This accessible book for graduate students and data scientists provides a solid background in probabilistic and statistical concepts relevant to data science. Emphasis is placed on practice, with examples throughout using real-world data that readers can implement from Python code available on the book's website.
This accessible book for graduate students and data scientists provides a solid background in probabilistic and statistical concepts relevant to data science. Emphasis is placed on practice, with examples throughout using real-world data that readers can implement from Python code available on the book's website.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Carlos Fernandez-Granda is Associate Professor of Mathematics and Data Science at New York University, where he has taught probability and statistics to data science students since 2015. The goal of his research is to design and analyze data science methodology, with a focus on machine learning, artificial intelligence, and their application to medicine, climate science, biology, and other scientific domains.
Inhaltsangabe
Preface Introduction and Overview 1. Probability 2. Discrete variables 3. Continuous variables 4. Multiple discrete variables 5. Multiple continuous variables 6. Discrete and continuous variables 7. Averaging 8. Correlation 9. Estimation of population parameters 10. Hypothesis testing 11. Principal component analysis and low-rank models 12. Regression and classification A. Datasets References Index.