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Forwarded from Machine Learning with Python

🔰 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.

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Step 1: Strengthen your math and statistics!

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

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Linear algebra: matrices, vectors, eigenvalues.

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Course: MIT 18.06 Linear Algebra

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Calculus: derivative, integral, optimization.

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Course: MIT Single Variable Calculus

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Statistics and probability: Bayes' theorem, hypothesis testing.

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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:

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Python: Pandas, NumPy, Matplotlib libraries

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Course: FreeCodeCamp Python Course

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

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Course: Stanford SQL Course

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

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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!

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Data cleaning: Working with missing values and detecting outliers.

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Course: Data Cleaning

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Data visualization: Matplotlib, Seaborn, Tableau

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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:

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Supervised learning: regression, classification.

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Unsupervised learning: clustering, PCA, anomaly detection.

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Deep learning: neural networks, CNN, RNN

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Course: CS229: Machine Learning

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🔢 Step 5: Working with Big Data and Cloud Technologies

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

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Big Data Tools: Hadoop, Spark, Dask

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Cloud platforms: AWS, GCP, Azure

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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.

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Kaggle competitions: solving real-world challenges.

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

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GitHub: Publish your projects on GitHub.

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Platform: Kaggle

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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.

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MLOps training: model versioning, monitoring, model retraining.

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Deployment models: Flask, FastAPI, Docker

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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.

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Read scientific articles: arXiv, Google Scholar

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Connect with the data community:

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Site: Papers with code

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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 #AIEnthusiast

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