Big_Data_Vule

Data Analyst | Data Scientist | Data Engineer | Business Intelligence | ML & Al | Statistics | Quant | Financial and Business Mathematics | Father of✌🏿

[Dan Kornas @DanKornas]

Your AI assistant doesn’t need to live inside one chat tab.

CORE is a self-hosted personal AI OS for builders who want one assistant connected to their tools, memory, tasks, and messaging surfaces.

It helps you hand off real work by watching app events and scratchpad tasks, loading context from memory and connectors, then running work through gateways, coding agents, terminal, or browser sessions with approval controls.

Key features:

• Always-on monitoring – watches connected apps, alerts, issues, and agent conversations so tasks can start from context
• Multiple interfaces – voice, scratchpad, messaging, and chat all route into the same memory and task system
• Memory + skills – temporal knowledge graph and reusable instructions keep preferences, decisions, and task rules available
• Connectors + toolkit – README lists 50+ app connectors and 1000+ actions across GitHub, Linear, Slack, Gmail, Calendar, Sentry, and more
• Gateway execution – can run Claude Code, Codex, terminal/browser sessions, open PRs, and require approval before risky actions

It’s public and self-hostable; the license file lists AGPL v3.0 plus Commons Clause terms.

Link in the reply 👇

[Python Coding @clcoding]

July Python Bootcamp Day 2

Join Free: https://t.co/iakdRQbXIy

[Darshil | Data Engineer👨🏻‍🔧 @parmardarshil07]

6 datasets that will actually teach you Data Engineering (real production-scale problems, not clean 500-row CSVs) 👇

Every beginner practices on the Titanic dataset.

Then they hit a real job and see 2 billion rows, messy formats, and access controls.

These 6 datasets close that gap:

☝️ 𝗡𝗬𝗖 𝗧𝗟𝗖 𝗧𝗿𝗶𝗽 𝗥𝗲𝗰𝗼𝗿𝗱𝘀
🚕 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝘣𝘪𝘨 𝘥𝘢𝘵𝘢 𝘣𝘢𝘴𝘪𝘤𝘴
→ Billions of taxi trips, already in Parquet
→ Practice partitioning, incremental loads, and Spark jobs
→ The best first "my laptop can't handle this" moment
→ https://t.co/FQHLxHZHhV (also on the AWS Open Data registry)

✌️ 𝗜𝗻𝘀𝘁𝗮𝗰𝗮𝗿𝘁 𝗠𝗮𝗿𝗸𝗲𝘁 𝗕𝗮𝘀𝗸𝗲𝘁
🛒 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝘥𝘢𝘵𝘢 𝘮𝘰𝘥𝘦𝘭𝘪𝘯𝘨
→ Real e-commerce tables: orders, products, users
→ Perfect for joins, star schemas, and dbt practice
→ Search "Instacart" on https://t.co/xXuxzV0wIP

🤟 𝗡𝗢𝗔𝗔 𝗪𝗲𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮 (𝗚𝗛𝗖𝗡 / 𝗜𝗦𝗗)
🌦️ 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝘤𝘭𝘦𝘢𝘯𝘪𝘯𝘨 𝘮𝘦𝘴𝘴𝘺 𝘥𝘢𝘵𝘢
→ Decades of weather readings from stations worldwide
→ Weird formats, missing values, quality flags — like real company data
→ Search GHCN or ISD on NOAA's data portals

🖖 𝗔𝗪𝗦 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆
☁️ 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝘤𝘭𝘰𝘶𝘥-𝘴𝘤𝘢𝘭𝘦 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨
→ Common Crawl (the web), Landsat (satellites), OpenStreetMap
→ Read directly from S3 — no downloads, just like production
→ https://t.co/UPTgZomxuL

✋ 𝗠𝗜𝗠𝗜𝗖-𝗜𝗜𝗜 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲
🏥 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝘸𝘰𝘳𝘬𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘴𝘦𝘯𝘴𝘪𝘵𝘪𝘷𝘦 𝘥𝘢𝘵𝘢
→ Real (de-identified) hospital records
→ You sign an access agreement first — exactly how regulated industries work
→ Search "MIMIC-III PhysioNet"

🤙 𝗡𝗬𝗖 𝟯𝟭𝟭 / 𝗖𝗶𝘁𝘆 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮
🏙️ 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝗔𝗣𝗜 𝘪𝘯𝘨𝘦𝘴𝘵𝘪𝘰𝘯
→ Millions of service requests, updated daily
→ Build a pipeline that pulls fresh data on a schedule — a real portfolio project
→ Search "311" on https://t.co/SWx9S5I3BA (DataSF and other cities work too)

My advice: don't collect all 6.

Pick ONE. Build ONE pipeline end to end. Put it on GitHub.

