Data Analytics

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Updated: May 26, 2026 at 8:44 AM

Data Analytics

Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho

@DataAnalyticsX is a growing community focused on analytics and explore and related topics

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Participant Growth (Last 17 Days)

Total: 28.6K

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Latest Posts

Data Analytics

May 16, 2026, 11:39 PM

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Language Models Interview Handbook

151 Interview Questions, Foundation Roadmaps, Python Examples,
Architecture Diagrams and Production Playbooks for Modern LLM

Help us grow

✅

1,370

Data Analytics

May 16, 2026, 11:39 PM

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🔖 10 Stanford courses on AI and ML — with official pages and all materials

▶️ https://www.youtube.com/playlist?list=PLoROMvodv4rOca_Ovz1DvdtWuz8BfSWL2
▶️ https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
▶️ https://www.youtube.com/playlist?list=PLoROMvodv4rP8nAmISxFINlGKSK4rbLKh
▶️ https://www.youtube.com/playlist?list=PLoROMvodv4rNRRGdS0rBbXOUGA0wjdh1X
▶️ https://www.youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdX
▶️ https://www.youtube.com/playlist?list=PLoROMvodv4rOaMFbaqxPDoLWjDaRAdP9D
▶️ https://www.youtube.com/playlist?list=PLoROMvodv4rOmsNzYBMe0gJY2XS8AQg16
▶️ https://www.youtube.com/playlist?list=PLoROMvodv4rObv1FMizXqumgVVdzX4_05
▶️ https://www.youtube.com/playlist?list=PLoROMvodv4rPOWA-omMM6STXaWW4FvJT8
▶️ https://www.youtube.com/playlist?list=PLoROMvodv4rOY23Y0BoGoBGgQ1zmU_MT_

They cover the entire spectrum: classic ML, LLM, and generative models — with theory and practice.

tags: #python #ML #LLM #AI

➡ https://t.me/MachineLearning9

Data Analytics

May 16, 2026, 11:39 PM

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🚨 26 Claude Code slash commands you should know already. Save this post !

Most Claude Code users are only using 10% of what it can do
Here are the shortcuts that change how you work :

📌 Session control:
→ /compact [instructions]: compresses your conversation, you control what stays
→ /resume [session]: pick up exactly where you left off
→ /branch [name]: explore an alternative path without touching your main session
→ /rewind: roll back to an earlier point in code or conversation

📌 Cost and usage:
→ /cost: see token usage for the session
→ /usage: check your plan limits and rate limit status
→ /extra-usage: keep working even when rate limits hit

📌 Project setup:
→ /init: creates a http://CLAUDE.md/ guide for your project
→ /memory: edits http://CLAUDE.md/ files, enables or disables auto-memory
→ /add-dir : adds a working directory for file access

📌 Code quality:
→ /diff: interactive diff viewer, uncommitted changes and per-turn diffs
→ /security-review: scans pending changes for vulnerabilities
→ /plan [description]: enters plan mode before Claude writes a single line

📌 Advanced control:
→ /permissions: manages allow, ask, and deny rules for tool use
→ /agents: configures sub-agent behavior
→ /mcp: manages MCP server connections and OAuth
→ /skills: lists all available skills, built-in and custom

Most people open Claude Code and just start typing
The ones shipping faster are using these before they write the first prompt

Which one did you not know about?
♻️ Repost if this helped someone on your team
🔰

