🚀 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.

🔗 Start learning now:
https://www.datacamp.com/courses/introduction-to-polars

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

🌟 Join the communities:

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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

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2️⃣ Then learn programming !

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

✅ Python: Link

✅ SQL language: Link

✅ Data Structures and Algorithms: Link

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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

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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

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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)

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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

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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

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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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👫 Preparing for Data Science Interviews


👨🏻‍💻 I've been collecting a variety of data science interview questions for different positions for a few weeks now.


✅ I covered everything, from basic to advanced:

Common Data Science and ML Questions (34 questions)

Regression (22 questions)

Classification (39 questions)

SVM algorithms, decision tree

Simple Bayes and statistical discussions and...

🚨 This list is regularly updated and categorized so that you can easily prepare for the interview step by step.👇


┌📝 Interview Questions
└🐱 GitHub-Repos

#DataScience #InterviewPrep #MLInterviews #DataScientist #MachineLearning #TechCareers #DSInterviewQuestions #GitHubResources #CareerInDataScience #CodingInterview

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Anyone trying to deeply understand Large Language Models.

Checkout

Foundations of Large Language Models

by Tong Xiao & Jingbo Zhu. It’s one of the clearest, most comprehensive resource.

⭐️ Paper Link: arxiv.org/pdf/2501.09223

#LLMs #LargeLanguageModels #AIResearch #DeepLearning #MachineLearning #AIResources #NLP #AITheory #FoundationModels #AIUnderstanding



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Supervised Learning: Classification and Regression

Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf

#SupervisedLearning #MachineLearning #Classification #Regression #MLNotes #DataScience #AIResources #MLTheory #MLLectures #LearnML

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