An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more and save time.
An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more and save time.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Taylor R. Brown is an assistant professor of statistics at the University of Virginia. His research interests include state space models, particle filtering, and Markov chain Monte Carlo algorithms. He obtained his Ph.D. in statistics from the University of Virginia.
Inhaltsangabe
1. Introduction 2. Basic Types 3. R vectors versus Numpy arrays and Pandas' Series 4. Numpy ndarrays Versus R's matrix and array Types 5. R's lists Versus Python's lists and dicts 6. Functions 7. Categorical Data 8. Data Frames Part 1. Introducing the Basics 10. Using Third-Party Code 11. Control Flow 12. Reshaping and Combining Data Sets 13. Visualization Part 2. Common Tasks and Patterns 14. An Introduction to Object-Oriented Programming 15. An Introduction to Functional Programming
1. Introduction 2. Basic Types 3. R vectors versus Numpy arrays and Pandas' Series 4. Numpy ndarrays Versus R's matrix and array Types 5. R's lists Versus Python's lists and dicts 6. Functions 7. Categorical Data 8. Data Frames Part 1. Introducing the Basics 10. Using Third-Party Code 11. Control Flow 12. Reshaping and Combining Data Sets 13. Visualization Part 2. Common Tasks and Patterns 14. An Introduction to Object-Oriented Programming 15. An Introduction to Functional Programming
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