Mathematical Methods in Data Science: A Practical, Intuitive Guide to the Core Math Behind Modern Analytics and Machine Learning
Adrian Klein
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Opening Credits
1/13/2026
Chapter 1: Why Math Matters When the Library Already Works
1/13/2026
Chapter 2: Data as Vectors: The Geometry of Tables and Features
1/13/2026
Chapter 3: Transformations and Dimensionality: Seeing Structure in High Dimensions
1/13/2026
Chapter 4: Uncertainty as a Language: Probability and Random Variables
1/13/2026
Chapter 5: Distributions, Expectation, and the Shape of Randomness
1/13/2026
Chapter 6: From Samples to Signals: Estimation, Confidence, and Testing
1/13/2026
Chapter 7: Climbing the Hill: Gradients, Optimization, and Convexity
1/13/2026
Chapter 8: Information, Loss, and Regularization: Taming Model Complexity
1/13/2026
Chapter 9: Under the Hood: Connecting Math to Core Data Science Algorithms
1/13/2026
Chapter 10: Becoming Math Confident: Learning Paths, Pitfalls, and Reading Papers
1/13/2026
Closing Credits
1/13/2026