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Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.
Explores scalability, persistence, and consistency in database systems and data-intensive applications, emphasizing the importance of state and trade-offs in data management.
Explores optimization strategies for deep learning accelerators, emphasizing data movement reduction through batching, dataflow optimizations, and compression.
Explores challenges in privacy-preserving data publishing, including failed de-identification examples and privacy threats, and presents a case study on Airbnb's efforts to address racist practices while protecting user privacy.
