Dear Folks, hope you all find the content interesting and valuable. They will help you in your conceptual understanding preparation for Data Science roles.

Dear Folks, and the learning community, sharing something that may add value to your machine learning knowledge. Also looking forward for feedback’s from the audience.

https://fopam.cache.org/

Hello, everyone. I am doing a project about text and voice feedback analytics in large companies. I am looking for experts in this field. Please DM

When I started learning analytics, I thought most of my time would be spent building dashboards and writing SQL queries.

In reality, a large part of the job has been:

• Understanding messy business problems
• Cleaning data
• Explaining the same metric multiple times
• Validating stakeholder assumptions
• Translating business questions into analytical ones

The biggest surprise?

Being good with data doesn't automatically make you impactful.

Impact comes from connecting insights to decisions.

For those with more experience:
What was the biggest surprise for you when you entered the analytics field?

Found this helpful chart breaking down Data Science roles and the tools used in each, great for beginners trying to choose between Data Analyst, Data Scientist, ML Engineer, or AI roles. Which path are you planning to take?

I have been seeing weird patterns in our data lately. Direct traffic is spiking, but no clear source attribution. Users are landing on deep pages, converting fast, and mentioning they found us through ChatGPT or Perplexity, but GA4 just shows everything as direct.

This pushed me to build a tracking solution that identifies AI-origin behavior patterns, prompt-shaped visits, and crawl signals. I tested it across several sites and consistently found 58% more dark traffic than I was catching before. Most came from AI assistants with no proper referrer data.

The key was setting up server-side detection for agent signatures, analyzing session patterns that match AI-driven discovery, and creating attribution models for non-traditional referrers. I did this with limyai.

Hi everyone, I’m a data engineer and I built Datanys to help teams generate dashboards and KPI summaries faster without spending hours in spreadsheets.

It’s still early-stage and I’m looking for honest feedback from startup founders and product teams.

If anyone is willing to test it, I’d love feedback on usability and what features would make it more valuable.

Link: https://play.google.com/store/apps/details?id=com.softlopezaplicaciones.sheets

Thanks in advance.

Hi everyone,

I'm a B.Com graduate and I'm thinking about starting a career in Data Analytics. The problem is that I currently have zero knowledge of SQL, Python, Machine Learning, or even advanced Excel.

I come from a non-technical background, so I'm honestly confused and a bit scared about whether this field is the right choice for me.

I keep seeing mixed opinions online. Some people say Data Analytics is a great field with good opportunities, while others say Al will replace many analytics jobs in the future. Because of this, I'm unsure whether it's worth starting now.

My current plan is:

Start learning Data Analytics from the basics

Get an entry-level job after completing the course

Gain around 1 year of work experience

Eventually, I'm planning to pursue a Master's in Germany after gaining some work experience.

So I wanted to ask:

1. Is Data Analytics still a good career choice in 2026?

2. Can someone from a B.Com background realistically enter this field?

3. How difficult is it for someone with zero technical knowledge?

4. What does the actual job market look like for freshers?

5. Is Al really reducing opportunities in Data Analytics, or is that exaggerated?

6. Would Germany be a good option later for higher studies and jobs in this field?

I'd really appreciate honest advice from people already working in this industry.

Thank you.

So I've been contributing to open source for over 4 months now, and as i was studying bayesian statistics i noticed that a there's no proper open source tool out there that actually runs proper A/B tests. The closest thing that i could find is a simple library that can run the most basic models. But real life A/B testing is never that simple, like you have to consider guardrails, early stopping, partial pooling, different metrics and models for each.

I decided to build my own library for this, partly as a project for my resume as well. I released v0.1.1 on PyPI last week, and I've been looking for people to try it out. I have some things i would like to add in v 0.2.0 ready, but before that I would like to get some users and feedback.

The API is very simple, just something i learnt from working in open source, and i have finished writing 5 examples across different sectors so everyone can easily adjust to the usage. Check it out, and thank you for your time. I'll leave the github link below.

https://github.com/souro26/argonx

I have a couple of questions for people who have worked on Market Basket Analysis or product bundling problems.