Data Analytics

May 16, 2026, 11:39 PM

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Data is everywhere. Clarity is rare.⁣
⁣
⁣
Behind every dashboard, SQL query, or machine learning model lies a common challenge — understanding the language of data.⁣
⁣
⁣
The 𝐀–𝐙 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 & 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 brings together 500+ essential terms across SQL, Python, Power BI, Excel, Statistics, and Machine Learning in one structured reference. ⁣
⁣
⁣
This is the layer many professionals underestimate.⁣
Not tools. Not dashboards.⁣
But the ability to understand, interpret, and communicate concepts with precision.⁣
⁣
⁣
𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐢𝐬 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞:⁣
- Clear definitions without unnecessary complexity⁣
- Concepts connected across tools and domains⁣
- Coverage from foundational terms to advanced analytics concepts⁣
- Useful for both technical execution and business communication⁣
⁣
⁣
𝐖𝐡𝐞𝐫𝐞 𝐭𝐡𝐢𝐬 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐢𝐦𝐩𝐚𝐜𝐭𝐟𝐮𝐥:⁣
- During interviews, when explaining concepts matters more than just knowing them⁣
- In projects, where misinterpreting a term can lead to incorrect insights⁣
- In stakeholder discussions, where clarity builds credibility⁣
- In learning journeys, where structured understanding accelerates growth⁣
⁣
⁣
𝐒𝐭𝐫𝐨𝐧𝐠 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬 𝐝𝐨𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐝𝐚𝐭𝐚. 𝐓𝐡𝐞𝐲 𝐬𝐩𝐞𝐚𝐤 𝐢𝐭𝐬 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐰𝐢𝐭𝐡 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞.⁣
⁣
⁣
#DataAnalytics #BusinessIntelligence #DataScience #SQL #Python #PowerBI #Excel #MachineLearning #Statistics #DataEngineering #AnalyticsCareer #DataLearning #DataProfessionals #CareerGrowth #InterviewPreparation

Data Analytics

May 16, 2026, 11:39 PM

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🔖 50 interview questions for LLM

A good warm-up before the interview: 50 questions on Large Language Models in one document. Not in-depth, but as a checklist to test your knowledge — just perfect.

tags: #LLM #ML #python #pytorch

➡

1,340

Data Analytics

May 16, 2026, 11:39 PM

🎥 Video

I found the BEST video explaining how LLMs work 👇

🔗 check out the full video here : https://lnkd.in/dvjZS89d

#LLM #ML #AI #Python

By:

1,520

Data Analytics

May 16, 2026, 11:39 PM

📷 Photo

🚀 Python isn’t just a programming language… it’s a career accelerator.

From data science to web development, automation to AI — Python is powering it all.

Here’s the reality 👇
When you combine Python with the right tools, you unlock entire industries:

▪️ Python + Pandas → Data manipulation
▪️ Python + Scikit-learn → Machine learning
▪️ Python + TensorFlow → Deep learning
▪️ Python + Matplotlib / Seaborn → Data visualization
▪️ Python + BeautifulSoup → Web scraping
▪️ Python + Selenium → Browser automation
▪️ Python + FastAPI → High-performance APIs
▪️ Python + SQLAlchemy → Database access
▪️ Python + Flask / Django → Web development
▪️ Python + OpenCV → Computer vision
▪️ Python + Pygame → Game development

💡 One language. Endless possibilities.

1,450

Data Analytics

May 16, 2026, 11:39 PM

Channel photo updated

Data Analytics

May 16, 2026, 11:39 PM

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𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗰𝗿𝗮𝗰𝗸 𝗔𝗜/𝗠𝗟 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀. 𝗬𝗼𝘂 𝗻𝗲𝗲𝗱 𝗮 𝘀𝗵𝗮𝗿𝗽 𝗽𝗿𝗲𝗽 𝘀𝘁𝗮𝗰𝗸.

Most people consume content.
Top candidates train with the right repos.

Here’s a tight 9-repo stack that actually moves the needle:

𝟭 ⤷ 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗿𝗶𝗺𝗲𝗿 (344k ⭐️)
https://lnkd.in/grVdK9X5
↳ Scale, trade-offs, real architecture thinking
↳ What you say in design rounds starts here

𝟮 ⤷ 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 (220k ⭐️)
https://lnkd.in/gdeUgjsi
↳ Clean implementations
↳ Sharpens fundamentals for coding + ML logic

𝟯 ⤷ 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗛𝗮𝗻𝗱𝗯𝗼𝗼𝗸 (139k ⭐️)
https://lnkd.in/gmwpV865
↳ End-to-end prep: DSA + design + behavioral
↳ Use it to structure your weeks

𝟰 ⤷ 𝗔𝘄𝗲𝘀𝗼𝗺𝗲 𝗠𝗟 (72.3k ⭐️)
https://lnkd.in/gSGVvmBB
↳ Curated ML resources, papers, tools
↳ Fill gaps fast without noise

