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Introduction to Data Science

Data Analysis and Prediction Algorithms with R
Sofort lieferbar | Lieferzeit: Sofort lieferbar I
ISBN-13:
9781000708035
Veröffentl:
2019
Seiten:
743
Autor:
Rafael A. Irizarry
eBook Typ:
EPUB
eBook Format:
EPUB
Kopierschutz:
0 - No protection
Sprache:
Englisch
Beschreibung:

The book begins by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning during the rest of the book.
I R. 1 Installing R and RStudio. 2. Getting Started with R and RStudio. 3. R Basics. 4. Programming basics. 5. The tidyverse. 6. Importing data. II Data Visualization. 7. Introduction to data visualization. 8. ggplot2. 9. Visualizing data distributions. 10. Data visualization in practice. 11. Data visualization principles. 12. Robust summaries. III Statistics with R. 13. Introduction to Statistics with R. 14. Probability. 15. Random variables. 16. Statistical Inference. 17. Statistical models. 18. Regression. 19. Linear Models. 20. Association is not causation. IV Data Wrangling. 21. Introduction to Data Wrangling. 22. Reshaping data. 23. Joining tables. 24. Web Scraping. 25. String Processing. 26. Parsing Dates and Times. 27. Text mining. V Machine Learning. 28. Introduction to Machine Learning. 29. Smoothing. 30. Cross validation. 31. The caret package. 32. Examples of algorithms. 33. Machine learning in practice. 34. Large datasets. 35. Clustering. VI Productivity tools. 36. Introduction to productivity tools. 37. Accessing the terminal and installing Git. 38. Organizing with Unix. 39. Git and GitHub. 40. Reproducible projects with RStudio and R markdown.

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