💡 A complete package for success in data science and machine learning interviews!

👩🏻‍💻 I found a GitHub repo full of resources you need to succeed in Data Science and Machine Learning interviews!

❓ What do you find in it?

1⃣ Practical cheat sheets: Important tips gathered in one place.

🔢 Cool books: resources worth your time!

🔢 Frequently Asked Interview Questions: Topics that are asked in most interviews and that you are likely to encounter.

🔢 Portfolio projects: To make your resume stronger.


✅ In short, a complete package for preparing for data science interviews, without the confusion!

🔗 Here is the repost link: 👇

🔗 Cracking the data science interview

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🚀 80 Python Interview Questions with Answers & Code! 🚀

✅ Why this resource? 
- Covers frequently asked questions in Python interviews 

📄 Each question comes with detailed answers and ready-to-use code snippets, making it perfect for beginners and experienced developers alike. Whether you're preparing for a job interview or leveling up your Python skills, this guide has you covered! 👀 

🔥 Don’t miss out! Save this, share it, and start preparing today! 💼 

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Title:
Master Machine Learning in Just 20 Days - Your Ultimate Guide! 🔥

Description:
Struggling to break into Data Science or ace ML interviews at top product-based companies?

This 20-day roadmap covers ML basics to advanced topics like tuning, deep learning, and deployment with top resources and practice questions!

What’s Inside:

✅ Supervised & Unsupervised Learning – Regression, Classification, Clustering
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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.

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