🏳️‍🌈 Learning Python for science is
✅ with these 8 awesome GitHub repos!


🖥 Repo: Project Based Learning

💬 One of the most famous educational repos with 230K+ stars that implements various algorithms and projects using Python.

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🖥 Repo: Real Python Materials

💬 Supplementary resources and exercises including project-based tutorials, guides, and practical exercises.

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🖥 Repo: Learn By Doing

💬 Project-based tutorials in AI and machine learning for all levels.

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🖥 Repo: Awesome Jupyter

💬 A curated collection of notebooks, tools, and powerful libraries for working with Jupyter.

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🖥 Repo: Python Mini Projects

💬 A collection of mini-projects like games and small apps that you can quickly run and practice.

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🖥 Repo: 100Projects of Code

💬 An educational challenge including 100 real projects; you practice and see your progress day by day.

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🖥 Repo: Data Science Projects

💬 Practical ideas and examples to start data science with Python.

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🖥 Repo: Python Project Scripts

💬 Small and large scripting projects, from beginner to advanced levels.

🔰 Simplify Your Code with `namedtuple` in Python

📋 This Python program shows how to use namedtuple to create lightweight, readable data structures instead of regular tuples!

✨ Example Output:

Alice 30 Paris

🔗 Learn More Here

🔅 Data Science Foundations: Data Mining in Python

🌐 Author: Barton Poulson
🔰 Level: Intermediate

⏰ Duration: 3h 3m

🌀 Learn the key concepts and skills behind one of the most important elements of data science: data mining.

📗 Topics: Data Mining, Python

📤 Join Learn Python for more courses

📂 Full description

Data mining is the area of data science that focuses on finding actionable patterns in large and diverse datasets: clusters of similar customers, trends over time that can only be spotted after disentangling seasonal and random effects, and new methods for predicting important outcomes. In this course, instructor Barton Poulson introduces you to data mining that uses the programming language Python. Barton goes over some preliminaries, such as the tools you may use for data mining. He discusses aspects of dimensionality reduction, then explains clustering, including hierarchical clustering, k-Means, DBSCAN, and more. Barton covers classification, including kNN and decision trees. He goes into association analysis and introduces you to Apriori, Eclat, and FP-Growth. Barton steps you through a time-series decomposition, then concludes with sentiment scoring and other text mining tools.

📱Learn Python
📱Data Science Foundations: Data Mining in Python