𝟱 ⤷ 𝗔𝘄𝗲𝘀𝗼𝗺𝗲 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 (76.6k ⭐️)
https://lnkd.in/gt3R-nhm
↳ Real systems, real trade-offs
↳ Turns theory into production thinking

𝟲 ⤷ 𝟭𝟬𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗠𝗟 (50.7k ⭐️)
https://lnkd.in/gyBQF3dv
↳ Daily reps → consistency
↳ Concepts + code together

𝟳 ⤷ 𝗠𝗮𝘁𝗵 𝗳𝗼𝗿 𝗠𝗟 (15.4k ⭐️)
https://lnkd.in/g_TYTu5J
↳ Linear algebra, stats, optimization
↳ Clears “I kinda get it” confusion

𝟴 ⤷ 𝗠𝗟 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 (8.1k ⭐️)
https://lnkd.in/dTt3syu2
↳ Focused ML interview questions
↳ Practice how you’ll actually be evaluated

𝟵 ⤷ 𝟲𝟱 𝗠𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘀 (4.6k ⭐️)
https://lnkd.in/dcT9EksV
↳ Fast revision set
↳ Perfect before interviews

𝟭𝟬 ⤷ 𝗛𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆

Fundamentals → Algorithms + Math
▼
System Design → Scalability
▼
ML Concepts → Practice Questions
▼
Daily Coding → Consistency

No jumping. No chaos. Just progression.

𝟭𝟭 ⤷ 𝗪𝗵𝗮𝘁 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲𝘀 𝘁𝗼𝗽 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀
↳ They don’t collect resources
↳ They reuse a small, high-signal stack
↳ They practice like it’s a system, not a mood

That’s the edge.

𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝟱𝟬 𝗿𝗲𝗽𝗼𝘀. 𝗬𝗼𝘂 𝗻𝗲𝗲𝗱 𝟵 𝗮𝗻𝗱 𝗮 𝗽𝗹𝗮𝗻.
🩵

Data Analytics

May 16, 2026, 11:39 PM

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Pandas Cheatsheet for Data Analysis

1,090

Data Analytics

Apr 2, 2026, 07:58 AM

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Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

429

Data Analytics

Apr 2, 2026, 07:58 AM

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Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

696

Data Analytics

Apr 2, 2026, 07:58 AM

📷 Photo

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist

✅ https://t.me/Codeprogrammer

1,670

Data Analytics

Apr 2, 2026, 07:58 AM

🎥 Video

🛫 ML Roadmap 2026 — a comprehensive guide to entering ML, LLM, and MLOps

A rather insightful ML roadmap has gone viral on GitHub: within it, the author has compiled a path from a foundation in mathematics, NumPy, and Pandas to LLM, agentic RAG, fine-tuning, MLOps, and interview preparation. The repository indeed includes sections on Karpathy, MCP, RLHF, LoRA/PEFT, and system design for AI interviews.

Conveniently, this isn't just a list of random links, but rather a structured route through the topics:
▶️ Foundations and tools;
▶️ Classic ML;
▶️ LLM and agents;
▶️ Engineering and MLOps;
▶️ Interview preparation.

➡️ GitHub link:
https://github.com/loganthorneloe/ml-roadmap

tags: #ml #llm

➡ https://t.me/CodeProgrammer

1,990

Data Analytics

Apr 2, 2026, 07:58 AM

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🚀𝗧𝗵𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗦𝗸𝗶𝗹𝗹𝘀 𝗘𝘃𝗲𝗿𝘆 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗠𝗮𝘀𝘁𝗲𝗿🐍

😅

1,700

Data Analytics

Apr 2, 2026, 07:58 AM

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🗂 Building our own mini-Skynet — a collection of 10 powerful AI repositories from big tech companies

1. https://github.com/microsoft/generative-ai-for-beginners and https://github.com/microsoft/ai-agents-for-beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.

2. https://github.com/rasbt/LLMs-from-scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".

3. https://github.com/openai/openai-cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.

4. https://github.com/facebookresearch/segment-anything and https://github.com/CompVis/stable-diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.

