🔥 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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📖 A comprehensive cheat sheet for working with Polars


🌟 Have you ever worked with pandas and thought that was the fastest way? I thought the same thing until I worked with Polars.

✏️ This cheat sheet explains everything about Polars in a concise and simple way. Not just theory! But also a bunch of real examples, practical experience, and projects that will really help you in the real world.

┌ 🐻‍❄️ Polars Cheat Sheet
├ ♾️ Google Colab
└ 📖 Doc

#Polars #DataEngineering #PythonLibraries #PandasAlternative #PolarsCheatSheet #DataScienceTools #FastDataProcessing #GoogleColab #DataAnalysis #PythonForDataScience



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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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Self-attention in LLMs, clearly explained

#SelfAttention #LLMs #Transformers #NLP #DeepLearning #MachineLearning #AIExplained #AttentionMechanism #AIConcepts #AIEducation

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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/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer

🥇 40+ Real and Free Data Science Projects

👨🏻‍💻 Real learning means implementing ideas and building prototypes. It's time to skip the repetitive training and get straight to real data science projects!

🔆 With the DataSimple.education website, you can access 40+ data science projects with Python completely free ! From data analysis and machine learning to deep learning and AI.

✏️ There are no beginner projects here; you work with real datasets. Each project is well thought out and guides you step by step. For example, you can build a stock forecasting model, analyze customer behavior, or even study the impact of major global events on your data.

┌🏳️‍🌈 40+ Python Data Science Projects
└ 🌎 Website

#DataScience #PythonProjects #MachineLearning #DeepLearning #AIProjects #RealWorldData #OpenSource #DataAnalysis #ProjectBasedLearning #LearnByBuilding

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🐍Looking to get started with Deep Learning using PyTorch?

This well-structured GitHub repository is a goldmine for beginners who want to learn PyTorch with hands-on examples and clear explanations📖.

🗂 What’s Inside?

🈂 Jupyter Notebooks with interactive code.
🧠 Step-by-step tutorials on Tensors, Autograd, and Neural Networks.
🖼 Real-world mini-projects like image classification.
⌛ Practical guides on using GPU with PyTorch.
✅ Beginner-friendly but also great for revision.

💡If you're serious about learning AI, this is one of the best free resources to kick off your journey🤝.

🖥 GitHub

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Mathematics for Computer Science

Book Details

- Discrete Mathematics: An Open Introduction
- By Oscar Levin
- 2025 Edition
- 547 pages

🔗 Download the Book
discrete.openmathbooks.org/pdfs/dmoi4.pdf

#MathematicsForCS #DiscreteMathematics #ComputerScience #MathForProgrammers #OpenSourceBooks #CSFundamentals #OscarLevin #MathForDevelopers #LearnDiscreteMath #CS2025



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