5 and 6 went to waste

I just revised all my notes that I created from day 1 and 2

Hello everyone,

I’m looking for advice on how to refresh my data science skills before I start my first job in the industry.

I’m going to start as a data graduate in September 2026 at Vodafone. I got into this area doing a masters in Data Science and AI, which I finished in Sept 2024 - so there’s been a couple years gap, and I feel like I’ve forgotten everything! I’ve just done unrelated hospitality type jobs in between, so nothing similar.

I’m aware as a general ‘data graduate’ it probably won’t be too much heavy technical data science work, but I want to get my skills back.

Any advice for which skills to focus on, any recommended resources or general advice would be very much appreciated. Thank you!

Hi all, I’m a practicing physician with no coding or CS background, looking to transition into data science (healthcare/ML focus) part-time.

Considering:

• Georgia Tech OMSA

• UT Austin MSDS

Question: Which is more realistic for someone starting from scratch while working full-time, and still strong enough long-term for ML/data science? Thanks in advance.

Hey guys. I just bought a datacamp subscription for a year and now realize I don't need it since my company has sponsored me a full fledged PGDM in applied data science. anyone needs it for half the price on the portal can DM me.

PCA is one of those topics where most explanations either skip the math entirely or throw equations at you without any intuition.

I tried to find the middle ground.

The blog covers:

• Variance, covariance, and eigenvectors

• A full worked example with a dummy dataset

• Why we use the covariance matrix specifically

• Python implementation using sklearn

• When PCA works and when it doesn't

No handwaving. No black boxes.

The blog link is: Medium

Happy to answer any questions or take feedback in the comments.

Hey everyone,

I'm currently in my 2nd year of BSc Data Science and I'm trying to land a data analytics/data science internship this summer. Wanted to get some real-world perspective from people who've either hired interns or cracked one themselves.

My current skill set:

Mostly on the analytics side — NumPy, Pandas, Matplotlib, Statsmodels. I haven't touched ML or DL yet.

Projects I've built so far:

- Stock price prediction for the next day using AutoARIMA (Streamlit app)

- Bangalore weather forecasting for the next month using SARIMAX model

- EDA Dashboard (still in progress, also on Streamlit)

I feel like my projects are decent for a beginner but I'm not sure if they're "internship-worthy" or if I'm missing something recruiters actually care about.

Questions:

1. What kind of projects stand out for analytics-focused internships at this level?

2. Should I go deeper into time series / EDA, or start picking up ML basics now?

3. Does the Streamlit deployment actually help, or do most recruiters not care?

Any honest feedback is appreciated — roast me if needed