5. https://github.com/jackfrued/Python-100-Days and https://github.com/jakevdp/PythonDataScienceHandbook
A powerful resource for Python and data analysis.

6. https://github.com/pathwaycom/llm-app and https://github.com/microsoft/ML-For-Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.

If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit.

tags: #github #LLM #AI #ML

➡️ https://t.me/CodeProgrammer

Data Analytics

Apr 2, 2026, 07:58 AM

📷 Photo

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist

✅ https://t.me/Codeprogrammer

1,350

Data Analytics

Apr 2, 2026, 07:58 AM

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🚀 Top 25 Machine Learning Architecture Questions (Every ML Engineer Should Know)

Machine Learning isn’t just about training models it’s about designing systems that scale, perform, and survive production.
If you’re preparing for ML interviews, system design rounds, or real-world MLOps work, these are the most important ML Architecture questions you should be comfortable answering

🧠 Core ML Architecture Concepts
1️⃣ What is Machine Learning architecture and why does it matter?
2️⃣ Batch inference vs Real-time inference
3️⃣ What is model serving and common tools used
4️⃣ Data drift: what it is and how to handle it
5️⃣ Feature stores and their role in ML systems
6️⃣ What is MLOps and why it’s critical

⚙️ Training, Optimization & Pipelines
7️⃣ Training vs fine-tuning
8️⃣ Regularization techniques (L1, L2, Dropout, Early stopping)
9️⃣ Model versioning in production
🔟 ML pipelines and workflow automation
1️⃣1️⃣ CI/CD for ML systems

🗄 Data, Embeddings & Databases
1️⃣2️⃣ Choosing the right database for ML
1️⃣3️⃣ What are embeddings and why they’re powerful
1️⃣4️⃣ Handling sensitive data (GDPR, HIPAA, security)

📊 Monitoring, Explainability & Scaling
1️⃣5️⃣ Monitoring tools for ML models
1️⃣6️⃣ Explainability vs Interpretability
1️⃣7️⃣ Horizontal vs Vertical scaling
1️⃣8️⃣ Ensuring reproducibility in ML
1️⃣9️⃣ Factors affecting ML latency

🚢 Deployment & Production Strategies
2️⃣0️⃣ Why Docker/containerization matters
2️⃣1️⃣ GPU-accelerated deployment — when & why
2️⃣2️⃣ A/B testing in ML systems
2️⃣3️⃣ Multi-model deployment strategies
2️⃣4️⃣ Model rollback strategies
2️⃣5️⃣ Designing ML architectures for scalability

Data Analytics

Apr 2, 2026, 07:58 AM

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Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

575

Data Analytics

Apr 2, 2026, 07:58 AM

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Machine Learning in Python (Course Notes)

I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!

Here’s what you’ll learn:

🔘 Linear Regression - The foundation of predictive modeling

🔘 Logistic Regression - Predicting probabilities and classifications

🔘 Clustering (K-Means, Hierarchical) - Making sense of unstructured data

🔘 Overfitting vs. Underfitting - The balancing act every ML engineer must master

🔘 OLS, R-squared, F-test - Key metrics to evaluate your models

https://t.me/CodeProgrammer || Share 🌐 and Like 👍

1,130

Data Analytics

Mar 27, 2026, 08:50 PM

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🎁 23 Years of SPOTO – Claim Your Free IT Certs Prep Kit!

🔥Whether you're preparing for #Python, #AI, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification – SPOTO has got you covered!

✅ Free Resources :
・Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/4lk4m3c
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Enter the Draw 👉: https://bit.ly/3NwkceD

👉 Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/Cnc5M5353oSBo3savBl397

💬 Want exam help? Chat with an admin now!
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⏰Last Chance – Get It Before It’s Gone!

989

Data Analytics

Mar 27, 2026, 08:50 PM

pinned a photo

Data Analytics

Mar 27, 2026, 08:50 PM

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🗂 A fresh deep learning course from MIT is now publicly available

A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.

The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.