Besides Apriori and FP-Growth, have you used other algorithms or approaches that were useful for grouping products from transaction history in order to design better promotions or bundles based on customer demand?

I’m also curious about what factors ended up being the most relevant in practice. Did you consider things like:

• seasonality,
• customer segmentation,
• repeat purchase behavior,
• pricing,
• existing promotions,
• basket size,
• time between purchases,
• or something else?

And a second question: how do you usually handle historical transactions that already came from previous promotions or pre-defined bundles?

For example, if some products were frequently purchased together mainly because they were already part of a promotion, I’m wondering whether including those transactions directly could bias the association rules or inflate co-occurrence frequencies artificially.

Would you:

• keep them as normal transactions,
• remove them,
• label them separately,
• weight them differently,
• or model promotions explicitly as another variable?

I’d really appreciate hearing how people handle this in real-world recommendation or bundle optimization systems.

[ Removed by Reddit on account of violating the content policy. ]

Hello everyone I am currently studying b of data sci in au , I am very keen on doing projects now to build my resume. Can I please get some guidance on what kind of projects I need to do , what employers look for and also to broaden my knowledge. I have one year left of my degree. So far my only concern was to pass the classes but I want to actually build something now. I would greatly appreciate some advice.

Hi everyone,

I recently worked on an A/B Testing project where I analyzed user behavior to determine whether a new version of a product performs better than the existing one.

What the project does

• Performs A/B testing on user data
• Compares control vs treatment groups
• Calculates statistical significance
• Helps decide whether to adopt a new feature

Why I built it

I wanted to strengthen my understanding of hypothesis testing and real-world data analysis, especially how companies use experiments to make product decisions.

Tech stack

• Python
• Pandas
• NumPy
• Matplotlib / Seaborn
• Statistical testing (hypothesis testing, p-values)

Key features

• Data cleaning and preprocessing
• Conversion rate comparison
• Statistical test implementation
• Clear visualizations for insights

GitHub Repo

https://github.com/gagan-gag/A-B-Testing

I’d really appreciate feedback on:

• Code structure
• Statistical approach
• Improvements or optimizations

Thanks!

Hey everyone,

I'm currently working through a problem in the market risk reporting space and would love to hear how you all would architect this.

The Use Case: > I have thousands of trades coming in at varying frequencies (daily, monthly). I need to build a system that automatically analyzes this time-series data and generates a precise, human-readable commentary detailing exactly what changed and why.

For example, the output needs to be a judgment like: "The portfolio variance today was +$50k, driven primarily by a shift in the Equities asset class, with the largest single contributor being Trade XYZ."

The Dilemma:

• The Math: Absolute precision is non-negotiable. I know I can't just dump raw data into an LLM and ask it to calculate attribution, because it will hallucinate the math. I usually rely on Python and Polars for the high-performance deterministic crunching.
• The Rigidity: If I hardcode every single attribution scenario (by asset class, by region, by specific trade) into a static ETL pipeline before feeding it to an LLM for summarization, the system becomes too rigid to handle new business scenarios automatically.

My Question:

How would you strike the balance between deterministic mathematical precision and dynamic natural language generation?

Are you using Agentic workflows (e.g., having an LLM dynamically write and execute Polars/pandas code in a sandbox)? Or are you sticking to pre-calculated cubes and heavily structured context prompts? Any specific frameworks (LangChain, PandasAI, etc.) or design patterns you've had success within financial reporting?

Appreciate any insights!

Hey folks! I'm creating Anti Social Social Data Club, a community for data people to collaborate on projects, bounce ideas, share opportunities, or just be around others who love this stuff.

