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🐼 "Comparison Between SQL and pandas" – A Handy Reference Guide

⚡️ As a data scientist, I often found myself switching back and forth between SQL and pandas during technical interviews. I was confident answering questions in SQL but sometimes struggled to translate the same logic into pandas – and vice versa.

🔸 To bridge this gap, I created a concise booklet in the form of a comparison table. It maps SQL queries directly to their equivalent pandas implementations, making it easy to understand and switch between both tools.

⚡ This reference guide has become an essential part of my interview prep. Before any interview, I quickly review it to ensure I’m ready to tackle data manipulation tasks using either SQL or pandas, depending on what’s required.

📕 Whether you're preparing for interviews or just want to solidify your understanding of both tools, this comparison guide is a great way to stay sharp and efficient.

#DataScience #SQL #pandas #InterviewPrep #Python #DataAnalysis #CareerGrowth #TechTips #Analytics

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🟣 AI Paper by Hand ✍️

[1] 𝗪𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀? 𝗡𝗼𝘁 𝗔𝗹𝗹 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗶𝘀 𝗡𝗲𝗲𝗱𝗲𝗱

[2] 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗦𝘁𝗿𝗶𝗻𝗴𝘀: 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗳𝗼𝗿 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻

[3] 𝗠𝗢𝗗𝗘𝗟 𝗦𝗪𝗔𝗥𝗠𝗦: 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 𝘁𝗼 𝗔𝗱𝗮𝗽𝘁 𝗟𝗟𝗠 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 𝘃𝗶𝗮 𝗦𝘄𝗮𝗿𝗺 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲

[4] 𝗧𝗛𝗜𝗡𝗞𝗜𝗡𝗚 𝗟𝗟𝗠𝗦: 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗙𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻

[5] 𝗢𝗽𝗲𝗻𝗩𝗟𝗔: 𝗔𝗻 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗩𝗶𝘀𝗶𝗼𝗻-𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲-𝗔𝗰𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹

[6] 𝗥𝗧-𝟭: 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗳𝗼𝗿 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗔𝘁 𝗦𝗰𝗮𝗹𝗲

[7] π𝟬: 𝗔 𝗩𝗶𝘀𝗶𝗼𝗻-𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲-𝗔𝗰𝘁𝗶𝗼𝗻 𝗙𝗹𝗼𝘄 𝗠𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗥𝗼𝗯𝗼𝘁 𝗖𝗼𝗻𝘁𝗿𝗼𝗹

[8] 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻: 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗟𝗼𝗻𝗴-𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗟𝗟𝗠 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝘃𝗶𝗮 𝗩𝗲𝗰𝘁𝗼𝗿 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹

[9] 𝗣-𝗥𝗔𝗚: 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗿 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗼𝗻 𝗘𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗘𝘃𝗲𝗿𝘆𝗱𝗮𝘆 𝗧𝗮𝘀𝗸

[10] 𝗥𝘂𝗔𝗚: 𝗟𝗲𝗮𝗿𝗻𝗲𝗱-𝗥𝘂𝗹𝗲-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗿 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀

[11] 𝗢𝗻 𝘁𝗵𝗲 𝗦𝘂𝗿𝗽𝗿𝗶𝘀𝗶𝗻𝗴 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀 𝗼𝗳 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿 𝗳𝗼𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀

[12] 𝗠𝗶𝘅𝘁𝘂𝗿𝗲-𝗼𝗳-𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀: 𝗔 𝗦𝗽𝗮𝗿𝘀𝗲 𝗮𝗻𝗱 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗮𝗹 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀

[13]-[14] 𝗘𝗱𝗶𝗳𝘆 𝟯𝗗: 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝟯𝗗 𝗔𝘀𝘀𝗲𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻

[15] 𝗕𝘆𝘁𝗲 𝗟𝗮𝘁𝗲𝗻𝘁 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿: 𝗣𝗮𝘁𝗰𝗵𝗲𝘀 𝗦𝗰𝗮𝗹𝗲 𝗕𝗲𝘁𝘁𝗲𝗿 𝗧𝗵𝗮𝗻 𝗧𝗼𝗸𝗲𝗻𝘀

[16]-[18] 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸-𝗩𝟯 (𝗣𝗮𝗿𝘁 𝟭-𝟯)

[19] 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻

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Best Statistics Notes ✅

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Scientific Visualization: Python + Matplotlib

📚 Download: https://inria.hal.science/hal-03427242/document

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📕 Mastering NumPy – From Basics to Advanced

NumPy is an essential library in the world of data science, widely recognized for its efficiency in numerical computations and data manipulation. This powerful tool simplifies complex operations with arrays, offering a faster and cleaner alternative to traditional Python lists and loops.