➡️ https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/

tags: #Python #DataScience #DeepLearning #AI

1,140

Data Analytics

Mar 27, 2026, 08:50 PM

📷 Photo

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist

✅ https://t.me/Codeprogrammer

1,030

Data Analytics

Mar 27, 2026, 08:50 PM

📎 File

This cheat sheet—part of our Complete Guide to #NumPy, #pandas, and #DataVisualization—offers a handy reference for essential pandas commands, focused on efficient #datamanipulation and analysis. Using examples from the Fortune 500 Companies #Dataset, it covers key pandas operations such as reading and writing data, selecting and filtering DataFrame values, and performing common transformations.

You'll find easy-to-follow examples for grouping, sorting, and aggregating data, as well as calculating statistics like mean, correlation, and summary statistics. Whether you're cleaning datasets, analyzing trends, or visualizing data, this cheat sheet provides concise instructions to help you navigate pandas’ powerful functionality.

Designed to be practical and actionable, this guide ensures you can quickly apply pandas’ versatile data manipulation tools in your workflow.

https://t.me/CodeProgrammer

1,600

Data Analytics

Mar 27, 2026, 08:50 PM

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SQL Cheat Sheet for Interview 2026

Master #SQL with this cheat sheet, covering querying, commands, filtering, aggregation and basics to advance. Perfect for coding interviews and tech job prep

Read: https://www.almabetter.com/bytes/cheat-sheet/sql

❤️

1,810

Data Analytics

Mar 27, 2026, 08:50 PM

📷 Photo

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
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Data Analytics

Mar 27, 2026, 08:50 PM

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Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory

Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.

If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?

In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.

Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory

https://t.me/CodeProgrammer 🌺

Data Analytics

Mar 23, 2026, 10:59 PM

📊 5 Useful Python Scripts for Automated Data Quality Checks

📌 Introduction

Data quality issues are pervasive and can lead to incorrect business decisions, broken analysis, and pipeline failures. Manual data validation is time-consuming and prone to errors, making it essential to automate the process. This article discusses five useful Python scripts for automated data quality checks, addressing common issues such as missing data, invalid data types, duplicate records, outliers, and cross-field inconsistencies.

📌 Main Content / Discussion

The five Python scripts are designed to handle specific data quality issues.

import pandas as pd
import numpy as np

# Example 1: Missing data analyzer script
def analyze_missing_data(df):
missing_data = df.isnull().sum()
return missing_data

# Example 2: Data type validator script
def validate_data_types(df, schema):
for column, dtype in schema.items():
if df[column].dtype != dtype:
print(f"Invalid data type for column {column}")
return df

# Example 3: Duplicate record detector script
def detect_duplicates(df):
duplicates = df.duplicated().sum()
return duplicates

# Example 4: Outlier detection script
def detect_outliers(df, column):
Q1 = df[column].quantile(0.25)
Q3 = df[column].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]
return outliers

# Example 5: Cross-field consistency checker script
def check_cross_field_consistency(df):
# Check for temporal consistency
df['start_date'] = pd.to_datetime(df['start_date'])
df['end_date'] = pd.to_datetime(df['end_date'])
inconsistencies = df[df['start_date'] > df['end_date']]
return inconsistencies

These scripts can be used to identify and address data quality issues, ensuring that the data is accurate, complete, and consistent.

📌 Conclusion

The five Python scripts discussed in this article provide a comprehensive solution for automated data quality checks. By using these scripts, data analysts and scientists can identify and address common data quality issues, ensuring that their data is reliable and accurate. The main insights from this article include the importance of automating data quality checks, the use of Python scripts for data validation, and the need for consistent data quality practices.
#DataQuality #DataValidation #PythonScripts #AutomatedDataQualityChecks #DataScience #MachineLearning

🔗 Read More https://www.kdnuggets.com/5-useful-python-scripts-for-automated-data-quality-checks

Data Analytics

Mar 23, 2026, 10:59 PM

Cheatsheet for Pandas to Polar

Getting started with Polars? This post shows you how to convert some familar Pandas commands to #Polars. But it also tries to go beyond that to introduce you to some of the more fundamental differences between Pandas and Polars.

https://www.rhosignal.com/posts/polars-pandas-cheatsheet/

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