🔰 How to become a data scientist in 2025?

👨🏻‍💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.


🔢 Step 1: Strengthen your math and statistics!

✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:

✅ Linear algebra: matrices, vectors, eigenvalues.

🔗 Course: MIT 18.06 Linear Algebra


✅ Calculus: derivative, integral, optimization.

🔗 Course: MIT Single Variable Calculus


✅ Statistics and probability: Bayes' theorem, hypothesis testing.

🔗 Course: Statistics 110

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🔢 Step 2: Learn to code.

✏️ Learn Python and become proficient in coding. The most important topics you need to master are:

✅ Python: Pandas, NumPy, Matplotlib libraries

🔗 Course: FreeCodeCamp Python Course

✅ SQL language: Join commands, Window functions, query optimization.

🔗 Course: Stanford SQL Course

✅ Data structures and algorithms: arrays, linked lists, trees.

🔗 Course: MIT Introduction to Algorithms

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🔢 Step 3: Clean and visualize data

✏️ Learn how to process and clean data and then create an engaging story from it!

✅ Data cleaning: Working with missing values ​​and detecting outliers.

🔗 Course: Data Cleaning

✅ Data visualization: Matplotlib, Seaborn, Tableau

🔗 Course: Data Visualization Tutorial

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🔢 Step 4: Learn Machine Learning

✏️ It's time to enter the exciting world of machine learning! You should know these topics:

✅ Supervised learning: regression, classification.

✅ Unsupervised learning: clustering, PCA, anomaly detection.

✅ Deep learning: neural networks, CNN, RNN


🔗 Course: CS229: Machine Learning

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🔢 Step 5: Working with Big Data and Cloud Technologies

✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.

✅ Big Data Tools: Hadoop, Spark, Dask

✅ Cloud platforms: AWS, GCP, Azure

🔗 Course: Data Engineering

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🔢 Step 6: Do real projects!

✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.

✅ Kaggle competitions: solving real-world challenges.

✅ End-to-End projects: data collection, modeling, implementation.

✅ GitHub: Publish your projects on GitHub.

🔗 Platform: Kaggle🔗 Platform: ods.ai

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🔢 Step 7: Learn MLOps and deploy models

✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.

✅ MLOps training: model versioning, monitoring, model retraining.

✅ Deployment models: Flask, FastAPI, Docker

🔗 Course: Stanford MLOps Course

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🔢 Step 8: Stay up to date and network

✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.

✅ Read scientific articles: arXiv, Google Scholar

✅ Connect with the data community:

🔗 Site: Papers with code
🔗 Site: AI Research at Google

#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data

🤖 AI/ML Roadmap

1️⃣ Math & Stats 🧮🔢: Learn Linear Algebra, Probability, and Calculus.
2️⃣ Programming 🐍💻: Master Python, NumPy, Pandas, and Matplotlib.
3️⃣ Machine Learning 📈🤖: Study Supervised & Unsupervised Learning, and Model Evaluation.
4️⃣ Deep Learning 🔥🧠: Understand Neural Networks, CNNs, RNNs, and Transformers.
5️⃣ Specializations 🎓🔬: Choose from NLP, Computer Vision, or Reinforcement Learning.
6️⃣ Big Data & Cloud ☁️📡: Work with SQL, NoSQL, AWS, and GCP.
7️⃣ MLOps & Deployment 🚀🛠️: Learn Flask, Docker, and Kubernetes.
8️⃣ Ethics & Safety ⚖️🛡️: Understand Bias, Fairness, and Explainability.
9️⃣ Research & Practice 📜🔍: Read Papers and Build Projects.
🔟 Projects 📂🚀: Compete in Kaggle and contribute to Open-Source.

React ❤️ for more

#ai

🤖 The Four Main Types of Artificial Intelligence

𝟏. 𝐍𝐚𝐫𝐫𝐨𝐰 𝐀𝐈 (𝐀𝐍𝐈 – Artificial Narrow Intelligence)
This is the AI we use today. It’s designed for specific tasks and doesn’t possess general intelligence.

Examples of Narrow AI:
- Chatbots like Siri or Alexa
- Recommendation engines (Netflix, Amazon)
- Facial recognition systems
- Self-driving car navigation

🧠 _It’s smart, but only within its lane._

𝟐. 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐀𝐈 (𝐀𝐆𝐈 – Artificial General Intelligence)
This is theoretical AI that can learn, reason, and perform any intellectual task a human can.

Key Traits:
- Understands context across domains
- Learns new tasks without retraining
- Thinks abstractly and creatively

🌐 _It’s like having a digital Einstein—but we’re not there yet._

𝟑. 𝐒𝐮𝐩𝐞𝐫𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐒𝐈 – Artificial Superintelligence)
This is the hypothetical future where AI surpasses human intelligence in every way.

Potential Capabilities:
- Solving complex global problems
- Mastering emotional intelligence
- Making decisions faster and more accurately than humans

🚀 _It’s the sci-fi dream—and concern—rolled into one._

𝟒. 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐀𝐈

Reactive Machines – Respond to inputs but don’t learn or remember (e.g., IBM’s Deep Blue)
Limited Memory – Learn from past data (e.g., self-driving cars)
Theory of Mind – Understand emotions and intentions (still theoretical)
Self-Aware AI – Possess consciousness and self-awareness (purely speculative)

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🧠 Bonus: Learning Styles in AI

Just like machine learning, AI systems use:
- Supervised Learning – Labeled data
- Unsupervised Learning – Pattern discovery
- Reinforcement Learning – Trial and error
- Semi-Supervised Learning – A mix of both

👍 #ai #artificialintelligence