One finished project beats six downloaded datasets.

Which dataset did I miss?

[SAROJ PARAJULI @Parajulisaroj16]

✅ Design effective questionnaires
✅ Analyze survey data in R
✅ Create insightful visualizations
✅ Learn with step-by-step examples
Perfect for researchers, students, and data analysts.
https://t.co/nn5fSUINEP
#RStats #DataScience #SurveyResearch #Statistics

[Jack @Tech_Rose1]

Claude is offering 18 official AI courses with certificates.

And it's 100% free:

1 - Claude 101. Learn Claude for everyday work.
https://t.co/xq6JsrstQK

2 - Master Claude Cowork. Build powerful agentic workflows right on your desktop.
https://t.co/lpOCz1jroN…

3 - Claude Code 101. Master vibe coding with Claude Code.
https://t.co/m5c0PWvBvF

4 - AI Fluency: Core concepts for AI literacy
https://t.co/zKS0xI2lGQ…

5 - Introduction to Agent Skills.
https://t.co/lpOCz1jroN…

6 - Building with the Claude API.
https://t.co/VPVdzz9uO6…

7 - Claude Code in Action. Integrate Claude Code into your dev workflow. Hands-on, practical, ship-focused.
https://t.co/7pzYYVBHCA…

8 - Intro to Model Context Protocol. Connect Claude to your local data. The ultimate game changer for context.
https://t.co/lpOCz1jroN…

9 - MCP: Advanced Topics. Build custom integrations and MCP servers. For heavy-duty scaling.
https://t.co/byEtnZjUPn…

10 - AI Fluency for Students. Use AI to study and research smarter. The ultimate cheat code for academics.
https://t.co/zlE8TeTK8l…

11 - AI Fluency for Educators.
https://t.co/zlE8TeTK8l…

12 - Teaching AI Fluency.
https://t.co/DTsNZ7h55M…

13 - AI Fluency for Nonprofits. Maximize your mission's impact. Do way more with way less.
https://t.co/zlE8TeTK8l…

14 - Claude with Amazon Bedrock. Deploy Claude on AWS infrastructure. Enterprise-grade scaling made easy.
https://t.co/l2LIFds3jn…

15 - Claude with Google Cloud's Vertex AI. Deploy Claude on Google Cloud infrastructure. Seamless cloud integration for scaling apps.
https://t.co/aoJi4klqfi…

16 - AI Fluency for Small Businesses
https://t.co/zlE8TeTK8l…

17 - AI Capabilities and Limitations
https://t.co/IIAnDvdHdL…

18 - Introduction to subagents
https://t.co/lpOCz1jroN…

Follow me @Tech_Rose1 for more AI IDEA.

[Job Corner @JOBCORNER247]

All listed SOC certifications are free:🚨

1. Coursera - Security Operations Center (SOC) Fundamentals
https://t.co/7uaxe8e2k2

2. Cisco SOC Analyst (CCST)
https://t.co/O1VmJONCLA

3. TryHackMe SOC Level 1
https://t.co/ipGpxQ6iJv

4. LetsDefend SOC Analyst Path
https://t.co/oPHak2oguW

5. Splunk Fundamentals 1 (SOC focus)
https://t.co/6hJOes84XY

Each program provides free training and an official certificate or badge at no cost.

[Matt Dancho (Business Science) @mdancho84]

🚨 Want to learn how to build + ship AI and Data Science projects (that businesses actually want in 2026)?

On July 14th, I am hosting a free workshop to help you get started with AI + DS projects in Python.

Register here (500 seats): https://t.co/P6jZxC0iBL

[Darshil | Data Engineer👨🏻‍🔧 @parmardarshil07]

These are the only 5 file formats you need to know if you want to ace your data engineering interview.

That one question trips up so many data engineers in interviews.

Learn these 5 formats, and you'll always have a sharp answer 👇

1️⃣ 𝗖𝗦𝗩 — the simplest one
Just rows and columns separated by commas.
Need one column? You still read the whole file.
→ Easy to use. Painfully slow on big data.

2️⃣ 𝗝𝗦𝗢𝗡 — flexible and nested
Human-readable and great for messy, semi-structured data.
APIs love it.
→ The catch: bigger files, slower at scale.

3️⃣ 𝗔𝘃𝗿𝗼 — the streaming favorite
Stores the schema together with the data, so you always know what you're reading.
Compresses well and handles schema changes smoothly.
→ Built for streaming, where data keeps evolving.

4️⃣ 𝗣𝗮𝗿𝗾𝘂𝗲𝘁 — columnar and fast
Stores data by column instead of by row.
So you read only the columns you need → much faster queries.
Compresses efficiently too → lower storage bills.

