Introduction to data science : data analysis and prediction algorithms with R
著者
書誌事項
Introduction to data science : data analysis and prediction algorithms with R
（Chapman & Hall/CRC data science series）
CRC Press, c2020
大学図書館所蔵 件 / 全6件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes index
内容説明・目次
内容説明
Covers the basics of R and the tidyverse
Demonstrate how to use ggplot2 to generate graphs and describe important Data Visualization principles
Introduces Data Wranglin topics such as web scrapping, using regular expressions, and joining and reshaping data tables using the tidyverse tools
Illustrates the importance of statistics in data analysis using case studies
Uses the caret package to build prediction algorithms including K-nearest Neighbors and Random Forests
Includes tools used on a day-to-day basis in data science projects including RStudio, UNIX/Linux shell, Git and GitHub, and knitr and R Markdown
目次
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.
「Nielsen BookData」 より
