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Python for Data Science
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This course offers comprehensive training in Python, covering everything from the fundamentals of the language and version control with Git and GitHub to advanced techniques in data analysis and artificial intelligence. Participants will explore data collection and manipulation through web scraping with Selenium and API consumption, complemented by data visualization with Seaborn and geospatial analysis using dynamic maps. Additionally, the course introduces the use of Large Language Models (LLMs) in AI, highlighting their applications and ethical considerations. With a practical approach, this course prepares participants to develop projects in data science, advanced programming, and intelligent data analysis.
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Discord
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Syllabus
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Causal inference and machine learning
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This course explores modern methods at the intersection of machine learning 🤖 and causal inference 🔍, equipping students with both predictive and explanatory tools for high-dimensional data. It covers:
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+ - High-dimensional linear models 📊 for prediction and inference
+ - Modern nonlinear methods 🌳 such as random forests and deep neural networks
+ - Randomized controlled trials (RCTs) 🎲 as the gold standard for causal estimation
+ - Causal DAGs 🔗 for representing and reasoning about causal relationships
+ - Double/debiased machine learning ⚖️ for valid inference after ML model selection
+ - Heterogeneous treatment effect estimation 🔀 using causal trees and forests
+ - Feature engineering with deep learning 🛠️ for causal and predictive modeling
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Discord
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Syllabus
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