๐จ๐ปโ๐ป 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.
๐ข Step 1: Strengthen your math and statistics!
โ๏ธ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
โ Linear algebra: matrices, vectors, eigenvalues.
๐ Course: MIT 18.06 Linear Algebra
โ Calculus: derivative, integral, optimization.
๐ Course: MIT Single Variable Calculus
โ Statistics and probability: Bayes' theorem, hypothesis testing.
๐ Course: Statistics 110
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๐ข Step 2: Learn to code.
โ๏ธ Learn Python and become proficient in coding. The most important topics you need to master are:
โ Python: Pandas, NumPy, Matplotlib libraries
๐ Course: FreeCodeCamp Python Course
โ SQL language: Join commands, Window functions, query optimization.
๐ Course: Stanford SQL Course
โ Data structures and algorithms: arrays, linked lists, trees.
๐ Course: MIT Introduction to Algorithms
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๐ข Step 3: Clean and visualize data
โ๏ธ Learn how to process and clean data and then create an engaging story from it!
โ Data cleaning: Working with missing values โโand detecting outliers.
๐ Course: Data Cleaning
โ Data visualization: Matplotlib, Seaborn, Tableau
๐ Course: Data Visualization Tutorial
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๐ข Step 4: Learn Machine Learning
โ๏ธ It's time to enter the exciting world of machine learning! You should know these topics:
โ Supervised learning: regression, classification.
โ Unsupervised learning: clustering, PCA, anomaly detection.
โ Deep learning: neural networks, CNN, RNN
๐ Course: CS229: Machine Learning
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๐ข Step 5: Working with Big Data and Cloud Technologies
โ๏ธ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
โ Big Data Tools: Hadoop, Spark, Dask
โ Cloud platforms: AWS, GCP, Azure
๐ Course: Data Engineering
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๐ข Step 6: Do real projects!
โ๏ธ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
โ Kaggle competitions: solving real-world challenges.
โ End-to-End projects: data collection, modeling, implementation.
โ GitHub: Publish your projects on GitHub.
๐ Platform: Kaggle๐ Platform: ods.ai
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๐ข Step 7: Learn MLOps and deploy models
โ๏ธ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
โ MLOps training: model versioning, monitoring, model retraining.
โ Deployment models: Flask, FastAPI, Docker
๐ Course: Stanford MLOps Course
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๐ข Step 8: Stay up to date and network
โ๏ธ 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.
โ Read scientific articles: arXiv, Google Scholar
โ Connect with the data community:
๐ Site: Papers with code
๐ Site: AI Research at Google
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