Side-by-side introduction of probability and statistics concepts for deeper understanding
Real-world datasets and examples to illustrate practical data science challenges
Python code, videos, slides, and solutions for hands-on learning
Coverage of advanced topics including principal component analysis and regression
Clear explanations of fundamental concepts such as overfitting and causal inference
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Report copyright infringement by Carlos Fernandez-Granda (Author) This self-contained guide introduces two pillars of data science, probability theory, and statistics, side by side, in order to illuminate the connections between statistical techniques and the probabilistic concepts they are based on. The topics covered in the book include r