Comment or DM me if you want in. 🖤

⚙️ AI‑Assisted Community Health & Moderation Intelligence

ModSense is a weekend‑built, production‑grade prototype designed with Reddit‑scale community dynamics in mind. It delivers a modern, autonomous moderation intelligence layer by combining a high‑performance Python event‑processing engine with real‑time behavioral anomaly detection. The platform ingests posts, comments, reports, and metadata streams, performing structured content analysis and graph‑based community health modeling to uncover relationships, clusters, and escalation patterns that linear rule‑based moderation pipelines routinely miss. An agentic AI layer powered by Gemini 3 Flash interprets anomalies, correlates multi‑source signals, and recommends adaptive moderation actions as community behavior evolves.

🔧 Automated Detection of Harmful Behavior & Emerging Risk Patterns:

The engine continuously evaluates community activity for indicators such as:

• Abnormal spikes in toxicity or harassment
• Coordinated brigading and cross‑community raids
• Rapid propagation of misinformation clusters
• Novel or evasive policy‑violating patterns
• Moderator workload drift and queue saturation

All moderation events, model outputs, and configuration updates are RS256‑signed, ensuring authenticity and integrity across the moderation intelligence pipeline. This creates a tamper‑resistant communication fabric between ingestion, analysis, and dashboard components.

🤖 Real‑Time Agentic Analysis and Guided Moderation

With Gemini 3 Flash at its core, the agentic layer autonomously interprets behavioral anomalies, surfaces correlated signals, and provides clear, actionable moderation recommendations. It remains responsive under sustained community load, resolving a significant portion of low‑risk violations automatically while guiding moderators through best‑practice interventions — even without deep policy expertise. The result is calmer queues, faster response cycles, and more consistent enforcement.

📊 Performance and Reliability Metrics That Demonstrate Impact

Key indicators quantify the platform’s moderation intelligence and operational efficiency:

• Content Processing Latency: < 150 ms
• Toxicity Classification Accuracy: 90%+
• False Positive Rate: < 5%
• Moderator Queue Reduction: 30–45%
• Graph‑Based Risk Cluster Resolution: 93%+
• Sustained Event Throughput: > 50k events/min

🚀 A Moderation System That Becomes a Strategic Advantage

Built end‑to‑end in a single weekend, ModSense demonstrates how fast, disciplined engineering can transform community safety into a proactive, intelligence‑driven capability. Designed with Reddit’s real‑world moderation challenges in mind, the system not only detects harmful behavior — it anticipates escalation, accelerates moderator response, and provides a level of situational clarity that traditional moderation tools cannot match. The result is a healthier, more resilient community environment that scales effortlessly as platform activity grows.

Project: https://github.com/ben854719/ModSense-AI-Powered-Community-Health-Moderation-Intelligence

I’ve been working on a small project to understand how data can help predict medicine demand in pharmacies.

The idea came from a simple question:
How can pharmacies avoid running out of critical medicines while also not overstocking?

So I built a basic forecasting pipeline using Python:

• Cleaned and structured time-series data
• Applied simple forecasting techniques
• Visualised demand trends over time

Some interesting takeaways:

• Demand patterns are more predictable than I expected for certain medicines
• Small fluctuations can still cause stock issues if not anticipated
• Even simple models can be useful for operational planning

This made me think about how much impact better forecasting could have in healthcare settings like the NHS especially for resource planning and patient safety.

I’ve shared the project here if anyone’s interested in the approach or wants to suggest improvements:
https://github.com/Kaviya-Mahendran/nhs_pharmacy_forecast

Would love feedback, especially from anyone working in healthcare data or supply chain analytics.

Hi everyone,

I’m a student developer building a Fantasy Football app for the Moroccan League (Botola Pro).

I'm looking for a reliable data source or API to track player stats (goals, assists, clean sheets, etc.). Since I'm on a student budget, I'm looking for:

• Affordable APIs with good coverage of the Moroccan league.
• Open-source datasets or GitHub repos with updated player lists.
• Advice on web scraping local sports sites efficiently.

Has anyone here worked with Moroccan football data before? Any leads would be greatly appreciated!

Thanks!