The "Mastering NumPy" booklet provides a comprehensive walkthrough—from array creation and indexing to mathematical/statistical operations and advanced topics like reshaping and stacking. All concepts are illustrated with clear, beginner-friendly examples, making it ideal for anyone aiming to boost their data handling skills.

#NumPy #Python #DataScience #MachineLearning #AI #BigData #DeepLearning #DataAnalysis

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A Gentle Introduction to Word Embedding and Text Vectorization

Ready to read: https://machinelearningmastery.com/a-gentle-introduction-to-word-embedding-and-text-vectorization/

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⚡ A beautiful booklet for learning deep learning in a smooth and concise way without diving into the world of complexity.

✅ I highly recommend reading this enjoyable booklet.

#DeepLearning #AI #MachineLearning #LearnAI #DeepLearningForBeginners

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🚀 DataCamp has officially partnered with Polars**—a cutting-edge DataFrame library designed for speed and efficiency!

To mark this exciting collaboration, **DataCamp is offering free access to its brand-new course *“Introduction to Polars”* for the next 90 days. 🎉

This course is a great opportunity for learners and professionals alike to master data cleaning, transformation, and analysis with Polars' high-performance engine, lazy execution, and powerful groupby operations.

Unlock the full potential of data workflows and explore how Polars can supercharge large-scale data processing.

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#DataScience #Polars #Python #BigData #DataEngineering #MachineLearning #DataAnalytics #OpenSource #DataCamp #FreeCourse #LearnDataScience

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🔥 How to become a data scientist in 2025?


1️⃣ First of all, strengthen your foundation (math and statistics) .

✏️ If you don't know math, you'll run into trouble wherever you go. Every model you build, every analysis you do, there's a world of math behind it. You need to know these things well:

✅ Linear Algebra: Link

✅ Calculus: Link

✅ Statistics and Probability: Link

➖➖➖➖➖➖

2️⃣ Then learn programming !

✏️ Without further ado, get started learning Python and SQL.

✅ Python: Link

✅ SQL language: Link

✅ Data Structures and Algorithms: Link

➖➖➖➖➖➖

3️⃣ Learn to clean and analyze data!

✏️ Data is always messy, and a data scientist must know how to organize it and extract insights from it.

✅ Data cleansing: Link

✅ Data visualization: Link

➖➖➖➖➖➖

4️⃣ Learn machine learning !

✏️ Once you've mastered the basic skills, it's time to enter the world of machine learning. Here's what you need to know:

◀️ Supervised learning: regression, classification

◀️ Unsupervised learning: clustering, dimensionality reduction

◀️ Deep learning: neural networks, CNN, RNN

✅ Stanford University CS229 course: Link

➖➖➖➖➖➖

5️⃣ Get to know big data and cloud computing !

✏️ Large companies are looking for people who can work with large volumes of data.

◀️ Big data tools (e.g. Hadoop, Spark, Dask)

◀️ Cloud services (AWS, GCP, Azure)

➖➖➖➖➖➖

6️⃣ Do a real project and build a portfolio !

✏️ Everything you've learned so far is worthless without a real project!

◀️ Participate in Kaggle and work with real data.

◀️ Do a project from scratch (from data collection to model deployment)

◀️ Put your code on GitHub.

✅ Open Source Data Science Projects: Link

➖➖➖➖➖➖

7️⃣ It's time to learn MLOps and model deployment!

✏️ Many people just build models but don't know how to deploy them. But companies want someone who can put the model into action!

◀️ Machine learning operationalization (monitoring, updating models)

◀️ Model deployment tools: Flask, FastAPI, Docker

✅ Stanford University MLOps Course: Link

➖➖➖➖➖➖

8️⃣ Always stay up to date and network!

✏️ Follow research articles on arXiv and Google Scholar.

✅ Papers with Code website: link

✅ AI Research at Google website: link

#DataScience #HowToBecomeADataScientist #ML2025 #Python #SQL #MachineLearning #MathForDataScience #BigData #MLOps #DeepLearning #AIResearch #DataVisualization #PortfolioProjects #CloudComputing #DSCareerPath



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