5️⃣ 𝗢𝗽𝗲𝗻 𝗧𝗮𝗯𝗹𝗲 𝗙𝗼𝗿𝗺𝗮𝘁𝘀 (Iceberg + Delta Lake) — the game-changer
These aren't just files. They add database powers on top of your data lake:
→ ACID transactions (safe, reliable writes)
→ Schema evolution (change the structure without breaking things)
→ Time travel (query older versions of your data)
They turn messy data lakes into systems you can actually trust.

𝗧𝗵𝗲 𝗾𝘂𝗶𝗰𝗸 𝗿𝘂𝗹𝗲 𝗼𝗳 𝘁𝗵𝘂𝗺𝗯:
CSV / JSON → small or external data.
Avro → streaming.
Parquet → analytics and heavy queries.
Open Table Formats → when your data lake needs to act like a database.

Know these 5, and the storage round stops being scary.

Which format do you reach for most? 👇

[Aman Kharwal @amankk_9]

Build an End-to-End AI Agent Application.
https://t.co/qlB9eDxr9o

[Dan Kornas @DanKornas]

Kaggle Competitions

What you will learn:
- Kaggle Competition - House Prices: Advanced Regression Techniques Part1
- Kaggle Competition - House Prices Regression Techniques(Hyperparameter Tuning)-Part 2
- Used Deep Learning Technique and did the Accuracy Increase? Part 3
- Kaggle Competition- Predict Stock Price Movement Based On News Headline using NLP
- Kaggle Competition- Implement A DNA Classifier using NLP
- Kaggle Competition- Predicting PIMA Diabetes Prediction using Machine Learning
- Kaggle Competition- Dengue or Malaria Prediction Using Transfer Learning VGG19

Link is in the first comment 👇

♻️ Share this with your network if you found it useful or insightful.

[Alex Freberg @Alex_TheAnalyst]

I’ve quietly been working on this project for almost a year and I’m excited to finally share it!

I wrote a book!

This book is about what to do once you actually have the job. It’s called “Beyond the Interview: Building Your Data Career”.

This is a really detailed and practical guide on how to grow your career quickly and efficiently. I share a lot of personal stories and give you behind the scenes into areas where I struggled and how I overcame them.

I’m holding the Final draft before it goes live, hence the “not for resale”, but I’ll be launching it in about 1 week!

[Python Programming @PythonPr]

How AI agents & Claude skills work (Clearly Explained)
https://t.co/1orw6k8ncU

[Python Programming @PythonPr]

SQL Data Quality Check

[Big_Data_Vule @data_vule]

While while

[Python Programming @PythonPr]

What is The Difference? Comment The Output

[Alex Freberg @Alex_TheAnalyst]

DAX is what separates Power BI users from Power BI developers.

Most people can drag fields onto a canvas and call it a dashboard.

But the moment someone asks for year-over-year growth, a running total, or a metric that respects filters the way the business actually thinks - you need DAX.

And the first real test is knowing when to use a calculated column vs a measure.

Get that wrong and your model bloats, your reports slow down, and your numbers stop making sense when someone clicks a slicer.

Get it right and you stop being the person who builds reports and you become the person who builds models other people build reports on.

[Python Programming @PythonPr]

SQL Data Warehouse from Scratch | Full Hands-On Data Engineering Project
https://t.co/5zSXMHhx85

[Ezekiel @ezekiel_aleke]

Excel + SQL + Power BI = Data Analytics

Excel + Financial Modeling = Financial Analysis

Excel + Dashboarding = Business Intelligence

Excel + A/B Testing + KPI Tracking = Product Analytics

Excel + Google Analytics + Reporting = Marketing Analytics

Excel + Sales Data + Forecasting = Sales Analysis

Excel + HR Metrics + Workforce Planning = HR Analytics

Excel + Risk Modeling + Scenario Analysis = Risk Analysis

Excel + Investment Analysis + Valuation = Investment Analysis

Excel + CRM Data + Performance Tracking = Customer Analytics

Excel + Project Tracking + Resource Allocation = Project Management

Excel can literally make you rich

[Ezekiel @ezekiel_aleke]

This course will single-handedly lock you in with SQL🔥

[Aman Kharwal @amankk_9]

40 Beginner-Friendly AI/ML Projects.
https://t.co/7namX16HlC

[SAROJ PARAJULI @Parajulisaroj16]

🚀 Sports are no longer won by talent alone.
They’re won by DATA. 📊🏆
https://t.co/cXKD6thsGT
#DataScience #RStats #SportsAnalytics #MachineLearning #Statistics #Python #Analytics #Sports #ArtificialGeneralIntelligence #DataVisualization